Panic, slash, or crash-Do black swans flap in stock markets?

NASA Astrophysics Data System (ADS)

Chen, Dar-Hsin; Huang, Han-Lin

2018-02-01

Stock transaction data typically present a time series that exhibits a somewhat confusing trend, making it difficult to issue any form of crisis warning. This study employs Fourier spectrum analysis to clearly show manic and irrational investors chasing prices. When clustering generates an enormous amount of unstable power, the result is a stock market collapsing into a danger area that can be taken as a warning signal. We thus take the Dow Jones Index as a typical stock market and employ daily data from 1994-2015. This study finds the investors' purchasing power through certain thresholds, analyses the performance characteristics of the spectrum, and denotes when a stock market is in a most serious crisis stage and in a second most serious correction stage. The result of our study indicates that the warning signal accurately measures a stock market crash that can be applicable to the Dow Jones Index, Nasdaq Index, and Germany ADX Index and to the emerging markets of Bovespa Index (Brazil) and Shanghai Index (China). Furthermore, this study provides a quantitative reference concerning the depth of market crashes.

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

PubMed Central

Pyo, Sujin; Lee, Jaewook; Cha, Mincheol

2017-01-01

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction. PMID:29136004

Multiscale Shannon entropy and its application in the stock market

NASA Astrophysics Data System (ADS)

Gu, Rongbao

2017-10-01

In this paper, we perform a multiscale entropy analysis on the Dow Jones Industrial Average Index using the Shannon entropy. The stock index shows the characteristic of multi-scale entropy that caused by noise in the market. The entropy is demonstrated to have significant predictive ability for the stock index in both long-term and short-term, and empirical results verify that noise does exist in the market and can affect stock price. It has important implications on market participants such as noise traders.

Stock Market Index Data and indicators for Day Trading as a Binary Classification problem.

PubMed

Bruni, Renato

2017-02-01

Classification is the attribution of labels to records according to a criterion automatically learned from a training set of labeled records. This task is needed in a huge number of practical applications, and consequently it has been studied intensively and several classification algorithms are available today. In finance, a stock market index is a measurement of value of a section of the stock market. It is often used to describe the aggregate trend of a market. One basic financial issue would be forecasting this trend. Clearly, such a stochastic value is very difficult to predict. However, technical analysis is a security analysis methodology developed to forecast the direction of prices through the study of past market data. Day trading consists in buying and selling financial instruments within the same trading day. In this case, one interesting problem is the automatic individuation of favorable days for trading. We model this problem as a binary classification problem, and we provide datasets containing daily index values, the corresponding values of a selection of technical indicators, and the class label, which is 1 if the subsequent time period is favorable for day trading and 0 otherwise. These datasets can be used to test the behavior of different approaches in solving the day trading problem.

Performance of technical trading rules: evidence from Southeast Asian stock markets.

PubMed

Tharavanij, Piyapas; Siraprapasiri, Vasan; Rajchamaha, Kittichai

2015-01-01

This paper examines the profitability of technical trading rules in the five Southeast Asian stock markets. The data cover a period of 14 years from January 2000 to December 2013. The instruments investigated are five Southeast Asian stock market indices: SET index (Thailand), FTSE Bursa Malaysia KLC index (Malaysia), FTSE Straits Times index (Singapore), JSX Composite index (Indonesia), and PSE composite index (the Philippines). Trading strategies investigated include Relative Strength Index, Stochastic oscillator, Moving Average Convergence-Divergence, Directional Movement Indicator and On Balance Volume. Performances are compared to a simple Buy-and-Hold. Statistical tests are also performed. Our empirical results show a strong performance of technical trading rules in an emerging stock market of Thailand but not in a more mature stock market of Singapore. The technical trading rules also generate statistical significant returns in the Malaysian, Indonesian and the Philippine markets. However, after taking transaction costs into account, most technical trading rules do not generate net returns. This fact suggests different levels of market efficiency among Southeast Asian stock markets. This paper finds three new insights. Firstly, technical indicators does not help much in terms of market timing. Basically, traders cannot expect to buy at a relative low price and sell at a relative high price by just using technical trading rules. Secondly, technical trading rules can be beneficial to individual investors as they help them to counter the behavioral bias called disposition effects which is the tendency to sell winning stocks too soon and holding on to losing stocks too long. Thirdly, even profitable strategies could not reliably predict subsequent market directions. They make money from having a higher average profit from profitable trades than an average loss from unprofitable ones.

Confidence and self-attribution bias in an artificial stock market.

PubMed

Bertella, Mario A; Pires, Felipe R; Rego, Henio H A; Silva, Jonathas N; Vodenska, Irena; Stanley, H Eugene

2017-01-01

Using an agent-based model we examine the dynamics of stock price fluctuations and their rates of return in an artificial financial market composed of fundamentalist and chartist agents with and without confidence. We find that chartist agents who are confident generate higher price and rate of return volatilities than those who are not. We also find that kurtosis and skewness are lower in our simulation study of agents who are not confident. We show that the stock price and confidence index-both generated by our model-are cointegrated and that stock price affects confidence index but confidence index does not affect stock price. We next compare the results of our model with the S&P 500 index and its respective stock market confidence index using cointegration and Granger tests. As in our model, we find that stock prices drive their respective confidence indices, but that the opposite relationship, i.e., the assumption that confidence indices drive stock prices, is not significant.

Asymmetric conditional volatility in international stock markets

NASA Astrophysics Data System (ADS)

Ferreira, Nuno B.; Menezes, Rui; Mendes, Diana A.

2007-08-01

Recent studies show that a negative shock in stock prices will generate more volatility than a positive shock of similar magnitude. The aim of this paper is to appraise the hypothesis under which the conditional mean and the conditional variance of stock returns are asymmetric functions of past information. We compare the results for the Portuguese Stock Market Index PSI 20 with six other Stock Market Indices, namely the SP 500, FTSE 100, DAX 30, CAC 40, ASE 20, and IBEX 35. In order to assess asymmetric volatility we use autoregressive conditional heteroskedasticity specifications known as TARCH and EGARCH. We also test for asymmetry after controlling for the effect of macroeconomic factors on stock market returns using TAR and M-TAR specifications within a VAR framework. Our results show that the conditional variance is an asymmetric function of past innovations raising proportionately more during market declines, a phenomenon known as the leverage effect. However, when we control for the effect of changes in macroeconomic variables, we find no significant evidence of asymmetric behaviour of the stock market returns. There are some signs that the Portuguese Stock Market tends to show somewhat less market efficiency than other markets since the effect of the shocks appear to take a longer time to dissipate.

Corruption and stock market development: A quantitative approach

NASA Astrophysics Data System (ADS)

Bolgorian, Meysam

2011-11-01

Studying the relation between corruption and economic factors and examining its consequences for economic development have attracted many economists and physicists in recent years. The purpose of this paper is to focus on the role of stock market development on corruption. Analyzing a data set of corruption and stock market development measures such as market capitalization and total value of share trading for 46 countries around the world for the period 2007-2009, we examine the dependence of the Corruption Perception Index (CPI) on stock market development. Our findings suggest that there exists a power-law dependence between corruption and stock market development. We also observe a negative relation between level of corruption and financial system improvement.

Has microblogging changed stock market behavior? Evidence from China

NASA Astrophysics Data System (ADS)

Jin, Xi; Shen, Dehua; Zhang, Wei

2016-06-01

This paper examines the stock market behavior for a long-lived subset of firms in Shanghai and Shenzhen CSI 300 Index (CSI 300 Index) both before and after the establishment of firms' Microblogging in Sina Weibo. The empirical results show a significant increase in the relative trading volume as well as the decreases in the daily expected stock return and firm-level volatility in the post-Sina Weibo period. These findings suggest that Sina Weibo as an alternative information interaction channel has changed the information environment for individual stock, enhanced the speed of information diffusion and therefore changed the overall stock market behavior.

Confidence and self-attribution bias in an artificial stock market

PubMed Central

Bertella, Mario A.; Pires, Felipe R.; Rego, Henio H. A.; Vodenska, Irena; Stanley, H. Eugene

2017-01-01

Using an agent-based model we examine the dynamics of stock price fluctuations and their rates of return in an artificial financial market composed of fundamentalist and chartist agents with and without confidence. We find that chartist agents who are confident generate higher price and rate of return volatilities than those who are not. We also find that kurtosis and skewness are lower in our simulation study of agents who are not confident. We show that the stock price and confidence index—both generated by our model—are cointegrated and that stock price affects confidence index but confidence index does not affect stock price. We next compare the results of our model with the S&P 500 index and its respective stock market confidence index using cointegration and Granger tests. As in our model, we find that stock prices drive their respective confidence indices, but that the opposite relationship, i.e., the assumption that confidence indices drive stock prices, is not significant. PMID:28231255

Market impact and structure dynamics of the Chinese stock market based on partial correlation analysis

NASA Astrophysics Data System (ADS)

Li, Xing; Qiu, Tian; Chen, Guang; Zhong, Li-Xin; Wu, Xiao-Run

2017-04-01

Partial correlation analysis is employed to study the market impact on the Chinese stock market from both the native and external markets. Whereas the native market index is observed to have a great impact on the market correlations for both the Shanghai and Shenzhen stock markets, some external stock indices of the United States, European and Asian stock markets show a slight influence on the Chinese market. The individual stock can be affected by different economic sectors, but the dominant influence is from the sector the stock itself belongs to or closely related to, and the finance and insurance sector shows a weaker correlation with other economic sectors. Moreover, the market structure similarity exhibits a negative correlation with the price return in most time, and the structure similarity decays with the time interval.

Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki

Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is a new element which is a criterion for decision making. In this paper, we combined GNP-IMX with Actor-Critic (GNP-IMX&AC) and create trading rules on stock markets. Evolution-based methods evolve their programs after enough period of time because they must calculate fitness values, however reinforcement learning can change programs during the period, therefore the trading rules can be created efficiently. In the simulation, the proposed method is trained using the stock prices of 10 brands in 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. The simulation results show that the proposed method can obtain larger profits than GNP-IMX without AC and Buy&Hold.

On the Feed-back Mechanism of Chinese Stock Markets

NASA Astrophysics Data System (ADS)

Lu, Shu Quan; Ito, Takao; Zhang, Jianbo

Feed-back models in the stock markets research imply an adjustment process toward investors' expectation for current information and past experiences. Error-correction and cointegration are often used to evaluate the long-run relation. The Efficient Capital Market Hypothesis, which had ignored the effect of the accumulation of information, cannot explain some anomalies such as bubbles and partial predictability in the stock markets. In order to investigate the feed-back mechanism and to determine an effective model, we use daily data of the stock index of two Chinese stock markets with the expectational model, which is one kind of geometric lag models. Tests and estimations of error-correction show that long-run equilibrium seems to be seldom achieved in Chinese stock markets. Our result clearly shows the common coefficient of expectations and fourth-order autoregressive disturbance exist in the two Chinese stock markets. Furthermore, we find the same coefficient of expectations has an autoregressive effect on disturbances in the two Chinese stock markets. Therefore the presence of such feed-back is also supported in Chinese stock markets.

Complexity in the Chinese stock market and its relationships with monetary policy intensity

NASA Astrophysics Data System (ADS)

Ying, Shangjun; Fan, Ying

2014-01-01

This paper introduces how to formulate the CSI300 evolving stock index using the Paasche compiling technique of weighed indexes after giving the GCA model. It studies dynamics characteristics of the Chinese stock market and its relationships with monetary policy intensity, based on the evolving stock index. It concludes by saying that it is possible to construct a dynamics equation of the Chinese stock market using three variables, and that it is useless to regular market-complexity according to changing intensity of external factors from a chaos point of view.

Relationships among Energy Price Shocks, Stock Market, and the Macroeconomy: Evidence from China

PubMed Central

Cong, Rong-Gang; Shen, Shaochuan

2013-01-01

This paper investigates the interactive relationships among China energy price shocks, stock market, and the macroeconomy using multivariate vector autoregression. The results indicate that there is a long cointegration among them. A 1% rise in the energy price index can depress the stock market index by 0.54% and the industrial value-adding growth by 0.037%. Energy price shocks also cause inflation and have a 5-month lag effect on stock market, which may result in the stock market “underreacting.” The energy price can explain stock market fluctuations better than the interest rate over a longer time period. Consequently, investors should pay greater attention to the long-term effect of energy on the stock market. PMID:23690737

Investigation of multifractality in the Brazilian stock market

NASA Astrophysics Data System (ADS)

Maganini, Natália Diniz; Da Silva Filho, Antônio Carlos; Lima, Fabiano Guasti

2018-05-01

Many studies point to a possible new stylized fact for financial time series: the multifractality. Several authors have already detected this characteristic in multiple time series in several countries. With that in mind and based on Multifractal Detrended Fluctuation Analysis (MFDFA) method, this paper analyzes the multifractality in the Brazilian market. This analysis is performed with daily data from IBOVESPA index (Brazilian stock exchange's main index) and other four highly marketable stocks in the Brazilian market (VALE5, ITUB4, BBDC4 and CIEL3), which represent more than 25% of the index composition, making up 1961 observations for each asset in the period from June 26 2009 to May 31 2017. We found that the studied stock prices and Brazilian index are multifractal, but that the multifractality degree is not the same for all the assets. The use of shuffled and surrogated series indicates that for the period and the actions considered the long-range correlations do not strongly influence the multifractality, but the distribution (fat tails) exerts a possible influence on IBOVESPA and CIEL3.

Coupling detrended fluctuation analysis of Asian stock markets

NASA Astrophysics Data System (ADS)

Wang, Qizhen; Zhu, Yingming; Yang, Liansheng; Mul, Remco A. H.

2017-04-01

This paper uses the coupling detrended fluctuation analysis (CDFA) method to investigate the multifractal characteristics of four Asian stock markets using three stock indices: stock price returns, trading volumes and the composite index. The results show that coupled correlations exist among the four stock markets and the coupled correlations have multifractal characteristics. We then use the chi square (χ2) test to identify the sources of multifractality. For the different stock indices, the contributions of a single series to multifractality are different. In other words, the contributions of each country to coupled correlations are different. The comparative analysis shows that the research on the combine effect of stock price returns and trading volumes may be more comprehensive than on an individual index. By comparing the strength of multifractality for original data with the residual errors of the vector autoregression (VAR) model, we find that the VAR model could not be used to describe the dynamics of the coupled correlations among four financial time series.

Estimating time-varying conditional correlations between stock and foreign exchange markets

NASA Astrophysics Data System (ADS)

Tastan, Hüseyin

2006-02-01

This study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.

Scaling and predictability in stock markets: a comparative study.

PubMed

Zhang, Huishu; Wei, Jianrong; Huang, Jiping

2014-01-01

Most people who invest in stock markets want to be rich, thus, many technical methods have been created to beat the market. If one knows the predictability of the price series in different markets, it would be easier for him/her to make the technical analysis, at least to some extent. Here we use one of the most basic sold-and-bought trading strategies to establish the profit landscape, and then calculate the parameters to characterize the strength of predictability. According to the analysis of scaling of the profit landscape, we find that the Chinese individual stocks are harder to predict than US ones, and the individual stocks are harder to predict than indexes in both Chinese stock market and US stock market. Since the Chinese (US) stock market is a representative of emerging (developed) markets, our comparative study on the markets of these two countries is of potential value not only for conducting technical analysis, but also for understanding physical mechanisms of different kinds of markets in terms of scaling.

Scaling and Predictability in Stock Markets: A Comparative Study

PubMed Central

Zhang, Huishu; Wei, Jianrong; Huang, Jiping

2014-01-01

Most people who invest in stock markets want to be rich, thus, many technical methods have been created to beat the market. If one knows the predictability of the price series in different markets, it would be easier for him/her to make the technical analysis, at least to some extent. Here we use one of the most basic sold-and-bought trading strategies to establish the profit landscape, and then calculate the parameters to characterize the strength of predictability. According to the analysis of scaling of the profit landscape, we find that the Chinese individual stocks are harder to predict than US ones, and the individual stocks are harder to predict than indexes in both Chinese stock market and US stock market. Since the Chinese (US) stock market is a representative of emerging (developed) markets, our comparative study on the markets of these two countries is of potential value not only for conducting technical analysis, but also for understanding physical mechanisms of different kinds of markets in terms of scaling. PMID:24632944

How long the singular value decomposed entropy predicts the stock market? - Evidence from the Dow Jones Industrial Average Index

NASA Astrophysics Data System (ADS)

Gu, Rongbao; Shao, Yanmin

2016-07-01

In this paper, a new concept of multi-scales singular value decomposition entropy based on DCCA cross correlation analysis is proposed and its predictive power for the Dow Jones Industrial Average Index is studied. Using Granger causality analysis with different time scales, it is found that, the singular value decomposition entropy has predictive power for the Dow Jones Industrial Average Index for period less than one month, but not for more than one month. This shows how long the singular value decomposition entropy predicts the stock market that extends Caraiani's result obtained in Caraiani (2014). On the other hand, the result also shows an essential characteristic of stock market as a chaotic dynamic system.

Quantifying the effect of investors' attention on stock market.

PubMed

Yang, Zhen-Hua; Liu, Jian-Guo; Yu, Chang-Rui; Han, Jing-Ti

2017-01-01

The investors' attention has been extensively used to predict the stock market. Different from existing proxies of the investors' attention, such as the Google trends, Baidu index (BI), we argue the collective attention from the stock trading platforms could reflect the investors' attention more closely. By calculated the increments of the attention volume for each stock (IAVS) from the stock trading platforms, we investigate the effect of investors' attention measured by the IAVS on the movement of the stock market. The experimental results for Chinese Securities Index 100 (CSI100) show that the BI is significantly correlated with the returns of CSI100 at 1% significance level only in 2014. However, it should be emphasized that the correlation of the new proposed measure, namely IAVS, is significantly at 1% significance level in 2014 and 2015. It shows that the effect of the measure IAVS on the movement of the stock market is more stable and significant than BI. This study yields important invest implications and better understanding of collective investors' attention.

Quantifying the effect of investors’ attention on stock market

PubMed Central

Yang, Zhen-Hua; Liu, Jian-Guo; Yu, Chang-Rui; Han, Jing-Ti

2017-01-01

The investors’ attention has been extensively used to predict the stock market. Different from existing proxies of the investors’ attention, such as the Google trends, Baidu index (BI), we argue the collective attention from the stock trading platforms could reflect the investors’ attention more closely. By calculated the increments of the attention volume for each stock (IAVS) from the stock trading platforms, we investigate the effect of investors’ attention measured by the IAVS on the movement of the stock market. The experimental results for Chinese Securities Index 100 (CSI100) show that the BI is significantly correlated with the returns of CSI100 at 1% significance level only in 2014. However, it should be emphasized that the correlation of the new proposed measure, namely IAVS, is significantly at 1% significance level in 2014 and 2015. It shows that the effect of the measure IAVS on the movement of the stock market is more stable and significant than BI. This study yields important invest implications and better understanding of collective investors’ attention. PMID:28542216

On fitting the Pareto Levy distribution to stock market index data: Selecting a suitable cutoff value

NASA Astrophysics Data System (ADS)

Coronel-Brizio, H. F.; Hernández-Montoya, A. R.

2005-08-01

The so-called Pareto-Levy or power-law distribution has been successfully used as a model to describe probabilities associated to extreme variations of stock markets indexes worldwide. The selection of the threshold parameter from empirical data and consequently, the determination of the exponent of the distribution, is often done using a simple graphical method based on a log-log scale, where a power-law probability plot shows a straight line with slope equal to the exponent of the power-law distribution. This procedure can be considered subjective, particularly with regard to the choice of the threshold or cutoff parameter. In this work, a more objective procedure based on a statistical measure of discrepancy between the empirical and the Pareto-Levy distribution is presented. The technique is illustrated for data sets from the New York Stock Exchange (DJIA) and the Mexican Stock Market (IPC).

A causality between fund performance and stock market

NASA Astrophysics Data System (ADS)

Kim, Ho-Yong; Kwon, Okyu; Oh, Gabjin

2016-02-01

We investigate whether the characteristic fund performance indicators (FPI), such as the fund return, the Net asset value (NAV) and the cash flow, are correlated with the asset price movement using information flows estimated by the Granger causality test. First, we find that the information flow of FPI is most sensitive to extreme events of the Korean stock market, which include negative events such as the sub-prime crisis and the impact of QE (quantitative easing) by the US subprime and Europe financial crisis as well as the positive events of the golden period of Korean Composite Stock Price Index (KOSPI), except for the fund cash flow. Second, both the fund return and the NAV exhibit significant correlations with the KOSPI, whereas the cash flow is not correlated with the stock market. This result suggests that the information resulting from the ability of the fund manager should influence stock market. Finally, during market crisis period, information flows between FPI and the Korean stock market are significantly positively correlated with the market volatility.

Time-varying long term memory in the European Union stock markets

NASA Astrophysics Data System (ADS)

Sensoy, Ahmet; Tabak, Benjamin M.

2015-10-01

This paper proposes a new efficiency index to model time-varying inefficiency in stock markets. We focus on European stock markets and show that they have different degrees of time-varying efficiency. We observe that the 2008 global financial crisis has an adverse effect on almost all EU stock markets. However, the Eurozone sovereign debt crisis has a significant adverse effect only on the markets in France, Spain and Greece. For the late members, joining EU does not have a uniform effect on stock market efficiency. Our results have important implications for policy makers, investors, risk managers and academics.

A wave function for stock market returns

NASA Astrophysics Data System (ADS)

Ataullah, Ali; Davidson, Ian; Tippett, Mark

2009-02-01

The instantaneous return on the Financial Times-Stock Exchange (FTSE) All Share Index is viewed as a frictionless particle moving in a one-dimensional square well but where there is a non-trivial probability of the particle tunneling into the well’s retaining walls. Our analysis demonstrates how the complementarity principle from quantum mechanics applies to stock market prices and of how the wave function presented by it leads to a probability density which exhibits strong compatibility with returns earned on the FTSE All Share Index. In particular, our analysis shows that the probability density for stock market returns is highly leptokurtic with slight (though not significant) negative skewness. Moreover, the moments of the probability density determined under the complementarity principle employed here are all convergent - in contrast to many of the probability density functions on which the received theory of finance is based.

Is there any connection between the network morphology and the fluctuations of the stock market index?

NASA Astrophysics Data System (ADS)

Stefan, F. M.; Atman, A. P. F.

2015-02-01

Models which consider behavioral aspects of the investors have attracted increasing interest in the Finance and Econophysics literature in the last years. Different behavioral profiles (imitation, anti-imitation, indifference) were proposed for the investors, which take their decision based on their trust network (neighborhood). Results from agent-based models have shown that most of the features observed in actual stock market indices can be replicated in simulations. Here, we present a deeper investigation of an agent based model considering different network morphologies (regular, random, small-world) for the investors' trust network, in an attempt to answer the question raised in the title. We study the model by considering four scenarios for the investors and different initial conditions to analyze their influence in the stock market fluctuations. We have characterized the stationary limit for each scenario tested, focusing on the changes introduced when complex networks were used, and calculated the Hurst exponent in some cases. Simulations showed interesting results suggesting that the fluctuations of the stock market index are strongly affected by the network morphology, a remarkable result which we believe was never reported or predicted before.

The impact of derivatives on Malaysian stock market

NASA Astrophysics Data System (ADS)

Malim, M. R.; Halim, F. A.; Murad, A.; Maad, H. A.; Annuar, N. F. M.

2017-09-01

The essential of derivatives has been discovered by researchers over recent decade. However, the conclusions made regarding the impact of derivatives on stock market volatility remains debatable. The main objective of this study is to examine the impact of derivatives on Malaysian stock market volatility by exploring FTSE Bursa Malaysia Kuala Lumpur Composite Index Futures (BMD FKLI) using FBM KLCI as the underlying asset. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) model was employed to realize the objective. The results have shown that the introduction of futures trading has decreased the volatility of Malaysian stock market. The volatility increased vigorously during the Asian financial crisis compared to the Global financial crisis. However, the role of futures as a risk transfer is agreed as it could improve the market by decreasing the volatility in the spot market.

What distinguishes individual stocks from the index?

NASA Astrophysics Data System (ADS)

Wagner, F.; Milaković, M.; Alfarano, S.

2010-01-01

Stochastic volatility models decompose the time series of financial returns into the product of a volatility factor and an iid noise factor. Assuming a slow dynamic for the volatility factor, we show via nonparametric tests that both the index as well as its individual stocks share a common volatility factor. While the noise component is Gaussian for the index, individual stock returns turn out to require a leptokurtic noise. Thus we propose a two-component model for stocks, given by the sum of Gaussian noise, which reflects market-wide fluctuations, and Laplacian noise, which incorporates firm-specific factors such as firm profitability or growth performance, both of which are known to be Laplacian distributed. In the case of purely Gaussian noise, the chi-squared probability for the density of individual stock returns is typically on the order of 10-20, while it increases to values of O(1) by adding the Laplace component.

Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

PubMed Central

Dai, Yonghui; Han, Dongmei; Dai, Weihui

2014-01-01

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659

Correlation and volatility in an Indian stock market: A random matrix approach

NASA Astrophysics Data System (ADS)

Kulkarni, Varsha; Deo, Nivedita

2007-11-01

We examine the volatility of an Indian stock market in terms of correlation of stocks and quantify the volatility using the random matrix approach. First we discuss trends observed in the pattern of stock prices in the Bombay Stock Exchange for the three-year period 2000 2002. Random matrix analysis is then applied to study the relationship between the coupling of stocks and volatility. The study uses daily returns of 70 stocks for successive time windows of length 85 days for the year 2001. We compare the properties of matrix C of correlations between price fluctuations in time regimes characterized by different volatilities. Our analyses reveal that (i) the largest (deviating) eigenvalue of C correlates highly with the volatility of the index, (ii) there is a shift in the distribution of the components of the eigenvector corresponding to the largest eigenvalue across regimes of different volatilities, (iii) the inverse participation ratio for this eigenvector anti-correlates significantly with the market fluctuations and finally, (iv) this eigenvector of C can be used to set up a Correlation Index, CI whose temporal evolution is significantly correlated with the volatility of the overall market index.

Cross-Correlation Asymmetries and Causal Relationships between Stock and Market Risk

PubMed Central

Borysov, Stanislav S.; Balatsky, Alexander V.

2014-01-01

We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock prices from the Standard & Poor's 500 index for 1994–2013. We focus on the behavior of the cross-correlations at the times of financial crises with significant jumps of market volatility. The observed historical dynamics showed that the dependence between the risks was almost linear during the US stock market downturn of 2002 and after the US housing bubble in 2007, remaining at that level until 2013. Moreover, the averaged cross-correlation function often had an asymmetric shape with respect to zero lag in the periods of high correlation. We develop the analysis by the application of the linear response formalism to study underlying causal relations. The calculated response functions suggest the presence of characteristic regimes near financial crashes, when the volatility of an individual stock follows the market volatility and vice versa. PMID:25162697

Cross-correlation asymmetries and causal relationships between stock and market risk.

PubMed

Borysov, Stanislav S; Balatsky, Alexander V

2014-01-01

We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock prices from the Standard & Poor's 500 index for 1994-2013. We focus on the behavior of the cross-correlations at the times of financial crises with significant jumps of market volatility. The observed historical dynamics showed that the dependence between the risks was almost linear during the US stock market downturn of 2002 and after the US housing bubble in 2007, remaining at that level until 2013. Moreover, the averaged cross-correlation function often had an asymmetric shape with respect to zero lag in the periods of high correlation. We develop the analysis by the application of the linear response formalism to study underlying causal relations. The calculated response functions suggest the presence of characteristic regimes near financial crashes, when the volatility of an individual stock follows the market volatility and vice versa.

Daily stock index return for the Canadian, UK, and US equity markets, compiled by Morgan Stanley Capital International, obtained from Datastream.

PubMed

Li, Leon

2018-02-01

The data presented in this article are related to the research article entitled "Testing and comparing the performance of dynamic variance and correlation models in value-at-risk estimation. North American Journal of Economics and Finance, 40, 116-135. doi:10.1016/j.najef.2017.02.006 (Li, 2017) [1]. Data on daily stock index return for the Canadian, UK, and US equity markets, as compiled by Morgan Stanley Capital International, are provided in this paper. The country indices comprise at least 80% of the stock market capitalization of each country. The data cover the period from January 1, 1990, through September 8, 2016, and include 6963 observations. All stock prices are stated in dollars.

Price and Volume Dynamics in the Japanese Stock Market

NASA Astrophysics Data System (ADS)

Yamashita, Hirofumi; Takayasu, Hideki; Takayasu, Misako

We investigated data of stocks listed on Tokyo Stock Exchange. Although the data we used contains limited number of limit orders around the best prices in the ask and bid sides, we could confirm some issues of the layered structure which is similar to that in FX markets. We show time series of a market impact index, which is made using high correlation between dynamics of price and volume of limit orders. In the last section, we remark differences in our observations comparing with the FX market case.

How High Frequency Trading Affects a Market Index

PubMed Central

Kenett, Dror Y.; Ben-Jacob, Eshel; Stanley, H. Eugene; gur-Gershgoren, Gitit

2013-01-01

The relationship between a market index and its constituent stocks is complicated. While an index is a weighted average of its constituent stocks, when the investigated time scale is one day or longer the index has been found to have a stronger effect on the stocks than vice versa. We explore how this interaction changes in short time scales using high frequency data. Using a correlation-based analysis approach, we find that in short time scales stocks have a stronger influence on the index. These findings have implications for high frequency trading and suggest that the price of an index should be published on shorter time scales, as close as possible to those of the actual transaction time scale. PMID:23817553

How Random is the Walk: Efficiency of Indian Stock and Futures Markets

NASA Astrophysics Data System (ADS)

Basu, Udayan Kumar

Time series of prices of stock and its rates of return has been one of the major areas of study in Econophysics. The price of a stock depends on a number of factors as well as information related thereto, and how quickly and effectively the price of a stock assimilates all such information decides the efficiency of the stock market. Instead of individual stocks, people often study the behaviour of stock indices to get a feel of the market as a whole, and the outcomes of such studies for the Dow Jones Industrial Average (DJIA), the Nasdaq Index and the S & P 500 Index have been listed in a number of articles. In this context, it has also been argued that for a market to be considered sufficiently liquid, correlation between successive price movements and rates of return should be insignificant, because any significant correlation would lead to an arbitrage opportunity that is expected to be rapidly exploited and thus washed out. The residual correlations are those little enough not to be profitable for strategies due to imperfect market conditions. Unless transaction costs or slippages or any other impediment exists, leading to some transactional inefficiency, arbitrages would take place to bring back the markets to a stage of insignifficant correlations [1, 2].

Optimization of investment portfolio weight of stocks affected by market index

NASA Astrophysics Data System (ADS)

Azizah, E.; Rusyaman, E.; Supian, S.

2017-01-01

Stock price assessment, selection of optimum combination, and measure the risk of a portfolio investment is one important issue for investors. In this paper single index model used for the assessment of the stock price, and formulation optimization model developed using Lagrange multiplier technique to determine the proportion of assets to be invested. The level of risk is estimated by using variance. These models are used to analyse the stock price data Lippo Bank and Bumi Putera.

Volatility-constrained multifractal detrended cross-correlation analysis: Cross-correlation among Mainland China, US, and Hong Kong stock markets

NASA Astrophysics Data System (ADS)

Cao, Guangxi; Zhang, Minjia; Li, Qingchen

2017-04-01

This study focuses on multifractal detrended cross-correlation analysis of the different volatility intervals of Mainland China, US, and Hong Kong stock markets. A volatility-constrained multifractal detrended cross-correlation analysis (VC-MF-DCCA) method is proposed to study the volatility conductivity of Mainland China, US, and Hong Kong stock markets. Empirical results indicate that fluctuation may be related to important activities in real markets. The Hang Seng Index (HSI) stock market is more influential than the Shanghai Composite Index (SCI) stock market. Furthermore, the SCI stock market is more influential than the Dow Jones Industrial Average stock market. The conductivity between the HSI and SCI stock markets is the strongest. HSI was the most influential market in the large fluctuation interval of 1991 to 2014. The autoregressive fractionally integrated moving average method is used to verify the validity of VC-MF-DCCA. Results show that VC-MF-DCCA is effective.

Lead-lag relationships between stock and market risk within linear response theory

NASA Astrophysics Data System (ADS)

Borysov, Stanislav; Balatsky, Alexander

2015-03-01

We study historical correlations and lead-lag relationships between individual stock risks (standard deviation of daily stock returns) and market risk (standard deviation of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over stocks, using historical stock prices from the Standard & Poor's 500 index for 1994-2013. The observed historical dynamics suggests that the dependence between the risks was almost linear during the US stock market downturn of 2002 and after the US housing bubble in 2007, remaining at that level until 2013. Moreover, the averaged cross-correlation function often had an asymmetric shape with respect to zero lag in the periods of high correlation. We develop the analysis by the application of the linear response formalism to study underlying causal relations. The calculated response functions suggest the presence of characteristic regimes near financial crashes, when individual stock risks affect market risk and vice versa. This work was supported by VR 621-2012-2983.

Stock Market Fluctuations and Self-Harm among Children and Adolescents in Hong Kong.

PubMed

Wong, Wilfred Hing-Sang; Lee, James Chun-Yin; Ho, Frederick Ka-Wing; Li, Tim Man-Ho; Ip, Patrick; Chow, Chun-Bong

2017-06-09

Although a few studies investigated the impact of stock market fluctuations on population health, the question of whether stock market fluctuations have an impact on self-harm in children and adolescents remain unanswered. This study therefore investigated the association between stock market fluctuations and self-harm among children and adolescents in Hong Kong. Daily self-harm attendance records were retrieved from all 18 local Accident and Emergency Departments (AED) from 2001 to 2012. 4931 children and adolescents who committed self-harm were included. The results indicated positive correlation between daily change in stock market index, Hang Seng Index (∇HSI, per 300 points), and daily self-harm incident risk of children and adolescents, without time lag between the two. The incident risk ratio for ∇HSI was 1.09 ( p = 0.0339) in children and 1.06 ( p = 0.0246) in adolescents. Importantly, non-trading days were found to impose significant protective effect in both groups against self-harm risk. Our results showed that stock market fluctuations were related to self-harm behaviors in children and adolescents. Parents and professionals should be educated about the potential harm of stock market fluctuations and the importance of effective parenting in reducing self-harm among children and adolescents.

Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market.

PubMed

Qiao, Haishu; Xia, Yue; Li, Ying

2016-01-01

This study used the dynamic conditional correlations (DCC) method to identify the linkage effects of Chinese stock market, and further detected the influence of network linkage effects on magnitude of security returns across different industries. Applying two physics-derived techniques, the minimum spanning tree and the hierarchical tree, we analyzed the stock interdependence within the network of the China Securities Index (CSI) industry index basket. We observed that that obvious linkage effects existed among stock networks. CII and CCE, CAG and ITH as well as COU, CHA and REI were confirmed as the core nodes in the three different networks respectively. We also investigated the stability of linkage effects by estimating the mean correlations and mean distances, as well as the normalized tree length of these indices. In addition, using the GMM model approach, we found inter-node influence within the stock network had a pronounced effect on stock returns. Our results generally suggested that there appeared to be greater clustering effect among the indexes belonging to related industrial sectors than those of diverse sectors, and network comovement was significantly affected by impactive financial events in the reality. Besides, stocks that were more central within the network of stock market usually had higher returns for compensation because they endured greater exposure to correlation risk.

Asymptotic behavior of the daily increment distribution of the IPC, the mexican stock market index

NASA Astrophysics Data System (ADS)

Coronel-Brizio, H. F.; Hernández-Montoya, A. R.

2005-02-01

In this work, a statistical analysis of the distribution of daily fluctuations of the IPC, the Mexican Stock Market Index is presented. A sample of the IPC covering the 13-year period 04/19/1990 - 08/21/2003 was analyzed and the cumulative probability distribution of its daily logarithmic variations studied. Results showed that the cumulative distribution function for extreme variations, can be described by a Pareto-Levy model with shape parameters alpha=3.634 +- 0.272 and alpha=3.540 +- 0.278 for its positive and negative tails respectively. This result is consistent with previous studies, where it has been found that 2.5< alpha <4 for other financial markets worldwide.

Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?

NASA Astrophysics Data System (ADS)

Peng, Huan; Chen, Ruoxun; Mei, Dexiang; Diao, Xiaohua

2018-07-01

In this paper, we use a comprehensive look to investigate whether the G7 stock markets can contain predictive information to help in forecasting the Chinese stock market volatility. Our out-of-sample empirical results indicate the kitchen sink (HAR-RV-SK) model is able to attain better performance than the benchmark model (HAR-RV) and other models, implying that the G7 stock markets can help in predicting the one-day volatility of the Chinese stock market. Moreover, the kitchen sink strategy can beat the strategy of the simple combination forecasts. Finally, the G7 stock markets can indeed contain useful information, which can increase the accuracy forecasts of the Chinese stock market.

Greed, fear and stock market dynamics

NASA Astrophysics Data System (ADS)

Westerhoff, Frank H.

2004-11-01

We present a behavioral stock market model in which traders are driven by greed and fear. In general, the agents optimistically believe in rising markets and thus buy stocks. But if stock prices change too abruptly, they panic and sell stocks. Our model mimics some stylized facts of stock market dynamics: (1) stock prices increase over time, (2) stock markets sometimes crash, (3) stock prices show little pair correlation between successive daily changes, and (4) periods of low volatility alternate with periods of high volatility. A strong feature of the model is that stock prices completely evolve according to a deterministic low-dimensional nonlinear law of motion.

Damped oscillations in the ratios of stock market indices

NASA Astrophysics Data System (ADS)

Wu, Ming-Chya

2012-02-01

A stock market index is an average of a group of stock prices with weights. Different stock market indices derived from various combinations of stocks may share similar trends in certain periods, while it is not expected that there are fixed relations among them. Here we report our investigations on the daily index data of Dow Jones Industry Average (DJIA), NASDAQ, and S&P500 from 1971/02/05 to 2011/06/30. By analyzing the index ratios using the empirical mode decomposition, we find that the ratios NASDAQ/DJIA and S&500/DJIA, normalized to 1971/02/05, approached and then retained the values of 2 and 1, respectively. The temporal variations of the ratios consist of global trends and oscillatory components including a damped oscillation in 8-year cycle and damping factors of 7183 days (NASDAQ/DJIA) and 138471 days (S&P500/DJIA). Anomalies in the ratios, corresponding to significant increases and decreases of indices, only appear in the time scale less than an 8-year cycle. Detrended fluctuation analysis and multiscale entropy analysis of the components with cycles less than a half-year manifest a behavior of self-adjustment in the ratios, and the behavior in S&500/DJIA is more significant than in NASDAQ/DJIA.

Persistence Probability Analyzed on the Taiwan STOCK Market

NASA Astrophysics Data System (ADS)

Chen, I.-Chun; Chen, Hung-Jung; Tseng, Hsen-Che

We report a numerical study of the Taiwan stock market, in which we used three data sources: the daily Taiwan stock exchange index (TAIEX) from January 1983 to May 2006, the daily OTC index from January 1995 to May 2006, and the one-min intraday data from February 2000 to December 2003. Our study is based on numerical estimates of persistence exponent θp, Hurst exponent H2, and fluctuation exponent h2. We also discuss the results concerning persistence probability P(t), qth-order price-price correlation function Gq(t), and qth-order normalized fluctuation function fq(t) among these indices.

Stock Market Expectations of Dutch Households

PubMed Central

Hurd, Michael; van Rooij, Maarten; Winter, Joachim

2013-01-01

Despite its importance for the analysis of life-cycle behavior and, in particular, retirement planning, stock ownership by private households is poorly understood. Among other approaches to investigate this puzzle, recent research has started to elicit private households’ expectations of stock market returns. This paper reports findings from a study that collected data over a two-year period both on households’ stock market expectations (subjective probabilities of gains or losses) and on whether they own stocks. We document substantial heterogeneity in financial market expectations. Expectations are correlated with stock ownership. Over the two years of our data, stock market prices increased, and expectations of future stock market price changes also increased, lending support to the view that expectations are influenced by recent stock gains or losses. PMID:23997423

Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market

PubMed Central

Qiao, Haishu; Xia, Yue; Li, Ying

2016-01-01

This study used the dynamic conditional correlations (DCC) method to identify the linkage effects of Chinese stock market, and further detected the influence of network linkage effects on magnitude of security returns across different industries. Applying two physics-derived techniques, the minimum spanning tree and the hierarchical tree, we analyzed the stock interdependence within the network of the China Securities Index (CSI) industry index basket. We observed that that obvious linkage effects existed among stock networks. CII and CCE, CAG and ITH as well as COU, CHA and REI were confirmed as the core nodes in the three different networks respectively. We also investigated the stability of linkage effects by estimating the mean correlations and mean distances, as well as the normalized tree length of these indices. In addition, using the GMM model approach, we found inter-node influence within the stock network had a pronounced effect on stock returns. Our results generally suggested that there appeared to be greater clustering effect among the indexes belonging to related industrial sectors than those of diverse sectors, and network comovement was significantly affected by impactive financial events in the reality. Besides, stocks that were more central within the network of stock market usually had higher returns for compensation because they endured greater exposure to correlation risk. PMID:27257816

Analytical study of index-coupled herd behavior in financial markets

NASA Astrophysics Data System (ADS)

Berman, Yonatan; Shapira, Yoash; Schwartz, Moshe

2016-12-01

Herd behavior in financial markets had been investigated extensively in the past few decades. Scholars have argued that the behavioral tendency of traders and investors to follow the market trend, notably reflected in indices both on short and long time scales, is substantially affecting the overall market behavior. Research has also been devoted to revealing these behaviors and characterizing the market herd behavior. In this paper we present a simple herd behavior model for the dynamics of financial variables by introducing a simple coupling mechanism of stock returns to the index return, deriving analytic expressions for statistical properties of the returns. We found that several important phenomena in the stock market, namely the correlations between stock market returns and the exponential decay of short-term autocorrelations, are derived from our model. These phenomena have been given various explanations and theories, with herd market behavior being one of the leading. We conclude that the coupling mechanism, which essentially encapsulates the herd behavior, indeed creates correlation and autocorrelation. We also show that this introduces a time scale to the system, which is the characteristic time lag between a change in the index and its effect on the return of a stock.

A spring-block analogy for the dynamics of stock indexes

NASA Astrophysics Data System (ADS)

Sándor, Bulcsú; Néda, Zoltán

2015-06-01

A spring-block chain placed on a running conveyor belt is considered for modeling stylized facts observed in the dynamics of stock indexes. Individual stocks are modeled by the blocks, while the stock-stock correlations are introduced via simple elastic forces acting in the springs. The dragging effect of the moving belt corresponds to the expected economic growth. The spring-block system produces collective behavior and avalanche like phenomena, similar to the ones observed in stock markets. An artificial index is defined for the spring-block chain, and its dynamics is compared with the one measured for the Dow Jones Industrial Average. For certain parameter regions the model reproduces qualitatively well the dynamics of the logarithmic index, the logarithmic returns, the distribution of the logarithmic returns, the avalanche-size distribution and the distribution of the investment horizons. A noticeable success of the model is that it is able to account for the gain-loss asymmetry observed in the inverse statistics. Our approach has mainly a pedagogical value, bridging between a complex socio-economic phenomena and a basic (mechanical) model in physics.

Price-volume multifractal analysis and its application in Chinese stock markets

NASA Astrophysics Data System (ADS)

Yuan, Ying; Zhuang, Xin-tian; Liu, Zhi-ying

2012-06-01

An empirical research on Chinese stock markets is conducted using statistical tools. First, the multifractality of stock price return series, ri(ri=ln(Pt+1)-ln(Pt)) and trading volume variation series, vi(vi=ln(Vt+1)-ln(Vt)) is confirmed using multifractal detrended fluctuation analysis. Furthermore, a multifractal detrended cross-correlation analysis between stock price return and trading volume variation in Chinese stock markets is also conducted. It is shown that the cross relationship between them is also found to be multifractal. Second, the cross-correlation between stock price Pi and trading volume Vi is empirically studied using cross-correlation function and detrended cross-correlation analysis. It is found that both Shanghai stock market and Shenzhen stock market show pronounced long-range cross-correlations between stock price and trading volume. Third, a composite index R based on price and trading volume is introduced. Compared with stock price return series ri and trading volume variation series vi, R variation series not only remain the characteristics of original series but also demonstrate the relative correlation between stock price and trading volume. Finally, we analyze the multifractal characteristics of R variation series before and after three financial events in China (namely, Price Limits, Reform of Non-tradable Shares and financial crisis in 2008) in the whole period of sample to study the changes of stock market fluctuation and financial risk. It is found that the empirical results verified the validity of R.

The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields

PubMed Central

Guo, Kun; Zhou, Wei-Xing; Cheng, Si-Wei; Sornette, Didier

2011-01-01

Using a recently introduced method to quantify the time-varying lead-lag dependencies between pairs of economic time series (the thermal optimal path method), we test two fundamental tenets of the theory of fixed income: (i) the stock market variations and the yield changes should be anti-correlated; (ii) the change in central bank rates, as a proxy of the monetary policy of the central bank, should be a predictor of the future stock market direction. Using both monthly and weekly data, we found very similar lead-lag dependence between the S&P 500 stock market index and the yields of bonds inside two groups: bond yields of short-term maturities (Federal funds rate (FFR), 3M, 6M, 1Y, 2Y, and 3Y) and bond yields of long-term maturities (5Y, 7Y, 10Y, and 20Y). In all cases, we observe the opposite of (i) and (ii). First, the stock market and yields move in the same direction. Second, the stock market leads the yields, including especially the FFR. Moreover, we find that the short-term yields in the first group lead the long-term yields in the second group before the financial crisis that started in mid-2007 and the inverse relationship holds afterwards. These results suggest that the Federal Reserve is increasingly mindful of the stock market behavior, seen as key to the recovery and health of the economy. Long-term investors seem also to have been more reactive and mindful of the signals provided by the financial stock markets than the Federal Reserve itself after the start of the financial crisis. The lead of the S&P 500 stock market index over the bond yields of all maturities is confirmed by the traditional lagged cross-correlation analysis. PMID:21857954

The US stock market leads the federal funds rate and treasury bond yields.

PubMed

Guo, Kun; Zhou, Wei-Xing; Cheng, Si-Wei; Sornette, Didier

2011-01-01

Using a recently introduced method to quantify the time-varying lead-lag dependencies between pairs of economic time series (the thermal optimal path method), we test two fundamental tenets of the theory of fixed income: (i) the stock market variations and the yield changes should be anti-correlated; (ii) the change in central bank rates, as a proxy of the monetary policy of the central bank, should be a predictor of the future stock market direction. Using both monthly and weekly data, we found very similar lead-lag dependence between the S&P 500 stock market index and the yields of bonds inside two groups: bond yields of short-term maturities (Federal funds rate (FFR), 3M, 6M, 1Y, 2Y, and 3Y) and bond yields of long-term maturities (5Y, 7Y, 10Y, and 20Y). In all cases, we observe the opposite of (i) and (ii). First, the stock market and yields move in the same direction. Second, the stock market leads the yields, including especially the FFR. Moreover, we find that the short-term yields in the first group lead the long-term yields in the second group before the financial crisis that started in mid-2007 and the inverse relationship holds afterwards. These results suggest that the Federal Reserve is increasingly mindful of the stock market behavior, seen as key to the recovery and health of the economy. Long-term investors seem also to have been more reactive and mindful of the signals provided by the financial stock markets than the Federal Reserve itself after the start of the financial crisis. The lead of the S&P 500 stock market index over the bond yields of all maturities is confirmed by the traditional lagged cross-correlation analysis.

Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

NASA Astrophysics Data System (ADS)

Yousif, Adil; Elfaki, Faiz

2017-12-01

The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

Empirical study of recent Chinese stock market

NASA Astrophysics Data System (ADS)

Jiang, J.; Li, W.; Cai, X.; Wang, Qiuping A.

2009-05-01

We investigate the statistical properties of the empirical data taken from the Chinese stock market during the time period from January, 2006 to July, 2007. By using the methods of detrended fluctuation analysis (DFA) and calculating correlation coefficients, we acquire the evidence of strong correlations among different stock types, stock index, stock volume turnover, A share (B share) seat number, and GDP per capita. In addition, we study the behavior of “volatility”, which is now defined as the difference between the new account numbers for two consecutive days. It is shown that the empirical power-law of the number of aftershock events exceeding the selected threshold is analogous to the Omori law originally observed in geophysics. Furthermore, we find that the cumulative distributions of stock return, trade volume and trade number are all exponential-like, which does not belong to the universality class of such distributions found by Xavier Gabaix et al. [Xavier Gabaix, Parameswaran Gopikrishnan, Vasiliki Plerou, H. Eugene Stanley, Nature, 423 (2003)] for major western markets. Through the comparison, we draw a conclusion that regardless of developed stock markets or emerging ones, “cubic law of returns” is valid only in the long-term absolute return, and in the short-term one, the distributions are exponential-like. Specifically, the distributions of both trade volume and trade number display distinct decaying behaviors in two separate regimes. Lastly, the scaling behavior of the relation is analyzed between dispersion and the mean monthly trade value for each administrative area in China.

Clustering stock market companies via chaotic map synchronization

NASA Astrophysics Data System (ADS)

Basalto, N.; Bellotti, R.; De Carlo, F.; Facchi, P.; Pascazio, S.

2005-01-01

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series to the coupling strengths between maps. The simulation of a chaotic map dynamics gives rise to a natural partition of the data, as companies belonging to the same industrial branch are often grouped together. The identification of clusters of companies of a given stock market index can be exploited in the portfolio optimization strategies.

Cross-sectional test of the Fama-French three-factor model: Evidence from Bangladesh stock market

NASA Astrophysics Data System (ADS)

Hasan, Md. Zobaer; Kamil, Anton Abdulbasah

2014-09-01

Stock market is an important part of a country's economy. It supports the country's economic development and progress by encouraging the efficiency and profitability of firms. This research was designed to examine the risk-return association of companies in the Dhaka Stock Exchange (DSE) market of Bangladesh by using the Fama-French three-factor model structure. The model is based on three factors, which are stock beta, SMB (difference in returns of the portfolio with small market capitalisation minus that with big market capitalisation) and HML (difference in returns of the portfolio with high book-to-market ratio minus that with low book-to-market ratio). This study focused on the DSE market as it is one of the frontier emerging stock markets of South Asia. For this study, monthly stock returns from 71 non-financial companies were used for the period of January 2002 to December 2011. DSI Index was used as a proxy for the market portfolio and Bangladesh government 3-Month T-bill rate was used as the proxy for the risk-free asset. It was found that large capital stocks outperform small capital stocks and stocks with lower book-to-market ratios outperform stocks with higher book-to-market ratios in the context of Bangladesh stock market.

Extreme values in the Chinese and American stock markets based on detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Cao, Guangxi; Zhang, Minjia

2015-10-01

This paper focuses on the comparative analysis of extreme values in the Chinese and American stock markets based on the detrended fluctuation analysis (DFA) algorithm using the daily data of Shanghai composite index and Dow Jones Industrial Average. The empirical results indicate that the multifractal detrended fluctuation analysis (MF-DFA) method is more objective than the traditional percentile method. The range of extreme value of Dow Jones Industrial Average is smaller than that of Shanghai composite index, and the extreme value of Dow Jones Industrial Average is more time clustering. The extreme value of the Chinese or American stock markets is concentrated in 2008, which is consistent with the financial crisis in 2008. Moreover, we investigate whether extreme events affect the cross-correlation between the Chinese and American stock markets using multifractal detrended cross-correlation analysis algorithm. The results show that extreme events have nothing to do with the cross-correlation between the Chinese and American stock markets.

Assessment of 48 Stock markets using adaptive multifractal approach

NASA Astrophysics Data System (ADS)

Ferreira, Paulo; Dionísio, Andreia; Movahed, S. M. S.

2017-11-01

In this paper, Stock market comovements are examined using cointegration, Granger causality tests and nonlinear approaches in context of mutual information and correlations. Since underlying data sets are affected by non-stationarities and trends, we also apply Adaptive Multifractal Detrended Fluctuation Analysis (AMF-DFA) and Adaptive Multifractal Detrended Cross-Correlation Analysis (AMF-DXA). We find only 170 pair of Stock markets cointegrated, and according to the Granger causality and mutual information, we realize that the strongest relations lies between emerging markets, and between emerging and frontier markets. According to scaling exponent given by AMF-DFA, h(q = 2) > 1, we find that all underlying data sets belong to non-stationary process. According to Efficient Market Hypothesis (EMH), only 8 markets are classified in uncorrelated processes at 2 σ confidence interval. 6 Stock markets belong to anti-correlated class and dominant part of markets has memory in corresponding daily index prices during January 1995 to February 2014. New-Zealand with H = 0 . 457 ± 0 . 004 and Jordan with H = 0 . 602 ± 0 . 006 are far from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost multifractal for all pair of Stock markets. The empirical relation, Hxy ≤ [Hxx +Hyy ] / 2, is confirmed. Mentioned relation for q > 0 is also satisfied while for q < 0 there is a deviation from this relation confirming behavior of markets for small fluctuations is affected by contribution of major pair. For larger fluctuations, the cross-correlation contains information from both local (internal) and global (external) conditions. Width of singularity spectrum for auto-correlation and cross-correlation are Δαxx ∈ [ 0 . 304 , 0 . 905 ] and Δαxy ∈ [ 0 . 246 , 1 . 178 ] , respectively. The wide range of singularity spectrum for cross-correlation confirms that the bilateral relation between Stock markets is more complex. The value of σDCCA indicates that all

The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH-BEKK model

NASA Astrophysics Data System (ADS)

Liu, Xueyong; An, Haizhong; Huang, Shupei; Wen, Shaobo

2017-01-01

Aiming to investigate the evolution of mean and volatility spillovers between oil and stock markets in the time and frequency dimensions, we employed WTI crude oil prices, the S&P 500 (USA) index and the MICEX index (Russia) for the period Jan. 2003-Dec. 2014 as sample data. We first applied a wavelet-based GARCH-BEKK method to examine the spillover features in frequency dimension. To consider the evolution of spillover effects in time dimension at multiple-scales, we then divided the full sample period into three sub-periods, pre-crisis period, crisis period, and post-crisis period. The results indicate that spillover effects vary across wavelet scales in terms of strength and direction. By analysis the time-varying linkage, we found the different evolution features of spillover effects between the Oil-US stock market and Oil-Russia stock market. The spillover relationship between oil and US stock market is shifting to short-term while the spillover relationship between oil and Russia stock market is changing to all time scales. That result implies that the linkage between oil and US stock market is weakening in the long-term, and the linkage between oil and Russia stock market is getting close in all time scales. This may explain the phenomenon that the US stock index and the Russia stock index showed the opposite trend with the falling of oil price in the post-crisis period.

Multifractal in Volatility of Family Business Stocks Listed on Casablanca STOCK Exchange

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

In this paper, we check for existence of multifractal in volatility of Moroccan family business stock returns and in volatility of Casablanca market index returns based on multifractal detrended fluctuation analysis (MF-DFA) technique. Empirical results show strong evidence of multifractal characteristics in volatility series of both family business stocks and market index. In addition, it is found that small variations in volatility of family business stocks are persistent, whilst small variations in volatility of market index are anti-persistent. However, large variations in family business volatility and market index volatility are both anti-persistent. Furthermore, multifractal spectral analysis based results show strong evidence that volatility in Moroccan family business companies exhibits more multifractality than volatility in the main stock market. These results may provide insightful information for risk managers concerned with family business stocks.

Mapping the Sensitivity of the Public Emotion to the Movement of STOCK Market Value: a Case Study of Manhattan

NASA Astrophysics Data System (ADS)

Kang, Y.; Wang, J.; Wang, Y.; Angsuesser, S.; Fei, T.

2017-09-01

We examined whether emotion expressed by users in social media can be influenced by stock market index or can predict the fluctuation of the stock market index. We collected the emotion data by using face detection technology and emotion cognition services for photos uploaded to Flickr. Each face's emotion was described in 8 dimensions the location was also recorded. An emotion score index was defined based on the combination of all 8 dimensions of emotion calculated by principal component analysis. The correlation coefficients between the stock market values and emotion scores are significant (R > 0.59 with p < 0.01). Using Granger Causality analysis for cause and effect detection, we found that users' emotion is influenced by stock market value change. A multiple linear regression model was established (R-square = 0.76) to explore the potential factors that influence the emotion score. Finally, a sensitivity map was created to show sensitive areas where human emotion is easily affected by the stock market changes. We concluded that in Manhattan region: (1) there is an obvious relationship between human emotion and stock market fluctuation; (2) emotion change follows the movements of the stock market; (3) the Times Square and Broadway Theatre are the most sensitive regions in terms of public emotional reaction to the economy represented by stock value.

The effect of Malaysia general election on stock market returns.

PubMed

Liew, Venus Khim-Sen; Rowland, Racquel

2016-01-01

During the latest episode of general election held in Malaysia, it is observed that the FBMKLCI index was lifted 62.52 points in a day soon after the announcement of election outcome. Moreover, the index registered a highest gain of 96.29 points in the middle of the intra-day trade. This suggests that investors who had got the right direction could make profitable intra-day trading the next trading day of the general election date. Results from statistical analysis uncover significant before-election-effect and after-election-effect from the most recent general elections held in Malaysia. Different subsets of macroeconomic variables are found to have significant role on stock market return depending on the market situation. Remarkably, when there was close fight between the two major political parties during the 2008 and 2013 election years, political uncertainty showed up its negative and significant role in influencing the stock market return. The major implication of these findings is that while investors may seek abnormal returns before and after the next general election, which is around the corner, they will have to pay attention on the influence of macroeconomic variables and political uncertainty on stock market return during the election year.

Predictability and co-movement relationships between conventional and Islamic stock market indexes: A multiscale exploration using wavelets

NASA Astrophysics Data System (ADS)

Saâdaoui, Foued; Naifar, Nader; Aldohaiman, Mohamed S.

2017-09-01

This paper investigates the dynamical relationship between conventional and Islamic stock markets using the wavelet-assisted cross-spectral, cross-correlation and causality analyses. Relying on bivariate time series from emerging and developed markets, the aim is to find and recognize local microscopic signs of convergence or divergence. The data set covers a period of exceptional instability in the financial system that was accompanied by a significant slump in the global economic environment. The empirical results demonstrate an obvious strong dependence between conventional and Islamic indexes at low-frequency, while the dependence becomes rather instable in the finest frequencies across different investment time horizons. The relationship also took a special different form in the crisis period compared to relatively calm periods. In developed markets, indexes were the most correlated over many periods and at many frequencies, while the relationship in emerging markets tended to be less manifest, especially for short-term horizons, offering investors different investment alternatives and portfolio diversification opportunities. The pre- and post-crisis causality investigations at the end of the study suggested a bidirectional relationship in most cases, thereby offering further perspectives on multivariate forecasting.

Equation-based model for the stock market

NASA Astrophysics Data System (ADS)

Xavier, Paloma O. C.; Atman, A. P. F.; de Magalhães, A. R. Bosco

2017-09-01

We propose a stock market model which is investigated in the forms of difference and differential equations whose variables correspond to the demand or supply of each agent and to the price. In the model, agents are driven by the behavior of their trust contact network as well by fundamental analysis. By means of the deterministic version of the model, the connection between such drive mechanisms and the price is analyzed: imitation behavior promotes market instability, finitude of resources is associated to stock index stability, and high sensitivity to the fair price provokes price oscillations. Long-range correlations in the price temporal series and heavy-tailed distribution of returns are observed for the version of the model which considers different proposals for stochasticity of microeconomic and macroeconomic origins.

Do Earthquakes Shake Stock Markets?

PubMed

Ferreira, Susana; Karali, Berna

2015-01-01

This paper examines how major earthquakes affected the returns and volatility of aggregate stock market indices in thirty-five financial markets over the last twenty years. Results show that global financial markets are resilient to shocks caused by earthquakes even if these are domestic. Our analysis reveals that, in a few instances, some macroeconomic variables and earthquake characteristics (gross domestic product per capita, trade openness, bilateral trade flows, earthquake magnitude, a tsunami indicator, distance to the epicenter, and number of fatalities) mediate the impact of earthquakes on stock market returns, resulting in a zero net effect. However, the influence of these variables is market-specific, indicating no systematic pattern across global capital markets. Results also demonstrate that stock market volatility is unaffected by earthquakes, except for Japan.

Economic Stress and Mental Health: The Relationship Between the Stock Market and Neurotic Disorder Doctor Visits.

PubMed

Chen, Chun-Chih; Lin, Ying-Tzu; Liu, Tsai-Ching; Chen, Chin-Shyan

2016-12-01

This paper investigates the relationship between the stock market and the neurotic disorder doctor visits. We use aggregate data, partition the population by age and gender and examine the impact of changes in the stock market on neurotic disorders. Using doctor visits as a proxy measure of morbidity, we find evidence of some relationship between neurotic disorder morbidity and stock market variations. A stock market falling in a single day and the accumulation of daily stock market drops are both associated with more neurotic disorder doctor visits. We also observe more neurotic disorder doctor visits during periods of a low stock index for the elderly, regardless of gender. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Stock Market Project.

ERIC Educational Resources Information Center

Distel, Brenda D.

This project is designed to teach students the process of buying stocks and to tracking their investments over the course of a semester. The goals of the course are to teach students about the relationships between conditions in the economy and the stock market; to predict the effect of an economic event on a specific stock or industry; to relate…

Empirical Examination of Fundamental Indexation in the German Market

NASA Astrophysics Data System (ADS)

Mihm, Max; Locarek-Junge, Hermann

Index Funds, Exchange Traded Funds and Derivatives give investors easy access to well diversified index portfolios. These index-based investment products exhibit low fees, which make them an attractive alternative to actively managed funds. Against this background, a new class of stock indices has been established based on the concept of “Fundamental Indexation”. The selection and weighting of index constituents is conducted by means of fundamental criteria like total assets, book value or number of employees. This paper examines the performance of fundamental indices in the German equity market. For this purpose, a backtest of five fundamental indices is conducted over the last 20 years. Furthermore the index returns are analysed under the assumption of an efficient as well as an inefficient market. Index returns in efficient markets are explained by applying the three factor model for stock returns of Fama and French (J Financ Econ 33(1):3-56, 1993). The results show that the outperformance of fundamental indices is partly due to a higher risk exposure, particularly to companies with a low price to book ratio. By relaxing the assumption of market efficiency, a return drag of capitalisation weighted indices can be deduced. Given a mean-reverting movement of prices, a direct connection between market capitalisation and index weighting leads to inferior returns.

Quantifying the Behavior of Stock Correlations Under Market Stress

PubMed Central

Preis, Tobias; Kenett, Dror Y.; Stanley, H. Eugene; Helbing, Dirk; Ben-Jacob, Eshel

2012-01-01

Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios. PMID:23082242

Do Earthquakes Shake Stock Markets?

PubMed Central

2015-01-01

This paper examines how major earthquakes affected the returns and volatility of aggregate stock market indices in thirty-five financial markets over the last twenty years. Results show that global financial markets are resilient to shocks caused by earthquakes even if these are domestic. Our analysis reveals that, in a few instances, some macroeconomic variables and earthquake characteristics (gross domestic product per capita, trade openness, bilateral trade flows, earthquake magnitude, a tsunami indicator, distance to the epicenter, and number of fatalities) mediate the impact of earthquakes on stock market returns, resulting in a zero net effect. However, the influence of these variables is market-specific, indicating no systematic pattern across global capital markets. Results also demonstrate that stock market volatility is unaffected by earthquakes, except for Japan. PMID:26197482

Evidence of increment of efficiency of the Mexican Stock Market through the analysis of its variations

NASA Astrophysics Data System (ADS)

Coronel-Brizio, H. F.; Hernández-Montoya, A. R.; Huerta-Quintanilla, R.; Rodríguez-Achach, M.

2007-07-01

It is well known that there exist statistical and structural differences between the stock markets of developed and emerging countries. In this work, and in order to find out if the efficiency of the Mexican Stock Market has been changing over time, we have performed and compared several analyses of the variations of the Mexican Stock Market index (IPC) and Dow Jones industrial average index (DJIA) for different periods of their historical daily data. We have analyzed the returns autocorrelation function (ACF) and used detrended fluctuation analysis (DFA) to study returns variations. We also analyze the volatility, mean value and standard deviation of both markets and compare their evolution. We conclude from the overall result of these studies, that they show compelling evidence of the increment of efficiency of the Mexican Stock Market over time. The data samples analyzed here, correspond to daily values of the IPC and DJIA for the period 10/30/1978-02/28/2006.

Influence of the Investor's Behavior on the Complexity of the Stock Market

NASA Astrophysics Data System (ADS)

Atman, A. P. F.; Gonçalves, Bruna Amin

2012-04-01

One of the pillars of the finance theory is the efficient-market hypothesis, which is used to analyze the stock market. However, in recent years, this hypothesis has been questioned by a number of studies showing evidence of unusual behaviors in the returns of financial assets ("anomalies") caused by behavioral aspects of the economic agents. Therefore, it is time to initiate a debate about the efficient-market hypothesis and the "behavioral finances." We here introduce a cellular automaton model to study the stock market complexity, considering different behaviors of the economical agents. From the analysis of the stationary standard of investment observed in the simulations and the Hurst exponents obtained for the term series of stock index, we draw conclusions concerning the complexity of the model compared to real markets. We also investigate which conditions of the investors are able to influence the efficient market hypothesis statements.

Comparable Stocks, Boundedly Rational Stock Markets and IPO Entry Rates

PubMed Central

Chok, Jay; Qian, Jifeng

2013-01-01

In this study, we examine how initial public offerings (IPO) entry rates are affected when stock markets are boundedly rational and IPO firms infer information from their counterparts in the market. We hypothesize a curvilinear relationship between the number of comparable stocks and initial public offerings (IPO) entry rates into the NASDAQ Stock Exchange. Furthermore, we argue that trading volume and changes in stock returns partially mediates the relationship between the number of comparable stocks and IPO entry rates. The statistical evidence provides strong support for the hypotheses. PMID:23690924

Comparable stocks, boundedly rational stock markets and IPO entry rates.

PubMed

Chok, Jay; Qian, Jifeng

2013-01-01

In this study, we examine how initial public offerings (IPO) entry rates are affected when stock markets are boundedly rational and IPO firms infer information from their counterparts in the market. We hypothesize a curvilinear relationship between the number of comparable stocks and initial public offerings (IPO) entry rates into the NASDAQ Stock Exchange. Furthermore, we argue that trading volume and changes in stock returns partially mediates the relationship between the number of comparable stocks and IPO entry rates. The statistical evidence provides strong support for the hypotheses.

STOCK MARKET CRASH AND EXPECTATIONS OF AMERICAN HOUSEHOLDS*

PubMed Central

HUDOMIET, PÉTER; KÉZDI, GÁBOR; WILLIS, ROBERT J.

2011-01-01

SUMMARY This paper utilizes data on subjective probabilities to study the impact of the stock market crash of 2008 on households’ expectations about the returns on the stock market index. We use data from the Health and Retirement Study that was fielded in February 2008 through February 2009. The effect of the crash is identified from the date of the interview, which is shown to be exogenous to previous stock market expectations. We estimate the effect of the crash on the population average of expected returns, the population average of the uncertainty about returns (subjective standard deviation), and the cross-sectional heterogeneity in expected returns (disagreement). We show estimates from simple reduced-form regressions on probability answers as well as from a more structural model that focuses on the parameters of interest and separates survey noise from relevant heterogeneity. We find a temporary increase in the population average of expectations and uncertainty right after the crash. The effect on cross-sectional heterogeneity is more significant and longer lasting, which implies substantial long-term increase in disagreement. The increase in disagreement is larger among the stockholders, the more informed, and those with higher cognitive capacity, and disagreement co-moves with trading volume and volatility in the market. PMID:21547244

The Stock Performance of C. Everett Koop Award Winners Compared With the Standard & Poor's 500 Index.

PubMed

Goetzel, Ron Z; Fabius, Raymond; Fabius, Dan; Roemer, Enid C; Thornton, Nicole; Kelly, Rebecca K; Pelletier, Kenneth R

2016-01-01

To explore the link between companies investing in the health and well-being programs of their employees and stock market performance. Stock performance of C. Everett Koop National Health Award winners (n = 26) was measured over time and compared with the average performance of companies comprising the Standard and Poor's (S&P) 500 Index. The Koop Award portfolio outperformed the S&P 500 Index. In the 14-year period tracked (2000-2014), Koop Award winners' stock values appreciated by 325% compared with the market average appreciation of 105%. This study supports prior and ongoing research demonstrating a higher market valuation--an affirmation of business success by Wall Street investors--of socially responsible companies that invest in the health and well-being of their workers when compared with other publicly traded firms.

Persistent collective trend in stock markets

NASA Astrophysics Data System (ADS)

Balogh, Emeric; Simonsen, Ingve; Nagy, Bálint Zs.; Néda, Zoltán

2010-12-01

Empirical evidence is given for a significant difference in the collective trend of the share prices during the stock index rising and falling periods. Data on the Dow Jones Industrial Average and its stock components are studied between 1991 and 2008. Pearson-type correlations are computed between the stocks and averaged over stock pairs and time. The results indicate a general trend: whenever the stock index is falling the stock prices are changing in a more correlated manner than in case the stock index is ascending. A thorough statistical analysis of the data shows that the observed difference is significant, suggesting a constant fear factor among stockholders.

Scale-free avalanche dynamics in the stock market

NASA Astrophysics Data System (ADS)

Bartolozzi, M.; Leinweber, D. B.; Thomas, A. W.

2006-10-01

Self-organized criticality (SOC) has been claimed to play an important role in many natural and social systems. In the present work we empirically investigate the relevance of this theory to stock-market dynamics. Avalanches in stock-market indices are identified using a multi-scale wavelet-filtering analysis designed to remove Gaussian noise from the index. Here, new methods are developed to identify the optimal filtering parameters which maximize the noise removal. The filtered time series is reconstructed and compared with the original time series. A statistical analysis of both high-frequency Nasdaq E-mini Futures and daily Dow Jones data is performed. The results of this new analysis confirm earlier results revealing a robust power-law behaviour in the probability distribution function of the sizes, duration and laminar times between avalanches. This power-law behaviour holds the potential to be established as a stylized fact of stock market indices in general. While the memory process, implied by the power-law distribution of the laminar times, is not consistent with classical models for SOC, we note that a power-law distribution of the laminar times cannot be used to rule out self-organized critical behaviour.

Index Cohesive Force Analysis Reveals That the US Market Became Prone to Systemic Collapses Since 2002

PubMed Central

Kenett, Dror Y.; Shapira, Yoash; Madi, Asaf; Bransburg-Zabary, Sharron; Gur-Gershgoren, Gitit; Ben-Jacob, Eshel

2011-01-01

Background The 2007–2009 financial crisis, and its fallout, has strongly emphasized the need to define new ways and measures to study and assess the stock market dynamics. Methodology/Principal Findings The S&P500 dynamics during 4/1999–4/2010 is investigated in terms of the index cohesive force (ICF - the balance between the stock correlations and the partial correlations after subtraction of the index contribution), and the Eigenvalue entropy of the stock correlation matrices. We found a rapid market transition at the end of 2001 from a flexible state of low ICF into a stiff (nonflexible) state of high ICF that is prone to market systemic collapses. The stiff state is also marked by strong effect of the market index on the stock-stock correlations as well as bursts of high stock correlations reminiscence of epileptic brain activity. Conclusions/Significance The market dynamical states, stability and transition between economic states was studies using new quantitative measures. Doing so shed new light on the origin and nature of the current crisis. The new approach is likely to be applicable to other classes of complex systems from gene networks to the human brain. PMID:21556323

The volatility of stock market prices.

PubMed

Shiller, R J

1987-01-02

If the volatility of stock market prices is to be understood in terms of the efficient markets hypothesis, then there should be evidence that true investment value changes through time sufficiently to justify the price changes. Three indicators of change in true investment value of the aggregate stock market in the United States from 1871 to 1986 are considered: changes in dividends, in real interest rates, and in a direct measure of intertemporal marginal rates of substitution. Although there are some ambiguities in interpreting the evidence, dividend changes appear to contribute very little toward justifying the observed historical volatility of stock prices. The other indicators contribute some, but still most of the volatility of stock market prices appears unexplained.

Analysing News for Stock Market Prediction

NASA Astrophysics Data System (ADS)

Ramalingam, V. V.; Pandian, A.; Dwivedi, shivam; Bhatt, Jigar P.

2018-04-01

Stock market means the aggregation of all sellers and buyers of stocks representing their ownership claims on the business. To be completely absolute about the investment on these stocks, proper knowledge about them as well as their pricing, for both present and future is very essential. Large amount of data is collected and parsed to obtain this essential information regarding the fluctuations in the stock market. This data can be any news or public opinions in general. Recently, many methods have been used, especially big unstructured data methods to predict the stock market values. We introduce another method of focusing on deriving the best statistical learning model for predicting the future values. The data set used is very large unstructured data collected from an online social platform, commonly known as Quindl. The data from this platform is then linked to a csv fie and cleaned to obtain the essential information for stock market prediction. The method consists of carrying out the NLP (Natural Language Processing) of the data and then making it easier for the system to understand, finds and identifies the correlation in between this data and the stock market fluctuations. The model is implemented using Python Programming Language throughout the entire project to obtain flexibility and convenience of the system.

Empirical behavior of a world stock index from intra-day to monthly time scales

NASA Astrophysics Data System (ADS)

Breymann, W.; Lüthi, D. R.; Platen, E.

2009-10-01

Most of the papers that study the distributional and fractal properties of financial instruments focus on stock prices or foreign exchange rates. This typically leads to mixed results concerning the distributions of log-returns and some multi-fractal properties of exchange rates, stock prices, and regional indices. This paper uses a well diversified world stock index as the central object of analysis. Such index approximates the growth optimal portfolio, which is demonstrated under the benchmark approach, it is the ideal reference unit for studying basic securities. When denominating this world index in units of a given currency, one measures the movements of the currency against the entire market. This provides a least disturbed observation of the currency dynamics. In this manner, one can expect to disentangle, e.g., the superposition of the two currencies involved in an exchange rate. This benchmark approach to the empirical analysis of financial data allows us to establish remarkable stylized facts. Most important is the observation that the repeatedly documented multi-fractal appearance of financial time series is very weak and much less pronounced than the deviation of the mono-scaling properties from Brownian-motion type scaling. The generalized Hurst exponent H(2) assumes typical values between 0.55 and 0.6. Accordingly, autocorrelations of log-returns decay according to a power law, and the quadratic variation vanishes when going to vanishing observation time step size. Furthermore, one can identify the Student t distribution as the log-return distribution of a well-diversified world stock index for long time horizons when a long enough data series is used for estimation. The study of dependence properties, finally, reveals that jumps at daily horizon originate primarily in the stock market while at 5min horizon they originate in the foreign exchange market. The principal message of the empirical analysis is that there is evidence that a diffusion model

The Stock Market Game: A Simulation of Stock Market Trading. Grades 5-8.

ERIC Educational Resources Information Center

Draze, Dianne

This guide to a unit on a simulation game about the stock market contains an instructional text and two separate simulations. Through directed lessons and reproducible worksheets, the unit teaches students about business ownership, stock exchanges, benchmarks, commissions, why prices change, the logistics of buying and selling stocks, and how to…

The Stock Performance of C. Everett Koop Award Winners Compared With the Standard & Poor's 500 Index

PubMed Central

Goetzel, Ron Z.; Fabius, Raymond; Fabius, Dan; Roemer, Enid C.; Thornton, Nicole; Kelly, Rebecca K.; Pelletier, Kenneth R.

2016-01-01

Objective: To explore the link between companies investing in the health and well-being programs of their employees and stock market performance. Methods: Stock performance of C. Everett Koop National Health Award winners (n = 26) was measured over time and compared with the average performance of companies comprising the Standard and Poor's (S&P) 500 Index. Results: The Koop Award portfolio outperformed the S&P 500 Index. In the 14-year period tracked (2000–2014), Koop Award winners’ stock values appreciated by 325% compared with the market average appreciation of 105%. Conclusions: This study supports prior and ongoing research demonstrating a higher market valuation—an affirmation of business success by Wall Street investors—of socially responsible companies that invest in the health and well-being of their workers when compared with other publicly traded firms. PMID:26716843

Online Stock Market Games for High Schools.

ERIC Educational Resources Information Center

Lopus, Jane; Placone, Dennis

2002-01-01

Identifies a Web site providing information about stock market simulations for high school economics courses. Divides the information into two tables: (1) the structure of online stock market games; and (2) the determination of portfolio values of online stock market games. States that changes and updates are available at Web sites. (JEH)

Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

NASA Astrophysics Data System (ADS)

Han, Rui-Qi; Xie, Wen-Jie; Xiong, Xiong; Zhang, Wei; Zhou, Wei-Xing

The correlation structure of a stock market contains important financial contents, which may change remarkably due to the occurrence of financial crisis. We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 Chinese stocks. Both raw correlation matrix and partial correlation matrix with respect to the market index in two time periods of one year are investigated. We find that the Chinese stocks have stronger average correlation and partial correlation in 2008 than in 2007 and the average partial correlation is significantly weaker than the average correlation in each period. Accordingly, the largest eigenvalue of the correlation matrix is remarkably greater than that of the partial correlation matrix in each period. Moreover, each largest eigenvalue and its eigenvector reflect an evident market effect, while other deviating eigenvalues do not. We find no evidence that deviating eigenvalues contain industrial sectorial information. Surprisingly, the eigenvectors of the second largest eigenvalues in 2007 and of the third largest eigenvalues in 2008 are able to distinguish the stocks from the two exchanges. We also find that the component magnitudes of the some largest eigenvectors are proportional to the stocks’ capitalizations.

The predictive power of singular value decomposition entropy for stock market dynamics

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2014-01-01

We use a correlation-based approach to analyze financial data from the US stock market, both daily and monthly observations from the Dow Jones. We compute the entropy based on the singular value decomposition of the correlation matrix for the components of the Dow Jones Industrial Index. Based on a moving window, we derive time varying measures of entropy for both daily and monthly data. We find that the entropy has a predictive ability with respect to stock market dynamics as indicated by the Granger causality tests.

Recurrence quantification analysis of global stock markets

NASA Astrophysics Data System (ADS)

Bastos, João A.; Caiado, Jorge

2011-04-01

This study investigates the presence of deterministic dependencies in international stock markets using recurrence plots and recurrence quantification analysis (RQA). The results are based on a large set of free float-adjusted market capitalization stock indices, covering a period of 15 years. The statistical tests suggest that the dynamics of stock prices in emerging markets is characterized by higher values of RQA measures when compared to their developed counterparts. The behavior of stock markets during critical financial events, such as the burst of the technology bubble, the Asian currency crisis, and the recent subprime mortgage crisis, is analyzed by performing RQA in sliding windows. It is shown that during these events stock markets exhibit a distinctive behavior that is characterized by temporary decreases in the fraction of recurrence points contained in diagonal and vertical structures.

Analysis of cyclical behavior in time series of stock market returns

NASA Astrophysics Data System (ADS)

Stratimirović, Djordje; Sarvan, Darko; Miljković, Vladimir; Blesić, Suzana

2018-01-01

In this paper we have analyzed scaling properties and cyclical behavior of the three types of stock market indexes (SMI) time series: data belonging to stock markets of developed economies, emerging economies, and of the underdeveloped or transitional economies. We have used two techniques of data analysis to obtain and verify our findings: the wavelet transform (WT) spectral analysis to identify cycles in the SMI returns data, and the time-dependent detrended moving average (tdDMA) analysis to investigate local behavior around market cycles and trends. We found cyclical behavior in all SMI data sets that we have analyzed. Moreover, the positions and the boundaries of cyclical intervals that we found seam to be common for all markets in our dataset. We list and illustrate the presence of nine such periods in our SMI data. We report on the possibilities to differentiate between the level of growth of the analyzed markets by way of statistical analysis of the properties of wavelet spectra that characterize particular peak behaviors. Our results show that measures like the relative WT energy content and the relative WT amplitude of the peaks in the small scales region could be used to partially differentiate between market economies. Finally, we propose a way to quantify the level of development of a stock market based on estimation of local complexity of market's SMI series. From the local scaling exponents calculated for our nine peak regions we have defined what we named the Development Index, which proved, at least in the case of our dataset, to be suitable to rank the SMI series that we have analyzed in three distinct groups.

Novel indexes based on network structure to indicate financial market

NASA Astrophysics Data System (ADS)

Zhong, Tao; Peng, Qinke; Wang, Xiao; Zhang, Jing

2016-02-01

There have been various achievements to understand and to analyze the financial market by complex network model. However, current studies analyze the financial network model but seldom present quantified indexes to indicate or forecast the price action of market. In this paper, the stock market is modeled as a dynamic network, in which the vertices refer to listed companies and edges refer to their rank-based correlation based on price series. Characteristics of the network are analyzed and then novel indexes are introduced into market analysis, which are calculated from maximum and fully-connected subnets. The indexes are compared with existing ones and the results confirm that our indexes perform better to indicate the daily trend of market composite index in advance. Via investment simulation, the performance of our indexes is analyzed in detail. The results indicate that the dynamic complex network model could not only serve as a structural description of the financial market, but also work to predict the market and guide investment by indexes.

Spillovers among regional and international stock markets

NASA Astrophysics Data System (ADS)

Huen, Tan Bee; Arsad, Zainudin; Chun, Ooi Po

2014-07-01

Realizing the greater risk by the increase in the level of financial market integration, this study investigates the dynamic of international and regional stock markets co-movement among Asian countries with the world leading market, the US. The data utilized in this study comprises of weekly closing prices for four stock indices, that consists of two developing markets (Malaysia and China) and two developed markets (Japan and the US), and encompasses the period from January 1996 to December 2012. Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model with the BEKK parameterization is employed to investigate the mean and volatility spillover effects among the selected stock indices. The results show significant mean spillover not only from the larger developed markets to smaller developing markets but also from the smaller developing markets to larger developed markets. Volatility spillover between the developed markets is found to be smaller than that between the developing markets. Conditional correlations among the stock markets are found to increase over the sample period. The findings of significant mean and volatility spillovers are considered as bad news for international investors as it reduces the benefit from portfolio diversification but act as useful information for investors to be more aware in diversifying their investment or stock selection.

H7N9 not only endanger human health but also hit stock marketing.

PubMed

Jiang, Yan; Zhang, Yi; Ma, Chunna; Wang, Quanyi; Xu, Chao; Donovan, Connor; Ali, Gholam; Xu, Tan; Sun, Wenjie

2017-01-01

This study aims to discuss the correlation between daily reported H7N9 cases and stock price indices in China. Information on daily reported H7N9 cases and stock market sectors indices between February 19, 2013 and March 31, 2014 were collected. A distributed lag non-linear model was used to describe the variation trend for the stock indices. The daily reported number of H7N9 cases was associated with the closing price of the Avian Influenza Sector Index (P < 0.05) and the opening price of the Shanghai Composite Index (P = 0.029). The Avian Influenza Sector Index decreased with increasing of daily reported case number when daily reported cases ≤ 4. Case number was associated with the opening/closing price of the Chinese Traditional Medicine Sector Index, the Biological Product Sector Index, and the Biomedicine Sector Index (P < 0.05). New or reemerging infectious diseases epidemic cause economic loss which is reflected in movements in stock prices.

Scaling analysis of stock markets

NASA Astrophysics Data System (ADS)

Bu, Luping; Shang, Pengjian

2014-06-01

In this paper, we apply the detrended fluctuation analysis (DFA), local scaling detrended fluctuation analysis (LSDFA), and detrended cross-correlation analysis (DCCA) to investigate correlations of several stock markets. DFA method is for the detection of long-range correlations used in time series. LSDFA method is to show more local properties by using local scale exponents. DCCA method is a developed method to quantify the cross-correlation of two non-stationary time series. We report the results of auto-correlation and cross-correlation behaviors in three western countries and three Chinese stock markets in periods 2004-2006 (before the global financial crisis), 2007-2009 (during the global financial crisis), and 2010-2012 (after the global financial crisis) by using DFA, LSDFA, and DCCA method. The findings are that correlations of stocks are influenced by the economic systems of different countries and the financial crisis. The results indicate that there are stronger auto-correlations in Chinese stocks than western stocks in any period and stronger auto-correlations after the global financial crisis for every stock except Shen Cheng; The LSDFA shows more comprehensive and detailed features than traditional DFA method and the integration of China and the world in economy after the global financial crisis; When it turns to cross-correlations, it shows different properties for six stock markets, while for three Chinese stocks, it reaches the weakest cross-correlations during the global financial crisis.

75 FR 16221 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing of Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-31

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing of Proposed Rule Change To Establish Strike Price Intervals and Trading Hours for Options on Index-Linked Securities March 23, 2010. Pursuant... 19b-4 thereunder,\\2\\ notice is hereby given that on March 11, 2010, The NASDAQ Stock Market LLC...

A model for the evaluation of systemic risk in stock markets

NASA Astrophysics Data System (ADS)

Caetano, Marco Antonio Leonel; Yoneyama, Takashi

2011-06-01

Systemic risk refers to the possibility of a collapse of an entire financial system or market, differing from the risk associated with any particular individual or a group pertaining to the system, which may include banks, government, brokers, and creditors. After the 2008 financial crisis, a significant amount of effort has been directed to the study of systemic risk and its consequences around the world. Although it is very difficult to predict when people begin to lose confidence in a financial system, it is possible to model the relationships among the stock markets of different countries and perform a Monte Carlo-type analysis to study the contagion effect. Because some larger and stronger markets influence smaller ones, a model inspired by a catalytic chemical model is proposed. In chemical reactions, reagents with higher concentrations tend to favor their conversion to products. In order to modulate the conversion process, catalyzers may be used. In this work, a mathematical modeling is proposed with bases on the catalytic chemical reaction model. More specifically, the Hang Seng and Dow Jones indices are assumed to dominate Ibovespa (the Brazilian Stock Market index), such that the indices of strong markets are taken as being analogous to the concentrations of the reagents and the indices of smaller markets as concentrations of products. The role of the catalyst is to model the degree of influence of one index on another. The actual data used to fit the model parameter consisted of the Hang Seng index, Dow Jones index, and Ibovespa, since 1993. “What if” analyses were carried out considering some intervention policies.

High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

NASA Astrophysics Data System (ADS)

Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

2008-02-01

Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

Quantifying the behavior of price dynamics at opening time in stock market

NASA Astrophysics Data System (ADS)

Ochiai, Tomoshiro; Takada, Hideyuki; Nacher, Jose C.

2014-11-01

The availability of huge volume of financial data has offered the possibility for understanding the markets as a complex system characterized by several stylized facts. Here we first show that the time evolution of the Japan’s Nikkei stock average index (Nikkei 225) futures follows the resistance and breaking-acceleration effects when the complete time series data is analyzed. However, in stock markets there are periods where no regular trades occur between the close of the market on one day and the next day’s open. To examine these time gaps we decompose the time series data into opening time and intermediate time. Our analysis indicates that for the intermediate time, both the resistance and the breaking-acceleration effects are still observed. However, for the opening time there are almost no resistance and breaking-acceleration effects, and volatility is always constantly high. These findings highlight unique dynamic differences between stock markets and forex market and suggest that current risk management strategies may need to be revised to address the absence of these dynamic effects at the opening time.

Impact of monetary policy changes on the Chinese monetary and stock markets

NASA Astrophysics Data System (ADS)

Tang, Yong; Luo, Yong; Xiong, Jie; Zhao, Fei; Zhang, Yi-Cheng

2013-10-01

The impact of monetary policy changes on the monetary market and stock market in China is investigated in this study. The changes of two major monetary policies, the interest rate and required reserve ratio, are analyzed in a study period covering seven years on the interbank monetary market and Shanghai stock market. We find that the monetary market is related to the macro economy trend and we also find that the monetary change surprises both of lowering and raising bring significant impacts to the two markets and the two markets respond to the changes differently. The results suggest that the impact of fluctuations is much larger for raising policy changes than lowering changes in the monetary market on policy announcing and effective dates. This is consistent with the “sign effect”, i.e. bad news brings a greater impact than good news. By studying the event window of each policy change, we also find that the “sign effect” still exists before and after each change in the monetary market. A relatively larger fluctuation is observed before the event date, which indicates that the monetary market might have a certain ability to predict a potential monetary change, while it is kept secret by the central bank before official announcement. In the stock market, we investigate how the returns and spreads of the Shanghai stock market index respond to the monetary changes. Evidences suggest the stock market is influenced but in a different way than the monetary market. The climbing of returns after the event dates for the lowering policy agrees with the theory that lowering changes can provide a monetary supply to boost the market and drive the stock returns higher but with a delay of 2 to 3 trading days on average. While in the bear market, the lowering policy brings larger volatility to the market on average than the raising ones. These empirical findings are useful for policymakers to understand how monetary policy changes impact the monetary and stock markets

Heterogeneous information-based artificial stock market

NASA Astrophysics Data System (ADS)

Pastore, S.; Ponta, L.; Cincotti, S.

2010-05-01

In this paper, an information-based artificial stock market is considered. The market is populated by heterogeneous agents that are seen as nodes of a sparsely connected graph. Agents trade a risky asset in exchange for cash. Besides the amount of cash and assets owned, each agent is characterized by a sentiment. Moreover, agents share their sentiments by means of interactions that are identified by the graph. Interactions are unidirectional and are supplied with heterogeneous weights. The agent's trading decision is based on sentiment and, consequently, the stock price process depends on the propagation of information among the interacting agents, on budget constraints and on market feedback. A central market maker (clearing house mechanism) determines the price process at the intersection of the demand and supply curves. Both closed- and open-market conditions are considered. The results point out the validity of the proposed model of information exchange among agents and are helpful for understanding the role of information in real markets. Under closed market conditions, the interaction among agents' sentiments yields a price process that reproduces the main stylized facts of real markets, e.g. the fat tails of the returns distributions and the clustering of volatility. Within open-market conditions, i.e. with an external cash inflow that results in asset price inflation, also the unitary root stylized fact is reproduced by the artificial stock market. Finally, the effects of model parameters on the properties of the artificial stock market are also addressed.

Multifractal structures for the Russian stock market

NASA Astrophysics Data System (ADS)

Ikeda, Taro

2018-02-01

In this paper, we apply the multifractal detrended fluctuation analysis (MFDFA) to the Russian stock price returns. To the best of our knowledge, this paper is the first to reveal the multifractal structures for the Russian stock market by financial crises. The contributions of the paper are twofold. (i) Finding the multifractal structures for the Russian stock market. The generalized Hurst exponents estimated become highly-nonlinear to the order of the fluctuation functions. (ii) Computing the multifractality degree according to Zunino et al. (2008). We find that the multifractality degree of the Russian stock market can be categorized within emerging markets, however, the Russian 1998 crisis and the global financial crisis dampen the degree when we consider the order of the polynomial trends in the MFDFA.

26 CFR 1.1296-2 - Definition of marketable stock.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 11 2010-04-01 2010-04-01 true Definition of marketable stock. 1.1296-2 Section... Definition of marketable stock. (a) General rule. For purposes of section 1296, the term marketable stock means— (1) Passive foreign investment company (PFIC) stock that is regularly traded, as defined in...

Stochastic cellular automata model for stock market dynamics

NASA Astrophysics Data System (ADS)

Bartolozzi, M.; Thomas, A. W.

2004-04-01

In the present work we introduce a stochastic cellular automata model in order to simulate the dynamics of the stock market. A direct percolation method is used to create a hierarchy of clusters of active traders on a two-dimensional grid. Active traders are characterized by the decision to buy, σi (t)=+1 , or sell, σi (t)=-1 , a stock at a certain discrete time step. The remaining cells are inactive, σi (t)=0 . The trading dynamics is then determined by the stochastic interaction between traders belonging to the same cluster. Extreme, intermittent events, such as crashes or bubbles, are triggered by a phase transition in the state of the bigger clusters present on the grid, where almost all the active traders come to share the same spin orientation. Most of the stylized aspects of the financial market time series, including multifractal proprieties, are reproduced by the model. A direct comparison is made with the daily closures of the S&P500 index.

STOCK Market Differences in Correlation-Based Weighted Network

NASA Astrophysics Data System (ADS)

Youn, Janghyuk; Lee, Junghoon; Chang, Woojin

We examined the sector dynamics of Korean stock market in relation to the market volatility. The daily price data of 360 stocks for 5019 trading days (from January, 1990 to August, 2008) in Korean stock market are used. We performed the weighted network analysis and employed four measures: the average, the variance, the intensity, and the coherence of network weights (absolute values of stock return correlations) to investigate the network structure of Korean stock market. We performed regression analysis using the four measures in the seven major industry sectors and the market (seven sectors combined). We found that the average, the intensity, and the coherence of sector (subnetwork) weights increase as market becomes volatile. Except for the "Financials" sector, the variance of sector weights also grows as market volatility increases. Based on the four measures, we can categorize "Financials," "Information Technology" and "Industrials" sectors into one group, and "Materials" and "Consumer Discretionary" sectors into another group. We investigated the distributions of intrasector and intersector weights for each sector and found the differences in "Financials" sector are most distinct.

Structure and dynamics of stock market in times of crisis

NASA Astrophysics Data System (ADS)

Zhao, Longfeng; Li, Wei; Cai, Xu

2016-02-01

Daily correlations among 322 S&P 500 constituent stocks are investigated by means of correlation-based (CB) network. By using the heterogeneous time scales, we identify global expansion and local clustering market behaviors during crises, which are mainly caused by community splits and inter-sector edge number decreases. The CB networks display distinctive community and sector structures. Graph edit distance is applied to capturing the dynamics of CB networks in which drastic structure reconfigurations can be observed during crisis periods. Edge statistics reveal the power-law nature of edges' duration time distribution. Despite the networks' strong structural changes during crises, we still find some long-duration edges that serve as the backbone of the stock market. Finally the dynamical change of network structure has shown its capability in predicting the implied volatility index (VIX).

Comparison of vector autoregressive (VAR) and vector error correction models (VECM) for index of ASEAN stock price

NASA Astrophysics Data System (ADS)

Suharsono, Agus; Aziza, Auliya; Pramesti, Wara

2017-12-01

Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship

Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market

NASA Astrophysics Data System (ADS)

Guo, Kun; Sun, Yi; Qian, Xin

2017-03-01

With the development of the social network, the interaction between investors in stock market became more fast and convenient. Thus, investor sentiment which can influence their investment decisions may be quickly spread and magnified through the network, and to a certain extent the stock market can be affected. This paper collected the user comments data from a popular professional social networking site of China stock market called Xueqiu, then the investor sentiment data can be obtained through semantic analysis. The dynamic analysis on relationship between investor sentiment and stock market is proposed based on Thermal Optimal Path (TOP) method. The results show that the sentiment data was not always leading over stock market price, and it can be used to predict the stock price only when the stock has high investor attention.

Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity

NASA Astrophysics Data System (ADS)

Xu, Ronghua; Wong, Wing-Keung; Chen, Guanrong; Huang, Shuo

2017-02-01

In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.

Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity

PubMed Central

Xu, Ronghua; Wong, Wing-Keung; Chen, Guanrong; Huang, Shuo

2017-01-01

In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development. PMID:28145494

Immediate causality network of stock markets

NASA Astrophysics Data System (ADS)

Zhou, Li; Qiu, Lu; Gu, Changgui; Yang, Huijie

2018-02-01

Extensive works show that a network of stocks within a single stock market stores rich information on evolutionary behaviors of the system, such as collapses and/or crises. But a financial event covers usually several markets or even the global financial system. This mismatch of scale leads to lack of concise information to coordinate the event. In this work by using the transfer entropy we reconstruct the influential network between ten typical stock markets distributed in the world. Interesting findings include, before a financial crisis the connection strength reaches a maximum, which can act as an early warning signal of financial crises. The markets in America are monodirectionally and strongly influenced by that in Europe and act as the center. Some strongly linked pairs have also close correlations. The findings are helpful in understanding the evolution and modelling the dynamical process of the global financial system. This method can be extended straightly to find early warning signals for physiological and ecological systems, etc.

Is there any overtrading in stock markets? The moderating role of big five personality traits and gender in a unilateral trend stock market.

PubMed

Zhang, Jian; Wang, Haocheng; Wang, Limin; Liu, Shuyi

2014-01-01

Overtrading is a common anomaly among stock investors. This study examines the relationship between overtrading and investment returns and the impact of the Big Five traits and gender on overtrading in a unilateral trend stock market using a simulated stock investment system. The data were derived from a sample of undergraduates from six universities who performed in a simulated stock investment situation and had their personality traits measured by the Big Five Personality Questionnaire. The results indicate that: (1) Overtrading was significant in rising stock markets, but not significant in falling markets. (2) The degree of female investors who overtraded was significant in rising markets. (3) The degree of overtrading investors who were high in extroversion or agreeableness was significant in rising markets. The implications of these results for more effective investment strategies are discussed.

Cointegration analysis and influence rank—A network approach to global stock markets

NASA Astrophysics Data System (ADS)

Yang, Chunxia; Chen, Yanhua; Niu, Lei; Li, Qian

2014-04-01

In this paper, cointegration relationships among 26 global stock market indices over the periods of sub-prime and European debt crisis and their influence rank are investigated by constructing and analyzing directed and weighted cointegration networks. The obtained results are shown as follows: the crises have changed cointegration relationships among stock market indices, their cointegration relationship increased after the Lehman Brothers collapse, while the degree of cointegration gradually decreased from the sub-prime to European debt crisis. The influence of US, Japan and China market indices are entirely distinguished over different periods. Before European debt crisis US stock market is a ‘global factor’ which leads the developed and emerging markets, while the influence of US stock market decreased evidently during the European debt crisis. Before sub-prime crisis, there is no significant evidence to show that other stock markets co-move with China stock market, while it becomes more integrated with other markets during the sub-prime and European debt crisis. Among developed and emerging stock markets, the developed stock markets lead the world stock markets before European debt crisis, while due to the shock of sub-prime and European debt crisis, their influences decreased and emerging stock markets replaced them to lead global stock markets.

Collective behavior of stock price movements in an emerging market

NASA Astrophysics Data System (ADS)

Pan, Raj Kumar; Sinha, Sitabhra

2007-10-01

To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed markets, such as the New York Stock Exchange (NYSE). This is shown to be due to the dominant influence of a common market mode on the stock prices. By comparison, interactions between related stocks, e.g., those belonging to the same business sector, are much weaker. This lack of distinct sector identity in emerging markets is explicitly shown by reconstructing the network of mutually interacting stocks. Spectral analysis of C for NSE reveals that, the few largest eigenvalues deviate from the bulk of the spectrum predicted by random matrix theory, but they are far fewer in number compared to, e.g., NYSE. We show this to be due to the relative weakness of intrasector interactions between stocks, compared to the market mode, by modeling stock price dynamics with a two-factor model. Our results suggest that the emergence of an internal structure comprising multiple groups of strongly coupled components is a signature of market development.

Multiscale Detrended Cross-Correlation Analysis of STOCK Markets

NASA Astrophysics Data System (ADS)

Yin, Yi; Shang, Pengjian

2014-06-01

In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997-2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and

Exploring Market State and Stock Interactions on the Minute Timescale

PubMed Central

Tan, Lei; Chen, Jun-Jie; Zheng, Bo; Ouyang, Fang-Yan

2016-01-01

A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is limited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more stable. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective. PMID:26900948

Exploring Market State and Stock Interactions on the Minute Timescale.

PubMed

Tan, Lei; Chen, Jun-Jie; Zheng, Bo; Ouyang, Fang-Yan

2016-01-01

A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is limited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more stable. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective.

Asymmetric MF-DCCA method based on risk conduction and its application in the Chinese and foreign stock markets

NASA Astrophysics Data System (ADS)

Cao, Guangxi; Han, Yan; Li, Qingchen; Xu, Wei

2017-02-01

The acceleration of economic globalization gradually shows the linkage of the stock markets in various counties and produces a risk conduction effect. An asymmetric MF-DCCA method is conducted based on the different directions of risk conduction (DMF-ADCCA) and by using the traditional MF-DCCA. To ensure that the empirical results are more objective and robust, this study selects the stock index data of China, the US, Germany, India, and Brazil from January 2011 to September 2014 using the asymmetric MF-DCCA method based on different risk conduction effects and nonlinear Granger causality tests to study the asymmetric cross-correlation between domestic and foreign stock markets. Empirical results indicate the existence of a bidirectional conduction effect between domestic and foreign stock markets, and the greater influence degree from foreign countries to domestic market compared with that from the domestic market to foreign countries.

Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market

NASA Astrophysics Data System (ADS)

Gong, Pu; Weng, Yingliang

2016-01-01

This paper generalizes a recently proposed spatial autoregressive model and introduces a spatiotemporal model for forecasting stock returns. We support the view that stock returns are affected not only by the absolute values of factors such as firm size, book-to-market ratio and momentum but also by the relative values of factors like trading volume ranking and market capitalization ranking in each period. This article studies a new method for constructing stocks' reference groups; the method is called quartile method. Applying the method empirically to the Shanghai Stock Exchange 50 Index, we compare the daily volatility forecasting performance and the out-of-sample forecasting performance of Value-at-Risk (VaR) estimated by different models. The empirical results show that the spatiotemporal model performs surprisingly well in terms of capturing spatial dependences among individual stocks, and it produces more accurate VaR forecasts than the other three models introduced in the previous literature. Moreover, the findings indicate that both allowing for serial correlation in the disturbances and using time-varying spatial weight matrices can greatly improve the predictive accuracy of a spatial autoregressive model.

Profitability of simple technical trading rules of Chinese stock exchange indexes

NASA Astrophysics Data System (ADS)

Zhu, Hong; Jiang, Zhi-Qiang; Li, Sai-Ping; Zhou, Wei-Xing

2015-12-01

Although technical trading rules have been widely used by practitioners in financial markets, their profitability still remains controversial. We here investigate the profitability of moving average (MA) and trading range break (TRB) rules by using the Shanghai Stock Exchange Composite Index (SHCI) from May 21, 1992 through December 31, 2013 and Shenzhen Stock Exchange Component Index (SZCI) from April 3, 1991 through December 31, 2013. The t-test is adopted to check whether the mean returns which are conditioned on the trading signals are significantly different from unconditioned returns and whether the mean returns conditioned on the buy signals are significantly different from the mean returns conditioned on the sell signals. We find that TRB rules outperform MA rules and short-term variable moving average (VMA) rules outperform long-term VMA rules. By applying White's Reality Check test and accounting for the data snooping effects, we find that the best trading rule outperforms the buy-and-hold strategy when transaction costs are not taken into consideration. Once transaction costs are included, trading profits will be eliminated completely. Our analysis suggests that simple trading rules like MA and TRB cannot beat the standard buy-and-hold strategy for the Chinese stock exchange indexes.

Is There Any Overtrading in Stock Markets? The Moderating Role of Big Five Personality Traits and Gender in a Unilateral Trend Stock Market

PubMed Central

Zhang, Jian; Wang, Haocheng; Wang, Limin; Liu, Shuyi

2014-01-01

Overtrading is a common anomaly among stock investors. This study examines the relationship between overtrading and investment returns and the impact of the Big Five traits and gender on overtrading in a unilateral trend stock market using a simulated stock investment system. The data were derived from a sample of undergraduates from six universities who performed in a simulated stock investment situation and had their personality traits measured by the Big Five Personality Questionnaire. The results indicate that: (1) Overtrading was significant in rising stock markets, but not significant in falling markets. (2) The degree of female investors who overtraded was significant in rising markets. (3) The degree of overtrading investors who were high in extroversion or agreeableness was significant in rising markets. The implications of these results for more effective investment strategies are discussed. PMID:24475235

Does Stock Market Performance Influence Retirement Intentions?

ERIC Educational Resources Information Center

Goda, Gopi Shah; Shoven, John B.; Slavov, Sita Nataraj

2012-01-01

Media reports predicted that the stock market decline in October 2008 would cause changes in retirement intentions, due to declines in retirement assets. We use panel data from the Health and Retirement Study to investigate the relationship between stock market performance and retirement intentions during 1998-2008, a period that includes the…

Financial liberalization and stock market cross-correlation: MF-DCCA analysis based on Shanghai-Hong Kong Stock Connect

NASA Astrophysics Data System (ADS)

Ruan, Qingsong; Zhang, Shuhua; Lv, Dayong; Lu, Xinsheng

2018-02-01

Based on the implementation of Shanghai-Hong Kong Stock Connect in China, this paper examines the effects of financial liberalization on stock market comovement using both multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DCCA) methods. Results based on MF-DFA confirm the multifractality of Shanghai and Hong Kong stock markets, and the market efficiency of Shanghai stock market increased after the implementation of this connect program. Besides, analysis based on MF-DCCA has verified the existence of persistent cross-correlation between Shanghai and Hong Kong stock markets, and the cross-correlation gets stronger after the launch of this liberalization program. Finally, we find that fat-tail distribution is the main source of multifractality in the cross-correlations before the stock connect program, while long-range correlation contributes to the multifractality after this program.

Analysis of portfolio optimization with lot of stocks amount constraint: case study index LQ45

NASA Astrophysics Data System (ADS)

Chin, Liem; Chendra, Erwinna; Sukmana, Agus

2018-01-01

To form an optimum portfolio (in the sense of minimizing risk and / or maximizing return), the commonly used model is the mean-variance model of Markowitz. However, there is no amount of lots of stocks constraint. And, retail investors in Indonesia cannot do short selling. So, in this study we will develop an existing model by adding an amount of lot of stocks and short-selling constraints to get the minimum risk of portfolio with and without any target return. We will analyse the stocks listed in the LQ45 index based on the stock market capitalization. To perform this analysis, we will use Solver that available in Microsoft Excel.

17 CFR 240.15g-2 - Penny stock disclosure document relating to the penny stock market.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 17 Commodity and Securities Exchanges 3 2013-04-01 2013-04-01 false Penny stock disclosure document relating to the penny stock market. 240.15g-2 Section 240.15g-2 Commodity and Securities Exchanges... Section 15(d) of the Act § 240.15g-2 Penny stock disclosure document relating to the penny stock market...

17 CFR 240.15g-2 - Penny stock disclosure document relating to the penny stock market.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 3 2011-04-01 2011-04-01 false Penny stock disclosure document relating to the penny stock market. 240.15g-2 Section 240.15g-2 Commodity and Securities Exchanges... Section 15(d) of the Act § 240.15g-2 Penny stock disclosure document relating to the penny stock market...

17 CFR 240.15g-2 - Penny stock disclosure document relating to the penny stock market.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 4 2014-04-01 2014-04-01 false Penny stock disclosure document relating to the penny stock market. 240.15g-2 Section 240.15g-2 Commodity and Securities Exchanges... Section 15(d) of the Act § 240.15g-2 Penny stock disclosure document relating to the penny stock market...

17 CFR 240.15g-2 - Penny stock disclosure document relating to the penny stock market.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 3 2012-04-01 2012-04-01 false Penny stock disclosure document relating to the penny stock market. 240.15g-2 Section 240.15g-2 Commodity and Securities Exchanges... Section 15(d) of the Act § 240.15g-2 Penny stock disclosure document relating to the penny stock market...

Geography and distance effect on financial dynamics in the Chinese stock market

NASA Astrophysics Data System (ADS)

Li, Xing; Qiu, Tian; Chen, Guang; Zhong, Li-Xin; Jiang, Xiong-Fei

2016-09-01

Geography effect is investigated for the Chinese stock market including the Shanghai and Shenzhen stock markets, based on the daily data of individual stocks. The stocks in the Shanghai city and the Guangdong province are found to greatly contribute to the Shanghai and Shenzhen markets in the geographical sector, respectively. By investigating a geographical correlation on a geographical parameter, the stock location is found to have an impact on the financial dynamics, except for the financial crisis time of the Shenzhen market. Stock distance effect is further studied, with the probability of the short distance observed to be much greater than that of the long distance. The distance is found to only affect the stock correlation of the Shanghai stock market, but has no effect on the Shenzhen stock market.

Evolutionary model of stock markets

NASA Astrophysics Data System (ADS)

Kaldasch, Joachim

2014-12-01

The paper presents an evolutionary economic model for the price evolution of stocks. Treating a stock market as a self-organized system governed by a fast purchase process and slow variations of demand and supply the model suggests that the short term price distribution has the form a logistic (Laplace) distribution. The long term return can be described by Laplace-Gaussian mixture distributions. The long term mean price evolution is governed by a Walrus equation, which can be transformed into a replicator equation. This allows quantifying the evolutionary price competition between stocks. The theory suggests that stock prices scaled by the price over all stocks can be used to investigate long-term trends in a Fisher-Pry plot. The price competition that follows from the model is illustrated by examining the empirical long-term price trends of two stocks.

Stock Market Savvy.

ERIC Educational Resources Information Center

Okula, Susan

2003-01-01

This issue of Keying In, the newsletter of the National Business Education Association, focuses upon teaching young adults how to develop both investment strategies and an understanding of the stock market. The first article, "Sound Investing Know-How: A Must for Today's Young Adults," describes how young adults can plan for their own…

Daily happiness and stock returns: Some international evidence

NASA Astrophysics Data System (ADS)

Zhang, Wei; Li, Xiao; Shen, Dehua; Teglio, Andrea

2016-10-01

In this paper, we examine the relations between the daily happiness sentiment extracted from Twitter and the stock market performance in 11 international stock markets. By partitioning this happiness sentiment into quintiles from the least to the happiest days, we first show that the contemporary correlation coefficients between happiness sentiment and index return in the 4 and most-happiness subgroups are higher than that in least, 2 and 3-happiness subgroups. Secondly, the happiness sentiment can provide additional explanatory power for index return in the most-happiness subgroup. Thirdly, the daily happiness can granger-cause the changes in index return for the majority of stock markets. Fourthly, we find that the index return and the range-based volatility of the most-happiness subgroup are larger than those of other subgroups. These results highlight the important role of social media in stock market.

Quantifying Stock Return Distributions in Financial Markets.

PubMed

Botta, Federico; Moat, Helen Susannah; Stanley, H Eugene; Preis, Tobias

2015-01-01

Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time scales. Here, we analyze a dataset comprising the stocks forming the Dow Jones Industrial Average at a second by second resolution in the period from January 2008 to July 2010 in order to quantify the distribution of changes in market prices at a range of time scales. We find that the tails of the distributions of logarithmic price changes, or returns, exhibit power law decays for time scales ranging from 300 seconds to 3600 seconds. For larger time scales, we find that the distributions tails exhibit exponential decay. Our findings may inform the development of models of market behavior across varying time scales.

Cross-correlations between West Texas Intermediate crude oil and the stock markets of the BRIC

NASA Astrophysics Data System (ADS)

Ma, Feng; Wei, Yu; Huang, Dengshi; Zhao, Lin

2013-11-01

In this paper, we investigate the cross-correlation properties between West Texas Intermediate crude oil and the stock markets of the BRIC. We use not only the qualitative analysis of the cross-correlation test, but also take the quantitative analysis of the MF-DXA, confirming the cross-correlation relationship between West Texas Intermediate crude oil and the stock markets of the BRIC (Brazil, Russia, India and China) respectively, which have strongly multifractal features, and the cross-correlations are more strongly multifractal in the short term than in the long term. Furthermore, based on the multifractal spectrum, we also find the multifractality strength between the crude oil WTI and Chinese stock market is stronger than the multifractality strength of other pairs. Based on the Iraq war (Mar 20, 2003) and the Financial crisis in 2008, we divide sample period into four segments to research the degree of the multifractal (ΔH) and the market efficiency (and the risk). Finally, we employ the technique of the rolling window to calculate the time-varying EI (efficiency index) and dependent on the EI, we can easily observe the change of stock markets. Furthermore, we explore the relationship between bivariate cross-correlation exponents (Hxy(q)) and the generalized Hurst exponents.

Networks of volatility spillovers among stock markets

NASA Astrophysics Data System (ADS)

Baumöhl, Eduard; Kočenda, Evžen; Lyócsa, Štefan; Výrost, Tomáš

2018-01-01

In our network analysis of 40 developed, emerging and frontier stock markets during the 2006-2014 period, we describe and model volatility spillovers during both the global financial crisis and tranquil periods. The resulting market interconnectedness is depicted by fitting a spatial model incorporating several exogenous characteristics. We document the presence of significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity market - volatility spillovers decrease when markets are characterized by greater temporal proximity. Volatility spillovers also present a high degree of interconnectedness, which is measured by high spatial autocorrelation. This finding is confirmed by spatial regression models showing that indirect effects are much stronger than direct effects; i.e., market-related changes in 'neighboring' markets (within a network) affect volatility spillovers more than changes in the given market alone, suggesting that spatial effects simply cannot be ignored when modeling stock market relationships. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.

Quantifying Stock Return Distributions in Financial Markets

PubMed Central

Botta, Federico; Moat, Helen Susannah; Stanley, H. Eugene; Preis, Tobias

2015-01-01

Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time scales. Here, we analyze a dataset comprising the stocks forming the Dow Jones Industrial Average at a second by second resolution in the period from January 2008 to July 2010 in order to quantify the distribution of changes in market prices at a range of time scales. We find that the tails of the distributions of logarithmic price changes, or returns, exhibit power law decays for time scales ranging from 300 seconds to 3600 seconds. For larger time scales, we find that the distributions tails exhibit exponential decay. Our findings may inform the development of models of market behavior across varying time scales. PMID:26327593

A multi-assets artificial stock market with zero-intelligence traders

NASA Astrophysics Data System (ADS)

Ponta, L.; Raberto, M.; Cincotti, S.

2011-01-01

In this paper, a multi-assets artificial financial market populated by zero-intelligence traders with finite financial resources is presented. The market is characterized by different types of stocks representing firms operating in different sectors of the economy. Zero-intelligence traders follow a random allocation strategy which is constrained by finite resources, past market volatility and allocation universe. Within this framework, stock price processes exhibit volatility clustering, fat-tailed distribution of returns and reversion to the mean. Moreover, the cross-correlations between returns of different stocks are studied using methods of random matrix theory. The probability distribution of eigenvalues of the cross-correlation matrix shows the presence of outliers, similar to those recently observed on real data for business sectors. It is worth noting that business sectors have been recovered in our framework without dividends as only consequence of random restrictions on the allocation universe of zero-intelligence traders. Furthermore, in the presence of dividend-paying stocks and in the case of cash inflow added to the market, the artificial stock market points out the same structural results obtained in the simulation without dividends. These results suggest a significative structural influence on statistical properties of multi-assets stock market.

The Stock Market Game: Classroom Use and Strategy.

ERIC Educational Resources Information Center

Wood, William C.; And Others

1992-01-01

Discusses the Stock Market Game in which teams of students buy and sell stocks. Reviews information on the costs and benefits of the game and its uses. Examines game strategies through the economics of capital markets. Concludes that substantial costs in class time may be outweighed by benefits in some classroom situations. (DK)

A Tale of Two Stock Markets

ERIC Educational Resources Information Center

Armstrong, Michelle Hine; Piercey, Victor I.; Greene-Hunley, Stephanie

2015-01-01

This article describes two different projects using the stock market as a context for learning. For both projects, students "bought" shares in individual companies, tracked stock prices for a period of time, and then "sold" their shares at a gain or loss. The projects are adaptable for students in late elementary school through…

Analysis of the efficiency-integration nexus of Japanese stock market

NASA Astrophysics Data System (ADS)

Rizvi, Syed Aun R.; Arshad, Shaista

2017-03-01

This paper attempts a novel approach in analysing the Japanese economy through a dual-dimension analysis of its stock market, examining the efficiency and market integration. Taking a period of 24 years, this study employs MFDFA and MGARCH to understand how the efficiency and integration of the stock market faired during different business cycle phases of the Japanese economy. The results showed improving efficiency over the time period. For the case of market integration, our findings conform to recent literature on business cycles and stock market integration that every succeeding recession creates a break into integration levels resulting in a decrease.

Risk-Adjusted Returns and Stock Market Games.

ERIC Educational Resources Information Center

Kagan, Gary; And Others

1995-01-01

Maintains that stock market games are designed to provide students with a background for investing in securities, especially stocks. Reviews two games used with secondary students, analyzes statistical data from these experiences, and considers weaknesses in the games. (CFR)

Group identification in Indonesian stock market

NASA Astrophysics Data System (ADS)

Nurriyadi Suparno, Ervano; Jo, Sung Kyun; Lim, Kyuseong; Purqon, Acep; Kim, Soo Yong

2016-08-01

The characteristic of Indonesian stock market is interesting especially because it represents developing countries. We investigate the dynamics and structures by using Random Matrix Theory (RMT). Here, we analyze the cross-correlation of the fluctuations of the daily closing price of stocks from the Indonesian Stock Exchange (IDX) between January 1, 2007, and October 28, 2014. The eigenvalue distribution of the correlation matrix consists of noise which is filtered out using the random matrix as a control. The bulk of the eigenvalue distribution conforms to the random matrix, allowing the separation of random noise from original data which is the deviating eigenvalues. From the deviating eigenvalues and the corresponding eigenvectors, we identify the intrinsic normal modes of the system and interpret their meaning based on qualitative and quantitative approach. The results show that the largest eigenvector represents the market-wide effect which has a predominantly common influence toward all stocks. The other eigenvectors represent highly correlated groups within the system. Furthermore, identification of the largest components of the eigenvectors shows the sector or background of the correlated groups. Interestingly, the result shows that there are mainly two clusters within IDX, natural and non-natural resource companies. We then decompose the correlation matrix to investigate the contribution of the correlated groups to the total correlation, and we find that IDX is still driven mainly by the market-wide effect.

A pre-crisis vs. crisis analysis of peripheral EU stock markets by means of wavelet transform and a nonlinear causality test

NASA Astrophysics Data System (ADS)

Polanco-Martínez, J. M.; Fernández-Macho, J.; Neumann, M. B.; Faria, S. H.

2018-01-01

This paper presents an analysis of EU peripheral (so-called PIIGS) stock market indices and the S&P Europe 350 index (SPEURO), as a European benchmark market, over the pre-crisis (2004-2007) and crisis (2008-2011) periods. We computed a rolling-window wavelet correlation for the market returns and applied a non-linear Granger causality test to the wavelet decomposition coefficients of these stock market returns. Our results show that the correlation is stronger for the crisis than for the pre-crisis period. The stock market indices from Portugal, Italy and Spain were more interconnected among themselves during the crisis than with the SPEURO. The stock market from Portugal is the most sensitive and vulnerable PIIGS member, whereas the stock market from Greece tends to move away from the European benchmark market since the 2008 financial crisis till 2011. The non-linear causality test indicates that in the first three wavelet scales (intraweek, weekly and fortnightly) the number of uni-directional and bi-directional causalities is greater during the crisis than in the pre-crisis period, because of financial contagion. Furthermore, the causality analysis shows that the direction of the Granger cause-effect for the pre-crisis and crisis periods is not invariant in the considered time-scales, and that the causality directions among the studied stock markets do not seem to have a preferential direction. These results are relevant to better understand the behaviour of vulnerable stock markets, especially for investors and policymakers.

Log-periodic view on critical dates of the Chinese stock market bubbles

NASA Astrophysics Data System (ADS)

Li, Chong

2017-01-01

We present an analysis of critical dates of three historical Chinese stock market bubbles (July 2006-Oct. 2007, Dec. 2007-Oct. 2008, Oct. 2014-June 2015) based on the Shanghai Shenzhen CSI 300 index (CSI300). This supports that the log-periodic power law singularity (LPPLS) model can describe well the behavior of super-exponential (power law with finite-time singularity) increase or decrease of the CSI300 index, suggesting that the LPPLS is available to predict the critical date. We also attempt to analyze the fitting parameter α of the LPPLS and the forecast gap which is between the last observed date and the expected critical date, proposing that the forecast gap is an alternative way for advanced warning of the market conversion.

Applications of statistical and atomic physics to the spectral line broadening and stock markets

NASA Astrophysics Data System (ADS)

Volodko, Dmitriy

The purpose of this investigation is the application of time correlation function methodology on the theoretical research of the shift of hydrogen and hydrogen-like spectral lines due to electrons and ions interaction with the spectral line emitters-dipole ionic-electronic shift (DIES) and the describing a behavior of stock-market in terms of a simple physical model simulation which obeys Levy statistical distribution---the same as that of the real stock-market index. Using Generalized Theory of Stark broadening of electrons in plasma we discovered a new source of the shift of hydrogen and hydrogen-like spectral lines that we called a dipole ionic-electronic shift (DIES). This shift results from the indirect coupling of electron and ion microfields in plasmas which is facilitated by the radiating atom/ion. We have shown that the DIES, unlike all previously known shifts, is highly nonlinear and has a different sign for different ranges of plasma parameters. The most favorable conditions for observing the DIES correspond to plasmas of high densities, but of relatively low temperature. For the Balmer-alpha line of hydrogen with the most favorable observational conditions Ne > 1018 cm-3, T < 2 eV, the DIES has been already confirmed experimentally. Based on the study of the time correlations and of the probability distribution of fluctuations in the stock market, we developed a relatively simple physical model, which simulates the Dow Jones Industrials index and makes short-term (a couple of days) predictions of its trend.

Network formation in a multi-asset artificial stock market

NASA Astrophysics Data System (ADS)

Wu, Songtao; He, Jianmin; Li, Shouwei; Wang, Chao

2018-04-01

A multi-asset artificial stock market is developed. In the market, stocks are assigned to a number of sectors and traded by heterogeneous investors. The mechanism of continuous double auction is employed to clear order book and form daily closed prices. Simulation results of prices at the sector level show an intra-sector similarity and inter-sector distinctiveness, and returns of individual stocks have stylized facts that are ubiquitous in the real-world stock market. We find that the market risk factor has critical impact on both network topology transition and connection formation, and that sector risk factors account for the formation of intra-sector links and sector-based local interaction. In addition, the number of community in threshold-based networks is correlated negatively and positively with the value of correlation coefficients and the ratio of intra-sector links, which are respectively determined by intensity of sector risk factors and the number of sectors.

Market impact and trading profile of hidden orders in stock markets.

PubMed

Moro, Esteban; Vicente, Javier; Moyano, Luis G; Gerig, Austin; Farmer, J Doyne; Vaglica, Gabriella; Lillo, Fabrizio; Mantegna, Rosario N

2009-12-01

We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are statistically reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power law; after the order is finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.

Market impact and trading profile of hidden orders in stock markets

NASA Astrophysics Data System (ADS)

Moro, Esteban; Vicente, Javier; Moyano, Luis G.; Gerig, Austin; Farmer, J. Doyne; Vaglica, Gabriella; Lillo, Fabrizio; Mantegna, Rosario N.

2009-12-01

We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are statistically reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power law; after the order is finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.

A hybrid approach EMD-HW for short-term forecasting of daily stock market time series data

NASA Astrophysics Data System (ADS)

Awajan, Ahmad Mohd; Ismail, Mohd Tahir

2017-08-01

Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Holt-Winter method (EMD-HW) is used to improve forecasting performances in financial time series. The strength of this EMD-HW lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 11 countries is applied to show the forecasting performance of the proposed EMD-HW. Based on the three forecast accuracy measures, the results indicate that EMD-HW forecasting performance is superior to traditional Holt-Winter forecasting method.

Chaotic behavior in Malaysian stock market: A study with recurrence quantification analysis

NASA Astrophysics Data System (ADS)

Niu, Betty Voon Wan; Noorani, Mohd Salmi Md; Jaaman, Saiful Hafizah

2016-11-01

The dynamics of stock market has been questioned for decades. Its behavior appeared random yet some found it behaves as chaos. Up to 5000 daily adjusted closing data of FTSE Bursa Malaysia Kuala Lumpur Composite Index (KLSE) was investigated through recurrence plot and recurrence quantification analysis. Results were compared between stochastic system, chaotic system and deterministic system. Results show that KLSE daily adjusted closing data behaves chaotically.

Impact of stock market structure on intertrade time and price dynamics.

PubMed

Ivanov, Plamen Ch; Yuen, Ainslie; Perakakis, Pandelis

2014-01-01

We analyse times between consecutive transactions for a diverse group of stocks registered on the NYSE and NASDAQ markets, and we relate the dynamical properties of the intertrade times with those of the corresponding price fluctuations. We report that market structure strongly impacts the scale-invariant temporal organisation in the transaction timing of stocks, which we have observed to have long-range power-law correlations. Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing on scales within a trading day, indicating influences of market structure. We also report a persistent decrease in correlation strength of intertrade times with increasing average intertrade time and with corresponding decrease in companies' market capitalization-a trend which is less pronounced for NASDAQ stocks. Surprisingly, we observe that stronger power-law correlations in intertrade times are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of intertrade times and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ markets, we demonstrate that the stronger correlations we find in intertrade times for NASDAQ stocks are associated with stronger correlations in absolute price returns and with higher volatility, suggesting that market structure may affect price behavior through information contained in transaction timing. These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure. Further, our results have implications for utilising transaction timing

Impact of Stock Market Structure on Intertrade Time and Price Dynamics

PubMed Central

Ivanov, Plamen Ch.; Yuen, Ainslie; Perakakis, Pandelis

2014-01-01

We analyse times between consecutive transactions for a diverse group of stocks registered on the NYSE and NASDAQ markets, and we relate the dynamical properties of the intertrade times with those of the corresponding price fluctuations. We report that market structure strongly impacts the scale-invariant temporal organisation in the transaction timing of stocks, which we have observed to have long-range power-law correlations. Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing on scales within a trading day, indicating influences of market structure. We also report a persistent decrease in correlation strength of intertrade times with increasing average intertrade time and with corresponding decrease in companies' market capitalization–a trend which is less pronounced for NASDAQ stocks. Surprisingly, we observe that stronger power-law correlations in intertrade times are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of intertrade times and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ markets, we demonstrate that the stronger correlations we find in intertrade times for NASDAQ stocks are associated with stronger correlations in absolute price returns and with higher volatility, suggesting that market structure may affect price behavior through information contained in transaction timing. These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure. Further, our results have implications for utilising transaction timing

Is the stock market efficient?

PubMed

Malkiel, B G

1989-03-10

A stock market is said to be efficient if it accurately reflects all relevant information in determining security prices. Critics have asserted that share prices are far too volatile to be explained by changes in objective economic events-the October 1987 crash being a case in point. Although the evidence is not unambiguous, reports of the death of the efficient market hypothesis appear premature.

Forecasting the value-at-risk of Chinese stock market using the HARQ model and extreme value theory

NASA Astrophysics Data System (ADS)

Liu, Guangqiang; Wei, Yu; Chen, Yongfei; Yu, Jiang; Hu, Yang

2018-06-01

Using intraday data of the CSI300 index, this paper discusses value-at-risk (VaR) forecasting of the Chinese stock market from the perspective of high-frequency volatility models. First, we measure the realized volatility (RV) with 5-minute high-frequency returns of the CSI300 index and then model it with the newly introduced heterogeneous autoregressive quarticity (HARQ) model, which can handle the time-varying coefficients of the HAR model. Second, we forecast the out-of-sample VaR of the CSI300 index by combining the HARQ model and extreme value theory (EVT). Finally, using several popular backtesting methods, we compare the VaR forecasting accuracy of HARQ model with other traditional HAR-type models, such as HAR, HAR-J, CHAR, and SHAR. The empirical results show that the novel HARQ model can beat other HAR-type models in forecasting the VaR of the Chinese stock market at various risk levels.

Extreme value modelling of Ghana stock exchange index.

PubMed

Nortey, Ezekiel N N; Asare, Kwabena; Mettle, Felix Okoe

2015-01-01

Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana stock exchange all-shares index (2000-2010) by applying the extreme value theory (EVT) to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before the EVT method was applied. The Peak Over Threshold approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model's goodness of fit was assessed graphically using Q-Q, P-P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the value at risk and expected shortfall risk measures at some high quantiles, based on the fitted GPD model.

Learning from the Market: Integrating "The Stock Market Game" (tm) across the Curriculum. EconomicsAmerica.

ERIC Educational Resources Information Center

National Council on Economic Education, New York, NY.

This book is designed to help teachers connect "The Stock Market Game" (tm) and the school curriculum. Three key economic themes developed in the lessons include: (1) stock buyers engage in economizing behavior; (2) market economies encourage the production of wealth; and (3) market activity takes place in the context of a legal…

Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price

NASA Astrophysics Data System (ADS)

Zhuang, Xiaoyang; Wei, Yu; Ma, Feng

2015-07-01

In this paper, the multifractality and efficiency degrees of ten important Chinese sectoral indices are evaluated using the methods of MF-DFA and generalized Hurst exponents. The study also scrutinizes the dynamics of the efficiency of Chinese sectoral stock market by the rolling window approach. The overall empirical findings revealed that all the sectoral indices of Chinese stock market exist different degrees of multifractality. The results of different efficiency measures have agreed on that the 300 Materials index is the least efficient index. However, they have a slight diffidence on the most efficient one. The 300 Information Technology, 300 Telecommunication Services and 300 Health Care indices are comparatively efficient. We also investigate the cross-correlations between the ten sectoral indices and WTI crude oil price based on Multifractal Detrended Cross-correlation Analysis. At last, some relevant discussions and implications of the empirical results are presented.

Hierarchical structure of stock price fluctuations in financial markets

NASA Astrophysics Data System (ADS)

Gao, Ya-Chun; Cai, Shi-Min; Wang, Bing-Hong

2012-12-01

The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they share. In this paper, the She-Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviating from Kolmogorov monofractal scaling of the velocity fluctuation in fluid turbulence, is applied to study and quantify the hierarchical structure of stock price fluctuations in financial markets. We therefore observed certain interesting results: (i) the hierarchical structure related to multifractal scaling generally presents in all the stock price fluctuations we investigated. (ii) The quantitatively statistical parameters that describe SL hierarchy are different between developed financial markets and emerging ones, distinctively. (iii) For the high-frequency stock price fluctuation, the hierarchical structure varies with different time periods. All these results provide a novel analogy in turbulence and financial market dynamics and an insight to deeply understand multifractality in financial markets.

What the 2008 stock market crash means for retirement security.

PubMed

Butrica, Barbara A; Smith, Karen E; Toder, Eric J

2010-10-01

The 2008 stock market crash raises concerns about retirement security, especially since the increased prevalence of 401(k) and similar retirement saving plans means that more Americans are now stakeholders in the equity market than in the past. Using a dynamic microsimulation model, this paper explores the ability of alternate future stock market scenarios to restore retirement assets. The authors find that those near retirement could fare the worst because they have no time to recoup their losses. Mid-career workers could fare better because they have more time to rebuild their wealth. They may even gain income if they buy stocks at low prices and get above-average rates of return. High-income groups will be the most affected because they are most likely to have financial assets and to be invested in the stock market.

Impact of Stock Market Structure on Intertrade Time and Price Dynamics

NASA Astrophysics Data System (ADS)

Yuen, Ainslie; Ivanov, Plamen Ch.

2005-08-01

The NYSE and NASDAQ stock markets have very different structures and there is continuing controversy over whether differences in stock price behaviour are due to market structure or company characteristics. As the influence of market structure on stock prices may be obscured by exogenous factors such as demand and supply, we hypothesize that modulation of the flow of transactions due to market operations may carry a stronger imprint of the internal market mechanism. We analyse times between consecutive transactions (ITT) for NYSE and NASDAQ stocks, and we relate the dynamical properties of the ITT with those of the corresponding price fluctuations. We find a robust scale-invariant temporal organisation in the ITT of stocks which is independent of individual company characteristics and industry sector, but which depends on market structure. We find that stocks registered on the NASDAQ exhibit stronger correlations in their transaction timing within a trading day, compared with NYSE stocks. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing within a trading day, after the move, suggesting influences of market structure. Surprisingly, we also observe that stronger power-law correlations in the ITT are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of ITT and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ, we demonstrate that the higher correlations we find in ITT for NASDAQ stocks are matched by higher correlations in absolute price returns and by higher volatility, suggesting that market structure may affect price behaviour through information contained in transaction timing.

Structural Evolutions of STOCK Markets Controlled by Generalized Entropy Principles of Complex Systems

NASA Astrophysics Data System (ADS)

Wang, Yi Jiao; Feng, Qing Yi; Chai, Li He

As one of the most important financial markets and one of the main parts of economic system, the stock market has become the research focus in economics. The stock market is a typical complex open system far from equilibrium. Many available models that make huge contribution to researches on market are strong in describing the market however, ignoring strong nonlinear interactions among active agents and weak in reveal underlying dynamic mechanisms of structural evolutions of market. From econophysical perspectives, this paper analyzes the complex interactions among agents and defines the generalized entropy in stock markets. Nonlinear evolutionary dynamic equation for the stock markets is then derived from Maximum Generalized Entropy Principle. Simulations are accordingly conducted for a typical case with the given data, by which the structural evolution of the stock market system is demonstrated. Some discussions and implications are finally provided.

The Stock Market: Risk vs. Uncertainty.

ERIC Educational Resources Information Center

Griffitts, Dawn

2002-01-01

This economics education publication focuses on the U.S. stock market and the risk and uncertainty that an individual faces when investing in the market. The material explains that risk and uncertainty relate to the same underlying concept randomness. It defines and discusses both concepts and notes that although risk is quantifiable, uncertainty…

The study of Thai stock market across the 2008 financial crisis

NASA Astrophysics Data System (ADS)

Kanjamapornkul, K.; Pinčák, Richard; Bartoš, Erik

2016-11-01

The cohomology theory for financial market can allow us to deform Kolmogorov space of time series data over time period with the explicit definition of eight market states in grand unified theory. The anti-de Sitter space induced from a coupling behavior field among traders in case of a financial market crash acts like gravitational field in financial market spacetime. Under this hybrid mathematical superstructure, we redefine a behavior matrix by using Pauli matrix and modified Wilson loop for time series data. We use it to detect the 2008 financial market crash by using a degree of cohomology group of sphere over tensor field in correlation matrix over all possible dominated stocks underlying Thai SET50 Index Futures. The empirical analysis of financial tensor network was performed with the help of empirical mode decomposition and intrinsic time scale decomposition of correlation matrix and the calculation of closeness centrality of planar graph.

The overnight effect on the Taiwan stock market

NASA Astrophysics Data System (ADS)

Tsai, Kuo-Ting; Lih, Jiann-Shing; Ko, Jing-Yuan

2012-12-01

This study examines statistical regularities among three components of stocks and indices: daytime (trading hour) return, overnight (off-hour session) return, and total (close-to-close) return. Owing to the fact that the Taiwan Stock Exchange (TWSE) has the longest non-trading periods among major markets, the TWSE is selected to explore the correlation among the three components and compare it with major markets such as the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automated Quotation (NASDAQ). Analysis results indicate a negative cross correlation between the sign of daytime return and the sign of overnight return; possibly explaining why most stocks feature a negative cross correlation between daytime return and overnight return [F. Wang, S.-J. Shieh, S. Havlin, H.E. Stanley, Statistical analysis of the overnight and daytime return, Phys. Rev. E 79 (2009) 056109]. Additionally, the cross correlation between the magnitude of returns is analyzed. According to those results, a larger magnitude of overnight return implies a higher probability that the sign of the following daytime return is the opposite of the sign of overnight return. Namely, the predictability of daytime return might be improved when a stock undergoes a large magnitude of overnight return. Furthermore, the cross correlations of 29 indices of worldwide markets are discussed.

A quantum anharmonic oscillator model for the stock market

NASA Astrophysics Data System (ADS)

Gao, Tingting; Chen, Yu

2017-02-01

A financially interpretable quantum model is proposed to study the probability distributions of the stock price return. The dynamics of a quantum particle is considered an analog of the motion of stock price. Then the probability distributions of price return can be computed from the wave functions that evolve according to Schrodinger equation. Instead of a harmonic oscillator in previous studies, a quantum anharmonic oscillator is applied to the stock in liquid market. The leptokurtic distributions of price return can be reproduced by our quantum model with the introduction of mixed-state and multi-potential. The trend following dominant market, in which the price return follows a bimodal distribution, is discussed as a specific case of the illiquid market.

Stylized facts in internal rates of return on stock index and its derivative transactions

NASA Astrophysics Data System (ADS)

Pichl, Lukáš; Kaizoji, Taisei; Yamano, Takuya

2007-08-01

Universal features in stock markets and their derivative markets are studied by means of probability distributions in internal rates of return on buy and sell transaction pairs. Unlike the stylized facts in normalized log returns, the probability distributions for such single asset encounters incorporate the time factor by means of the internal rate of return, defined as the continuous compound interest. Resulting stylized facts are shown in the probability distributions derived from the daily series of TOPIX, S & P 500 and FTSE 100 index close values. The application of the above analysis to minute-tick data of NIKKEI 225 and its futures market, respectively, reveals an interesting difference in the behavior of the two probability distributions, in case a threshold on the minimal duration of the long position is imposed. It is therefore suggested that the probability distributions of the internal rates of return could be used for causality mining between the underlying and derivative stock markets. The highly specific discrete spectrum, which results from noise trader strategies as opposed to the smooth distributions observed for fundamentalist strategies in single encounter transactions may be useful in deducing the type of investment strategy from trading revenues of small portfolio investors.

Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach

NASA Astrophysics Data System (ADS)

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

2014-09-01

Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.

Tests of nonuniversality of the stock return distributions in an emerging market

NASA Astrophysics Data System (ADS)

Mu, Guo-Hua; Zhou, Wei-Xing

2010-12-01

There is convincing evidence showing that the probability distributions of stock returns in mature markets exhibit power-law tails and both the positive and negative tails conform to the inverse cubic law. It supports the possibility that the tail exponents are universal at least for mature markets in the sense that they do not depend on stock market, industry sector, and market capitalization. We investigate the distributions of intraday returns at different time scales ( Δt=1 , 5, 15, and 30 min) of all the A-share stocks traded in the Chinese stock market, which is the largest emerging market in the world. We find that the returns can be well fitted by the q -Gaussian distribution and the tails have power-law relaxations with the exponents increasing with Δt and being well outside the Lévy stable regime for individual stocks. We provide statistically significant evidence showing that, at small time scales Δt<15min , the exponents logarithmically decrease with the turnover rate and increase with the market capitalization. When Δt>15min , no conclusive evidence is found for a possible dependence of the tail exponent on the turnover rate or the market capitalization. Our findings indicate that the intraday return distributions at small time scales are not universal in emerging stock markets but might be universal at large time scales.

Multifractal detrended cross-correlations between crude oil market and Chinese ten sector stock markets

NASA Astrophysics Data System (ADS)

Yang, Liansheng; Zhu, Yingming; Wang, Yudong; Wang, Yiqi

2016-11-01

Based on the daily price data of spot prices of West Texas Intermediate (WTI) crude oil and ten CSI300 sector indices in China, we apply multifractal detrended cross-correlation analysis (MF-DCCA) method to investigate the cross-correlations between crude oil and Chinese sector stock markets. We find that the strength of multifractality between WTI crude oil and energy sector stock market is the highest, followed by the strength of multifractality between WTI crude oil and financial sector market, which reflects a close connection between energy and financial market. Then we do vector autoregression (VAR) analysis to capture the interdependencies among the multiple time series. By comparing the strength of multifractality for original data and residual errors of VAR model, we get a conclusion that vector auto-regression (VAR) model could not be used to describe the dynamics of the cross-correlations between WTI crude oil and the ten sector stock markets.

A self-similar hierarchy of the Korean stock market

NASA Astrophysics Data System (ADS)

Lim, Gyuchang; Min, Seungsik; Yoo, Kun-Woo

2013-01-01

A scaling analysis is performed on market values of stocks listed on Korean stock exchanges such as the KOSPI and the KOSDAQ. Different from previous studies on price fluctuations, market capitalizations are dealt with in this work. First, we show that the sum of the two stock exchanges shows a clear rank-size distribution, i.e., the Zipf's law, just as each separate one does. Second, by abstracting Zipf's law as a γ-sequence, we define a self-similar hierarchy consisting of many levels, with the numbers of firms at each level forming a geometric sequence. We also use two exponential functions to describe the hierarchy and derive a scaling law from them. Lastly, we propose a self-similar hierarchical process and perform an empirical analysis on our data set. Based on our findings, we argue that all money invested in the stock market is distributed in a hierarchical way and that a slight difference exists between the two exchanges.

Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

NASA Astrophysics Data System (ADS)

Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.

2015-06-01

This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.

Unraveling chaotic attractors by complex networks and measurements of stock market complexity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Hongduo; Li, Ying, E-mail: mnsliy@mail.sysu.edu.cn

2014-03-15

We present a novel method for measuring the complexity of a time series by unraveling a chaotic attractor modeled on complex networks. The complexity index R, which can potentially be exploited for prediction, has a similar meaning to the Kolmogorov complexity (calculated from the Lempel–Ziv complexity), and is an appropriate measure of a series' complexity. The proposed method is used to research the complexity of the world's major capital markets. None of these markets are completely random, and they have different degrees of complexity, both over the entire length of their time series and at a level of detail. However,more » developing markets differ significantly from mature markets. Specifically, the complexity of mature stock markets is stronger and more stable over time, whereas developing markets exhibit relatively low and unstable complexity over certain time periods, implying a stronger long-term price memory process.« less

Hot money and China's stock market volatility: Further evidence using the GARCH-MIDAS model

NASA Astrophysics Data System (ADS)

Wei, Yu; Yu, Qianwen; Liu, Jing; Cao, Yang

2018-02-01

This paper investigates the influence of hot money on the return and volatility of the Chinese stock market using a nonlinear Granger causality test and a new GARCH-class model based on mixed data sampling regression (GARCH-MIDAS). The empirical results suggest that no linear or nonlinear causality exists between the growth rate of hot money and the Chinese stock market return, implying that the Chinese stock market is not driven by hot money and vice versa. However, hot money has a significant positive impact on the long-term volatility of the Chinese stock market. Furthermore, the dependence between the long-term volatility caused by hot money and the total volatility of the Chinese stock market is time-variant, indicating that huge volatilities in the stock market are not always triggered by international speculation capital flow and that Chinese authorities should further focus on more systemic reforms in the trading rules and on effectively regulating the stock market.

Antibubble and prediction of China's stock market and real-estate

NASA Astrophysics Data System (ADS)

Zhou, Wei-Xing; Sornette, Didier

2004-06-01

We show that the Chinese stock markets are quite different and decoupled from Western markets (which include Tokyo). We document a well-developed log-periodic power-law antibubble in China's stock market, which started in August 2001. We argue that the current stock market antibubble is sustained by a contemporary active unsustainable real-estate bubble in China. The characteristic parameters of the antibubble have exhibited remarkable stability over one year (October 2002-October 2003). Many tests, including predictability over different horizons and time periods, confirm the high significance of the antibubble detection. Based on an analysis including data up to 2003/10/28, we have predicted that the Chinese stock market will stop its negative trend around the end of 2003 and start going up, appreciating by at least 25% in the following 6 months. We present a partial assessment of this prediction at the time of revision of this manuscript (early January 2004). Notwithstanding the immature nature of the Chinese equity market and the strong influence of government policy, we have found maybe even stronger imprints of herding than in other mature markets. This is maybe due indeed to the immaturity of the Chinese market which seems to attract short-term investors more interested in fast gains than in long-term investments, thus promoting speculative herding.

How the 2008 stock market crash and seasons affect total and cardiac deaths in Los Angeles County.

PubMed

Schwartz, Bryan Glen; Pezzullo, John Christopher; McDonald, Scott Andrew; Poole, William Kenneth; Kloner, Robert Alan

2012-05-15

Various stressors trigger cardiac death. The objective was to investigate a possible relation between a stock market crash and cardiac death in a large population within the United States. We obtained daily stock market data (Dow Jones Industrial Average Index), death certificate data for daily deaths in Los Angeles County (LA), and annual LA population estimates for 2005 through 2008. The 4 years death rate curves (2005 through 2008) were averaged into a single curve to illustrate annual trends. Data were "deseasonalized" by subtracting from the daily observed value the average value for that day of year. There was marked seasonal variation in total and cardiac death rates. Even in the mild LA climate, death rates were higher in winter versus summer including total death (+17%), circulatory death (+24%), coronary heart disease death (+28%), and myocardial infarction death (+38%) rates (p <0.0001 for each). Absolute coronary heart disease death rates have decreased since 1985. After accounting for seasonal variation, the large stock market crash in October 2008 did not affect death rates in LA. Death rates remained at or below seasonal averages during the stock market crash. In conclusion, after correcting for seasonal variation, the stock market crash in October 2008 was not associated with an increase in total or cardiac death in LA. Annual coronary heart disease death rates continue to decrease. However, seasonal variation (specifically winter) remains a trigger for death and coronary heart disease death even in LA where winters are mild. Copyright © 2012 Elsevier Inc. All rights reserved.

Structural Break, Stock Prices of Clean Energy Firms and Carbon Market

NASA Astrophysics Data System (ADS)

Wang, Yubao; Cai, Junyu

2018-03-01

This paper uses EU ETS carbon future price and Germany/UK clean energy firms stock indices to study the relationship between carbon market and clean energy market. By structural break test, it is found that the ‘non-stationary’ variables judged by classical unit root test do own unit roots and need taking first difference. After analysis of VAR and Granger causality test, no causal relationships are found between the two markets. However, when Hsiao’s version of causality test is employed, carbon market is found to have power in explaining the movement of stock prices of clean energy firms, and stock prices of clean energy firms also affect the carbon market.

A fuzzy logic model to forecast stock market momentum in Indonesia's property and real estate sector

NASA Astrophysics Data System (ADS)

Penawar, H. K.; Rustam, Z.

2017-07-01

The Capital market has the important role in Indonesia's economy. The capital market does not only support the economy of Indonesia but also being an indicator Indonesia's economy improvement. Something that has been traded in the capital market is stock (stock market). Nowadays, the stock market is full of uncertainty. That uncertainty values make predicting stock market is all that we have to do before we make a decision in the stock market. One that can be predicted in the stock market is momentum. To forecast stock market momentum, it can use fuzzy logic model. In the process of modeling, it will be used 14 days historical data that consisting the value of open, high, low, and close, to predict the next 5 days momentum categories. There are three momentum categories namely Bullish, Neutral, and Bearish. To illustrate the fuzzy logic model, we will use stocks data from several companies that listed on Indonesia Stock Exchange (IDX) in property and real estate sector.

Fractal stock markets: International evidence of dynamical (in)efficiency.

PubMed

Bianchi, Sergio; Frezza, Massimiliano

2017-07-01

The last systemic financial crisis has reawakened the debate on the efficient nature of financial markets, traditionally described as semimartingales. The standard approaches to endow the general notion of efficiency of an empirical content turned out to be somewhat inconclusive and misleading. We propose a topological-based approach to quantify the informational efficiency of a financial time series. The idea is to measure the efficiency by means of the pointwise regularity of a (stochastic) function, given that the signature of a martingale is that its pointwise regularity equals 12. We provide estimates for real financial time series and investigate their (in)efficient behavior by comparing three main stock indexes.

Fractal stock markets: International evidence of dynamical (in)efficiency

NASA Astrophysics Data System (ADS)

Bianchi, Sergio; Frezza, Massimiliano

2017-07-01

The last systemic financial crisis has reawakened the debate on the efficient nature of financial markets, traditionally described as semimartingales. The standard approaches to endow the general notion of efficiency of an empirical content turned out to be somewhat inconclusive and misleading. We propose a topological-based approach to quantify the informational efficiency of a financial time series. The idea is to measure the efficiency by means of the pointwise regularity of a (stochastic) function, given that the signature of a martingale is that its pointwise regularity equals 1/2 . We provide estimates for real financial time series and investigate their (in)efficient behavior by comparing three main stock indexes.

Dynamic evolution of cross-correlations in the Chinese stock market.

PubMed

Ren, Fei; Zhou, Wei-Xing

2014-01-01

The analysis of cross-correlations is extensively applied for the understanding of interconnections in stock markets and the portfolio risk estimation. Current studies of correlations in Chinese market mainly focus on the static correlations between return series, and this calls for an urgent need to investigate their dynamic correlations. Our study aims to reveal the dynamic evolution of cross-correlations in the Chinese stock market, and offer an exact interpretation for the evolution behavior. The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different time periods, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes. Our results provide new perspectives for the understanding of the dynamic evolution of cross-correlations in the Chines stock markets, and the result of risk estimation is valuable for the application of risk management.

Dynamic Evolution of Cross-Correlations in the Chinese Stock Market

PubMed Central

Ren, Fei; Zhou, Wei-Xing

2014-01-01

The analysis of cross-correlations is extensively applied for the understanding of interconnections in stock markets and the portfolio risk estimation. Current studies of correlations in Chinese market mainly focus on the static correlations between return series, and this calls for an urgent need to investigate their dynamic correlations. Our study aims to reveal the dynamic evolution of cross-correlations in the Chinese stock market, and offer an exact interpretation for the evolution behavior. The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different time periods, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes. Our results provide new perspectives for the understanding of the dynamic evolution of cross-correlations in the Chines stock markets, and the result of risk estimation is valuable for the application of risk management. PMID:24867071

Are Vietnam and Chinese stock markets out of the US contagion effect in extreme events?

NASA Astrophysics Data System (ADS)

Nguyen, Cuong; Ishaq Bhatti, M.; Henry, Darren

2017-08-01

This paper employs Chi-plots, Kendall (K)-plots and three different copula functions to empirically examine the tail dependence between the US stock market and stock markets in Vietnam and China in order to test contagion effects pre- and post- the US subprime mortgage crisis. The results based on data between 2003 and 2011 indicate the presence of left tail dependence before and after the crisis suggesting no change in dependence structure, but there exists stronger left tail dependence between the US and Vietnam stock markets. It is observed that the US and Vietnam stock markets are more prone to crashing than booming together. For the Chinese market, the US and Shanghai stock markets exhibit left tail dependence before the crisis, but no evidence of post-crisis tail dependency. On the contrary, the Shenzhen stock market is independent of the US market before and after the crisis which implies that an extreme event in the US market is less likely to influence the Shenzhen stock market. This suggests that there is significant potential for risk diversification by investing in the Shenzhen market by US investors after the financial crisis. These results have not been documented in the existing literature and provide a new insight into risk diversification between the two important Asian emerging stock markets.

Stock markets are not what we think they are: the key roles of cross-ownership and corporate treasury stock

NASA Astrophysics Data System (ADS)

Roehner, Bertrand M.

2005-03-01

We describe, document and statistically test three mechanisms by which corporations can influence or even control stock prices: (i) Parent and holding companies wield control over other publicly traded companies. (ii) Through clever management of treasury stock based on buyback programs and stock issuance, stock price fluctuations can be amplified or curbed. The shock of September 11, 2001 is used to test this effect. (iii) Finally, historical evidence shows that there is a close interdependence between the level of stock prices on the one hand and merger and acquisition activity on the other hand: on average, a 10% increase in the number of mergers brings about a 3% increase in the overall level of stock prices. If one adds up buybacks, initial public offerings and takeover transactions, all of which depend upon strategic decisions taken by corporate management, they represent on average 7.2% of the trade on the New York Stock Exchange over the period 1987-2003 (as much as 12% in specific years such as 1988). This perspective, in which the Boards of Directors of major companies “shepherd” the market, offers a natural interpretation of the so-called “herd behavior” observed in stock markets. The traditional view holds that, by driving profit expectations, corporations have an indirect role in shaping the market. In this paper, we suggest that over the last decades they became more and more the direct moving force of stock markets.

Scaling and volatility of breakouts and breakdowns in stock price dynamics.

PubMed

Liu, Lu; Wei, Jianrong; Huang, Jiping

2013-01-01

Because the movement of stock prices is not only ubiquitous in financial markets but also crucial for investors, extensive studies have been done to understand the law behind it. In particular, since the financial crisis in 2008, researchers have a more interest in investigating large market volatilities in order to grasp changing market trends. In this work, we analyze the breakouts and breakdowns of both the Standard & Poor's 500 Index in the US stock market and the Shanghai Composite Index in the Chinese stock market. The breakout usually represents an ongoing upward trend in technical analysis while the breakdown represents an ongoing downward trend. Based on the renormalization method, we introduce two parameters to quantize breakouts and breakdowns, respectively. We discover scaling behavior, characterized by power-law distributions for both the breakouts and breakdowns in the two financial markets with different power-law exponents, which reflect different market volatilities. In detail, the market volatility for breakdowns is usually larger than that for breakouts. Moreover, as an emerging market, the Chinese stock market has larger market volatilities for both the breakouts and breakdowns than the US stock market (a mature market). Further, the short-term volatilities show similar features for both the US stock market and the Chinese stock market. However, the medium-term volatilities in the US stock market are almost symmetrical for the breakouts and breakdowns, whereas those in the Chinese stock market appear to be asymmetrical for the breakouts and breakdowns. The methodology presented here provides a way to understand scaling and hence volatilities of breakouts and breakdowns in stock price dynamics. Our findings not only reveal the features of market volatilities but also make a comparison between mature and emerging financial markets.

Scaling and Volatility of Breakouts and Breakdowns in Stock Price Dynamics

PubMed Central

Liu, Lu; Wei, Jianrong; Huang, Jiping

2013-01-01

Background Because the movement of stock prices is not only ubiquitous in financial markets but also crucial for investors, extensive studies have been done to understand the law behind it. In particular, since the financial crisis in 2008, researchers have a more interest in investigating large market volatilities in order to grasp changing market trends. Methodology/Principal Findings In this work, we analyze the breakouts and breakdowns of both the Standard & Poor’s 500 Index in the US stock market and the Shanghai Composite Index in the Chinese stock market. The breakout usually represents an ongoing upward trend in technical analysis while the breakdown represents an ongoing downward trend. Based on the renormalization method, we introduce two parameters to quantize breakouts and breakdowns, respectively. We discover scaling behavior, characterized by power-law distributions for both the breakouts and breakdowns in the two financial markets with different power-law exponents, which reflect different market volatilities. In detail, the market volatility for breakdowns is usually larger than that for breakouts. Moreover, as an emerging market, the Chinese stock market has larger market volatilities for both the breakouts and breakdowns than the US stock market (a mature market). Further, the short-term volatilities show similar features for both the US stock market and the Chinese stock market. However, the medium-term volatilities in the US stock market are almost symmetrical for the breakouts and breakdowns, whereas those in the Chinese stock market appear to be asymmetrical for the breakouts and breakdowns. Conclusions/Signicance The methodology presented here provides a way to understand scaling and hence volatilities of breakouts and breakdowns in stock price dynamics. Our findings not only reveal the features of market volatilities but also make a comparison between mature and emerging financial markets. PMID:24376577

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

PubMed

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

PubMed Central

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J.

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483

Generating Stock Trading Rules Using Genetic Network Programming with Flag Nodes and Adjustment of Importance Indexes

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Chen, Yan; Hirasawa, Kotaro

Genetic Network Programming (GNP) is an evolutionary algorithm which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is one of the criterions for decision making. However, the values of IMXs must be deteminined by our experience/knowledge. Therefore in this paper, IMXs are adjusted appropriately during the stock trading in order to predict the rise and fall of the stocks. Moreover, newly defined flag nodes are introduced to GNP, which can appropriately judge the current situation of the stock prices, and also contributes to the use of many kinds of nodes in GNP program. In the simulation, programs are evolved using the stock prices of 20 companies. Then the generalization ability is tested and compared with GNP without flag nodes, GNP without IMX adjustment and Buy&Hold.

Evolutions of fluctuation modes and inner structures of global stock markets

NASA Astrophysics Data System (ADS)

Yan, Yan; Wang, Lei; Liu, Maoxin; Chen, Xiaosong

2016-09-01

The paper uses empirical data, including 42 globally main stock indices in the period 1996-2014, to systematically study the evolution of fluctuation modes and inner structures of global stock markets. The data are large in scale considering both time and space. A covariance matrix-based principle fluctuation mode analysis (PFMA) is used to explore the properties of the global stock markets. It has been ignored by previous studies that covariance matrix is more suitable than the correlation matrix to be the basis of PFMA. It is found that the principle fluctuation modes of global stock markets are in the same directions, and global stock markets are divided into three clusters, which are found to be closely related to the countries’ locations with exceptions of China, Russia and Czech Republic. A time-stable correlation network constructing method is proposed to solve the problem of high-level statistical uncertainty when the estimated periods are very short, and the complex dynamic network (CDN) is constructed to investigate the evolution of inner structures. The results show when the clusters emerge and how long the clusters exist. When the 2008 financial crisis broke out, the indices form one cluster. After these crises, only the European cluster still exists. These findings complement the previous studies, and can help investors and regulators to understand the global stock markets.

Impact of global financial crisis on stylized facts between energy markets and stock markets

NASA Astrophysics Data System (ADS)

Leng, Tan Kim; Cheong, Chin Wen; Hooi, Tan Siow

2014-06-01

Understanding the stylized facts is extremely important and has becomes a hot issue nowadays. However, recent global financial crisis that started from United States had spread all over the world and adversely affected the commodities and financial sectors of both developed and developing countries. This paper tends to examine the impact of crisis on stylized facts between energy and stock markets using ARCH-family models based on the experience over 2008 global financial crisis. Empirical results denote that there is long lasting, persists and positively significant the autocorrelation function of absolute returns and their squares in both markets for before and during crisis. Besides that, leverage effects are found in stock markets whereby bad news has a greater impact on volatility than good news for both before and during crisis. However, crisis does not indicate any impact on risk-return tradeoff for both energy and stock markets. For forecasting evaluations, GARCH model and FIAPARCH model indicate superior out of sample forecasts for before and during crisis respectively.

Comparison between global financial crisis and local stock disaster on top of Chinese stock network

NASA Astrophysics Data System (ADS)

Xia, Lisi; You, Daming; Jiang, Xin; Guo, Quantong

2018-01-01

The science of complex network theory can be usefully applied in many important fields, one of which is the finance. In these practical cases, a massive dataset can be represented as a very large network with certain attributes associated with its nodes and edges. As one of the most important components of financial market, stock market has been attracting more and more attention. In this paper, we propose a threshold model to build Chinese stock market networks and study the topological properties of these networks. To be specific, we compare the effects of different crises, namely the 2008 global crisis and the stock market disaster in 2015, on the threshold networks. Prices of the stocks belonging to the Shanghai and Shenzhen 300 index are considered for three periods: the global crisis, common period and the stock market disaster. We find the probability distribution of the cross-correlations of the stocks during the stock market disaster is fatter than that of others. Besides, the thresholds of cross-correlations are assigned to obtain the threshold networks and the power-law of degree distribution in these networks are observed in a certain range of threshold values. The networks during the stock market disaster also appear to have larger mean degree and modularity, which reveals the strong correlations among these stock prices. Our findings to some extent crosscheck the liquidity shortage reason which is believed to result in the outbreak of the stock market disaster. Moreover, we hope that this paper could give us a deeper understanding of the market's behavior and also lead to interesting future research about the problems of modern finance theory.

On the integration of financial markets: How strong is the evidence from five international stock markets?

NASA Astrophysics Data System (ADS)

Bentes, Sónia R.

2015-07-01

This paper examines the integration of financial markets using data from five international stock markets in the context of globalization. The theoretical basis of this study relies on the price theory and the Law of One Price, which was adjusted to the framework of financial markets. When price levels are nonstationary, cointegration and the error correction model constitute a powerful tool for the empirical examination of market integration. The error correction model provides a fully dynamic framework that allows to separating the long and the short run effects of the integration process. A dataset encompassing the daily stock price series of the PSI 20 (Portugal), IBEX 35 (Spain), FTSE 100 (UK), NIKKEI 225 (Japan) and SP 500 (US) indices from January 4th 1999 to September 19th 2014 is employed. The results highlight that these five stock markets are linked together by just one long-run relationship, although short-run movements are also present, which causes distinct deviations from the long-run equilibrium relationship. Endogeneity prevails in the system as a whole. While market integration in the sense of the Law of One Price holds, pairwise full price transmission has limited evidence. The results therefore show that stock market price movements are highly nonlinear and complex.

Initial value sensitivity of the Chinese stock market and its relationship with the investment psychology

NASA Astrophysics Data System (ADS)

Ying, Shangjun; Li, Xiaojun; Zhong, Xiuqin

2015-04-01

This paper discusses the initial value sensitivity (IVS) of Chinese stock market, including the single stock market and the Chinese A-share stock market, with respect to real markets and evolving models. The aim is to explore the relationship between IVS of the Chinese A-share stock market and the investment psychology based on the evolving model of genetic cellular automaton (GCA). We find: (1) The Chinese stock market is sensitively dependent on the initial conditions. (2) The GCA model provides a considerable reliability in complexity simulation (e.g. the IVS). (3) The IVS of stock market is positively correlated with the imitation probability when the intensity of the imitation psychology reaches a certain threshold. The paper suggests that the government should seek to keep the imitation psychology under a certain level, otherwise it may induce severe fluctuation to the market.

Value at Risk on Composite Price Share Index Stock Data

NASA Astrophysics Data System (ADS)

Oktaviarina, A.

2018-01-01

The financial servicest authority was declared Let’s Save Campaign on n commemoration of the World Savings Day that falls on this day, October 31, 2016. The activity was greeted enthusiastically by Indonesia Stock Exchange by taking out the slogan Let’s Save The Stocks. Stock is a form of investment that is expected to benefit in the future despite has risks. Value at Risk (VaR) is a method that can measure how much the risk of a financial investment. Composite Stock Price Indeks is the stock price index used by Indonesia Stock Exchange as stock volatility benchmarks in Indonesia. This study aimed to estimate Value at Risk (VaR) on closing price Composite Price Share Index Stock data on the period 20 September 2016 until 20 September 2017. Box-Pierce test results p value=0.9528 which is greater than a, that shows homoskedasticity. Value at Risk (VaR) with Variance Covariance Method is Rp.3.054.916,07 which means with 99% confindence interval someone who invests Rp.100.000.000,00 will get Rp.3.054.916,07 as a maximum loss.

Research on energy stock market associated network structure based on financial indicators

NASA Astrophysics Data System (ADS)

Xi, Xian; An, Haizhong

2018-01-01

A financial market is a complex system consisting of many interacting units. In general, due to the various types of information exchange within the industry, there is a relationship between the stocks that can reveal their clear structural characteristics. Complex network methods are powerful tools for studying the internal structure and function of the stock market, which allows us to better understand the stock market. Applying complex network methodology, a stock associated network model based on financial indicators is created. Accordingly, we set threshold value and use modularity to detect the community network, and we analyze the network structure and community cluster characteristics of different threshold situations. The study finds that the threshold value of 0.7 is the abrupt change point of the network. At the same time, as the threshold value increases, the independence of the community strengthens. This study provides a method of researching stock market based on the financial indicators, exploring the structural similarity of financial indicators of stocks. Also, it provides guidance for investment and corporate financial management.

Statistical properties and pre-hit dynamics of price limit hits in the Chinese stock markets.

PubMed

Wan, Yu-Lei; Xie, Wen-Jie; Gu, Gao-Feng; Jiang, Zhi-Qiang; Chen, Wei; Xiong, Xiong; Zhang, Wei; Zhou, Wei-Xing

2015-01-01

Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of price limit hits are not well studied partially due to the fact that main stock markets such as the US markets and most European markets do not set price limits. Here, we perform detailed analyses of the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 to investigate the statistical properties of price limit hits and the dynamical evolution of several important financial variables before stock price hits its limits. We compare the properties of up-limit hits and down-limit hits. We also divide the whole period into three bullish periods and three bearish periods to unveil possible differences during bullish and bearish market states. To uncover the impacts of stock capitalization on price limit hits, we partition all stocks into six portfolios according to their capitalizations on different trading days. We find that the price limit trading rule has a cooling-off effect (object to the magnet effect), indicating that the rule takes effect in the Chinese stock markets. We find that price continuation is much more likely to occur than price reversal on the next trading day after a limit-hitting day, especially for down-limit hits, which has potential practical values for market practitioners.

Hidden cross-correlation patterns in stock markets based on permutation cross-sample entropy and PCA

NASA Astrophysics Data System (ADS)

Lin, Aijing; Shang, Pengjian; Zhong, Bo

2014-12-01

In this article, we investigate the hidden cross-correlation structures in Chinese stock markets and US stock markets by performing PCSE combined with PCA approach. It is suggested that PCSE can provide a more faithful and more interpretable description of the dynamic mechanism between time series than cross-correlation matrix. We show that this new technique can be adapted to observe stock markets especially during financial crisis. In order to identify and compare the interactions and structures of stock markets during financial crisis, as well as in normal periods, all the samples are divided into four sub-periods. The results imply that the cross-correlations between Chinese group are stronger than the US group in the most sub-periods. In particular, it is likely that the US stock markets are more integrated with each other during global financial crisis than during Asian financial crisis. However, our results illustrate that Chinese stock markets are not immune from the global financial crisis, although less integrated with other markets if they are compared with US stock markets.

A network analysis of the Chinese stock market

NASA Astrophysics Data System (ADS)

Huang, Wei-Qiang; Zhuang, Xin-Tian; Yao, Shuang

2009-07-01

In many practical important cases, a massive dataset can be represented as a very large network with certain attributes associated with its vertices and edges. Stock markets generate huge amounts of data, which can be use for constructing the network reflecting the market’s behavior. In this paper, we use a threshold method to construct China’s stock correlation network and then study the network’s structural properties and topological stability. We conduct a statistical analysis of this network and show that it follows a power-law model. We also detect components, cliques and independent sets in this network. These analyses allows one to apply a new data mining technique of classifying financial instruments based on stock price data, which provides a deeper insight into the internal structure of the stock market. Moreover, we test the topological stability of this network and find that it displays a topological robustness against random vertex failures, but it is also fragile to intentional attacks. Such a network stability property would be also useful for portfolio investment and risk management.

Are Price Limits Effective? An Examination of an Artificial Stock Market.

PubMed

Zhang, Xiaotao; Ping, Jing; Zhu, Tao; Li, Yuelei; Xiong, Xiong

2016-01-01

We investigated the inter-day effects of price limits policies that are employed in agent-based simulations. To isolate the impact of price limits from the impact of other factors, we built an artificial stock market with higher frequency price limits hitting. The trading mechanisms in this market are the same as the trading mechanisms in China's stock market. Then, we designed a series of simulations with and without price limits policy. The results of these simulations demonstrate that both upper and lower price limits can cause a volatility spillover effect and a trading interference effect. The process of price discovery will be delayed if upper price limits are imposed on a stock market; however, this phenomenon does not occur when lower price limits are imposed.

Quantifying Wikipedia Usage Patterns Before Stock Market Moves

NASA Astrophysics Data System (ADS)

Moat, Helen Susannah; Curme, Chester; Avakian, Adam; Kenett, Dror Y.; Stanley, H. Eugene; Preis, Tobias

2013-05-01

Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises. Here, we investigate whether data generated through Internet usage contain traces of attempts to gather information before trading decisions were taken. We present evidence in line with the intriguing suggestion that data on changes in how often financially related Wikipedia pages were viewed may have contained early signs of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of decision making.

Quantifying Wikipedia Usage Patterns Before Stock Market Moves

PubMed Central

Moat, Helen Susannah; Curme, Chester; Avakian, Adam; Kenett, Dror Y.; Stanley, H. Eugene; Preis, Tobias

2013-01-01

Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises. Here, we investigate whether data generated through Internet usage contain traces of attempts to gather information before trading decisions were taken. We present evidence in line with the intriguing suggestion that data on changes in how often financially related Wikipedia pages were viewed may have contained early signs of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of decision making.

Dynamical Analysis of Stock Market Instability by Cross-correlation Matrix

NASA Astrophysics Data System (ADS)

Takaishi, Tetsuya

2016-08-01

We study stock market instability by using cross-correlations constructed from the return time series of 366 stocks traded on the Tokyo Stock Exchange from January 5, 1998 to December 30, 2013. To investigate the dynamical evolution of the cross-correlations, crosscorrelation matrices are calculated with a rolling window of 400 days. To quantify the volatile market stages where the potential risk is high, we apply the principal components analysis and measure the cumulative risk fraction (CRF), which is the system variance associated with the first few principal components. From the CRF, we detected three volatile market stages corresponding to the bankruptcy of Lehman Brothers, the 2011 Tohoku Region Pacific Coast Earthquake, and the FRB QE3 reduction observation in the study period. We further apply the random matrix theory for the risk analysis and find that the first eigenvector is more equally de-localized when the market is volatile.

Statistical Properties and Pre-Hit Dynamics of Price Limit Hits in the Chinese Stock Markets

PubMed Central

Wan, Yu-Lei; Xie, Wen-Jie; Gu, Gao-Feng; Jiang, Zhi-Qiang; Chen, Wei; Xiong, Xiong; Zhang, Wei; Zhou, Wei-Xing

2015-01-01

Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders’ short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of price limit hits are not well studied partially due to the fact that main stock markets such as the US markets and most European markets do not set price limits. Here, we perform detailed analyses of the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 to investigate the statistical properties of price limit hits and the dynamical evolution of several important financial variables before stock price hits its limits. We compare the properties of up-limit hits and down-limit hits. We also divide the whole period into three bullish periods and three bearish periods to unveil possible differences during bullish and bearish market states. To uncover the impacts of stock capitalization on price limit hits, we partition all stocks into six portfolios according to their capitalizations on different trading days. We find that the price limit trading rule has a cooling-off effect (object to the magnet effect), indicating that the rule takes effect in the Chinese stock markets. We find that price continuation is much more likely to occur than price reversal on the next trading day after a limit-hitting day, especially for down-limit hits, which has potential practical values for market practitioners. PMID:25874716

Stroke: a Hidden Danger of Margin Trading in Stock Markets.

PubMed

Lin, Shu-Hui; Wang, Chien-Ho; Liu, Tsai-Ching; Chen, Chin-Shyan

2015-10-01

Using 10-year population data from 2000 through 2009 in Taiwan, this is the first paper to analyze the relationship between margin trading in stock markets and stroke hospitalizations. The results show that 3 and 6 days after an increase of margin trading in the Taiwan stock markets are associated with greater stoke hospitalizations. In general, a 1 % increase in total margin trading positions is associated with an increment of 2.5 in the total number of stroke hospitalizations, where the mean number of hospital admissions is 233 cases a day. We further examine the effects of margin trading by gender and age groups and find that the effects of margin trading are significant for males and those who are 45-74 years old only. In summary, buying stocks with money you do not have is quite risky, especially if the prices of those stocks fall past a certain level or if there is a sudden and severe drop in the stock market. There is also a hidden danger to one's health from margin trading. A person should be cautious before conducting margin trading, because while it can be quite profitable, danger always lurks just around the corner.

Stock markets and criticality in the current economic crisis

NASA Astrophysics Data System (ADS)

da Silva, Roberto; Zembrzuski, Marcelo; Correa, Fabio C.; Lamb, Luis C.

2010-12-01

We show that the current economic crisis has led the market to exhibit a non-critical behavior. We do so by analyzing the quantitative parameters of time series from the main assets of the Brazilian Stock Market BOVESPA. By monitoring global persistence we show a deviation of power law behavior during the crisis in a strong analogy with spin systems (from where this concept was originally conceived). Such behavior is corroborated by an emergent heavy tail of absolute return distribution and also by the magnitude autocorrelation exponent. Comparisons with universal exponents obtained in the international stock markets are also performed. This suggests how a thorough analysis of suitable exponents can bring a possible way of forecasting market crises characterized by non-criticality.

Relationship between suicide rate and economic growth and stock market in the People's Republic of China: 2004-2013.

PubMed

Yin, Honglei; Xu, Lin; Shao, Yechang; Li, Liping; Wan, Chengsong

2016-01-01

The objective of this study was to estimate the features of suicide rate and its association with economic development and stock market during the past decade in the People's Republic of China. Official data were gathered and analyzed in the People's Republic of China during the period 2004-2013. Nationwide suicide rate was stratified by four year age-groups, sex, urban/rural areas, and regions (East, Central, and West). Annual economic indexes including gross domestic product (GDP) per capita and rural and urban income per capita were all adjusted for inflation. Variation coefficient of market index (VCMI) was also included as an economic index to measure the fluctuation of the stock market. Negative binomial regression was performed to examine the time trend of region-level suicide rates and effects of sex, age, urban/rural area, region, and economic index on the suicide rates. Suicide rates of each age-group, sex, urban/rural area, and region were generally decreased from 2004 to 2013, while annual GDP per capita and rural and urban income per capita were generally increased by year. VCMI fluctuated largely, which peaked around 2009 and decreased after that time. Negative binomial regression showed that the decreased suicide rate in East and Central rural areas was the main cause of the decrease in suicide rate in the People's Republic of China. Suicide rate in the People's Republic of China for the study period increased with age and was higher in rural than in urban area, higher in males than in females, and the highest in the Central region. When GDP per capita increased by 2,787 RMB, the suicide rate decreased by 0.498 times. VCMI showed no significant relationship with suicide rate in the negative binomial regression. Suicide rate decreased in 2004-2013; varied among different age-groups, sex, urban/rural areas, and regions; and was negatively associated with the economic growth in the People's Republic of China. Stock market showed no relationship with

Universal Behavior of Extreme Price Movements in Stock Markets

PubMed Central

Fuentes, Miguel A.; Gerig, Austin; Vicente, Javier

2009-01-01

Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evidence that a modification to the random model—adding a slow, but significant, fluctuation to the standard deviation of the process—accurately explains the probability of different-sized price changes, including the relative high frequency of extreme movements. Furthermore, we show that this process is similar across stocks so that their price fluctuations can be characterized by a single curve. Because the behavior of price fluctuations is rooted in the characteristics of volatility, we expect our results to bring increased interest to stochastic volatility models, and especially to those that can produce the properties of volatility reported here. PMID:20041178

Viscoelasticity and pattern formations in stock market indices

NASA Astrophysics Data System (ADS)

Gündüz, Güngör; Gündüz, Aydın

2017-06-01

of stock index value although they describe different properties. Entropy fluctuates at fast increase and fast fall of index value, and fluctuation becomes very high at minimum values of the index. The curvature of a circle passing from the two ends of the vector and the point of intersection of its horizontal and vertical components designates the reactivity involved in the market and the radius of circle behaves somehow similar to entropy and Wiener noise. The change of entropy and Wiener noise with radius exhibits patterns with four branches.

Enhanced index tracking modelling in portfolio optimization

NASA Astrophysics Data System (ADS)

Lam, W. S.; Hj. Jaaman, Saiful Hafizah; Ismail, Hamizun bin

2013-09-01

Enhanced index tracking is a popular form of passive fund management in stock market. It is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the risk. Enhanced index tracking aims to generate excess return over the return achieved by the index without purchasing all of the stocks that make up the index by establishing an optimal portfolio. The objective of this study is to determine the optimal portfolio composition and performance by using weighted model in enhanced index tracking. Weighted model focuses on the trade-off between the excess return and the risk. The results of this study show that the optimal portfolio for the weighted model is able to outperform the Malaysia market index which is Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.

Multifractal characterization of energy stocks in China: A multifractal detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Yang, Liansheng; Zhu, Yingming; Wang, Yudong

2016-06-01

In this paper, we investigate the impacts of oil price changes on energy stocks in Chinese stock market from the multifractal perspective. The well-known multifractal detrended fluctuation analysis (MF-DFA) is applied to detect the multifractality. We find that both returns and volatilities of energy industry index display apparent multifractal behavior. Oil market activity is an important source of multifractality in energy stocks index in addition to long-range correlations and fat-tail distributions.

Variable diffusion in stock market fluctuations

NASA Astrophysics Data System (ADS)

Hua, Jia-Chen; Chen, Lijian; Falcon, Liberty; McCauley, Joseph L.; Gunaratne, Gemunu H.

2015-02-01

We analyze intraday fluctuations in several stock indices to investigate the underlying stochastic processes using techniques appropriate for processes with nonstationary increments. The five most actively traded stocks each contains two time intervals during the day where the variance of increments can be fit by power law scaling in time. The fluctuations in return within these intervals follow asymptotic bi-exponential distributions. The autocorrelation function for increments vanishes rapidly, but decays slowly for absolute and squared increments. Based on these results, we propose an intraday stochastic model with linear variable diffusion coefficient as a lowest order approximation to the real dynamics of financial markets, and to test the effects of time averaging techniques typically used for financial time series analysis. We find that our model replicates major stylized facts associated with empirical financial time series. We also find that ensemble averaging techniques can be used to identify the underlying dynamics correctly, whereas time averages fail in this task. Our work indicates that ensemble average approaches will yield new insight into the study of financial markets' dynamics. Our proposed model also provides new insight into the modeling of financial markets dynamics in microscopic time scales.

78 FR 40523 - Order Exempting Market Makers Participating in NASDAQ Stock Market LLC's Market Quality Program...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-05

... SECURITIES AND EXCHANGE COMMISSION [Release No. 34-69892] Order Exempting Market Makers Participating in NASDAQ Stock Market LLC's Market Quality Program From Section 11(d)(1) of the Securities Exchange Act of 1934 and Rule 11d1-2 Thereunder June 28, 2013. On March 13, 2013, the Securities and Exchange Commission (``Commission'') approved a...

Are Price Limits Effective? An Examination of an Artificial Stock Market

PubMed Central

Zhu, Tao; Li, Yuelei; Xiong, Xiong

2016-01-01

We investigated the inter-day effects of price limits policies that are employed in agent-based simulations. To isolate the impact of price limits from the impact of other factors, we built an artificial stock market with higher frequency price limits hitting. The trading mechanisms in this market are the same as the trading mechanisms in China’s stock market. Then, we designed a series of simulations with and without price limits policy. The results of these simulations demonstrate that both upper and lower price limits can cause a volatility spillover effect and a trading interference effect. The process of price discovery will be delayed if upper price limits are imposed on a stock market; however, this phenomenon does not occur when lower price limits are imposed. PMID:27513330

Examining the efficiency and interdependence of US credit and stock markets through MF-DFA and MF-DXA approaches

NASA Astrophysics Data System (ADS)

Shahzad, Syed Jawad Hussain; Nor, Safwan Mohd; Mensi, Walid; Kumar, Ronald Ravinesh

2017-04-01

This study examines the power law properties of 11 US credit and stock markets at the industry level. We use multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DXA) to first investigate the relative efficiency of credit and stock markets and then evaluate the mutual interdependence between CDS-equity market pairs. The scaling exponents of the MF-DFA approach suggest that CDS markets are relatively more inefficient than their equity counterparts. However, Banks and Financial credit markets are relatively more efficient. Basic Materials (both CDS and equity indices) is the most inefficient sector of the US economy. The cross-correlation exponents obtained through MF-DXA also suggest that the relationship of the CDS and equity sectors within and across markets is multifractal for all pairs. Within the CDS market, Basic Materials is the most dependent sector, whereas equity market sectors can be divided into two distinct groups based on interdependence. The pair-wise dependence between Basic Materials sector CDSs and the equity index is also the highest. The degree of cross-correlation shows that the sectoral pairs of CDS and equity markets belong to a persistent cross-correlated series within selected time intervals.

Monte Carlo Simulation of Microscopic Stock Market Models

NASA Astrophysics Data System (ADS)

Stauffer, Dietrich

Computer simulations with random numbers, that is, Monte Carlo methods, have been considerably applied in recent years to model the fluctuations of stock market or currency exchange rates. Here we concentrate on the percolation model of Cont and Bouchaud, to simulate, not to predict, the market behavior.

Multiscale multifractal time irreversibility analysis of stock markets

NASA Astrophysics Data System (ADS)

Jiang, Chenguang; Shang, Pengjian; Shi, Wenbin

2016-11-01

Time irreversibility is one of the most important properties of nonstationary time series. Complex time series often demonstrate even multiscale time irreversibility, such that not only the original but also coarse-grained time series are asymmetric over a wide range of scales. We study the multiscale time irreversibility of time series. In this paper, we develop a method called multiscale multifractal time irreversibility analysis (MMRA), which allows us to extend the description of time irreversibility to include the dependence on the segment size and statistical moments. We test the effectiveness of MMRA in detecting multifractality and time irreversibility of time series generated from delayed Henon map and binomial multifractal model. Then we employ our method to the time irreversibility analysis of stock markets in different regions. We find that the emerging market has higher multifractality degree and time irreversibility compared with developed markets. In this sense, the MMRA method may provide new angles in assessing the evolution stage of stock markets.

What stock market returns to expect for the future?

PubMed

Diamond, P A

2000-01-01

In evaluating proposals for reforming Social Security that involve stock investments, the Office of the Chief Actuary (OCACT) has generally used a 7.0 percent real return for stocks. The 1994-96 Advisory Council specified that OCACT should use that return in making its 75-year projections of investment-based reform proposals. The assumed ultimate real return on Treasury bonds of 3.0 percent implies a long-run equity premium of 4.0 percent. There are two equity-premium concepts: the realized equity premium, which is measured by the actual rates of return; and the required equity premium, which investors expect to receive for being willing to hold available stocks and bonds. Over the past two centuries, the realized premium was 3.5 percent on average, but 5.2 percent for 1926 to 1998. Some critics argue that the 7.0 percent projected stock returns are too high. They base their arguments on recent developments in the capital market, the current high value of the stock market, and the expectation of slower economic growth. Increased use of mutual funds and the decline in their costs suggest a lower required premium, as does the rising fraction of the American public investing in stocks. The size of the decrease is limited, however, because the largest cost savings do not apply to the very wealthy and to large institutional investors, who hold a much larger share of the stock market's total value than do new investors. These trends suggest a lower equity premium for projections than the 5.2 percent of the past 75 years. Also, a declining required premium is likely to imply a temporary increase in the realized premium because a rising willingness to hold stocks tends to increase their price. Therefore, it would be a mistake during a transition period to extrapolate what may be a temporarily high realized return. In the standard (Solow) economic growth model, an assumption of slower long-run growth lowers the marginal product of capital if the savings rate is constant

The cross-correlation analysis of multi property of stock markets based on MM-DFA

NASA Astrophysics Data System (ADS)

Yang, Yujun; Li, Jianping; Yang, Yimei

2017-09-01

In this paper, we propose a new method called DH-MXA based on distribution histograms of Hurst surface and multiscale multifractal detrended fluctuation analysis. The method allows us to investigate the cross-correlation characteristics among multiple properties of different stock time series. It may provide a new way of measuring the nonlinearity of several signals. It also can provide a more stable and faithful description of cross-correlation of multiple properties of stocks. The DH-MXA helps us to present much richer information than multifractal detrented cross-correlation analysis and allows us to assess many universal and subtle cross-correlation characteristics of stock markets. We show DH-MXA by selecting four artificial data sets and five properties of four stock time series from different countries. The results show that our proposed method can be adapted to investigate the cross-correlation of stock markets. In general, the American stock markets are more mature and less volatile than the Chinese stock markets.

Statistical properties of short-selling and margin-trading activities and their impacts on returns in the Chinese stock markets

NASA Astrophysics Data System (ADS)

Gao, Yan; Gao, Yao

2015-11-01

We investigate the collective behaviors of short-selling and margin-trading between Chinese stocks and their impacts on the co-movements of stock returns by cross-correlation and partial correlation analyses. We find that the collective behaviors of margin-trading are largely attributed to the index cohesive force, while those of short-selling are mainly due to some direct interactions between stocks. Interestingly, the dominant role the finance industry plays in the collective behaviors of short-selling could make it more important in affecting the co-movement structure of stock returns by strengthening its relationship with the market index. By detecting the volume-return and volume-volatility relationships, we find that the investors of the two leverage activities are positively triggered by individual stock volatility first, and next, at the return level, margin-buyers show trend-following properties, while short-sellers are probably informative traders who trade on the information impulse of specific firms. However, the return predictability of the two leverage trading activities and their impacts on stock volatility are not significant. Moreover, both tails of the cumulative distributions of the two leverage trading activities are found following the stretched exponential law better than the power-law.

Autoregressive processes with exponentially decaying probability distribution functions: applications to daily variations of a stock market index.

PubMed

Porto, Markus; Roman, H Eduardo

2002-04-01

We consider autoregressive conditional heteroskedasticity (ARCH) processes in which the variance sigma(2)(y) depends linearly on the absolute value of the random variable y as sigma(2)(y) = a+b absolute value of y. While for the standard model, where sigma(2)(y) = a + b y(2), the corresponding probability distribution function (PDF) P(y) decays as a power law for absolute value of y-->infinity, in the linear case it decays exponentially as P(y) approximately exp(-alpha absolute value of y), with alpha = 2/b. We extend these results to the more general case sigma(2)(y) = a+b absolute value of y(q), with 0 < q < 2. We find stretched exponential decay for 1 < q < 2 and stretched Gaussian behavior for 0 < q < 1. As an application, we consider the case q=1 as our starting scheme for modeling the PDF of daily (logarithmic) variations in the Dow Jones stock market index. When the history of the ARCH process is taken into account, the resulting PDF becomes a stretched exponential even for q = 1, with a stretched exponent beta = 2/3, in a much better agreement with the empirical data.

Static and dynamic factors in an information-based multi-asset artificial stock market

NASA Astrophysics Data System (ADS)

Ponta, Linda; Pastore, Stefano; Cincotti, Silvano

2018-02-01

An information-based multi-asset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented. In the market, agents trade risky assets in exchange for cash. Beside the amount of cash and of stocks owned, each agent is characterized by sentiments and agents share their sentiments by means of interactions that are determined by sparsely connected networks. A central market maker (clearing house mechanism) determines the price processes for each stock at the intersection of the demand and the supply curves. Single stock price processes exhibit volatility clustering and fat-tailed distribution of returns whereas multivariate price process exhibits both static and dynamic stylized facts, i.e., the presence of static factors and common trends. Static factors are studied making reference to the cross-correlation of returns of different stocks. The common trends are investigated considering the variance-covariance matrix of prices. Results point out that the probability distribution of eigenvalues of the cross-correlation matrix of returns shows the presence of sectors, similar to those observed on real empirical data. As regarding the dynamic factors, the variance-covariance matrix of prices point out a limited number of assets prices series that are independent integrated processes, in close agreement with the empirical evidence of asset price time series of real stock markets. These results remarks the crucial dependence of statistical properties of multi-assets stock market on the agents' interaction structure.

The dynamic correlation between policy uncertainty and stock market returns in China

NASA Astrophysics Data System (ADS)

Yang, Miao; Jiang, Zhi-Qiang

2016-11-01

The dynamic correlation is examined between government's policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.

76 FR 77883 - Self-Regulatory Organizations; NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-14

...-Regulatory Organizations; NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of a Proposed... that on November 28, 2011, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange'') filed with the... equities business on the NASDAQ Stock Market LLC are not subject to the fees in Rule 7003(b...

Structural changes and out-of-sample prediction of realized range-based variance in the stock market

NASA Astrophysics Data System (ADS)

Gong, Xu; Lin, Boqiang

2018-03-01

This paper aims to examine the effects of structural changes on forecasting the realized range-based variance in the stock market. Considering structural changes in variance in the stock market, we develop the HAR-RRV-SC model on the basis of the HAR-RRV model. Subsequently, the HAR-RRV and HAR-RRV-SC models are used to forecast the realized range-based variance of S&P 500 Index. We find that there are many structural changes in variance in the U.S. stock market, and the period after the financial crisis contains more structural change points than the period before the financial crisis. The out-of-sample results show that the HAR-RRV-SC model significantly outperforms the HAR-BV model when they are employed to forecast the 1-day, 1-week, and 1-month realized range-based variances, which means that structural changes can improve out-of-sample prediction of realized range-based variance. The out-of-sample results remain robust across the alternative rolling fixed-window, the alternative threshold value in ICSS algorithm, and the alternative benchmark models. More importantly, we believe that considering structural changes can help improve the out-of-sample performances of most of other existing HAR-RRV-type models in addition to the models used in this paper.

Prediction of stock market characteristics using neural networks

NASA Astrophysics Data System (ADS)

Pandya, Abhijit S.; Kondo, Tadashi; Shah, Trupti U.; Gandhi, Viraf R.

1999-03-01

International stocks trading, currency and derivative contracts play an increasingly important role for many investors. Neural network is playing a dominant role in predicting the trends in stock markets and in currency speculation. In most economic applications, the success rate using neural networks is limited to 70 - 80%. By means of the new approach of GMDH (Group Method of Data Handling) neural network predictions can be improved further by 10 - 15%. It was observed in our study, that using GMDH for short, noisy or inaccurate data sample resulted in the best-simplified model. In the GMDH model accuracy of prediction is higher and the structure is simpler than that of the usual full physical model. As an example, prediction of the activity on the stock exchange in New York was considered. On the basis of observations in the period of Jan '95 to July '98, several variables of the stock market (S&P 500, Small Cap, Dow Jones, etc.) were predicted. A model portfolio using various stocks (Amgen, Merck, Office Depot, etc.) was built and its performance was evaluated based on neural network forecasting of the closing prices. Comparison of results was made with various neural network models such as Multilayer Perceptrons with Back Propagation, and the GMDH neural network. Variations of GMDH were studied and analysis of their performance is reported in the paper.

Multifractal analysis of Moroccan family business stock returns

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-11-01

In this paper, long-range temporal correlations at different scales in Moroccan family business stock returns are investigated. For comparison purpose, presence of multifractality is also investigated in Casablanca Stock Exchange (CSE) major indices: MASI which is the all shares index and MADEX which is the index of most liquid shares. It is found that return series of both family business companies and major stock market indices show strong evidence of multifractality. In particular, empirical results reveal that short (long) fluctuations in family business stock returns are less (more) persistent (anti-persistent) than short fluctuations in market indices. In addition, both serial correlation and distribution characteristics significantly influence the strength of the multifractal spectrums of CSE and family business stocks returns. Furthermore, results from multifractal spectrum analysis suggest that family business stocks are less risky. Thus, such differences in price dynamics could be exploited by investors and forecasters in active portfolio management.

Non-linear characteristics and long-range correlations in Asian stock markets

NASA Astrophysics Data System (ADS)

Jiang, J.; Ma, K.; Cai, X.

2007-05-01

We test several non-linear characteristics of Asian stock markets, which indicates the failure of efficient market hypothesis and shows the essence of fractal of the financial markets. In addition, by using the method of detrended fluctuation analysis (DFA) to investigate the long range correlation of the volatility in the stock markets, we find that the crossover phenomena exist in the results of DFA. Further, in the region of small volatility, the scaling behavior is more complicated; in the region of large volatility, the scaling exponent is close to 0.5, which suggests the market is more efficient. All these results may indicate the possibility of characteristic multifractal scaling behaviors of the financial markets.

26 CFR 1.1296-2 - Definition of marketable stock.

Code of Federal Regulations, 2011 CFR

2011-04-01

... marketable stock means— (1) Passive foreign investment company (PFIC) stock that is regularly traded, as... income of the foreign corporation for its taxable year is passive income, as defined in section 1297(a)(1... corporation during its taxable year which produce passive income or which are held for the production of...

Statistical properties of the stock and credit market: RMT and network topology

NASA Astrophysics Data System (ADS)

Lim, Kyuseong; Kim, Min Jae; Kim, Sehyun; Kim, Soo Yong

We analyzed the dependence structure of the credit and stock market using random matrix theory and network topology. The dynamics of both markets have been spotlighted throughout the subprime crisis. In this study, we compared these two markets in view of the market-wide effect from random matrix theory and eigenvalue analysis. We found that the largest eigenvalue of the credit market as a whole preceded that of the stock market in the beginning of the financial crisis and that of two markets tended to be synchronized after the crisis. The correlation between the companies of both markets became considerably stronger after the crisis as well.

Complex network analysis of conventional and Islamic stock market in Indonesia

NASA Astrophysics Data System (ADS)

Rahmadhani, Andri; Purqon, Acep; Kim, Sehyun; Kim, Soo Yong

2015-09-01

The rising popularity of Islamic financial products in Indonesia has become a new interesting topic to be analyzed recently. We introduce a complex network analysis to compare conventional and Islamic stock market in Indonesia. Additionally, Random Matrix Theory (RMT) has been added as a part of reference to expand the analysis of the result. Both of them are based on the cross correlation matrix of logarithmic price returns. Closing price data, which is taken from June 2011 to July 2012, is used to construct logarithmic price returns. We also introduce the threshold value using winner-take-all approach to obtain scale-free property of the network. This means that the nodes of the network that has a cross correlation coefficient below the threshold value should not be connected with an edge. As a result, we obtain 0.5 as the threshold value for all of the stock market. From the RMT analysis, we found that there is only market wide effect on both stock market and no clustering effect has been found yet. From the network analysis, both of stock market networks are dominated by the mining sector. The length of time series of closing price data must be expanded to get more valuable results, even different behaviors of the system.

Effect of market factors on the short-time pricing of stock-exchange metals

NASA Astrophysics Data System (ADS)

Bogdanov, S. V.; Shevelev, I. M.; Chernyi, S. A.

2016-12-01

The open trade on the world market is estimated using information of one-day exchange prices of nonferrous and precious metals, oil, reduced crude, and gasoline and the main world stock indices in the time period from January 1, 2009 to December 31, 2015. It is found that the short-term changes in the prices of nonferrous metals are determined by the prices on the metal market. The changes in the prices of energy carriers and the stock trade on the stock market weakly influence the pricing of nonferrous and precious metals. The prices of metals depend on the situation during trade on commodity exchanges, and the stock market indirectly influences the exchange prices of metals through changes in the share prices of the companies that produce copper, aluminum, and zinc.

Information theory applied to econophysics: stock market behaviors

NASA Astrophysics Data System (ADS)

Vogel, Eugenio E.; Saravia, Gonzalo

2014-08-01

The use of data compressor techniques has allowed to recognize magnetic transitions and their associated critical temperatures [E.E. Vogel, G. Saravia, V. Cortez, Physica A 391, 1591 (2012)]. In the present paper we introduce some new concepts associated to data recognition and extend the use of these techniques to econophysics to explore the variations of stock market indicators showing that information theory can help to recognize different regimes. Modifications and further developments to previously introduced data compressor wlzip are introduced yielding two measurements. Additionally, we introduce an algorithm that allows to tune the number of significant digits over which the data compression is due to act complementing, this with an appropriate method to round off the truncation. The application is done to IPSA, the main indicator of the Chilean Stock Market during the year 2010 due to availability of quality data and also to consider a rare effect: the earthquake of the 27th of February on that year which is as of now the sixth strongest earthquake ever recorded by instruments (8.8 Richter scale) according to United States Geological Survey. Along the year 2010 different regimes are recognized. Calm days show larger compression than agitated days allowing for classification and recognition. Then the focus turns onto selected days showing that it is possible to recognize different regimes with the data of the last hour (60 entries) allowing to determine actions in a safer way. The "day of the week" effect is weakly present but "the hour of the day" effect is clearly present; its causes and implications are discussed. This effect also establishes the influence of Asian, European and American stock markets over the smaller Chilean Stock Market. Then dynamical studies are conducted intended to search a system that can help to realize in real time about sudden variations of the market; it is found that information theory can be really helpful in this respect.

Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

PubMed

Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

PubMed Central

Banik, Shipra; Khodadad Khan, A. F. M.; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange. PMID:24701205

Anticipating Stock Market Movements with Google and Wikipedia

NASA Astrophysics Data System (ADS)

Moat, Helen Susannah; Curme, Chester; Stanley, H. Eugene; Preis, Tobias

Many of the trading decisions that have led to financial crises are captured by vast, detailed stock market datasets. Here, we summarize two of our recent studies which investigate whether Internet usage data contain traces of attempts to gather information before such trading decisions were taken. By analyzing changes in how often Internet users searched for financially related information on Google (Preis et al., Sci Rep 3:1684, 2013) and Wikipedia (Moat et al., Sci Rep 3:1801, 2013), patterns are found that may be interpreted as "early warning signs" of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of economic decision making.

Portfolio optimization in enhanced index tracking with goal programming approach

NASA Astrophysics Data System (ADS)

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

2014-09-01

Enhanced index tracking is a popular form of passive fund management in stock market. Enhanced index tracking aims to generate excess return over the return achieved by the market index without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio to maximize the mean return and minimize the risk. The objective of this paper is to determine the portfolio composition and performance using goal programming approach in enhanced index tracking and comparing it to the market index. Goal programming is a branch of multi-objective optimization which can handle decision problems that involve two different goals in enhanced index tracking, a trade-off between maximizing the mean return and minimizing the risk. The results of this study show that the optimal portfolio with goal programming approach is able to outperform the Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.

Quantifying the semantics of search behavior before stock market moves.

PubMed

Curme, Chester; Preis, Tobias; Stanley, H Eugene; Moat, Helen Susannah

2014-08-12

Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.

Quantifying the semantics of search behavior before stock market moves

PubMed Central

Curme, Chester; Preis, Tobias; Stanley, H. Eugene; Moat, Helen Susannah

2014-01-01

Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events. PMID:25071193

Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study.

PubMed

Bhattacharjee, Biplab; Shafi, Muhammad; Acharjee, Animesh

2016-01-01

The socio-economic systems today possess high levels of both interconnectedness and interdependencies, and such system-level relationships behave very dynamically. In such situations, it is all around perceived that influence is a perplexing power that has an overseeing part in affecting the dynamics and behaviours of involved ones. As a result of the force & direction of influence, the transformative change of one entity has a cogent aftereffect on the other entities in the system. The current study employs directed weighted networks for investigating the influential relationship patterns existent in a typical equity market as an outcome of inter-stock interactions happening at the market level, the sectorial level and the industrial level. The study dataset is derived from 335 constituent stocks of 'Standard & Poor Bombay Stock Exchange 500 index' and study period is 1st June 2005 to 30th June 2015. The study identifies the set of most dynamically influential stocks & their respective temporal pattern at three hierarchical levels: the complete equity market, different sectors, and constituting industry segments of those sectors. A detailed influence relationship analysis is performed for the sectorial level network of the construction sector, and it was found that stocks belonging to the cement industry possessed high influence within this sector. Also, the detailed network analysis of construction sector revealed that it follows scale-free characteristics and power law distribution. In the industry specific influence relationship analysis for cement industry, methods based on threshold filtering and minimum spanning tree were employed to derive a set of sub-graphs having temporally stable high-correlation structure over this ten years period.

Do stock prices drive people crazy?

PubMed

Lin, Chung-Liang; Chen, Chin-Shyan; Liu, Tsai-Ching

2015-03-01

This is the first research to examine a potential relation between stock market volatility and mental disorders. Using data on daily incidences of mental disorders in Taiwan over 4000 days from 1998 through 2009 to assess the time-series relation between stock price movements and mental disorders, we observe that stock price fluctuation clearly affects the hospitalization of mental disorders. We find that during a 12-year follow-up period, a low stock price index, a daily fall in the stock price index and consecutive daily falls in the stock price index are all associated with greater of mental disorders hospitalizations. A 1000-point fall in the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) increases the number of daily mental disorders hospitalizations by 4.71%. A 1% fall in the TAIEX in one single day increases daily hospitalizations for mental disorders by 0.36%. When the stock price index falls one consecutive day, it causes a daily increase of approximately 0.32% hospitalizations due to mental disorders on that day. Stock price index is found to be significant for both gender and all age groups. In addition, daily change is significant for both gender and middle-age groups, whereas accumulated change is significant for males and people aged 45-64. Stockholdings can help people accumulate wealth, but they can also increase mental disorders hospitalizations. In other words, stock price fluctuations do drive people crazy. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2014; all rights reserved.

Risk assessment and stock market volatility in the Eurozone: 1986-2014

NASA Astrophysics Data System (ADS)

Menezes, Rui; Oliveira, Álvaro

2015-04-01

This paper studies the stock market return's volatility in the Eurozone as an input for evaluating the market risk. Stock market returns are endogenously determined by long-term interest rate changes and so is the return's conditional variance. The conditional variance is the time-dependent variance of the underlying variable. In other words, it is the variance of the returns measured at each moment t, so it changes through time depending on the specific market structure at each time observation. Thus, a multivariate EGARCH model is proposed to capture the complex nature of this network. By network, in this context, we mean the chain of stock exchanges that co-move and interact in such a way that a shock in one of them propagates up to the other ones (contagion). Previous studies provide evidence that the Eurozone stock exchanges are deeply integrated. The results indicate that asymmetry and leverage effects exist along with fat tails and endogeneity. In-sample and out-of-sample forecasting tests provide clear evidence that the multivariate EGARCH model performs better than the univariate counterpart to predict the behavior of returns both before and after the 2008 crisis.

A heterogeneous artificial stock market model can benefit people against another financial crisis.

PubMed

Yang, Haijun; Chen, Shuheng

2018-01-01

This paper presents results of an artificial stock market and tries to make it more consistent with the statistical features of real stock data. Based on the SFI-ASM, a novel model is proposed to make agents more close to the real world. Agents are divided into four kinds in terms of different learning speeds, strategy-sizes, utility functions, and level of intelligence; and a crucial parameter has been found to ensure system stability. So, some parameters are appended to make the model which contains zero-intelligent and less-intelligent agents run steadily. Moreover, considering real stock markets change violently due to the financial crisis; the real stock markets are divided into two segments, before the financial crisis and after it. The optimal modified model before the financial crisis fails to replicate the statistical features of the real market after the financial crisis. Then, the optimal model after the financial crisis is shown. The experiments indicate that the optimal model after the financial crisis is able to replicate several of real market phenomena, including the first-order autocorrelation, kurtosis, standard deviation of yield series and first-order autocorrelation of yield square. We point out that there is a structural change in stock markets after the financial crisis, which can benefit people forecast the financial crisis.

Dependence structure of the commodity and stock markets, and relevant multi-spread strategy

NASA Astrophysics Data System (ADS)

Kim, Min Jae; Kim, Sehyun; Jo, Yong Hwan; Kim, Soo Yong

2011-10-01

Understanding the dependence structure between the commodity and stock markets is a crucial issue in constructing a portfolio. It can also help us to discover new opportunities to implement spread trading using multiple assets classified in the two different markets. This study analyzed the dependence structure of the commodity and stock markets using the random matrix theory technique and network analysis. Our results show that the stock and commodity markets must be handled as completely separated asset classes except for the oil and gold markets, so the performance enhancement of the mean-variance portfolio is significant as expected. In light of the fact that WTI 1 month futures and four oil-related stocks are strongly correlated, they were selected as basic ingredients to complement the multi-spread convergence trading strategy using a machine learning technique called the AdaBoost algorithm. The performance of this strategy for non-myopic investors, who can endure short-term loss, can be enhanced significantly on a risk measurement basis.

17 CFR 240.15g-2 - Penny stock disclosure document relating to the penny stock market.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 3 2010-04-01 2010-04-01 false Penny stock disclosure document relating to the penny stock market. 240.15g-2 Section 240.15g-2 Commodity and Securities Exchanges SECURITIES AND EXCHANGE COMMISSION (CONTINUED) GENERAL RULES AND REGULATIONS, SECURITIES EXCHANGE ACT OF 1934...

A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.

Time series momentum and contrarian effects in the Chinese stock market

NASA Astrophysics Data System (ADS)

Shi, Huai-Long; Zhou, Wei-Xing

2017-10-01

This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-specific characteristics. Our findings indicate that there is a time series momentum effect in the short run and a contrarian effect in the long run in the Chinese stock market. The performances of the time series momentum and contrarian strategies are highly dependent on the look-back and holding periods and firm-specific characteristics.

Stochastic Frontier Model Approach for Measuring Stock Market Efficiency with Different Distributions

PubMed Central

Hasan, Md. Zobaer; Kamil, Anton Abdulbasah; Mustafa, Adli; Baten, Md. Azizul

2012-01-01

The stock market is considered essential for economic growth and expected to contribute to improved productivity. An efficient pricing mechanism of the stock market can be a driving force for channeling savings into profitable investments and thus facilitating optimal allocation of capital. This study investigated the technical efficiency of selected groups of companies of Bangladesh Stock Market that is the Dhaka Stock Exchange (DSE) market, using the stochastic frontier production function approach. For this, the authors considered the Cobb-Douglas Stochastic frontier in which the technical inefficiency effects are defined by a model with two distributional assumptions. Truncated normal and half-normal distributions were used in the model and both time-variant and time-invariant inefficiency effects were estimated. The results reveal that technical efficiency decreased gradually over the reference period and that truncated normal distribution is preferable to half-normal distribution for technical inefficiency effects. The value of technical efficiency was high for the investment group and low for the bank group, as compared with other groups in the DSE market for both distributions in time- varying environment whereas it was high for the investment group but low for the ceramic group as compared with other groups in the DSE market for both distributions in time-invariant situation. PMID:22629352

Stochastic frontier model approach for measuring stock market efficiency with different distributions.

PubMed

Hasan, Md Zobaer; Kamil, Anton Abdulbasah; Mustafa, Adli; Baten, Md Azizul

2012-01-01

The stock market is considered essential for economic growth and expected to contribute to improved productivity. An efficient pricing mechanism of the stock market can be a driving force for channeling savings into profitable investments and thus facilitating optimal allocation of capital. This study investigated the technical efficiency of selected groups of companies of Bangladesh Stock Market that is the Dhaka Stock Exchange (DSE) market, using the stochastic frontier production function approach. For this, the authors considered the Cobb-Douglas Stochastic frontier in which the technical inefficiency effects are defined by a model with two distributional assumptions. Truncated normal and half-normal distributions were used in the model and both time-variant and time-invariant inefficiency effects were estimated. The results reveal that technical efficiency decreased gradually over the reference period and that truncated normal distribution is preferable to half-normal distribution for technical inefficiency effects. The value of technical efficiency was high for the investment group and low for the bank group, as compared with other groups in the DSE market for both distributions in time-varying environment whereas it was high for the investment group but low for the ceramic group as compared with other groups in the DSE market for both distributions in time-invariant situation.

The mean time-limited crash rate of stock price

NASA Astrophysics Data System (ADS)

Li, Yun-Xian; Li, Jiang-Cheng; Yang, Ai-Jun; Tang, Nian-Sheng

2017-05-01

In this article we investigate the occurrence of stock market crash in an economy cycle. Bayesian approach, Heston model and statistical-physical method are considered. Specifically, Heston model and an effective potential are employed to address the dynamic changes of stock price. Bayesian approach has been utilized to estimate the Heston model's unknown parameters. Statistical physical method is used to investigate the occurrence of stock market crash by calculating the mean time-limited crash rate. The real financial data from the Shanghai Composite Index is analyzed with the proposed methods. The mean time-limited crash rate of stock price is used to describe the occurrence of stock market crash in an economy cycle. The monotonous and nonmonotonous behaviors are observed in the behavior of the mean time-limited crash rate versus volatility of stock for various cross correlation coefficient between volatility and price. Also a minimum occurrence of stock market crash matching an optimal volatility is discovered.

Traders' behavioral coupling and market phase transition

NASA Astrophysics Data System (ADS)

Ma, Rong; Zhang, Yin; Li, Honggang

2017-11-01

Traditional economic theory is based on the assumption that traders are completely independent and rational; however, trading behavior in the real market is often coupled by various factors. This paper discusses behavioral coupling based on the stock index in the stock market, focusing on the convergence of traders' behavior, its effect on the correlation of stock returns and market volatility. We find that the behavioral consensus in the stock market, the correlation degree of stock returns, and the market volatility all exhibit significant phase transitions with stronger coupling.

Effects of daylight savings time changes on stock market volatility.

PubMed

Berument, M Hakan; Dogan, Nukhet; Onar, Bahar

2010-04-01

The presence of daylight savings time effects on stock returns and on stock volatility was investigated using an EGARCH specification to model the conditional variance. The evidence gathered from the major United States stock markets for the period between 1967 and 2007 did not support the existence of the daylight savings time effect on stock returns or on volatility. Returns on the first business day following daylight savings time changes were not lower nor was the volatility higher, as would be expected if there were an effect.

Estimation of a simple agent-based model of financial markets: An application to Australian stock and foreign exchange data

NASA Astrophysics Data System (ADS)

Alfarano, Simone; Lux, Thomas; Wagner, Friedrich

2006-10-01

Following Alfarano et al. [Estimation of agent-based models: the case of an asymmetric herding model, Comput. Econ. 26 (2005) 19-49; Excess volatility and herding in an artificial financial market: analytical approach and estimation, in: W. Franz, H. Ramser, M. Stadler (Eds.), Funktionsfähigkeit und Stabilität von Finanzmärkten, Mohr Siebeck, Tübingen, 2005, pp. 241-254], we consider a simple agent-based model of a highly stylized financial market. The model takes Kirman's ant process [A. Kirman, Epidemics of opinion and speculative bubbles in financial markets, in: M.P. Taylor (Ed.), Money and Financial Markets, Blackwell, Cambridge, 1991, pp. 354-368; A. Kirman, Ants, rationality, and recruitment, Q. J. Econ. 108 (1993) 137-156] of mimetic contagion as its starting point, but allows for asymmetry in the attractiveness of both groups. Embedding the contagion process into a standard asset-pricing framework, and identifying the abstract groups of the herding model as chartists and fundamentalist traders, a market with periodic bubbles and bursts is obtained. Taking stock of the availability of a closed-form solution for the stationary distribution of returns for this model, we can estimate its parameters via maximum likelihood. Expanding our earlier work, this paper presents pertinent estimates for the Australian dollar/US dollar exchange rate and the Australian stock market index. As it turns out, our model indicates dominance of fundamentalist behavior in both the stock and foreign exchange market.

Multiscale sample entropy and cross-sample entropy based on symbolic representation and similarity of stock markets

NASA Astrophysics Data System (ADS)

Wu, Yue; Shang, Pengjian; Li, Yilong

2018-03-01

A modified multiscale sample entropy measure based on symbolic representation and similarity (MSEBSS) is proposed in this paper to research the complexity of stock markets. The modified algorithm reduces the probability of inducing undefined entropies and is confirmed to be robust to strong noise. Considering the validity and accuracy, MSEBSS is more reliable than Multiscale entropy (MSE) for time series mingled with much noise like financial time series. We apply MSEBSS to financial markets and results show American stock markets have the lowest complexity compared with European and Asian markets. There are exceptions to the regularity that stock markets show a decreasing complexity over the time scale, indicating a periodicity at certain scales. Based on MSEBSS, we introduce the modified multiscale cross-sample entropy measure based on symbolic representation and similarity (MCSEBSS) to consider the degree of the asynchrony between distinct time series. Stock markets from the same area have higher synchrony than those from different areas. And for stock markets having relative high synchrony, the entropy values will decrease with the increasing scale factor. While for stock markets having high asynchrony, the entropy values will not decrease with the increasing scale factor sometimes they tend to increase. So both MSEBSS and MCSEBSS are able to distinguish stock markets of different areas, and they are more helpful if used together for studying other features of financial time series.

Machine learning in sentiment reconstruction of the simulated stock market

NASA Astrophysics Data System (ADS)

Goykhman, Mikhail; Teimouri, Ali

2018-02-01

In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to reconstruct the transition probabilities matrix of the Markov sentiment process and recover the underlying sentiment states from the observed stock price behavior. We demonstrate that the Hidden Markov Model can successfully recover the transition probabilities matrix for the hidden sentiment process of the Markov Chain type. We also demonstrate that the Recurrent Neural Network can successfully recover the hidden sentiment states from the observed simulated stock price time series.

Cosmetic surgery volume and its correlation with the major US stock market indices.

PubMed

Gordon, Chad R; Pryor, Landon; Afifi, Ahmed M; Benedetto, Paul X; Langevin, C J; Papay, Francis; Yetman, Randall; Zins, James E

2010-01-01

As a consumer-driven industry, cosmetic plastic surgery is subject to ebbs and flows as the economy changes. There have been many predictions about the short, intermediate, and long-term impact on cosmetic plastic surgery as a result of difficulties in the current economic climate, but no studies published in the literature have quantified a direct correlation. The authors investigate a possible correlation between cosmetic surgery volume and the economic trends of the three major US stock market indices. A volume analysis for the time period from January 1992 to October 2008 was performed (n = 7360 patients, n = 8205 procedures). Four cosmetic procedures-forehead lift (FL), rhytidectomy (Rh), breast augmentation (BA), and liposuction (Li)-were chosen; breast reduction (BRd), breast reconstruction (BRc), and carpal tunnel release (CTR) were selected for comparison. Case volumes for each procedure and fiscal quarter were compared to the trends of the S&P 500, Dow Jones (DOW), and NASDAQ (NASD) indices. Pearson correlation statistics were used to evaluate a relationship between the market index trends and surgical volume. P values <.05 were considered statistically significant. Three of the four cosmetic surgery procedures investigated (Rh, n = 1540; Li, n = 1291; BA, n = 1959) demonstrated a direct (ie, positive) statistical correlation to all three major market indices. FL (n =312) only correlated to the NASD (P = .021) and did not reach significance with the S&P 500 (P = .077) or DOW (P = .14). BRd and BRc demonstrated a direct correlation to two of the three stock market indices, whereas CTR showed an inverse (ie, negative) correlation to two of the three indices. This study, to our knowledge, is the first to suggest a direct correlation of four cosmetic and two reconstructive plastic surgery procedures to the three major US stock market indices and further emphasizes the importance of a broad-based plastic surgery practice in times of economic recession.

Testing the stability of the 2000 US stock market “antibubble”

NASA Astrophysics Data System (ADS)

Zhou, Wei-Xing; Sornette, Didier

2005-03-01

Since August 2000, the stock market in the USA as well as most other western markets have depreciated almost in synchrony according to complex patterns of drops and local rebounds. In (Quantitative Finance 2 (2002) 468), we have proposed to describe this phenomenon using the concept of a log-periodic power law antibubble, characterizing behavioral herding between investors leading to a competition between positive and negative feedbacks in the pricing process. A monthly prediction for the future evolution of the US S&P 500 index has been issued, monitored and updated in ( http://www.ess.ucla.edu/faculty/sornette/prediction/index.asp#prediction), which is still running as the article goes to press. Here, we test the possible existence of a regime switching in the US S&P 500 antibubble. First, we find some evidence that the antibubble has exhibited a transition in log-periodicity described by a so-called second-order log-periodicity. Second, we develop a battery of tests to detect a possible end of the antibubble of the first order which suggest that the antibubble was alive in August 2003 but has ended in the USA, when expressed in the local US dollar currency. Our tests provide quantitative measures to diagnose the end of an antibubble. Such diagnostic is not instantaneous and requires from three to six months within the new regime before assessing its existence with confidence. From the perspective of foreign investors in their currencies (S&P 500 denominated in British pound or in euro) or when expressed in gold so as to correct for an arguably artificial US$ valuation associated with the Federal Reserve interest rate and monetary policy, we find that the S&P 500 antibubble is still alive and running its course. Similar analyses performed on the major European stock markets (CAC 40 of France, DAX of Germany, and FTSE 100 of United Kingdom) show that the antibubble is also present and continuing there.

A heterogeneous artificial stock market model can benefit people against another financial crisis

PubMed Central

2018-01-01

This paper presents results of an artificial stock market and tries to make it more consistent with the statistical features of real stock data. Based on the SFI-ASM, a novel model is proposed to make agents more close to the real world. Agents are divided into four kinds in terms of different learning speeds, strategy-sizes, utility functions, and level of intelligence; and a crucial parameter has been found to ensure system stability. So, some parameters are appended to make the model which contains zero-intelligent and less-intelligent agents run steadily. Moreover, considering real stock markets change violently due to the financial crisis; the real stock markets are divided into two segments, before the financial crisis and after it. The optimal modified model before the financial crisis fails to replicate the statistical features of the real market after the financial crisis. Then, the optimal model after the financial crisis is shown. The experiments indicate that the optimal model after the financial crisis is able to replicate several of real market phenomena, including the first-order autocorrelation, kurtosis, standard deviation of yield series and first-order autocorrelation of yield square. We point out that there is a structural change in stock markets after the financial crisis, which can benefit people forecast the financial crisis. PMID:29912893

Identifying the Bottom Line after a Stock Market Crash

NASA Astrophysics Data System (ADS)

Roehner, B. M.

In this empirical paper we show that in the months following a crash there is a distinct connection between the fall of stock prices and the increase in the range of interest rates for a sample of bonds. This variable, which is often referred to as the interest rate spread variable, can be considered as a statistical measure for the disparity in lenders' opinions about the future; in other words, it provides an operational definition of the uncertainty faced by economic agents. The observation that there is a strong negative correlation between stock prices and the spread variable relies on the examination of eight major crashes in the United States between 1857 and 1987. That relationship which has remained valid for one and a half century in spite of important changes in the organization of financial markets can be of interest in the perspective of Monte Carlo simulations of stock markets.

The Dow is Killing Me: Risky Health Behaviors and the Stock Market.

PubMed

Cotti, Chad; Dunn, Richard A; Tefft, Nathan

2015-07-01

We investigate how risky health behaviors and self-reported health vary with the Dow Jones Industrial Average (DJIA) and during stock market crashes. Because stock market indices are leading indicators of economic performance, this research contributes to our understanding of the macroeconomic determinants of health. Existing studies typically rely on the unemployment rate to proxy for economic performance, but this measure captures only one of many channels through which the economic environment may influence individual health decisions. We find that large, negative monthly DJIA returns, decreases in the level of the DJIA, and stock market crashes are widely associated with worsening self-reported mental health and more cigarette smoking, binge drinking, and fatal car accidents involving alcohol. These results are consistent with predictions from rational addiction models and have implications for research on the association between consumption and stock prices. Copyright © 2014 John Wiley & Sons, Ltd.

A Two Population Model for the Stock Market Problem

NASA Astrophysics Data System (ADS)

Skiadas, Christos H.

The development of the last year disaster in the Stock Markets all over the world gave rise to reconsidering the previous models used. It is clear that, even in an organized international or national context, large fluctuations and sudden losses may occur. This paper explores a two populations' model. The populations are conflicting into the same environment (a Stock Market) by following the main rules present, that is mutual interaction between adopters, potential adopters, word-of-mouth communication and of course by taking into consideration the innovation diffusion process. The proposed model has special futures expressed by third order terms providing characteristic stationary points.

Using the Stock Market to Teach Physics

NASA Astrophysics Data System (ADS)

Faux, David A.; Hearn, Stephen

2004-11-01

Students are interested in money. Personal finance is an important issue for most students, especially as they move into university education and take a greater control of their own finances. Many are also interested in stock markets and their ability to allow someone to make, and lose, large sums of money, with their interest fueled by the boom in technology-based stocks of 2000/2001 followed by their subsequent dramatic collapse and the publicizing of so-called "rogue-traders." There is also a much greater ownership of stocks by families following public offerings, stock-based savings products, and the ability to trade stocks online. Consequently, there has been a steady growth of finance and finance-related courses available within degree programs in response to the student demand, with many students motivated by the huge salaries commanded by those with a successful career in the financial sector. We report here details of a joint project between Charterhouse School and the University of Surrey designed to exploit the excitement of finance to teach elements of the high school (age 16-18) curriculum through modeling and simulation.

On fractality and chaos in Moroccan family business stock returns and volatility

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-05-01

The purpose of this study is to examine existence of fractality and chaos in returns and volatilities of family business companies listed on the Casablanca Stock Exchange (CSE) in Morocco, and also in returns and volatility of the CSE market index. Detrended fluctuation analysis based Hurst exponent and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model are used to quantify fractality in returns and volatility time series respectively. Besides, the largest Lyapunov exponent is employed to quantify chaos in both time series. The empirical results from sixteen family business companies follow. For return series, fractality analysis show that most of family business returns listed on CSE exhibit anti-persistent dynamics, whilst market returns have persistent dynamics. Besides, chaos tests show that business family stock returns are not chaotic while market returns exhibit evidence of chaotic behaviour. For volatility series, fractality analysis shows that most of family business stocks and market index exhibit long memory in volatility. Furthermore, results from chaos tests show that volatility of family business returns is not chaotic, whilst volatility of market index is chaotic. These results may help understanding irregularities patterns in Moroccan family business stock returns and volatility, and how they are different from market dynamics.

New Results on Gain-Loss Asymmetry for Stock Markets Time Series

NASA Astrophysics Data System (ADS)

Grudziecki, M.; Gnatowska, E.; Karpio, K.; Orłowski, A.; Załuska-Kotur, M.

2008-09-01

A method called investment horizon approach was successfully used to analyze stock markets of many different countries. Here we apply a version of this method to study characteristics of the Polish Pioneer mutual funds. We decided to analyze Pioneer because of its longest involvement in investing on the Polish market. Moreover, it apparently manages the biggest amount of money among all similar institutions in Poland. We compare various types of Pioneer mutual funds, characterized by different financial instruments they invest in. Previously, investment horizon approach produced different characteristics of emerging markets as opposed to mature ones, providing a possible way to quantify stock market maturity. Here we generalize the above mentioned results for mutual funds of various types.

76 FR 51447 - Self-Regulatory Organizations; Chicago Stock Exchange, Inc.; Notice of Filing of Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

... trading pause process during periods of extraordinary market volatility as a pilot in S&P 500 Index stocks... Exchange LLC, The NASDAQ Stock Market LLC, New York Stock Exchange LLC, NYSE Amex LLC, NYSE Arca, Inc... Organizations; Chicago Stock Exchange, Inc.; Notice of Filing of Proposed Rule Change To Amend Article 20, Rule...

Volatility measurement with directional change in Chinese stock market: Statistical property and investment strategy

NASA Astrophysics Data System (ADS)

Ma, Junjun; Xiong, Xiong; He, Feng; Zhang, Wei

2017-04-01

The stock price fluctuation is studied in this paper with intrinsic time perspective. The event, directional change (DC) or overshoot, are considered as time scale of price time series. With this directional change law, its corresponding statistical properties and parameter estimation is tested in Chinese stock market. Furthermore, a directional change trading strategy is proposed for invest in the market portfolio in Chinese stock market, and both in-sample and out-of-sample performance are compared among the different method of model parameter estimation. We conclude that DC method can capture important fluctuations in Chinese stock market and gain profit due to the statistical property that average upturn overshoot size is bigger than average downturn directional change size. The optimal parameter of DC method is not fixed and we obtained 1.8% annual excess return with this DC-based trading strategy.

Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient

NASA Astrophysics Data System (ADS)

Wang, Gang-Jin; Xie, Chi; Chen, Shou; Yang, Jiao-Jiao; Yang, Ming-Yan

2013-09-01

In this study, we first build two empirical cross-correlation matrices in the US stock market by two different methods, namely the Pearson’s correlation coefficient and the detrended cross-correlation coefficient (DCCA coefficient). Then, combining the two matrices with the method of random matrix theory (RMT), we mainly investigate the statistical properties of cross-correlations in the US stock market. We choose the daily closing prices of 462 constituent stocks of S&P 500 index as the research objects and select the sample data from January 3, 2005 to August 31, 2012. In the empirical analysis, we examine the statistical properties of cross-correlation coefficients, the distribution of eigenvalues, the distribution of eigenvector components, and the inverse participation ratio. From the two methods, we find some new results of the cross-correlations in the US stock market in our study, which are different from the conclusions reached by previous studies. The empirical cross-correlation matrices constructed by the DCCA coefficient show several interesting properties at different time scales in the US stock market, which are useful to the risk management and optimal portfolio selection, especially to the diversity of the asset portfolio. It will be an interesting and meaningful work to find the theoretical eigenvalue distribution of a completely random matrix R for the DCCA coefficient because it does not obey the Marčenko-Pastur distribution.

Profitability of Contrarian Strategies in the Chinese Stock Market.

PubMed

Shi, Huai-Long; Jiang, Zhi-Qiang; Zhou, Wei-Xing

2015-01-01

This paper reexamines the profitability of loser, winner and contrarian portfolios in the Chinese stock market using monthly data of all stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period from January 1997 to December 2012. We find evidence of short-term and long-term contrarian profitability in the whole sample period when the estimation and holding horizons are 1 month or longer than 12 months and the annualized return of contrarian portfolios increases with the estimation and holding horizons. We perform subperiod analysis and find that the long-term contrarian effect is significant in both bullish and bearish states, while the short-term contrarian effect disappears in bullish states. We compare the performance of contrarian portfolios based on different grouping manners in the estimation period and unveil that decile grouping outperforms quintile grouping and tertile grouping, which is more evident and robust in the long run. Generally, loser portfolios and winner portfolios have positive returns and loser portfolios perform much better than winner portfolios. Both loser and winner portfolios in bullish states perform better than those in the whole sample period. In contrast, loser and winner portfolios have smaller returns in bearish states, in which loser portfolio returns are significant only in the long term and winner portfolio returns become insignificant. These results are robust to the one-month skipping between the estimation and holding periods and for the two stock exchanges. Our findings show that the Chinese stock market is not efficient in the weak form. These findings also have obvious practical implications for financial practitioners.

Herd behaviors in the stock and foreign exchange markets

NASA Astrophysics Data System (ADS)

Kim, Kyungsik; Yoon, Seong-Min; Kim, Yup

2004-10-01

The herd behavior of returns for the won-dollar exchange rate and the Korean stock price index (KOSPI) is analyzed in Korean financial markets. It is reported that the probability distribution P( R) of returns R for three types of herding parameter satisfies the power-law behavior P( R)≃ R- β with the exponents β=2.2 (the won-dollar exchange rate) and 2.4 (the KOSPI). When the herding parameter h satisfies h⩾2.33, the crash regime in which P( R) increases with the increasing R appears. The active state of the transaction exists to decrease for h>2.33. Especially, we find that the distribution of normalized returns shows a crossover to a Gaussian distribution when the time step Δ t=252 is used. Our results will also be compared to the other well-known analyses.

76 FR 10418 - Self-Regulatory Organizations; NASDAQ Stock Market, LLC; Notice of Filing of Proposed Rule Change...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-24

...-Regulatory Organizations; NASDAQ Stock Market, LLC; Notice of Filing of Proposed Rule Change To Amend The..., 2011, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange'') filed with the Securities and Exchange... The NASDAQ Stock Market LLC proposes to amend the By-Laws of its parent corporation, The NASDAQ OMX...

Collective Behavior of Market Participants during Abrupt Stock Price Changes

PubMed Central

Maskawa, Jun-ichi

2016-01-01

Under uncertainty, human and animal collectives often respond stochastically to events they encounter. Human or animal individuals behave depending on others’ actions, and sometimes follow choices that are sub-optimal for individuals. Such mimetic behaviors are enhanced during emergencies, creating collective behavior of a group. A stock market that is about to crash, as markets did immediately after the Lehman Brothers bankruptcy, provides illustrative examples of such behaviors. We provide empirical evidence proving the existence of collective behavior among stock market participants in emergent situations. We investigated the resolution of extreme supply-and-demand order imbalances by increased balancing counter orders: buy and sell orders for excess supply and demand respectively, during times of price adjustment, so-called special quotes on the Tokyo Stock Exchange. Counter orders increase positively depending on the quantity of revealed counter orders: the accumulated orders in the book until then. Statistics of the coming counter order are well described using a logistic regression model with the ratio of revealed orders until then to the finally revealed orders as the explanatory variable. Results given here show that the market participants make Bayesian estimations of optimal choices to ascertain whether to order using information about orders of other participants. PMID:27513335

Collective Behavior of Market Participants during Abrupt Stock Price Changes.

PubMed

Maskawa, Jun-Ichi

2016-01-01

Under uncertainty, human and animal collectives often respond stochastically to events they encounter. Human or animal individuals behave depending on others' actions, and sometimes follow choices that are sub-optimal for individuals. Such mimetic behaviors are enhanced during emergencies, creating collective behavior of a group. A stock market that is about to crash, as markets did immediately after the Lehman Brothers bankruptcy, provides illustrative examples of such behaviors. We provide empirical evidence proving the existence of collective behavior among stock market participants in emergent situations. We investigated the resolution of extreme supply-and-demand order imbalances by increased balancing counter orders: buy and sell orders for excess supply and demand respectively, during times of price adjustment, so-called special quotes on the Tokyo Stock Exchange. Counter orders increase positively depending on the quantity of revealed counter orders: the accumulated orders in the book until then. Statistics of the coming counter order are well described using a logistic regression model with the ratio of revealed orders until then to the finally revealed orders as the explanatory variable. Results given here show that the market participants make Bayesian estimations of optimal choices to ascertain whether to order using information about orders of other participants.

Information transfer across intra/inter-structure of CDS and stock markets

NASA Astrophysics Data System (ADS)

Lim, Kyuseong; Kim, Sehyun; Kim, Soo Yong

2017-11-01

We investigate the information flow between industrial sectors in credit default swap and stock markets in the United States based on transfer entropy. Both markets have been studied with respect to dynamics and relations. Our approach considers the intra-structure of each financial market as well as the inter-structure between two markets through a moving window in order to scan a period from 2005 to 2012. We examine the information transfer with different k, especially k = 3, k = 5 and k = 7. Analysis indicates that the cases with k = 3 and k = 7 show the opposite trends but similar characteristics. Change in transfer entropy for intra-structure of CDS market precedes that of stock market in view of the entire time windows. Abrupt rise and fall in inter-structural information transfer between two markets are detected at the periods related to the financial crises, which can be considered as early warnings.

The properties of realized volatility and realized correlation: Evidence from the Indian stock market

NASA Astrophysics Data System (ADS)

Gkillas (Gillas), Konstantinos; Vortelinos, Dimitrios I.; Saha, Shrabani

2018-02-01

This paper investigates the properties of realized volatility and correlation series in the Indian stock market by employing daily data converting to monthly frequency of five different stock indices from January 2, 2006 to November 30, 2014. Using non-parametric estimation technique the properties examined include normality, long-memory, asymmetries, jumps, and heterogeneity. The realized volatility is a useful technique which provides a relatively accurate measure of volatility based on the actual variance which is beneficial for asset management in particular for non-speculative funds. The results show that realized volatility and correlation series are not normally distributed, with some evidence of persistence. Asymmetries are also evident in both volatilities and correlations. Both jumps and heterogeneity properties are significant; whereas, the former is more significant than the latter. The findings show that properties of volatilities and correlations in Indian stock market have similarities as that show in the stock markets in developed countries such as the stock market in the United States which is more prevalent for speculative business traders.

HMO behavior and stock market valuation: what does Wall Street reward and punish?

PubMed

Pauly, M V; Hillman, A L; Furukawa, M F; McCullough, J S

2001-01-01

This article analyzes the variation in returns to owning stock in investor-owned health maintenance organizations (IOHMOs) for the period 1994-1997. The average return (measured by the change in the market value of the stock plus dividends) was close to zero, but returns were positive and high for firms operating in local markets that were and remained less competitive, with large nationwide scope, and with less rapidly growing panels of contracted physicians. Indicators of a firm's strategic direction were abstracted from their annual reports; firms pursuing a merger or acquisition strategy, and those emphasizing a utilization review strategy, showed lower returns than those that did not. Other strategy and market variables were not related to stock market returns over this period, and were also generally not related to price-earnings ratios. This analysis supports the view that competitive HMO markets best constrain profits to investor-owned firms.

Simplified stock markets described by number operators

NASA Astrophysics Data System (ADS)

Bagarello, F.

2009-06-01

In this paper we continue our systematic analysis of the operatorial approach previously proposed in an economical context and we discuss a mixed toy model of a simplified stock market, i.e. a model in which the price of the shares is given as an input. We deduce the time evolution of the portfolio of the various traders of the market, as well as of other observable quantities. As in a previous paper, we solve the equations of motion by means of a fixed point like approximation.

Changes of hierarchical network in local and world stock market

NASA Astrophysics Data System (ADS)

Patwary, Enayet Ullah; Lee, Jong Youl; Nobi, Ashadun; Kim, Doo Hwan; Lee, Jae Woo

2017-10-01

We consider the cross-correlation coefficients of the daily returns in the local and global stock markets. We generate the minimal spanning tree (MST) using the correlation matrix. We observe that the MSTs change their structure from chain-like networks to star-like networks during periods of market uncertainty. We quantify the measure of the hierarchical network utilizing the value of the hierarchy measured by the hierarchical path. The hierarchy and betweenness centrality characterize the state of the market regarding the impact of crises. During crises, the non-financial company is established as the central node of the MST. However, before the crisis and during stable periods, the financial company is occupying the central node of the MST in the Korean and the U.S. stock markets. The changes in the network structure and the central node are good indicators of an upcoming crisis.

76 FR 79262 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Order Granting Approval of Proposed...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-21

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Order Granting Approval of Proposed Rule Change To... Select Markets December 15, 2011. I. Introduction On August 30, 2011, The NASDAQ Stock Market LLC... Global and Global Select Markets. The proposed rule change was published in the Federal Register on...

The normalisation of terror: the response of Israel's stock market to long periods of terrorism.

PubMed

Peleg, Kobi; Regens, James L; Gunter, James T; Jaffe, Dena H

2011-01-01

Man-made disasters such as acts of terrorism may affect a society's resiliency and sensitivity to prolonged physical and psychological stress. The Israeli Tel Aviv stock market TA-100 Index was used as an indicator of reactivity to suicide terror bombings. After accounting for factors such as world market changes and attack severity and intensity, the analysis reveals that although Israel's financial base remained sensitive to each act of terror across the entire period of the Second Intifada (2000-06), sustained psychological resilience was indicated with no apparent overall market shift. In other words, we saw a 'normalisation of terror' following an extended period of continued suicide bombings. The results suggest that investors responded to less transitory global market forces, indicating sustained resilience and long-term market confidence. Future studies directly measuring investor expectations and reactions to man-made disasters, such as terrorism, are warranted. © 2011 The Author(s). Disasters © Overseas Development Institute, 2011.

The predictive power of Japanese candlestick charting in Chinese stock market

NASA Astrophysics Data System (ADS)

Chen, Shi; Bao, Si; Zhou, Yu

2016-09-01

This paper studies the predictive power of 4 popular pairs of two-day bullish and bearish Japanese candlestick patterns in Chinese stock market. Based on Morris' study, we give the quantitative details of definition of long candlestick, which is important in two-day candlestick pattern recognition but ignored by several previous researches, and we further give the quantitative definitions of these four pairs of two-day candlestick patterns. To test the predictive power of candlestick patterns on short-term price movement, we propose the definition of daily average return to alleviate the impact of correlation among stocks' overlap-time returns in statistical tests. To show the robustness of our result, two methods of trend definition are used for both the medium-market-value and large-market-value sample sets. We use Step-SPA test to correct for data snooping bias. Statistical results show that the predictive power differs from pattern to pattern, three of the eight patterns provide both short-term and relatively long-term prediction, another one pair only provide significant forecasting power within very short-term period, while the rest three patterns present contradictory results for different market value groups. For all the four pairs, the predictive power drops as predicting time increases, and forecasting power is stronger for stocks with medium market value than those with large market value.

Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market

NASA Astrophysics Data System (ADS)

Cui, Ling-xiao; Long, Wen

2016-11-01

Dynamic mode decomposition (DMD) is an effective method to capture the intrinsic dynamical modes of complex system. In this work, we adopt DMD method to discover the evolutionary patterns in stock market and apply it to Chinese A-share stock market. We design two strategies based on DMD algorithm. The strategy which considers only timing problem can make reliable profits in a choppy market with no prominent trend while fails to beat the benchmark moving-average strategy in bull market. After considering the spatial information from spatial-temporal coherent structure of DMD modes, we improved the trading strategy remarkably. Then the DMD strategies profitability is quantitatively evaluated by performing SPA test to correct the data-snooping effect. The results further prove that DMD algorithm can model the market patterns well in sideways market.

Market Impact and Order Book Characteristics in the Korean Futures Market

NASA Astrophysics Data System (ADS)

Lee, Junghoon; Youn, Janghyuk; Chang, Woojin

We have examined the order book characteristics and market impact on the Korean stock index futures market (KOSPI 200 index futures). The distribution of order volumes generally follows power-law distribution. The estimated exponents are 1.9 for market order, 2.5 for limit order, and 2.1 for cancel order. This result is different from the case of stocks where the exponent of market order is larger than that of limit order. The order likelihood is distinctively high in every 50's of order volume, which implies the behavioral characteristics of human preference on round-up numbers. The distributions of bid-ask spread and the best quotes volume provide the evidence of the liquidity of KOSPI 200 index futures market. We have obtained the concave relationship between market impact and transaction volume as well. Finally, the market response behavior is observed regarding various transaction sizes. The size of market response is estimated to be proportional to the size of transaction. Also, the larger the transaction size is, the longer it takes to recover the stability from the impact triggered by transaction.

The time-varying correlation between policy uncertainty and stock returns: Evidence from China

NASA Astrophysics Data System (ADS)

Xiong, Xiong; Bian, Yuxiang; Shen, Dehua

2018-06-01

In this paper, we use a new policy uncertainty index to investigate the time-varying correlation between economic policy uncertainty (EPU) and Chinese stock market returns. The correlation is examined in the period from January 1995 to December 2016. We show that absolute changes in EPU have a significant impact on stock market returns. Specifically, empirical results based on the DCC-GARCH model reveal that the correlation between EPU and stock returns has large fluctuations, especially during a financial crisis; in addition, the impact of EPU on the Shanghai stock market is greater than on the Shenzhen stock market. Robustness results reveal that the impact of EPU on state-owned enterprises is larger than on non-state enterprises. All of these results highlight the important role of EPU in the Chinese stock market, and shed light on such issues for future research.

Profitability of Contrarian Strategies in the Chinese Stock Market

PubMed Central

Shi, Huai-Long; Jiang, Zhi-Qiang; Zhou, Wei-Xing

2015-01-01

This paper reexamines the profitability of loser, winner and contrarian portfolios in the Chinese stock market using monthly data of all stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period from January 1997 to December 2012. We find evidence of short-term and long-term contrarian profitability in the whole sample period when the estimation and holding horizons are 1 month or longer than 12 months and the annualized return of contrarian portfolios increases with the estimation and holding horizons. We perform subperiod analysis and find that the long-term contrarian effect is significant in both bullish and bearish states, while the short-term contrarian effect disappears in bullish states. We compare the performance of contrarian portfolios based on different grouping manners in the estimation period and unveil that decile grouping outperforms quintile grouping and tertile grouping, which is more evident and robust in the long run. Generally, loser portfolios and winner portfolios have positive returns and loser portfolios perform much better than winner portfolios. Both loser and winner portfolios in bullish states perform better than those in the whole sample period. In contrast, loser and winner portfolios have smaller returns in bearish states, in which loser portfolio returns are significant only in the long term and winner portfolio returns become insignificant. These results are robust to the one-month skipping between the estimation and holding periods and for the two stock exchanges. Our findings show that the Chinese stock market is not efficient in the weak form. These findings also have obvious practical implications for financial practitioners. PMID:26368537

Fractional Market Model and its Verification on the Warsaw STOCK Exchange

NASA Astrophysics Data System (ADS)

Kozłowska, Marzena; Kasprzak, Andrzej; Kutner, Ryszard

We analyzed the rising and relaxation of the cusp-like local peaks superposed with oscillations which were well defined by the Warsaw Stock Exchange index WIG in a daily time horizon. We found that the falling paths of all index peaks were described by a generalized exponential function or the Mittag-Leffler (ML) one superposed with various types of oscillations. However, the rising paths (except the first one of WIG which rises exponentially and the most important last one which rises again according to the ML function) can be better described by bullish anti-bubbles or inverted bubbles.2-4 The ML function superposed with oscillations is a solution of the nonhomogeneous fractional relaxation equation which defines here our Fractional Market Model (FMM) of index dynamics which can be also called the Rheological Model of Market. This solution is a generalized analog of an exactly solvable fractional version of the Standard or Zener Solid Model of viscoelastic materials commonly used in modern rheology.5 For example, we found that the falling paths of the index can be considered to be a system in the intermediate state lying between two complex ones, defined by short and long-time limits of the Mittag-Leffler function; these limits are given by the Kohlrausch-Williams-Watts (KWW) law for the initial times, and the power-law or the Nutting law for asymptotic time. Some rising paths (i.e., the bullish anti-bubbles) are a kind of log-periodic oscillations of the market in the bullish state initiated by a crash. The peaks of the index can be viewed as precritical or precrash ones since: (i) the financial market changes its state too early from the bullish to bearish one before it reaches a scaling region (defined by the diverging power-law of return per unit time), and (ii) they are affected by a finite size effect. These features could be a reminiscence of a significant risk aversion of the investors and their finite number, respectively. However, this means that the

Relationship between suicide rate and economic growth and stock market in the People’s Republic of China: 2004–2013

PubMed Central

Yin, Honglei; Xu, Lin; Shao, Yechang; Li, Liping; Wan, Chengsong

2016-01-01

Objectives The objective of this study was to estimate the features of suicide rate and its association with economic development and stock market during the past decade in the People’s Republic of China. Methods Official data were gathered and analyzed in the People’s Republic of China during the period 2004–2013. Nationwide suicide rate was stratified by four year age-groups, sex, urban/rural areas, and regions (East, Central, and West). Annual economic indexes including gross domestic product (GDP) per capita and rural and urban income per capita were all adjusted for inflation. Variation coefficient of market index (VCMI) was also included as an economic index to measure the fluctuation of the stock market. Negative binomial regression was performed to examine the time trend of region-level suicide rates and effects of sex, age, urban/rural area, region, and economic index on the suicide rates. Results Suicide rates of each age-group, sex, urban/rural area, and region were generally decreased from 2004 to 2013, while annual GDP per capita and rural and urban income per capita were generally increased by year. VCMI fluctuated largely, which peaked around 2009 and decreased after that time. Negative binomial regression showed that the decreased suicide rate in East and Central rural areas was the main cause of the decrease in suicide rate in the People’s Republic of China. Suicide rate in the People’s Republic of China for the study period increased with age and was higher in rural than in urban area, higher in males than in females, and the highest in the Central region. When GDP per capita increased by 2,787 RMB, the suicide rate decreased by 0.498 times. VCMI showed no significant relationship with suicide rate in the negative binomial regression. Conclusion Suicide rate decreased in 2004–2013; varied among different age-groups, sex, urban/rural areas, and regions; and was negatively associated with the economic growth in the People’s Republic of

Who wins? Study of long-run trader survival in an artificial stock market

NASA Astrophysics Data System (ADS)

Cincotti, Silvano; M. Focardi, Sergio; Marchesi, Michele; Raberto, Marco

2003-06-01

We introduce a multi-asset artificial financial market with finite amount of cash and number of stocks. The background trading is characterized by a random trading strategy constrained by the finiteness of resources and by market volatility. Stock price processes exhibit volatility clustering, fat-tailed distribution of returns and reversion to the mean. Three active trading strategies have been introduced and studied in two different market conditions: steady market and growing market with asset inflation. We show that the profitability of each strategy depends both on the periodicity of portfolio reallocation and on the market condition. The best performing strategy is the one that exploits the mean reversion characteristic of asset price processes.

A global network topology of stock markets: Transmitters and receivers of spillover effects

NASA Astrophysics Data System (ADS)

Shahzad, Syed Jawad Hussain; Hernandez, Jose Areola; Rehman, Mobeen Ur; Al-Yahyaee, Khamis Hamed; Zakaria, Muhammad

2018-02-01

This paper applies a bivariate cross-quantilogram approach to examine the spillover network structure in the stock markets of 58 countries according to bearish, normal and bullish market scenarios. Our aim is to identify the strongest interdependencies, the directionality of the spillover risk effects, and to detect those equity markets with the potential to cause global systemic risk. The results highlight the role of the US and Canadian equity markets as major spillover transmitters, while the stock markets of Romania, Taiwan and Mexico act mainly as spillover receivers. Particularly strong spillovers are observed from the Canadian and US equity markets towards the Irish market, and from the Brazilian equity market towards the Kenyan equivalent. The equity market networks suggest that only the US equity market can trigger systemic risk on a global scale. Implications of the results are discussed.

Effects of global financial crisis on network structure in a local stock market

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

2014-08-01

This study considers the effects of the 2008 global financial crisis on threshold networks of a local Korean financial market around the time of the crisis. Prices of individual stocks belonging to KOSPI 200 (Korea Composite Stock Price Index 200) are considered for three time periods, namely before, during, and after the crisis. Threshold networks are constructed from fully connected cross-correlation networks, and thresholds of cross-correlation coefficients are assigned to obtain threshold networks. At the high threshold, only one large cluster consisting of firms in the financial sector, heavy industry, and construction is observed during the crisis. However, before and after the crisis, there are several fragmented clusters belonging to various sectors. The power law of the degree distribution in threshold networks is observed within the limited range of thresholds. Threshold networks are fatter during the crisis than before or after the crisis. The clustering coefficient of the threshold network follows the power law in the scaling range.

Understanding the complex dynamics of stock markets through cellular automata

NASA Astrophysics Data System (ADS)

Qiu, G.; Kandhai, D.; Sloot, P. M. A.

2007-04-01

We present a cellular automaton (CA) model for simulating the complex dynamics of stock markets. Within this model, a stock market is represented by a two-dimensional lattice, of which each vertex stands for a trader. According to typical trading behavior in real stock markets, agents of only two types are adopted: fundamentalists and imitators. Our CA model is based on local interactions, adopting simple rules for representing the behavior of traders and a simple rule for price updating. This model can reproduce, in a simple and robust manner, the main characteristics observed in empirical financial time series. Heavy-tailed return distributions due to large price variations can be generated through the imitating behavior of agents. In contrast to other microscopic simulation (MS) models, our results suggest that it is not necessary to assume a certain network topology in which agents group together, e.g., a random graph or a percolation network. That is, long-range interactions can emerge from local interactions. Volatility clustering, which also leads to heavy tails, seems to be related to the combined effect of a fast and a slow process: the evolution of the influence of news and the evolution of agents’ activity, respectively. In a general sense, these causes of heavy tails and volatility clustering appear to be common among some notable MS models that can confirm the main characteristics of financial markets.

Dataset for petroleum based stock markets and GAUSS codes for SAMEM.

PubMed

Khalifa, Ahmed A A; Bertuccelli, Pietro; Otranto, Edoardo

2017-02-01

This article includes a unique data set of a balanced daily (Monday, Tuesday and Wednesday) for oil and natural gas volatility and the oil rich economies' stock markets for Saudi Arabia, Qatar, Kuwait, Abu Dhabi, Dubai, Bahrain and Oman, using daily data over the period spanning Oct. 18, 2006-July 30, 2015. Additionally, we have included unique GAUSS codes for estimating the spillover asymmetric multiplicative error model (SAMEM) with application to Petroleum-Based Stock Market. The data, the model and the codes have many applications in business and social science.

The position profiles of order cancellations in an emerging stock market

NASA Astrophysics Data System (ADS)

Gu, Gao-Feng; Xiong, Xiong; Ren, Fei; Zhou, Wei-Xing; Zhang, Wei

2013-04-01

Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price level in the limit-order book (LOB) and the position in the queue at each price level. We study the profiles of these two order cancellation positions through rebuilding the limit-order book using the order flow data of 23 liquid stocks traded on the Shenzhen Stock Exchange in the year 2003. We find that the profiles of relative price levels where cancellations occur obey a log-normal distribution. After normalizing the relative price level by removing the factor of order numbers stored at the price level, we find that the profiles exhibit a power-law scaling behavior on the right tails for both buy and sell orders. When focusing on the order cancellation positions in the queue at each price level, we find that the profiles increase rapidly in the front of the queue, and then fluctuate around a constant value till the end of the queue. These profiles are similar for different stocks. In addition, the profiles of cancellation positions can be fitted by an exponent function for both buy and sell orders. These two kinds of cancellation profiles seem universal for different stocks investigated and exhibit minor asymmetry between buy and sell orders. Our empirical findings shed new light on the order cancellation dynamics and pose constraints on the construction of order-driven stock market models.

78 FR 25329 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-30

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of..., The NASDAQ Stock Market LLC (``NASDAQ'' or the ``Exchange'') filed with the Securities and Exchange..., including the Nasdaq Market Center, the FINRA/NASDAQ Trade Reporting Facility, and FINRA's OTCBB Service...

Multifractals in Western Major STOCK Markets Historical Volatilities in Times of Financial Crisis

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

In this paper, the generalized Hurst exponent is used to investigate multifractal properties of historical volatility (CHV) in stock market price and return series before, during and after 2008 financial crisis. Empirical results from NASDAQ, S&P500, TSE, CAC40, DAX, and FTSE stock market data show that there is strong evidence of multifractal patterns in HV of both price and return series. In addition, financial crisis deeply affected the behavior and degree of multifractality in volatility of Western financial markets at price and return levels.

78 FR 23611 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-19

... simultaneously trade different asset classes within the same strategy. Because cash equities and options markets...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of a... April 1, 2013, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange'') filed with the Securities and...

76 FR 76795 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-08

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of...\\ notice is hereby given that on November 22, 2011, The NASDAQ Stock Market LLC (``NASDAQ'' or the... the proposal, market participants will be given the additional options of (1) assigning a group...

77 FR 52375 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-29

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of Proposed Rule Change To Eliminate Market Maker Pre-Opening Obligations on NOM August 23, 2012. Pursuant to... is hereby given that on August 10, 2012, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange...

77 FR 33256 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-05

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of... is hereby given that on May 22, 2012, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange'') filed... Order Fee,\\3\\ aimed at reducing inefficient order entry practices of certain market participants that...

Sign realized jump risk and the cross-section of stock returns: Evidence from China's stock market.

PubMed

Chao, Youcong; Liu, Xiaoqun; Guo, Shijun

2017-01-01

Using 5-minute high frequency data from the Chinese stock market, we employ a non-parametric method to estimate Fama-French portfolio realized jumps and investigate whether the estimated positive, negative and sign realized jumps could forecast or explain the cross-sectional stock returns. The Fama-MacBeth regression results show that not only have the realized jump components and the continuous volatility been compensated with risk premium, but also that the negative jump risk, the positive jump risk and the sign jump risk, to some extent, could explain the return of the stock portfolios. Therefore, we should pay high attention to the downside tail risk and the upside tail risk.

Interdependence between Greece and other European stock markets: A comparison of wavelet and VMD copula, and the portfolio implications

NASA Astrophysics Data System (ADS)

Shahzad, Syed Jawad Hussain; Kumar, Ronald Ravinesh; Ali, Sajid; Ameer, Saba

2016-09-01

The interdependence of Greece and other European stock markets and the subsequent portfolio implications are examined in wavelet and variational mode decomposition domain. In applying the decomposition techniques, we analyze the structural properties of data and distinguish between short and long term dynamics of stock market returns. First, the GARCH-type models are fitted to obtain the standardized residuals. Next, different copula functions are evaluated, and based on the conventional information criteria and time varying parameter, Joe-Clayton copula is chosen to model the tail dependence between the stock markets. The short-run lower tail dependence time paths show a sudden increase in comovement during the global financial crises. The results of the long-run dependence suggest that European stock markets have higher interdependence with Greece stock market. Individual country's Value at Risk (VaR) separates the countries into two distinct groups. Finally, the two-asset portfolio VaR measures provide potential markets for Greece stock market investment diversification.

78 FR 12116 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Designation of a Longer...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-21

... from NASDAQ Rule 2460 (Payment for Market Making). Accordingly, the Commission, pursuant to Section 19...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Designation of a Longer Period for... Market Quality Program February 14, 2013. On December 7, 2012, The NASDAQ Stock Market LLC (``Exchange...

Modeling Long-term Behavior of Stock Market Prices Using Differential Equations

NASA Astrophysics Data System (ADS)

Yang, Xiaoxiang; Zhao, Conan; Mazilu, Irina

2015-03-01

Due to incomplete information available in the market and uncertainties associated with the price determination process, the stock prices fluctuate randomly during a short period of time. In the long run, however, certain economic factors, such as the interest rate, the inflation rate, and the company's revenue growth rate, will cause a gradual shift in the stock price. Thus, in this paper, a differential equation model has been constructed in order to study the effects of these factors on the stock prices. The model obtained accurately describes the general trends in the AAPL and XOM stock price changes over the last ten years.

76 FR 76786 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-08

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of... 29, 2011, The NASDAQ Stock Market LLC (the ``Exchange'' or ``NASDAQ'') filed with the Securities and... Options Market (``NOM'') to eliminate from its rules two order types and two data feeds that are not in...

78 FR 29193 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-17

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of a Proposed Rule Change To Remove Pilot Restrictions From NASDAQ's Qualified Market Maker and NBBO Setter...\\ thereunder, notice is hereby given that on May 1, 2013, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange...

77 FR 20658 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-05

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of... 19b-4 thereunder,\\2\\ notice is hereby given that on March 23, 2012, The NASDAQ Stock Market LLC...-market price stability. Pegged Orders are orders that, once entered, adjust in price automatically, in...

75 FR 18250 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-09

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of Proposed Rule Change Relating to Pricing for Option Orders Routed to Away Markets April 1, 2010. Pursuant...\\ notice is hereby given that on March 25, 2010, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange...

77 FR 34453 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-11

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of a Proposed Rule Change To Update the NASDAQ Options Market Message Traffic Mitigation Rule June 5, 2012...\\ thereunder, notice is hereby given that, on May 29, 2012, The NASDAQ Stock Market LLC (``NASDAQ'') filed with...

Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market.

PubMed

Kenett, Dror Y; Tumminello, Michele; Madi, Asaf; Gur-Gershgoren, Gitit; Mantegna, Rosario N; Ben-Jacob, Eshel

2010-12-20

What are the dominant stocks which drive the correlations present among stocks traded in a stock market? Can a correlation analysis provide an answer to this question? In the past, correlation based networks have been proposed as a tool to uncover the underlying backbone of the market. Correlation based networks represent the stocks and their relationships, which are then investigated using different network theory methodologies. Here we introduce a new concept to tackle the above question--the partial correlation network. Partial correlation is a measure of how the correlation between two variables, e.g., stock returns, is affected by a third variable. By using it we define a proxy of stock influence, which is then used to construct partial correlation networks. The empirical part of this study is performed on a specific financial system, namely the set of 300 highly capitalized stocks traded at the New York Stock Exchange, in the time period 2001-2003. By constructing the partial correlation network, unlike the case of standard correlation based networks, we find that stocks belonging to the financial sector and, in particular, to the investment services sub-sector, are the most influential stocks affecting the correlation profile of the system. Using a moving window analysis, we find that the strong influence of the financial stocks is conserved across time for the investigated trading period. Our findings shed a new light on the underlying mechanisms and driving forces controlling the correlation profile observed in a financial market.

78 FR 48744 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-09

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of a...,\\2\\ notice is hereby given that, on July 26, 2013, The NASDAQ Stock Market LLC (``NASDAQ'' or..., Section 6 (Series of Options Contracts Open for Trading) of the rules of the NASDAQ Options Market (``NOM...

78 FR 49578 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-14

... market center. LIST is a routing strategy that is used by firms that wish for their orders to participate...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of a... hereby given that on August 5, 2013, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange'') filed with...

Multilayer Stock Forecasting Model Using Fuzzy Time Series

PubMed Central

Javedani Sadaei, Hossein; Lee, Muhammad Hisyam

2014-01-01

After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS. PMID:24605058

Stock volatility as a risk factor for coronary heart disease death.

PubMed

Ma, Wenjuan; Chen, Honglei; Jiang, Lili; Song, Guixiang; Kan, Haidong

2011-04-01

The volatility of financial markets may cause substantial emotional and physical stress among investors. We hypothesize that this may have adverse effects on cardiovascular health. The Chinese stock markets were extremely volatile between 2006 and 2008. We, therefore, examined the relationship between daily change of the Shanghai Stock Exchange (SSE) Composite Index (referred as the Index) and coronary heart disease (CHD) deaths from 1 January 2006 to 31 December 2008 in Shanghai, the financial capital of China. Daily death and stock performance data were collected from the Shanghai Center for Disease Control and Prevention and SSE, respectively. Data were analysed with over-dispersed generalized linear Poisson models, controlling for long-term and seasonal trends of CHD mortality, day of the week, Index closing value, weather conditions, and air pollution levels. We observed a U-shaped relationship between the Index change and CHD deaths: both rising and falling of the Index were associated with more deaths and the fewest deaths coincided with little or no change of the index. We also examined the absolute daily change of the Index in relation to CHD deaths: in a 1-day lag model, each 100-point change of the Index corresponded to 5.17% (95% confidence interval: 1.71, 8.63%) increase in CHD deaths. Further analysis showed that the association was stronger for out-of-hospital CHD death than for in-hospital death. We found that CHD deaths fluctuated with daily stock changes in Shanghai, suggesting that stock volatility may adversely affect cardiovascular health.

78 FR 76347 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-17

... they remain in effect until executed or the end of either regular market hours at 4:00 p.m. or the end...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of...\\ thereunder, notice is hereby given that on November 29, 2013, The NASDAQ Stock Market LLC (``NASDAQ'' or...

The mutual causality analysis between the stock and futures markets

NASA Astrophysics Data System (ADS)

Yao, Can-Zhong; Lin, Qing-Wen

2017-07-01

In this paper we employ the conditional Granger causality model to estimate the information flow, and find that the improved model outperforms the Granger causality model in revealing the asymmetric correlation between stocks and futures in the Chinese market. First, we find that information flows estimated by Granger causality tests from futures to stocks are greater than those from stocks to futures. Additionally, average correlation coefficients capture some important characteristics between stock prices and information flows over time. Further, we find that direct information flows estimated by conditional Granger causality tests from stocks to futures are greater than those from futures to stocks. Besides, the substantial increases of information flows and direct information flows exhibit a certain degree of synchronism with the occurrences of important events. Finally, the comparative analysis with the asymmetric ratio and the bootstrap technique demonstrates the slight asymmetry of information flows and the significant asymmetry of direct information flows. It reveals that the information flows from futures to stocks are slightly greater than those in the reverse direction, while the direct information flows from stocks to futures are significantly greater than those in the reverse direction.

Comparison between goal programming and cointegration approaches in enhanced index tracking

NASA Astrophysics Data System (ADS)

Lam, Weng Siew; Jamaan, Saiful Hafizah Hj.

2013-04-01

Index tracking is a popular form of passive fund management in stock market. Passive management is a buy-and-hold strategy that aims to achieve rate of return similar to the market return. Index tracking problem is a problem of reproducing the performance of a stock market index, without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio that minimizes risk or tracking error. An improved index tracking (enhanced index tracking) is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the tracking error. Enhanced index tracking aims to generate excess return over the return achieved by the index. The objective of this study is to compare the portfolio compositions and performances by using two different approaches in enhanced index tracking problem, which are goal programming and cointegration. The result of this study shows that the optimal portfolios for both approaches are able to outperform the Malaysia market index which is Kuala Lumpur Composite Index. Both approaches give different optimal portfolio compositions. Besides, the cointegration approach outperforms the goal programming approach because the cointegration approach gives higher mean return and lower risk or tracking error. Therefore, the cointegration approach is more appropriate for the investors in Malaysia.

Sign realized jump risk and the cross-section of stock returns: Evidence from China's stock market

PubMed Central

Chao, Youcong; Liu, Xiaoqun; Guo, Shijun

2017-01-01

Using 5-minute high frequency data from the Chinese stock market, we employ a non-parametric method to estimate Fama-French portfolio realized jumps and investigate whether the estimated positive, negative and sign realized jumps could forecast or explain the cross-sectional stock returns. The Fama-MacBeth regression results show that not only have the realized jump components and the continuous volatility been compensated with risk premium, but also that the negative jump risk, the positive jump risk and the sign jump risk, to some extent, could explain the return of the stock portfolios. Therefore, we should pay high attention to the downside tail risk and the upside tail risk. PMID:28771514

75 FR 21688 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Order Granting Accelerated Approval...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-26

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Order Granting Accelerated Approval of Proposed Rule... 20, 2010. I. Introduction On March 11, 2010, The NASDAQ Stock Market LLC (``Nasdaq'' or ``Exchange.... Strike prices for ETF options are permitted in $1 or greater intervals where the strike price is $200 or...

Global financial crisis and weak-form efficiency of Islamic sectoral stock markets: An MF-DFA analysis

NASA Astrophysics Data System (ADS)

Mensi, Walid; Tiwari, Aviral Kumar; Yoon, Seong-Min

2017-04-01

This paper estimates the weak-form efficiency of Islamic stock markets using 10 sectoral stock indices (basic materials, consumer services, consumer goods, energy, financials, health care, industrials, technology, telecommunication, and utilities). The results based on the multifractal detrended fluctuation analysis (MF-DFA) approach show time-varying efficiency for the sectoral stock markets. Moreover, we find that they tend to show high efficiency in the long term but moderate efficiency in the short term, and that these markets become less efficient after the onset of the global financial crisis. These results have several significant implications in terms of asset allocation for investors dealing with Islamic markets.

Index Fund Selections with GAs and Classifications Based on Turnover

NASA Astrophysics Data System (ADS)

Orito, Yukiko; Motoyama, Takaaki; Yamazaki, Genji

It is well known that index fund selections are important for the risk hedge of investment in a stock market. The`selection’means that for`stock index futures’, n companies of all ones in the market are selected. For index fund selections, Orito et al.(6) proposed a method consisting of the following two steps : Step 1 is to select N companies in the market with a heuristic rule based on the coefficient of determination between the return rate of each company in the market and the increasing rate of the stock price index. Step 2 is to construct a group of n companies by applying genetic algorithms to the set of N companies. We note that the rule of Step 1 is not unique. The accuracy of the results using their method depends on the length of time data (price data) in the experiments. The main purpose of this paper is to introduce a more`effective rule’for Step 1. The rule is based on turnover. The method consisting of Step 1 based on turnover and Step 2 is examined with numerical experiments for the 1st Section of Tokyo Stock Exchange. The results show that with our method, it is possible to construct the more effective index fund than the results of Orito et al.(6). The accuracy of the results using our method depends little on the length of time data (turnover data). The method especially works well when the increasing rate of the stock price index over a period can be viewed as a linear time series data.

A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.

PubMed

Araújo, Ricardo de A

2012-04-01

Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature. Copyright © 2011 Elsevier Ltd. All rights reserved.

Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model

NASA Astrophysics Data System (ADS)

Rounaghi, Mohammad Mahdi; Nassir Zadeh, Farzaneh

2016-08-01

We investigated the presence and changes in, long memory features in the returns and volatility dynamics of S&P 500 and London Stock Exchange using ARMA model. Recently, multifractal analysis has been evolved as an important way to explain the complexity of financial markets which can hardly be described by linear methods of efficient market theory. In financial markets, the weak form of the efficient market hypothesis implies that price returns are serially uncorrelated sequences. In other words, prices should follow a random walk behavior. The random walk hypothesis is evaluated against alternatives accommodating either unifractality or multifractality. Several studies find that the return volatility of stocks tends to exhibit long-range dependence, heavy tails, and clustering. Because stochastic processes with self-similarity possess long-range dependence and heavy tails, it has been suggested that self-similar processes be employed to capture these characteristics in return volatility modeling. The present study applies monthly and yearly forecasting of Time Series Stock Returns in S&P 500 and London Stock Exchange using ARMA model. The statistical analysis of S&P 500 shows that the ARMA model for S&P 500 outperforms the London stock exchange and it is capable for predicting medium or long horizons using real known values. The statistical analysis in London Stock Exchange shows that the ARMA model for monthly stock returns outperforms the yearly. ​A comparison between S&P 500 and London Stock Exchange shows that both markets are efficient and have Financial Stability during periods of boom and bust.

A complex network for studying the transmission mechanisms in stock market

NASA Astrophysics Data System (ADS)

Long, Wen; Guan, Lijing; Shen, Jiangjian; Song, Linqiu; Cui, Lingxiao

2017-10-01

This paper introduces a new complex network to describe the volatility transmission mechanisms in stock market. The network can not only endogenize stock market's volatility but also figure out the direction of volatility spillover. In this model, we first use BEKK-GARCH to estimate the volatility spillover effects among Chinese 18 industry sectors. Then, based on the ARCH coefficients and GARCH coefficients, the directional shock networks and variance networks in different stages are constructed separately. We find that the spillover effects and network structures changes in different stages. The results of the topological stability test demonstrate that the connectivity of networks becomes more fragile to selective attacks than stochastic attacks.

Correlation analysis of the Korean stock market: Revisited to consider the influence of foreign exchange rate

NASA Astrophysics Data System (ADS)

Jo, Sang Kyun; Kim, Min Jae; Lim, Kyuseong; Kim, Soo Yong

2018-02-01

We investigated the effect of foreign exchange rate in a correlation analysis of the Korean stock market using both random matrix theory and minimum spanning tree. We collected data sets which were divided into two types of stock price, the original stock price in Korean Won and the price converted into US dollars at contemporary foreign exchange rates. Comparing the random matrix theory based on the two different prices, a few particular sectors exhibited substantial differences while other sectors changed little. The particular sectors were closely related to economic circumstances and the influence of foreign financial markets during that period. The method introduced in this paper offers a way to pinpoint the effect of exchange rate on an emerging stock market.

77 FR 62290 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-12

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of... hereby given that on September 27, 2012, The NASDAQ Stock Market LLC (``NASDAQ'' or the ``Exchange... uniform registration form changes; (2) electronic fingerprint processing; (3) Web EFT TM , which allows...

78 FR 62814 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Order Approving a Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-22

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Order Approving a Proposed Rule Change To Assume... Authority and Supervision September 30, 2013. On July 31, 2013, The NASDAQ Stock Market LLC (``NASDAQ'' or...) Manipulation patterns that monitor solely NASDAQ activity, including patterns that monitor the Exchange's...

77 FR 70857 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Designation of Longer...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-27

... 2, 2012, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange'') filed with the Securities and... SECURITIES AND EXCHANGE COMMISSION [Release No. 34-68279; File No. SR-NASDAQ-2012-117] Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Designation of Longer Period for Commission...

76 FR 75924 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-05

...-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of... November 22, 2011, The NASDAQ Stock Market LLC (``Exchange''), filed with the Securities and Exchange... open orders, use of routing strategies and liquidity code designation. The data provided by QView will...

78 FR 59740 - Self-Regulatory Organizations; the NASDAQ Stock Market LLC; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-27

...-Regulatory Organizations; the NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of... is hereby given that on September 9, 2013, The NASDAQ Stock Market LLC (``NASDAQ'' or the ``Exchange.../or Professional liquidity based on increasing percentages of total industry customer equity and ETF...

Stock price analysis of sustainable foreign investment companies in Indonesia

NASA Astrophysics Data System (ADS)

Fachrudin, Khaira Amalia

2018-03-01

The stock price is determined by demand and supply in the stock market. Stock price reacts to information. Sustainable investment is an investment that considers environmental sustainability and human rights. This study aims to predict the probability of above average stock price by including the sustainability index as one of its variables. The population is all foreign investment companies in Indonesia. The target population is companies that distribute dividends – also as a sample. The analysis tool is a logistic regression. At 5% alpha, it was found that sustainability index did not have the probability to increase stock price average. The significant effects are free cash flow and cost of debt. However, sustainability index can increase the Negelkarke R square. The implication is that the awareness of sustainability is still necesary to be improved because from the research result it can be seen that investors only consider the risk and return.

75 FR 65044 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Order Granting Approval to a Proposed...