Stock Market Index Computer Programs.
ERIC Educational Resources Information Center
Rowley, Eric
1986-01-01
Provides two computer programs, written in BASIC, to calculate average stock market price levels. The programs allow students to work directly from the raw price data that appear daily in the financial news. Teaching suggestions are provided. (JDH)
Stock market index prediction using neural networks
NASA Astrophysics Data System (ADS)
Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok
1994-03-01
A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.
The Index cohesive effect on stock market correlations
NASA Astrophysics Data System (ADS)
Shapira, Y.; Kenett, D. Y.; Ben-Jacob, E.
2009-12-01
We present empirical examination and reassessment of the functional role of the market Index, using datasets of stock returns for eight years, by analyzing and comparing the results for two very different markets: 1) the New York Stock Exchange (NYSE), representing a large, mature market, and 2) the Tel Aviv Stock Exchange (TASE), representing a small, young market. Our method includes special collective (holographic) analysis of stock-Index correlations, of nested stock correlations (including the Index as an additional ghost stock) and of bare stock correlations (after subtraction of the Index return from the stocks returns). Our findings verify and strongly substantiate the assumed functional role of the index in the financial system as a cohesive force between stocks, i.e., the correlations between stocks are largely due to the strong correlation between each stock and the Index (the adhesive effect), rather than inter-stock dependencies. The Index adhesive and cohesive effects on the market correlations in the two markets are presented and compared in a reduced 3-D principal component space of the correlation matrices (holographic presentation). The results provide new insights into the interplay between an index and its constituent stocks in TASE-like versus NYSE-like markets.
Portfolio optimization for index tracking modelling in Malaysia stock market
NASA Astrophysics Data System (ADS)
Siew, Lam Weng; Jaaman, Saiful Hafizah; Ismail, Hamizun
2016-06-01
Index tracking is an investment strategy in portfolio management which aims to construct an optimal portfolio to generate similar mean return with the stock market index mean return without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using the optimization model which adopts regression approach in tracking the benchmark stock market index return. In this study, the data consists of weekly price of stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2013. The results of this study show that the optimal portfolio is able to track FBMKLCI Index at minimum tracking error of 1.0027% with 0.0290% excess mean return over the mean return of FBMKLCI Index. The significance of this study is to construct the optimal portfolio using optimization model which adopts regression approach in tracking the stock market index without purchasing all index components.
Qiu, Mingyue; Song, Yu
2016-01-01
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055
Qiu, Mingyue; Song, Yu
2016-01-01
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055
Xiong, Xiong; Nan, Ding; Yang, Yang; Yongjie, Zhang
2015-01-01
This paper explores a method of managing the risk of the stock index futures market and the cross-market through analyzing the effectiveness of price limits on the Chinese Stock Index 300 futures market. We adopt a cross-market artificial financial market (include the stock market and the stock index futures market) as a platform on which to simulate the operation of the CSI 300 futures market by changing the settings of price limits. After comparing the market stability under different price limits by appropriate liquidity and volatility indicators, we find that enhancing price limits or removing price limits both play a negative impact on market stability. In contrast, a positive impact exists on market stability if the existing price limit is maintained (increase of limit by10%, down by 10%) or it is broadened to a proper extent. Our study provides reasonable advice for a price limit setting and risk management for CSI 300 futures. PMID:26571135
2015-01-01
This paper explores a method of managing the risk of the stock index futures market and the cross-market through analyzing the effectiveness of price limits on the Chinese Stock Index 300 futures market. We adopt a cross-market artificial financial market (include the stock market and the stock index futures market) as a platform on which to simulate the operation of the CSI 300 futures market by changing the settings of price limits. After comparing the market stability under different price limits by appropriate liquidity and volatility indicators, we find that enhancing price limits or removing price limits both play a negative impact on market stability. In contrast, a positive impact exists on market stability if the existing price limit is maintained (increase of limit by10%, down by 10%) or it is broadened to a proper extent. Our study provides reasonable advice for a price limit setting and risk management for CSI 300 futures. PMID:26571135
A Comparison of Forecasting the Index of the Korean Stock Market
NASA Astrophysics Data System (ADS)
Shin, Young-Geun; Park, Sang-Sung; Jang, Dong-Sik
2009-08-01
According to the increase of an impact foreigner investor have on the Korean stock market, it is very importance to analyze the investment pattern of the foreigner investors in order to predict the movement of the Korean stock market. Firstly, in this study we collected various factors which influence the Korean stock market in the previous literatures about the movement of stock market. Secondly, Factors which influence significantly to KOPSI 200 Index among the collected factors are extracted through the stepwise selection used in regression analysis. Finally we predicted the movement of the Korean stock market using Back-Propagation Neural Network (BPN) and Support Vector Machine (SVM). And we have done a comparison analysis of obtained results through these methods. As a result of the experiments, prediction accuracy using SVM showed better result than using BPN.
NASA Astrophysics Data System (ADS)
Zhang, L.; Yu, C.; Sun, J. Q.
2015-03-01
It is difficult to simulate the dynamical behavior of actual financial markets indexes effectively, especially when they have nonlinear characteristics. So it is significant to propose a mathematical model with these characteristics. In this paper, we investigate a generalized Weierstrass-Mandelbrot function (WMF) model with two nonlinear characteristics: fractal dimension D where 2 > D > 1.5 and Hurst exponent (H) where 1 > H > 0.5 firstly. And then we study the dynamical behavior of H for WMF as D and the spectrum of the time series γ change in three-dimensional space, respectively. Because WMF and the actual stock market indexes have two common features: fractal behavior using fractal dimension and long memory effect by Hurst exponent, we study the relationship between WMF and the actual stock market indexes. We choose a random value of γ and fixed value of D for WMF to simulate the S&P 500 indexes at different time ranges. As shown in the simulation results of three-dimensional space, we find that γ is important in WMF model and different γ may have the same effect for the nonlinearity of WMF. Then we calculate the skewness and kurtosis of actual Daily S&P 500 index in different time ranges which can be used to choose the value of γ. Based on these results, we choose appropriate γ, D and initial value into WMF to simulate Daily S&P 500 indexes. Using the fit line method in two-dimensional space for the simulated values, we find that the generalized WMF model is effective for simulating different actual stock market indexes in different time ranges. It may be useful for understanding the dynamical behavior of many different financial markets.
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.
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.
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.
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…
Increasing market efficiency in the stock markets
NASA Astrophysics Data System (ADS)
Yang, Jae-Suk; Kwak, Wooseop; Kaizoji, Taisei; Kim, In-Mook
2008-01-01
We study the temporal evolutions of three stock markets; Standard and Poor's 500 index, Nikkei 225 Stock Average, and the Korea Composite Stock Price Index. We observe that the probability density function of the log-return has a fat tail but the tail index has been increasing continuously in recent years. We have also found that the variance of the autocorrelation function, the scaling exponent of the standard deviation, and the statistical complexity decrease, but that the entropy density increases as time goes over time. We introduce a modified microscopic spin model and simulate the model to confirm such increasing and decreasing tendencies in statistical quantities. These findings indicate that these three stock markets are becoming more efficient.
ERIC Educational Resources Information Center
Anderson, Christine; Cook, Stan
1990-01-01
Offers a field-tested stock market unit designed to develop mathematical skills involving fractions and decimals, basic understanding of the stock market, and hypothesis testing skills in real world situations. Includes tables displaying questions, tally sheets, and instructions, as well as a list of related activities. (MDH)
Arbitrage and Volatility in Chinese Stock's Markets
NASA Astrophysics Data System (ADS)
Lu, Shu Quan; Ito, Takao; Zhang, Jianbo
From the point of view of no-arbitrage pricing, what matters is how much volatility the stock has, for volatility measures the amount of profit that can be made from shorting stocks and purchasing options. With the short-sales constraints or in the absence of options, however, high volatility is likely to mean arbitrage from stock market. As emerging stock markets for China, investors are increasingly concerned about volatilities of Chinese two stock markets. We estimate volatility's models for Chinese stock markets' indexes using Markov chain Monte Carlo (MCMC) method and GARCH. We find that estimated values of volatility parameters are very high for all data frequencies. It suggests that stock returns are extremely volatile even at long term intervals in Chinese markets. Furthermore, this result could be considered that there seems to be arbitrage opportunities in Chinese stock markets.
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 financial…
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. PMID:12005968
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.
Market Confidence Predicts Stock Price: Beyond Supply and Demand.
Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing
2016-01-01
Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price. PMID:27391816
Market Confidence Predicts Stock Price: Beyond Supply and Demand
Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing
2016-01-01
Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price. PMID:27391816
Stocks low, marketers confident
Mantho, M.
1997-01-01
This has been a nerve wracking season as we looked at inadequate inventory spurring prices ever upward. We have watched the American Petroleum Association`s figures on refiner stocks with considerable dismay as they consistantly fell behind year ago inventory. The anxiety extended to how much oil was in marketers` bulkplants and finally in customer tanks. And so, we asked our reporting panel to help us get a fix on how much oil was available for our customers. We asked the questions in early November and so all our figures are for that month. First we asked the capacity of their bulk tanks. And then how many gallons they had on hand November 1, 1996 and the same date of 1995. From these figures, we were able to get estimates of oil inventories. Marketers bulk tanks were 47.5% filled on November 1 which meant that there was on hand at this level, 36 gallons of heating oil for each customer. At that point in the season, customer tanks were 58% filled which translated into 218 gallons.
Do Earthquakes Shake Stock Markets?
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
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.
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.
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.
Quantum Brownian motion model for the stock market
NASA Astrophysics Data System (ADS)
Meng, Xiangyi; Zhang, Jian-Wei; Guo, Hong
2016-06-01
It is believed by the majority today that the efficient market hypothesis is imperfect because of market irrationality. Using the physical concepts and mathematical structures of quantum mechanics, we construct an econophysical framework for the stock market, based on which we analogously map massive numbers of single stocks into a reservoir consisting of many quantum harmonic oscillators and their stock index into a typical quantum open system-a quantum Brownian particle. In particular, the irrationality of stock transactions is quantitatively considered as the Planck constant within Heisenberg's uncertainty relationship of quantum mechanics in an analogous manner. We analyze real stock data of Shanghai Stock Exchange of China and investigate fat-tail phenomena and non-Markovian behaviors of the stock index with the assistance of the quantum Brownian motion model, thereby interpreting and studying the limitations of the classical Brownian motion model for the efficient market hypothesis from a new perspective of quantum open system dynamics.
A controllable laboratory stock market for modeling real stock markets
NASA Astrophysics Data System (ADS)
An, Kenan; Li, Xiaohui; Yang, Guang; Huang, Jiping
2013-10-01
Based on the different research approaches, econophysics can be divided into three directions: empirical econophysics, computational econophysics, and experimental econophysics. Because empirical econophysics lacks controllability that is needed to study the impacts of different external conditions and computational econophysics has to adopt artificial decision-making processes that are often deviated from those of real humans, experimental econophysics tends to overcome these problems by offering controllability and using real humans in laboratory experiments. However, to our knowledge, the existing laboratory experiments have not convincingly reappeared the stylized facts (say, scaling) that have been revealed for real economic/financial markets by econophysicists. A most important reason is that in these experiments, discrete trading time makes these laboratory markets deviated from real markets where trading time is naturally continuous. Here we attempt to overcome this problem by designing a continuous double-auction stock-trading market and conducting several human experiments in laboratory. As an initial work, the present artificial financial market can reproduce some stylized facts related to clustering and scaling. Also, it predicts some other scaling in human behavior dynamics that is hard to achieve in real markets due to the difficulty in getting the data. Thus, it becomes possible to study real stock markets by conducting controlled experiments on such laboratory stock markets producing high frequency data.
NASA Astrophysics Data System (ADS)
Gu, Rongbao; Xiong, Wei; Li, Xinjie
2015-12-01
This paper analyzes the predictive ability of the singular value decomposition entropy for the Shenzhen Component Index based on different scales. It is found that, the predictive ability of the entropy for the index is affected by the width of moving time windows and the structural break in stock market. By moving time windows with one year, the predictive power of singular value decomposition entropy of Shenzhen stock market for its component index is found after the reform of non-tradable shares.
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.
The lead-lag relationship between stock index and stock index futures: A thermal optimal path method
NASA Astrophysics Data System (ADS)
Gong, Chen-Chen; Ji, Shen-Dan; Su, Li-Ling; Li, Sai-Ping; Ren, Fei
2016-02-01
The study of lead-lag relationship between stock index and stock index futures is of great importance for its wide application in hedging and portfolio investments. Previous works mainly use conventional methods like Granger causality test, GARCH model and error correction model, and focus on the causality relation between the index and futures in a certain period. By using a non-parametric approach-thermal optimal path (TOP) method, we study the lead-lag relationship between China Securities Index 300 (CSI 300), Hang Seng Index (HSI), Standard and Poor 500 (S&P 500) Index and their associated futures to reveal the variance of their relationship over time. Our finding shows evidence of pronounced futures leadership for well established index futures, namely HSI and S&P 500 index futures, while index of developing market like CSI 300 has pronounced leadership. We offer an explanation based on the measure of an indicator which quantifies the differences between spot and futures prices for the surge of lead-lag function. Our results provide new perspectives for the understanding of the dynamical evolution of lead-lag relationship between stock index and stock index futures, which is valuable for the study of market efficiency and its applications.
Is the stock market efficient?
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. PMID:17808264
Abnormal statistical properties of stock indexes during a financial crash
NASA Astrophysics Data System (ADS)
Li, Wei-Shen; Liaw, Sy-Sang
2015-03-01
We investigate minute indexes of stock markets in 10 countries during financial crashes by dividing them into several stages according to their stock price tendencies: plunging stage (stage 1), fluctuating or rebounding stage (stage 2), and soaring stage (stage 3). The tail distributions of the returns satisfy a power law for developed markets but show a dual power-law structure for emerging markets. Prominent dual fractal structures are found during the plunging stage in developed markets, and after the plunging stage in emerging markets. The fractal analysis on the sign series of the returns yields similar dual fractal properties. The magnitude series of the returns provides surprising results during a crash. We find that developed markets have strong and weak long-range persistence in plunging and soaring stage respectively, while emerging markets behave oppositely. These results indicate that different types of markets are influenced strongly by the external shock of a crisis at different stages.
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)
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.
Comparable stocks, boundedly rational stock markets and IPO entry rates.
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
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
Stock Market Expectations of Dutch Households
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
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.
Looking Forward to the Stock Market.
ERIC Educational Resources Information Center
Dickneider, William
1992-01-01
Discusses the advantages of using the stock market to add new dimensions to social studies classes. Suggests that changes in society will make knowledge of financial markets essential for students. Includes two lesson plans with handouts that use the Disney company and changes in the operation of the stock market to capture student interest. (DK)
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.
Scaling and Predictability in Stock Markets: A Comparative Study
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
Scaling and predictability in stock markets: a comparative study.
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
Students Invest in the Stock Market
ERIC Educational Resources Information Center
Parker, George O.
1977-01-01
How one teacher motivated students to learn about the stock market by allowing them to actually invest money. Class discussion covered inexpensive ways to buy stock, choosing securities, and buying and selling stock. Suggestions are offered for adapting this project for use at the secondary level. (TA)
Stock market stability: Diffusion entropy analysis
NASA Astrophysics Data System (ADS)
Li, Shouwei; Zhuang, Yangyang; He, Jianmin
2016-05-01
In this article, we propose a method to analyze the stock market stability based on diffusion entropy, and conduct an empirical analysis of Dow Jones Industrial Average. Empirical results show that this method can reflect the volatility and extreme cases of the stock market.
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…
Pattern Prediction in Stock Market
NASA Astrophysics Data System (ADS)
Kaushik, Saroj; Singhal, Naman
In this paper, we have presented a new approach to predict pattern of the financial time series in stock market for next 10 days and compared it with the existing method of exact value prediction [2, 3, and 4]. The proposed pattern prediction technique performs better than value prediction. It has been shown that the average for pattern prediction is 58.7% while that for value prediction is 51.3%. Similarly, maximum for pattern and value prediction are 100% and 88.9% respectively. It is of more practical significance if one can predict an approximate pattern that can be expected in the financial time series in the near future rather than the exact value. This way one can know the periods when the stock will be at a high or at a low and use the information to buy or sell accordingly. We have used Support Vector Machine based prediction system as a basis for predicting pattern. MATLAB has been used for implementation.
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.
Scaling behavior in ranking mobility of Chinese stock market
NASA Astrophysics Data System (ADS)
Wu, Ke; Xiong, Wanting; Weng, Xin; Wang, Yougui
2014-08-01
As an aggregate measure of the variations in individuals, the analysis of mobility provides a substantial and comprehensive perspective into the complexity of socio-economic systems. In this paper, we introduced the ranking mobility index to measure the ranking variations of the stocks in Chinese stock market over time. Using the daily data of 837 constituent stocks of the Shanghai A-Stock Composite Index from January 1, 2002 to December 31, 2012, we examined respectively the dependence of ranking mobility with respect to the absolute return, trading volume and turnover ratio on the sampling time interval. The scaling property is observed in all three relations. The fact of long relaxation times gives evidence of long memory property in the stock ranking orders.
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.
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.
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…
Stock or stroke? Stock market movement and stroke incidence in Taiwan.
Chen, Chun-Chih; Chen, Chin-Shyan; Liu, Tsai-Ching; Lin, Ying-Tzu
2012-12-01
This paper investigates the impact of stock market movement on incidences of stroke utilizing population-based aggregate data in Taiwan. Using the daily data from the Taiwan Stock Exchange Capitalization Weighted Stock Index and from the National Health Insurance Research Database during 2001/1/1-2007/12/31, which consist of 2556 observations, we examine the effects of stock market on stroke incidence - the level effect and the daily change effects. In general, we find that both a low stock index level and a daily fall in the stock index are associated with greater incidences of stroke. We further partition the data on sex and age. The level effect is found to be significant for either gender, in the 45-64 and 65 ≥ age groups. In addition, two daily change effects are found to be significant for males and the elderly. Although stockholdings can increase wealth, they can also increase stroke incidence, thereby representing a cost to health. PMID:22951009
The volatility of stock market prices.
Shiller, R J
1987-01-01
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. PMID:17769311
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.
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.
Relationships among Energy Price Shocks, Stock Market, and the Macroeconomy: Evidence from China
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
Relationships among energy price shocks, stock market, and the macroeconomy: evidence from China.
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
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…
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.
Quantifying the Behavior of Stock Correlations Under Market Stress
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
Modeling stock markets through bosonic operators
NASA Astrophysics Data System (ADS)
Bagarello, Fabio
2008-11-01
We review our results on a quantum-like approach recently developed in the attempt of modeling a simplified stock-market. Under suitable approximations we deduce the time evolution of the portfolio of the various traders of the market, as well as of other observable quantities.
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…
Quantifying Stock Return Distributions in Financial Markets
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
Quantifying Stock Return Distributions in Financial Markets.
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
26 CFR 1.1296-2 - Definition of marketable stock.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 26 Internal Revenue 11 2011-04-01 2011-04-01 false Definition of marketable stock. 1.1296-2....1296-2 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,...
Influence network in the Chinese stock market
NASA Astrophysics Data System (ADS)
Gao, Ya-Chun; Zeng, Yong; Cai, Shi-Min
2015-03-01
In a stock market, the price fluctuations are interactive, that is, one listed company can influence others. In this paper, we seek to study the influence relationships among listed companies by constructing a directed network on the basis of the Chinese stock market. This influence network shows distinct topological properties. In particular, a few large companies that can lead the tendency of the stock market are recognized. Furthermore, by analyzing the subnetworks of listed companies distributed in several significant economic sectors, it is found that the influence relationships are totally different from one economic sector to another, of which three types of connectivity as well as hub-like listed companies are identified. In addition, the rankings of listed companies obtained from the centrality metrics of the influence network are compared with those according to the assets, which gives inspiration to uncover and understand the importance of listed companies on the stock market. These empirical results are meaningful in providing these topological properties of the Chinese stock market and economic sectors as well as revealing the interactive influence relationships among listed companies.
Empirical investigation of stock price dynamics in an emerging market
NASA Astrophysics Data System (ADS)
Palágyi, Zoltán; Mantegna, Rosario N.
1999-07-01
We study the development of an emerging market - the Budapest Stock Exchange - by investigating the time evolution of some statistical properties of heavily traded stocks. Moving quarter by quarter over a period of two and a half years we analyze the scaling properties of the standard deviation of intra-day log-price changes. We observe scaling using both seconds and ticks as units of time. For the investigated stocks a Levy shape is a good approximation to the probability density function of tick-by-tick log-price changes in each quarter: the index of the distribution follows an increasing trend, suggesting it could be used as a measure of market efficiency.
26 CFR 1.1296-2 - Definition of marketable stock.
Code of Federal Regulations, 2010 CFR
2010-04-01
... multiple tiers. If an exchange in a foreign country has more than one tier or market level on which stock... 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...
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.
How High Frequency Trading Affects a Market Index
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
Zoo Techniques in the Stock Market.
ERIC Educational Resources Information Center
Berzosa, Maria Jose Garcia
1999-01-01
Outlines a stock market activity that has helped English for specific purposes students in business school learn technical vocabulary. The objective is to minimize confusion by using visuals with written and spoken words, and to find effective ways of promoting learning and generating motivation. (Author/VWL)
Analysis in correlation for the Korean stock market
NASA Astrophysics Data System (ADS)
Jung, Woo-Sung; Chae, Seungbyung; Yang, Jae-Suk; Kwon, Okyu; Moon, Hie-Tae
2005-05-01
The correlation between stock price changes is useful information. Through the correlation matrix, we construct a portfolio with its minimum spanning tree. We make the minimum spanning tree of the Korean stock market, a representative emerging market, which is different from that of the mature market. It is due to the emerging market's less abundant liquidity than the mature market. And we find the distribution of the correlation coefficient is different for several periods. As the market is developing, many changes from inside and outside the market occurs, and several parameters of the stock market network are changed. The Korean stock market is under an evolution.
Progress in physical properties of Chinese stock markets
NASA Astrophysics Data System (ADS)
Liang, Yuan; Yang, Guang; Huang, Ji-Ping
2013-08-01
In the past two decades, statistical physics was brought into the field of finance, applying new methods and concepts to financial time series and developing a new interdiscipline "econophysics". In this review, we introduce several commonly used methods for stock time series in econophysics including distribution functions, correlation functions, detrended fluctuation analysis method, detrended moving average method, and multifractal analysis. Then based on these methods, we review some statistical properties of Chinese stock markets including scaling behavior, long-term correlations, cross-correlations, leverage effects, antileverage effects, and multifractality. Last, based on an agent-based model, we develop a new option pricing model — financial market model that shows a good agreement with the prices using real Shanghai Index data. This review is helpful for people to understand and research statistical physics of financial markets.
Extreme value modelling of Ghana stock exchange index.
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. PMID:26587364
Collective Behavior of Stock Prices as a Precursor to Market Crash
NASA Astrophysics Data System (ADS)
Maskawa, J.
We study precursors to the global market crash that occurred onall main stock exchanges throughout the world in October 2008 about three weeks after the bankruptcy of Lehman Brothers Holdings Inc. on 15 September. We examine the collective behavior of stock returns and analyze the market mode, which is a market-wide collective mode, with constituent issues of the FTSE 100 index listed on the London Stock Exchange. Before the market crash, a sharp rise in a measure of the collective behavior was observed. It was shown to be associated with news including the words ``financial crisis". They did not impact stock prices severely alone, but they exacerbated the pessimistic mood that prevailed among stock market participants. Such news increased after the Lehman shock preceding the market crash. The variance increased along with the cumulative amount of news according to a power law.
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.
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.
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).
STOCK MARKET CRASH AND EXPECTATIONS OF AMERICAN HOUSEHOLDS*
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
26 CFR 1.1296-1 - Mark to market election for marketable stock.
Code of Federal Regulations, 2012 CFR
2012-04-01
... 26 Internal Revenue 11 2012-04-01 2012-04-01 false Mark to market election for marketable stock. 1... Losses § 1.1296-1 Mark to market election for marketable stock. (a) Definitions—(1) Eligible RIC. An... respect to any section 1296 stock, the excess, if any, of— (A) The amount of mark to market gain...
26 CFR 1.1296-1 - Mark to market election for marketable stock.
Code of Federal Regulations, 2013 CFR
2013-04-01
... 26 Internal Revenue 11 2013-04-01 2013-04-01 false Mark to market election for marketable stock. 1... Losses § 1.1296-1 Mark to market election for marketable stock. (a) Definitions—(1) Eligible RIC. An... respect to any section 1296 stock, the excess, if any, of— (A) The amount of mark to market gain...
26 CFR 1.1296-1 - Mark to market election for marketable stock.
Code of Federal Regulations, 2014 CFR
2014-04-01
... 26 Internal Revenue 11 2014-04-01 2014-04-01 false Mark to market election for marketable stock. 1... Losses § 1.1296-1 Mark to market election for marketable stock. (a) Definitions—(1) Eligible RIC. An... respect to any section 1296 stock, the excess, if any, of— (A) The amount of mark to market gain...
26 CFR 1.1296-1 - Mark to market election for marketable stock.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 26 Internal Revenue 11 2011-04-01 2011-04-01 false Mark to market election for marketable stock. 1... Losses § 1.1296-1 Mark to market election for marketable stock. (a) Definitions—(1) Eligible RIC. An... respect to any section 1296 stock, the excess, if any, of— (A) The amount of mark to market gain...
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.
Fear and its implications for stock markets
NASA Astrophysics Data System (ADS)
Simonsen, I.; Ahlgren, P. T. H.; Jensen, M. H.; Donangelo, R.; Sneppen, K.
2007-05-01
The value of stocks, indices and other assets, are examples of stochastic processes with unpredictable dynamics. In this paper, we discuss asymmetries in short term price movements that can not be associated with a long term positive trend. These empirical asymmetries predict that stock index drops are more common on a relatively short time scale than the corresponding raises. We present several empirical examples of such asymmetries. Furthermore, a simple model featuring occasional short periods of synchronized dropping prices for all stocks constituting the index is introduced with the aim of explaining these facts. The collective negative price movements are imagined triggered by external factors in our society, as well as internal to the economy, that create fear of the future among investors. This is parameterized by a “fear factor” defining the frequency of synchronized events. It is demonstrated that such a simple fear factor model can reproduce several empirical facts concerning index asymmetries. It is also pointed out that in its simplest form, the model has certain shortcomings.
Examining the dynamic interactions on volatilities of paired stock markets
NASA Astrophysics Data System (ADS)
Lee, Jun Shean; Sek, Siok Kun
2015-02-01
We conduct empirical analyses to investigate the interaction between volatilities of paired stock markets. The main objective of this study is to reveal possibility of spillover effects among stock markets which can determine the performances of stock returns and trade volumes of stocks. In particular, we seek to investigate if there exist two-way causal relationships on the volatilities in two stock markets in two groups of countries, i.e. between emerging markets of ASEAN-5 and between emerging and advanced countries. Our study is focused in Malaysia stock market and the paired relationship with its neighbouring countries (ASEAN5) and advanced countries (Japan and U.S.) respectively. The multivariate GARCH(1,1) model is applied in studying the interactions on the volatilities of paired stock markets. The results are compared between neighbouring countries and with that of advanced countries. The results are expected to reveal linkages between volatilities of stock markets and the dynamic relationships across markets. The results provide useful information in studying the performances of stock markets and predicting the stock movements by incorporating the external impacts from foreign stock markets.
The Stock Performance of C. Everett Koop Award Winners Compared With the Standard & Poor's 500 Index
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
Performance of technical trading rules: evidence from Southeast Asian stock markets.
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. PMID:26435898
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.
Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies
NASA Astrophysics Data System (ADS)
Li, Ping; Wang, Bing-Hong
2007-05-01
We present a model of complex network generated from Hang Seng index (HSI) of Hong Kong stock market, which encodes stock market relevant both interconnections and interactions between fluctuation patterns of HSI in the network topologies. In the network, the nodes (edges) represent all kinds of patterns of HSI fluctuation (their interconnections). Based on network topological statistic, we present efficient algorithms, measuring betweenness centrality ( BC) and inverse participation ratio ( IPR) of network adjacency matrix, for detecting topological important nodes. We have at least obtained three uniform nodes of topological importance, and find the three nodes, i.e. 18.7% nodes undertake 71.9% betweenness centrality and closely correlate other nodes. From these topological important nodes, we can extract hidden significant fluctuation patterns of HSI. We also find these patterns are independent the time intervals scales. The results contain important physical implication, i.e. the significant patterns play much more important roles in both information control and transport of stock market, and should be useful for us to more understand fluctuations regularity of stock market index. Moreover, we could conclude that Hong Kong stock market, rather than a random system, is statistically stable, by comparison to random networks.
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)
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.
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.
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.
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.
Modeling and computing of stock index forecasting based on neural network and Markov chain.
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
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.
Time varying market efficiency of the GCC stock markets
NASA Astrophysics Data System (ADS)
Charfeddine, Lanouar; Khediri, Karim Ben
2016-02-01
This paper investigates the time-varying levels of weak-form market efficiency for the GCC stock markets over the period spanning from May 2005 to September 2013. We use two empirical approaches: (1) the generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) model with state space time varying parameter (Kalman filter), and (2) a rolling technique sample test of the fractional long memory parameter d. As long memory estimation methods, we use the detrended fluctuation analysis (DFA) technique, the modified R/S statistic, the exact local whittle (ELW) and the feasible Exact Local Whittle (FELW) methods. Moreover, we use the Bai and Perron (1998, 2003) multiple structural breaks technique to test and date the time varying behavior of stock market efficiency. Empirical results show that GCC markets have different degrees of time-varying efficiency, and also have experiencing periods of efficiency improvement. Results also show evidence of structural breaks in all GCC markets. Moreover, we observe that the recent financial shocks such as Arab spring and subprime crises have a significant impact on the time path evolution of market efficiency.
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.
Using the Stock Market Game to Teach Economics.
ERIC Educational Resources Information Center
Lopus, Jane Schaerges
1985-01-01
The Stock Market Game is a computerized simulation that involves high school students in trading eligible New York Stock Exchange stocks in an effort to make capital gains. Time required is ten weeks. The game is described; economic concepts that can be taught using the game are listed. (RM)
Statistics of extreme events in Chinese stock markets
NASA Astrophysics Data System (ADS)
Wu, Gan-Hua; Qiu, Lu; Mutua, Stephen; Li, Xin-Li; Yang, Yue; Yang, Hui-Jie; Jiang, Yan
2014-12-01
We investigate the impact of financial factors on daily volume recurrent time intervals in the developing Chinese stock markets. The tails of probability distribution functions (PDFs) of volume recurrent intervals behave as a power-law, and the scaling exponent decreases with the increase of stock lifetime, which are similar to those in the US stock markets, and they are typical representatives of developed markets. The difference is that the power-law exponent values remain almost the same with the changes of market capitalization, mean volume, and mean trading value, respectively. These findings enrich the results for event statistics for financial 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.
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.
Cross-Correlation Asymmetries and Causal Relationships between Stock and Market Risk
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.
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
Robot traders can prevent extreme events in complex stock markets
NASA Astrophysics Data System (ADS)
Suhadolnik, Nicolas; Galimberti, Jaqueson; Da Silva, Sergio
2010-11-01
If stock markets are complex, monetary policy and even financial regulation may be useless to prevent bubbles and crashes. Here, we suggest the use of robot traders as an anti-bubble decoy. To make our case, we put forward a new stochastic cellular automata model that generates an emergent stock price dynamics as a result of the interaction between traders. After introducing socially integrated robot traders, the stock price dynamics can be controlled, so as to make the market more Gaussian.
An information flow among industry sectors in the Korean stock market
NASA Astrophysics Data System (ADS)
Oh, Gabjin; Oh, Tamina; Kim, Hoyong; Kwon, Okyu
2014-12-01
We investigate the information flow among 22 industry sectors in the Korean stock market by using the symbolic transfer entropy (STE) method. We consider the daily index of 22 industry sectors in the Korean Composite Stock Price Index (KOSPI) from January 3, 2000 to March 30, 2012. We measure the degree of asymmetry in the information flow and the amount of information flow among the industry sectors before, during, and after the subprime crisis in order to analyze how to relate them to the market crisis. We find that the amount of information flow and the number of connectedness during the financial crisis in the Korean stock market are higher than those before and after the market crisis. In addition, we find the role of the insurance sector, which is related to risk management, increases as information source after the crisis.
The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields
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.
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
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.
Exploring Market State and Stock Interactions on the Minute Timescale.
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
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
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].
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.
Multifractal detrended cross-correlations between the Chinese exchange market and stock market
NASA Astrophysics Data System (ADS)
Cao, Guangxi; Xu, Longbing; Cao, Jie
2012-10-01
Based on the daily price data of the Chinese Yuan (RMB)/US dollar exchange rate and the Shanghai Stock Composite Index, we conducted an empirical analysis of the cross-correlations between the Chinese exchange market and stock market using the multifractal cross-correlation analysis method. The results demonstrate the overall significance of the cross-correlation based on the analysis of a statistic. Multifractality exists in cross-correlations, and the cross-correlated behavior of small fluctuations is more persistent than that of large fluctuations. Moreover, using the rolling windows method, we find that the cross-correlations between the Chinese exchange market and stock market vary with time and are especially sensitive to the reform of the RMB exchange rate regime. The previous reduction in the flexibility of the RMB exchange rate in July 2008 strengthened the persistence of cross-correlations and decreased the degree of multifractality, whereas the enhancement of the flexibility of the RMB exchange rate in June 2010 weakened the persistence of cross-correlations and increased the multifractality. Finally, several relevant discussions are provided to verify the robustness of our empirical analysis.
Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market.
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
Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market
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
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.
Cross-response in correlated financial markets: individual stocks
NASA Astrophysics Data System (ADS)
Wang, Shanshan; Schäfer, Rudi; Guhr, Thomas
2016-04-01
Previous studies of the stock price response to trades focused on the dynamics of single stocks, i.e. they addressed the self-response. We empirically investigate the price response of one stock to the trades of other stocks in a correlated market, i.e. the cross-responses. How large is the impact of one stock on others and vice versa? - This impact of trades on the price change across stocks appears to be transient instead of permanent as we discuss from the viewpoint of market efficiency. Furthermore, we compare the self-responses on different scales and the self- and cross-responses on the same scale. We also find that the cross-correlation of the trade signs turns out to be a short-memory process.
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
Federal Register 2010, 2011, 2012, 2013, 2014
2012-08-08
... Understandings with various stock exchanges. Given the capitalization of these indexes and the deep and liquid... deep and liquid markets for the securities underlying both the MSCI EM Index and the MSCI EAFE Index... the Commission's Internet comment form ( http://www.sec.gov/rules/sro.shtml ); or Send an email...
Visualization of a stock market correlation matrix
NASA Astrophysics Data System (ADS)
Rea, Alethea; Rea, William
2014-04-01
This paper presents a novel application of Neighbor-Net, a clustering algorithm developed for constructing a phylogenetic network in the field of evolutionary biology, to visualizing a correlation matrix. We apply Neighbor-Net as implemented in the SplitsTree software package to 48 stocks listed on the New Zealand Stock Exchange. We show that by visualizing the correlation matrix using a Neighbor-Net splits graph and its associated circular ordering of the stocks that some of the problems associated with understanding the large number of correlations between the individual stocks can be overcome. We compare the visualization of Neighbor-Net with that provided by hierarchical clustering trees and minimum spanning trees. The use of Neighbor-Net networks, or splits graphs, yields greater insight into how closely individual stocks are related to each other in terms of their correlations and suggests new avenues of research into how to construct small diversified stock portfolios.
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 environment in…
Using the Stock Market for Relevance in Teaching Number Sense
ERIC Educational Resources Information Center
Leonard, Jacqueline; Campbell, Louise L.
2004-01-01
This article describes the lessons introduced to middle school children in the Washington, D.C., area on the stock market. Students kept eight weeks of records and learned about decimals and place value. (Contains 5 figures.)
Bullish on Mathematics: Using Stock Market Simulations To Enhance Learning.
ERIC Educational Resources Information Center
Alsup, John K.; Altmyer, Donald J.
2002-01-01
Describes how a stock market simulation can be an excellent tool for motivating students to learn real-world mathematics in a middle school classroom. Details four activities that can accompany the simulation. (Contains 12 references.) (YDS)
Granger causality stock market networks: Temporal proximity and preferential attachment
NASA Astrophysics Data System (ADS)
Výrost, Tomáš; Lyócsa, Štefan; Baumöhl, Eduard
2015-06-01
The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, a preferential attachment between stock markets exists, i.e. the probability of the presence of spillover effects between any given two markets increases with their degree of connectedness to others.
Possible causes of long-range dependence in the Brazilian stock market
NASA Astrophysics Data System (ADS)
Cajueiro, Daniel O.; Tabak, Benjamin M.
2005-01-01
While the presence of long-range dependence in the asset returns seems to be a stylized fact, the issue of arguing the possible causes of this phenomena is totally obscure. Trying to shed light in this problem, we investigate the possible sources of the long-range dependence phenomena in the Brazilian Stock Market. For this purpose, we employ a sample which comprises stocks traded in the Brazilian financial market (BOVESPA Index). The Hurst exponent here is considered as our measure of long-range dependence and it is evaluated by six different methods. We have found evidence of statistically significant rank correlation between specific variables of the Brazilian firms which subscribe stocks and the long-range dependence phenomena present in these stocks.
Statistical Properties of Stock Market Eigensignals
NASA Astrophysics Data System (ADS)
Kwapien, J.; Drożdż, S.; Oświecimka, P.
2005-08-01
By using the correlation matrix approach, we decompose the evolution of a set of the 100 largest American companies into the components (portfolios) defined by the eigenvectors of the correlation matrix. Among the results, we show that a number of the non-random components exceeds the previous estimates based on much shorter time series of daily returns. This indicates that for short signals the bulk of random eigenvalues defined by Random Matrix Theory can comprise also a significant amount of information. We also show that the components corresponding to a few largest eigenvalues and describing the most collective part of the market evolution reveal strong nonlinear correlation structure in contrast to the other components. All the components are multifractal. Moreover, by using a modified definition of the correlation matrix, we are able to decompose the daily pattern of the German DAX30 index into components which can characterize the recurrent events occurring at precise moments of a trading day.
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.
Evolution of Shanghai STOCK Market Based on Maximal Spanning Trees
NASA Astrophysics Data System (ADS)
Yang, Chunxia; Shen, Ying; Xia, Bingying
2013-01-01
In this paper, using a moving window to scan through every stock price time series over a period from 2 January 2001 to 11 March 2011 and mutual information to measure the statistical interdependence between stock prices, we construct a corresponding weighted network for 501 Shanghai stocks in every given window. Next, we extract its maximal spanning tree and understand the structure variation of Shanghai stock market by analyzing the average path length, the influence of the center node and the p-value for every maximal spanning tree. A further analysis of the structure properties of maximal spanning trees over different periods of Shanghai stock market is carried out. All the obtained results indicate that the periods around 8 August 2005, 17 October 2007 and 25 December 2008 are turning points of Shanghai stock market, at turning points, the topology structure of the maximal spanning tree changes obviously: the degree of separation between nodes increases; the structure becomes looser; the influence of the center node gets smaller, and the degree distribution of the maximal spanning tree is no longer a power-law distribution. Lastly, we give an analysis of the variations of the single-step and multi-step survival ratios for all maximal spanning trees and find that two stocks are closely bonded and hard to be broken in a short term, on the contrary, no pair of stocks remains closely bonded for a long time.
A quantum-like approach to the stock market
NASA Astrophysics Data System (ADS)
Aerts, Diederik; D'Hooghe, Bart; Sozzo, Sandro
2012-03-01
Modern approaches to stock pricing in quantitative finance are typically founded on the Black-Scholes model and the underlying random walk hypothesis. Empirical data indicate that this hypothesis works well in stable situations but, in abrupt transitions such as during an economical crisis, the random walk model fails and alternative descriptions are needed. For this reason, several proposals have been recently forwarded which are based on the formalism of quantum mechanics. In this paper we apply the SCoP formalism, elaborated to provide an operational foundation of quantum mechanics, to the stock market. We argue that a stock market is an intrinsically contextual system where agents' decisions globally influence the market system and stocks prices, determining a nonclassical behavior. More specifically, we maintain that a given stock does not generally have a definite value, e.g., a price, but its value is actualized as a consequence of the contextual interactions in the trading process. This contextual influence is responsible of the non-Kolmogorovian quantumlike behavior of the market at a statistical level. Then, we propose a sphere model within our hidden measurement formalism that describes a buying/selling process of a stock and shows that it is intuitively reasonable to assume that the stock has not a definite price until it is traded. This result is relevant in our opinion since it provides a theoretical support to the use of quantum models in finance.
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
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.
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
Using the Stock Market Game in the Social Studies Classroom.
ERIC Educational Resources Information Center
Cox, Allen C.
1997-01-01
Describes an educational game designed to help students (fourth grade through adult) understand how financial markets work within the free enterprise system and how the basic economic principles impact the stock market. Discusses the paper and Internet versions of the game and provides some teaching guidelines. (MJP)
26 CFR 1.1296-1 - Mark to market election for marketable stock.
Code of Federal Regulations, 2010 CFR
2010-04-01
... stock sold as being from the second lot, Corp A recognizes $100 of long term capital loss pursuant to... 26 Internal Revenue 11 2010-04-01 2010-04-01 true Mark to market election for marketable stock. 1... (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES Special Rules for Determining Capital Gains and Losses §...
Multifractal cross-correlation spectra analysis on Chinese stock markets
NASA Astrophysics Data System (ADS)
Zhao, Xiaojun; Shang, Pengjian; Shi, Wenbin
2014-05-01
In this paper, the long-range cross-correlation of Chinese stock indices is systematically studied. The multifractal detrended cross-correlation analysis (MF-DXA) appears to be one of the most effective methods in detecting long-range cross-correlation of two non-stationary variables. The Legendre spectrum and the large deviations spectrum are extended to the cross-correlation case, so as to present multifractal structure of stock return and volatility series. It is characterized of the multifractality in Chinese stock markets, partly due to clusters of local detrended covariance with large and small magnitudes.
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.
Quantifying the semantics of search behavior before stock market moves
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
Quantifying the semantics of search behavior before stock market moves.
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. PMID:25071193
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
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.
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.
An autocatalytic network model for stock markets
NASA Astrophysics Data System (ADS)
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2015-02-01
The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.
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.
Stroke: a Hidden Danger of Margin Trading in Stock Markets.
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. PMID:26014381
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.
Spillover effects of oil price shocks across stock markets
NASA Astrophysics Data System (ADS)
Ng, Zhan Jian; Sek, Siok Kun
2014-12-01
Oil price shock can impose detrimental effects to an economy. In this study, we empirically study the spillover effects of oil price shock on determining volatilities of stock markets across the main oil importing and oil producing countries. In particular, we are interested to compare the relative impact of oil price shock on the volatilities of stock markets and how each stock market reacts to oil price shock for oil importing and oil producing countries. We focus the study in four main oil importer and four oil producers respectively using the daily data starting from January 2009 to December 2013. The multivariate GARCH(1,1) model is applied for the purpose of this study. The results of the study suggest that there exist spillover effect between crude oil price and stock returns for all the countries. The short run persistency of spillover effect in oil-exporting countries is lower than oil-importing countries but the long run persistency of spillover effect in oil-exporting countries is higher than oil-importing countries. In general the short run persistency is smaller and the long run persistency is very high. The results hold for volatility of oil price and stock returns and also spillover volatility in all countries.
Fractal Analysis of Prime Indian STOCK Market Indices
NASA Astrophysics Data System (ADS)
Samadder, Swetadri; Ghosh, Koushik; Basu, Tapasendra
2013-03-01
The purpose of the present work is to study the fractal behaviour of prime Indian stock exchanges, namely Bombay Stock Exchange Sensitivity Index (BSE Sensex) and National Stock Exchange (NSE). To analyze the monofractality of these indices we have used Higuchi method and Katz method separately. By applying Mutifractal Detrended Fluctuation Analysis (MFDFA) technique we have calculated the generalized Hurst exponents, multifractal scaling exponents and generalized multifractal dimensions for the present indices. We have deduced Hölder exponents as well as singularity spectra for BSE and NSE. It has been observed that both the stock exchanges are possessing self-similarity at different small ranges separately and inhomogeneously. By comparing the multifractal behaviour of the BSE and NSE indices, we have found that the second one exhibits a richer multifractal feature than the first one.
Temporal evolution into a more efficient stock market
NASA Astrophysics Data System (ADS)
Yang, Jae-Suk; Kaizoji, Taisei; Kwak, Wooseop
2011-06-01
Using the price change and the log return of 10 stock market indices, we examine the temporal evolution of the time scale. The 10 stock markets had similar properties. Their log-return time series had patterns and long-range correlations until the mid-1990s. In the 2000s, however, the long-range correlations for most markets shortened, and the patterns weakened. These phenomena were due to advances in communication infrastructure such as the Internet and internet-based trading systems, which increased the speed of information dissemination. We examined the temporal evolution of the time scale in the markets by comparing the probability density function of log returns for the 2000s with that in the 1990s and by using the minimum entropy density method.
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.
Cointegration-based financial networks study in Chinese stock market
NASA Astrophysics Data System (ADS)
Tu, Chengyi
2014-05-01
We propose a method based on cointegration instead of correlation to construct financial complex network in Chinese stock market. The network is obtained starting from the matrix of p-value calculated by Engle-Granger cointegration test between all pairs of stocks. Then some tools for filtering information in complex network are implemented to prune the complete graph described by the above matrix, such as setting a level of statistical significance as a threshold and Planar Maximally Filtered Graph. We also calculate Partial Correlation Planar Graph of these stocks to compare the above networks. Last, we analyze these directed, weighted and non-symmetric networks by using standard methods of network analysis, including degree centrality, PageRank, HITS, local clustering coefficient, K-shell and strongly and weakly connected components. The results shed a new light on the underlying mechanisms and driving forces in a financial market and deepen our understanding of financial complex network.
Efficiency of Thai stock markets: Detrended fluctuation analysis
NASA Astrophysics Data System (ADS)
Sukpitak, Jessada; Hengpunya, Varagorn
2016-09-01
The evolution of Hurst exponent of SET index over time, as a measure of market efficiency, is examined by DFA method. It is found that, during the study period, Hurst exponent tends to decrease to the ideal value 0.5, i.e., the market becomes more efficient. This finding readily conforms to the assertion that emerging markets are becoming more efficient. Additionally, the development of Hurst exponent during November 2006 to March 2015 of SET index compared to MAI index is investigated. The result shows that the deviation of the Hurst exponent from 0.5 for the MAI index is larger than that of the SET index. This implies that SET is more efficient than MAI and thus supports the assumption that market capitalization has significant influence on market efficiency.
The Stock Market and Economic Principles: A Curriculum Project.
ERIC Educational Resources Information Center
Savino, Ronald J.
This paper outlines how to teach macroeconomic principles through mock stock market investing while helping students develop economic awareness, interest, and comprehension on a more sophisticated level. The basic textbook is "The Economy Today" (B. R. Schiller). After two weeks of teaching basic economic concepts and vocabulary, such as…
Unraveling chaotic attractors by complex networks and measurements of stock market complexity
Cao, Hongduo; Li, Ying
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, 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.
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.
Multivariate Markov chain modeling for stock markets
NASA Astrophysics Data System (ADS)
Maskawa, Jun-ichi
2003-06-01
We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.
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.
Federal Register 2010, 2011, 2012, 2013, 2014
2011-12-16
... COMMISSION Self-Regulatory Organizations; NASDAQ Stock Market LLC; Notice of Filing and Immediate... hereby given that on November 30, 2011. The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange'') filed... of the Proposed Rule Change The NASDAQ Stock Market LLC proposes to amend Rule 7056 entitled...
Federal Register 2010, 2011, 2012, 2013, 2014
2012-02-14
... COMMISSION Self-Regulatory Organizations; NASDAQ Stock Market LLC; Notice of Filing and Immediate... that on January 31, 2012, The NASDAQ Stock Market LLC (``NASDAQ'' or ``Exchange'') filed with the... Change The NASDAQ Stock Market LLC proposes to modify Chapter XV, entitled ``Option Fees,'' at Sec....
Confidence and the stock market: an agent-based approach.
Bertella, Mario A; Pires, Felipe R; Feng, Ling; Stanley, Harry Eugene
2014-01-01
Using a behavioral finance approach we study the impact of behavioral bias. We construct an artificial market consisting of fundamentalists and chartists to model the decision-making process of various agents. The agents differ in their strategies for evaluating stock prices, and exhibit differing memory lengths and confidence levels. When we increase the heterogeneity of the strategies used by the agents, in particular the memory lengths, we observe excess volatility and kurtosis, in agreement with real market fluctuations--indicating that agents in real-world financial markets exhibit widely differing memory lengths. We incorporate the behavioral traits of adaptive confidence and observe a positive correlation between average confidence and return rate, indicating that market sentiment is an important driver in price fluctuations. The introduction of market confidence increases price volatility, reflecting the negative effect of irrationality in market behavior. PMID:24421888
Confidence and the Stock Market: An Agent-Based Approach
Bertella, Mario A.; Pires, Felipe R.; Feng, Ling; Stanley, Harry Eugene
2014-01-01
Using a behavioral finance approach we study the impact of behavioral bias. We construct an artificial market consisting of fundamentalists and chartists to model the decision-making process of various agents. The agents differ in their strategies for evaluating stock prices, and exhibit differing memory lengths and confidence levels. When we increase the heterogeneity of the strategies used by the agents, in particular the memory lengths, we observe excess volatility and kurtosis, in agreement with real market fluctuations—indicating that agents in real-world financial markets exhibit widely differing memory lengths. We incorporate the behavioral traits of adaptive confidence and observe a positive correlation between average confidence and return rate, indicating that market sentiment is an important driver in price fluctuations. The introduction of market confidence increases price volatility, reflecting the negative effect of irrationality in market behavior. PMID:24421888
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.
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.
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.
The normalisation of terror: the response of Israel's stock market to long periods of terrorism.
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. PMID:20735455
Analysis of network clustering behavior of the Chinese stock market
NASA Astrophysics Data System (ADS)
Chen, Huan; Mai, Yong; Li, Sai-Ping
2014-11-01
Random Matrix Theory (RMT) and the decomposition of correlation matrix method are employed to analyze spatial structure of stocks interactions and collective behavior in the Shanghai and Shenzhen stock markets in China. The result shows that there exists prominent sector structures, with subsectors including the Real Estate (RE), Commercial Banks (CB), Pharmaceuticals (PH), Distillers&Vintners (DV) and Steel (ST) industries. Furthermore, the RE and CB subsectors are mostly anti-correlated. We further study the temporal behavior of the dataset and find that while the sector structures are relatively stable from 2007 through 2013, the correlation between the real estate and commercial bank stocks shows large variations. By employing the ensemble empirical mode decomposition (EEMD) method, we show that this anti-correlation behavior is closely related to the monetary and austerity policies of the Chinese government during the period of study.
Collective Behavior of Market Participants during Abrupt Stock Price Changes.
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
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
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.
Profitability of Contrarian Strategies in the Chinese Stock Market
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
Profitability of Contrarian Strategies in the Chinese Stock Market.
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
The cellular automaton model of investment behavior in the stock market
NASA Astrophysics Data System (ADS)
Wei, Yi-ming; Ying, Shang-jun; Fan, Ying; Wang, Bing-Hong
2003-07-01
The modeling theory and method using cellular automata are applied to the study on the complexity in the stock market. An evolution model based on cellular automaton for the investment behavior in the stock market is formulated. The simulation results and analyses of various states of the stock market show that investors’ imitation degree and the macro factors are the key determinants to the stability of the stock market. We observed that more diversity in the investment views of agents and lower imitation among investors are in favor of the normal development of the stock market.
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.
Characterization of stock market regimes by data compression
NASA Astrophysics Data System (ADS)
Vogel, Eugenio E.; Saravia, Gonzalo
2011-03-01
It has been shown that data compression can characterize magnetic phases (Physica A 388 (2009) 4075). In the introduction of this presentation we briefly review this result. We then go onto introducing a new data compressor (wlzip) developed by us to optimize recognition of meaningful patterns in the compressing procedure, yielding sharp transition curves at the magnetic critical temperatures. The advantages of the new compressor, such as better definition and tuning capabilities are presented. The rest of the talk consists of applying wlzip to the Chilean stock market along several months during 2010. The accumulated daily data allow to recognizing days with different types of activity. Moreover, the data recorded every minute allow to analyzing the ``present'' status of the stock market by applying wlzip to the data of the last hour or couple of hours. Possible extensions of the application of this technique to other fields are discussed. Partial support from Fondecyt 1100156, ICM and CEDENNA is acknowledged.
The multiscale analysis between stock market time series
NASA Astrophysics Data System (ADS)
Shi, Wenbin; Shang, Pengjian
2015-11-01
This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.
Generalized Bogoliubov Polariton Model: An Application to Stock Exchange Market
NASA Astrophysics Data System (ADS)
Thuy Anh, Chu; Anh, Truong Thi Ngoc; Lan, Nguyen Tri; Viet, Nguyen Ai
2016-06-01
A generalized Bogoliubov method for investigation non-simple and complex systems was developed. We take two branch polariton Hamiltonian model in second quantization representation and replace the energies of quasi-particles by two distribution functions of research objects. Application to stock exchange market was taken as an example, where the changing the form of return distribution functions from Boltzmann-like to Gaussian-like was studied.
Avalanche Dynamics and Trading Friction Effects on STOCK Market Returns
NASA Astrophysics Data System (ADS)
Iori, Giulia
We propose a model with heterogeneous interacting traders which can explain some of the stylized facts of stock market returns. A generalized version of the Random Field Ising Model (RFIM) is introduced to describe trading behavior. Imitation effects, which induce agents to trade, can generate avalanches in trading volume and large gaps in demand and supply. A trade friction is introduced which, by responding to price movements, creates a feedback mechanism on future trading and generates volatility clustering.
Mason, Chris; Mason, Julian; Culme-Seymour, Emily J; Bonfiglio, Gregory A; Reeve, Brock C
2013-06-01
During Q4 2012 and Q1 2013, the cell therapy industry made strong progress in translation and commercialization. Continued development of the companies included in a dedicated stock market index suggests emergence of this industry as a distinct healthcare sector. PMID:23746973
ERIC Educational Resources Information Center
Maier, Mark H.
2002-01-01
Reviews "Learning from the Market: Integrating 'The Stock Market Game' across the Curriculum" guide for teachers in grades 4 to 12. Believes the guide suffers from errors of fact and omission. Suggests corrections and alternative activities that enable instructors to continue to use the material. (JEH)
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.
Stock markets and quantum dynamics: A second quantized description
NASA Astrophysics Data System (ADS)
Bagarello, F.
2007-12-01
In this paper we continue our description of stock markets in terms of some non-abelian operators which are used to describe the portfolio of the various traders and other observable quantities. After a first prototype model with only two traders, we discuss a more realistic model of market involving an arbitrary number of traders. For both models we find approximated solutions for the time evolution of the portfolio of each trader. In particular, for the more realistic model, we use the stochastic limit approach and a fixed point like approximation.
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.
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.
What stock market returns to expect for the future?
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
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.
An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA
NASA Astrophysics Data System (ADS)
Rizvi, Syed Aun R.; Dewandaru, Ginanjar; Bacha, Obiyathulla I.; Masih, Mansur
An efficient market has been theoretically proven to be a key component for effective and efficient resource allocation in an economy. This paper incorporates econophysics with Efficient Market Hypothesis to undertake a comparative analysis of Islamic and developed countries’ markets by extending the understanding of their multifractal nature. By applying the Multifractal Detrended Fluctuation Analysis (MFDFA) we calculated the generalized Hurst exponents, multifractal scaling exponents and generalized multifractal dimensions for 22 broad market indices. The findings provide a deeper understanding of the markets in Islamic countries, where they have traces of highly efficient performance particularly in crisis periods. A key finding is the empirical evidence of the impact of the ‘stage of market development’ on the efficiency of the market. If Islamic countries aim to improve the efficiency of resource allocation, an important area to address is to focus, among others, on enhancing the stage of market development.
Scale-dependent price fluctuations for the Indian stock market
NASA Astrophysics Data System (ADS)
Matia, K.; Pal, M.; Salunkay, H.; Stanley, H. E.
2004-06-01
Classic studies of the probability density of price fluctuations g for stocks and foreign exchanges of several highly developed economies have been interpreted using a power law probability density function P(g) ~ g-(α + 1) with exponent values α > 2. To test the ubiquity of this relationship we analyze daily returns for the period November 1994 June 2002 for the 49 largest stocks of the National Stock Exchange which has the highest trade volume in India. We find the surprising result that P(g) decays as an exponential function P(g) ~ exp [ - βg] with a characteristic decay scale β = 1.51 ± 0.05 for the negative tail and β = 1.34 ± 0.04 for the positive tail. The exponential function is significantly different from the power law function observed for highly developed economies. Thus, we conclude that the stock market of the less highly developed economy of India belongs to a different class from that of highly developed countries.
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
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
Clustering of Casablanca stock market based on hurst exponent estimates
NASA Astrophysics Data System (ADS)
Lahmiri, Salim
2016-08-01
This paper deals with the problem of Casablanca Stock Exchange (CSE) topology modeling as a complex network during three different market regimes: general trend characterized by ups and downs, increasing trend, and decreasing trend. In particular, a set of seven different Hurst exponent estimates are used to characterize long-range dependence in each industrial sector generating process. They are employed in conjunction with hierarchical clustering approach to examine the co-movements of the Casablanca Stock Exchange industrial sectors. The purpose is to investigate whether cluster structures are similar across variable, increasing and decreasing regimes. It is observed that the general structure of the CSE topology has been considerably changed over 2009 (variable regime), 2010 (increasing regime), and 2011 (decreasing regime) time periods. The most important findings follow. First, in general a high value of Hurst exponent is associated to a variable regime and a small one to a decreasing regime. In addition, Hurst estimates during increasing regime are higher than those of a decreasing regime. Second, correlations between estimated Hurst exponent vectors of industrial sectors increase when Casablanca stock exchange follows an upward regime, whilst they decrease when the overall market follows a downward regime.
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.
How long is the memory of the US stock market?
NASA Astrophysics Data System (ADS)
Ferreira, Paulo; Dionísio, Andreia
2016-06-01
The Efficient Market Hypothesis (EMH), one of the most important hypothesis in financial economics, argues that return rates have no memory (correlation) which implies that agents cannot make abnormal profits in financial markets, due to the possibility of arbitrage operations. With return rates for the US stock market, we corroborate the fact that with a linear approach, return rates do not show evidence of correlation. However, linear approaches might not be complete or global, since return rates could suffer from nonlinearities. Using detrended cross-correlation analysis and its correlation coefficient, a methodology which analyzes long-range behavior between series, we show that the long-range correlation of return rates only ends in the 149th lag, which corresponds to about seven months. Does this result undermine the EMH?
17 CFR 240.15g-2 - Penny stock disclosure document relating to the penny stock market.
Code of Federal Regulations, 2011 CFR
2011-04-01
... required by paragraph (a) of this section for the period specified in 17 CFR 240.17a-4(b) of this chapter... 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...
Dynamics of cross-correlations in the stock market
NASA Astrophysics Data System (ADS)
Rosenow, Bernd; Gopikrishnan, Parameswaran; Plerou, Vasiliki; Eugene Stanley, H.
2003-06-01
Co-movements of stock price fluctuations are described by the cross-correlation matrix C. The application of random matrix theory (RMT) allows to distinguish between spurious correlations in C due to measurement noise and true correlations containing economically meaningful information. By calculating cross-correlations for different time windows, we study the time dependence of eigenvectors of C, which are related to economic sectors, and the time evolution of the largest eigenvalue, which describes the average correlation strength. We use these results to forecast cross-correlations, and test the quality of our forecast by constructing investments in the stock market which expose the invested capital to a minimum level of risk only.
How the 2008 stock market crash and seasons affect total and cardiac deaths in Los Angeles County.
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. PMID:22381159
Soccer and stock market risk: empirical evidence from the Istanbul Stock Exchange.
Berument, M Hakan; Ceylan, Nildag Basak
2013-06-01
There is an emerging but important literature on the effects of sport events such as soccer on stock market returns. After a soccer team's win, agents discount future events more favorably and increase risk tolerance. Similarly, after a loss, risk tolerance decreases. This paper directly assesses risk tolerance after a sports event by using daily data from the three major soccer teams in Turkey (Beşiktaşç Fenerbahge and Galatasaray). Results provide evidence that risk tolerance increases after a win, but similar patterns were not found after a loss. PMID:24245071
Federal Register 2010, 2011, 2012, 2013, 2014
2011-01-19
... COMMISSION Self-Regulatory Organizations; NASDAQ Stock Market LLC; Notice of Filing and Immediate Effectiveness of Proposed Rule Change Relating to Routing Fees for the NASDAQ Options Market January 12, 2011...,\\2\\ notice is hereby given that on January 6, 2011, The NASDAQ Stock Market LLC (``NASDAQ''...
Optimality problem of network topology in stocks market analysis
NASA Astrophysics Data System (ADS)
Djauhari, Maman Abdurachman; Gan, Siew Lee
2015-02-01
Since its introduction fifteen years ago, minimal spanning tree has become an indispensible tool in econophysics. It is to filter the important economic information contained in a complex system of financial markets' commodities. Here we show that, in general, that tool is not optimal in terms of topological properties. Consequently, the economic interpretation of the filtered information might be misleading. To overcome that non-optimality problem, a set of criteria and a selection procedure of an optimal minimal spanning tree will be developed. By using New York Stock Exchange data, the advantages of the proposed method will be illustrated in terms of the power-law of degree distribution.
Stock market speculation: Spontaneous symmetry breaking of economic valuation
NASA Astrophysics Data System (ADS)
Sornette, Didier
2000-09-01
Firm foundation theory estimates a security's firm fundamental value based on four determinants: expected growth rate, expected dividend payout, the market interest rate and the degree of risk. In contrast, other views of decision-making in the stock market, using alternatives such as human psychology and behavior, bounded rationality, agent-based modeling and evolutionary game theory, expound that speculative and crowd behavior of investors may play a major role in shaping market prices. Here, we propose that the two views refer to two classes of companies connected through a "phase transition". Our theory is based on (1) the identification of the fundamental parity symmetry of prices (p→-p), which results from the relative direction of payment flux compared to commodity flux and (2) the observation that a company's risk-adjusted growth rate discounted by the market interest rate behaves as a control parameter for the observable price. We find a critical value of this control parameter at which a spontaneous symmetry-breaking of prices occurs, leading to a spontaneous valuation in absence of earnings, similarly to the emergence of a spontaneous magnetization in Ising models in absence of a magnetic field. The low growth rate phase is described by the firm foundation theory while the large growth rate phase is the regime of speculation and crowd behavior. In practice, while large "finite-time horizon" effects round off the predicted singularities, our symmetry-breaking speculation theory accounts for the apparent over-pricing and the high volatility of fast growing companies on the stock markets.
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.
Kamstra, Mark J; Kramer, Lisa A; Levi, Maurice D
2013-02-01
In a 2011 reply to our 2010 comment in this journal, Berument and Dogen maintained their challenge to the existence of the negative daylight-saving effect in stock returns reported by Kamstra, Kramer, and Levi in 2000. Unfortunately, in their reply, Berument and Dogen ignored all of the points raised in the comment, failing even to cite the Kamstra, et al. comment. Berument and Dogen continued to use inappropriate estimation techniques, over-parameterized models, and low-power tests and perhaps most surprisingly even failed to replicate results they themselves reported in their previous paper, written by Berument, Dogen, and Onar in 2010. The findings reported by Berument and Dogen, as well as by Berument, Dogen, and Onar, are neither well-supported nor well-reasoned. We maintain our original objections to their analysis, highlight new serious empirical and theoretical problems, and emphasize that there remains statistically significant evidence of an economically large negative daylight-saving effect in U.S. stock returns. The issues raised in this rebuttal extend beyond the daylight-saving effect itself, touching on methodological points that arise more generally when deciding how to model financial returns data. PMID:23654029
ERIC Educational Resources Information Center
Morris, Michael W.; Sheldon, Oliver J.; Ames, Daniel R.; Young, Maia J.
2007-01-01
We investigated two types of metaphors in stock market commentary. "Agent" metaphors describe price trajectories as volitional actions, whereas "object" metaphors describe them as movements of inanimate objects. Study 1 examined the consequences of commentators' metaphors for their investor audience. Agent metaphors, compared with object metaphors…
Microscopic analysis of currency and stock exchange markets
NASA Astrophysics Data System (ADS)
Kador, L.
1999-08-01
Recently it was shown that distributions of short-term price fluctuations in foreign-currency exchange exhibit striking similarities to those of velocity differences in turbulent flows. Similar profiles represent the spectral-diffusion behavior of impurity molecules in disordered solids at low temperatures. It is demonstrated that a microscopic statistical theory of the spectroscopic line shapes can be applied to the other two phenomena. The theory interprets the financial data in terms of information which becomes available to the traders and their reactions as a function of time. The analysis shows that there is no characteristic time scale in financial markets, but that instead stretched-exponential or algebraic memory functions yield good agreement with the price data. For an algebraic function, the theory yields truncated Lévy distributions which are often observed in stock exchange markets.
Pattern of trends in stock markets as revealed by the renormalization method
NASA Astrophysics Data System (ADS)
Zhang, H. S.; Shen, X. Y.; Huang, J. P.
2016-08-01
Predicting the movement of prices is a challenging topic in financial markets. So far, many investigations have been performed to help understand the dynamics of stock prices. In this work, we utilize the renormalization method to analyze the scaling and pattern of stock price trends. According to the analysis of length and changing velocity of the price trends, we find that there exist asymmetric phenomena of the trends in American stock market. In addition, a stronger Herd behavior is also discovered in the Chinese stock market. Since the Chinese (American) stock market is a representative of emerging (mature) market, the study on comparing the markets between these two countries is of potential value, which can leave us a wiser about both the pattern of the markets and the underlying physical mechanisms.
Web search queries can predict stock market volumes.
Bordino, Ilaria; Battiston, Stefano; Caldarelli, Guido; Cristelli, Matthieu; Ukkonen, Antti; Weber, Ingmar
2012-01-01
We live in a computerized and networked society where many of our actions leave a digital trace and affect other people's actions. This has lead to the emergence of a new data-driven research field: mathematical methods of computer science, statistical physics and sociometry provide insights on a wide range of disciplines ranging from social science to human mobility. A recent important discovery is that search engine traffic (i.e., the number of requests submitted by users to search engines on the www) can be used to track and, in some cases, to anticipate the dynamics of social phenomena. Successful examples include unemployment levels, car and home sales, and epidemics spreading. Few recent works applied this approach to stock prices and market sentiment. However, it remains unclear if trends in financial markets can be anticipated by the collective wisdom of on-line users on the web. Here we show that daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily volumes of queries related to the same stocks. In particular, query volumes anticipate in many cases peaks of trading by one day or more. Our analysis is carried out on a unique dataset of queries, submitted to an important web search engine, which enable us to investigate also the user behavior. We show that the query volume dynamics emerges from the collective but seemingly uncoordinated activity of many users. These findings contribute to the debate on the identification of early warnings of financial systemic risk, based on the activity of users of the www. PMID:22829871
Web Search Queries Can Predict Stock Market Volumes
Bordino, Ilaria; Battiston, Stefano; Caldarelli, Guido; Cristelli, Matthieu; Ukkonen, Antti; Weber, Ingmar
2012-01-01
We live in a computerized and networked society where many of our actions leave a digital trace and affect other people’s actions. This has lead to the emergence of a new data-driven research field: mathematical methods of computer science, statistical physics and sociometry provide insights on a wide range of disciplines ranging from social science to human mobility. A recent important discovery is that search engine traffic (i.e., the number of requests submitted by users to search engines on the www) can be used to track and, in some cases, to anticipate the dynamics of social phenomena. Successful examples include unemployment levels, car and home sales, and epidemics spreading. Few recent works applied this approach to stock prices and market sentiment. However, it remains unclear if trends in financial markets can be anticipated by the collective wisdom of on-line users on the web. Here we show that daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily volumes of queries related to the same stocks. In particular, query volumes anticipate in many cases peaks of trading by one day or more. Our analysis is carried out on a unique dataset of queries, submitted to an important web search engine, which enable us to investigate also the user behavior. We show that the query volume dynamics emerges from the collective but seemingly uncoordinated activity of many users. These findings contribute to the debate on the identification of early warnings of financial systemic risk, based on the activity of users of the www. PMID:22829871
Information theory in econophysics: stock market and retirement funds
NASA Astrophysics Data System (ADS)
Vogel, Eugenio; Saravia, G.; Astete, J.; Díaz, J.; Erribarren, R.; Riadi, F.
2013-03-01
Information theory can help to recognize magnetic phase transitions, what can be seen as a way to recognize different regimes. This is achieved by means of zippers specifically designed to compact data in a meaningful way at is the case for compressor wlzip. In the present contribution we first apply wlzip to the Chilean stock market interpreting the compression rates for the files storing the minute variation of the IPSA indicator. Agitated days yield poor compression rates while calm days yield high compressibility. We then correlate this behavior to the value of the five retirement funds related to the Chilean economy. It is found that the covariance between the profitability of the retirement funds and the compressibility of the IPSA values of previous day is high for those funds investing in risky stocks. Surprisingly, there seems to be no great difference among the three riskier funds contrary to what could be expected from the limitations on the portfolio composition established by the laws that regulate this market.
Interest rate change and Omori dynamics in the Stock Market
NASA Astrophysics Data System (ADS)
Petersen, Alexander; Wang, Fengzhong; Havlin, Shlomo; Stanley, H. Eugene
2009-03-01
I present the behavior of U.S. markets on the day of U.S. Federal Open Market Commission (FOMC) meetings from the perspective of Statistical Physics. The announcement of key U.S. Federal Reserve rate changes causes a small financial shock, where the dynamics before and after the announcement can be described by an Omori law. We find that markets respond sharply to the news in a complex way reminiscent of physical earthquakes described by the Omori law, which describes the power-law relaxation of aftershocks following a singular perturbation. We find Omori laws in both the volatility of the price (also known as the absolute returns) and the volume traded, using 1-minute resolution financial time series. These results suggest that the perturbative response of the stock market is the same for both financial news and financial crises. The intraday response can be measured by the Omori power-law exponent φ, which has opposite sign before and after the announcement. We estimate the magnitude of news by relating φ to the behavior of the U. S. Treasury Bill before and after FOMC announcements.
Quantum spatial-periodic harmonic model for daily price-limited stock markets
NASA Astrophysics Data System (ADS)
Meng, Xiangyi; Zhang, Jian-Wei; Xu, Jingjing; Guo, Hong
2015-11-01
We investigate the behaviors of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is considered to be oscillating and damping in a quantum spatial-periodic harmonic oscillator potential well. A complicated non-linear relation including inter-band positive correlation and intra-band negative correlation between the volatility and trading volume of a stock is numerically derived with the energy band structure of the model concerned. The effectiveness of price limit is re-examined, with some observed characteristics of price-limited stock markets in China studied by applying our quantum model.
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.
ERIC Educational Resources Information Center
Keller, Heath; Cox, James R.
2004-01-01
A collaborative effort between business and chemistry resulted in a class project called the Proteomics Stock Market Project. The project includes biochemical and marketing concepts, technology, writing assignments and group work.
NASA Astrophysics Data System (ADS)
Lin, Aijing; Ma, Hui; Shang, Pengjian
2015-10-01
Here we propose the new method DH-MMA, based on multiscale multifractal detrended fluctuation analysis(MMA), to investigate the scaling properties in stock markets. It is demonstrated that our approach can provide a more stable and faithful description of the scaling properties in comprehensive range rather than fixing the window length and slide length. It allows the assessment of more universal and subtle scaling characteristics. We illustrate DH-MMA by selecting power-law artificial data sets and six stock markets from US and China. The US stocks exhibit very strong multifractality for positive values of q, however, the Chinese stocks show stronger multifractality for negative q than positive q. In general, the US stock markets show similar behaviors, but Chinese stock markets display distinguishing characteristics.
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.
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.
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.
Statistical properties and pre-hit dynamics of price limit hits in the Chinese stock markets.
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
Statistical Properties and Pre-Hit Dynamics of Price Limit Hits in the Chinese Stock Markets
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
Liquidity Spillover in International Stock Markets through Distinct Time Scales
Righi, Marcelo Brutti; Vieira, Kelmara Mendes
2014-01-01
This paper identifies liquidity spillovers through different time scales based on a wavelet multiscaling method. We decompose daily data from U.S., British, Brazilian and Hong Kong stock markets indices in order to calculate the scale correlation between their illiquidities. The sample is divided in order to consider non-crisis, sub-prime crisis and Eurozone crisis. We find that there are changes in correlations of distinct scales and different periods. Association in finest scales is smaller than in coarse scales. There is a rise on associations in periods of crisis. In frequencies, there is predominance for significant distinctions involving the coarsest scale, while for crises periods there is predominance for distinctions on the finest scale. PMID:24465918
Liquidity spillover in international stock markets through distinct time scales.
Righi, Marcelo Brutti; Vieira, Kelmara Mendes
2014-01-01
This paper identifies liquidity spillovers through different time scales based on a wavelet multiscaling method. We decompose daily data from U.S., British, Brazilian and Hong Kong stock markets indices in order to calculate the scale correlation between their illiquidities. The sample is divided in order to consider non-crisis, sub-prime crisis and Eurozone crisis. We find that there are changes in correlations of distinct scales and different periods. Association in finest scales is smaller than in coarse scales. There is a rise on associations in periods of crisis. In frequencies, there is predominance for significant distinctions involving the coarsest scale, while for crises periods there is predominance for distinctions on the finest scale. PMID:24465918
Can percolation theory be applied to the stock market?
NASA Astrophysics Data System (ADS)
Stauffer, Dietrich
1998-11-01
The fluctuations of the stock market - the price changes per unit time - seem to deviate from Gaussians for short time steps. Power laws, exponentials, and multifractal descriptions have been offered to explain this short-time behavior. Microscopic models dealing with the decisions of single traders on the market have tried to reproduce this behavior. Possibly the simplest of these models is the herding approach of Cont and Bouchaud. Here a total of Nt traders cluster together randomly as in percolation theory. Each cluster randomly decides by buy or sell an amount proportional to its size, or not to trade. Monte Carlo simulations in two to seven dimensions at the percolation threshold depend on the number N of clusters trading within one time step. For N 1, the changes follow a power law; for 1 N Nt they are bell-shaped with power-law tails; for N Nt they crossover to a Gaussian. The correlations in the absolute value of the change decay slowly with time. Thus percolation not only describes the origin of life or the boiling of your breakfast egg, but also explains why we are not rich.
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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.
Effects of Technical Traders in a Synthetic Stock Market
NASA Astrophysics Data System (ADS)
Bernaschi, M.; Castiglione, F.
In Ref. 1, a new model for the description of the financial markets dynamics has been proposed. Traders move on a two dimensional lattice and interact by means of mechanisms of mutual influence. In the present paper, we present results from large-scale simulations of the same model enhanced by the introduction of rational traders modeled as moving-averages followers. The dynamics now accounts for log-normal distribution of volatility which is consistent with some observation of real financial indexes7 at least for the central part of the distribution.
Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.
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
Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions
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
Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA
NASA Astrophysics Data System (ADS)
Cao, Guangxi; Cao, Jie; Xu, Longbing
2013-02-01
We utilized asymmetric multifractal detrended fluctuation analysis in this study to examine the asymmetric multifractal scaling behavior of Chinese stock markets with uptrends or downtrends. Results show that the multifractality degree of Chinese stock markets with uptrends is stronger than that of Chinese stock markets with downtrends. Correlation asymmetries are more evident in large fluctuations than in small fluctuations. By discussing the source of asymmetric multifractality, we find that multifractality is related to long-range correlations when the market is going up, whereas it is related to fat-tailed distribution when the market is going down. The main source of asymmetric scaling behavior in the Shanghai stock market are long-range correlations, whereas that in the Shenzhen stock market is fat-tailed distribution. An analysis of the time-varying feature of scaling asymmetries shows that the evolution trends of these scaling asymmetries are similar in the two Chinese stock markets. Major financial and economical events may enhance scaling asymmetries.
Impact of stock market structure on intertrade time and price dynamics.
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
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
A study of correlations in the stock market
NASA Astrophysics Data System (ADS)
Sharma, Chandradew; Banerjee, Kinjal
2015-08-01
We study the various sectors of the Bombay Stock Exchange (BSE) for a period of 8 years from April 2006 to March 2014. Using the data of daily returns of a period of eight years we make a direct model free analysis of the pattern of the sectorial indices movement and the correlations among them. Our analysis shows significant auto correlation among the individual sectors and also strong cross-correlation among sectors. We also find that auto correlations in some of the sectors persist in time. This is a very significant result and has not been reported so far in Indian context. These findings will be very useful in model building for prediction of price movement of equities, derivatives and portfolio management. We show that the Random Walk Hypothesis is not applicable in modeling the Indian market and mean-variance-skewness-kurtosis based portfolio optimization might be required. We also find that almost all sectors are highly correlated during large fluctuation periods and have only moderate correlation during normal periods.
Cross-correlation analysis of stock markets using EMD and EEMD
NASA Astrophysics Data System (ADS)
Xu, Mengjia; Shang, Pengjian; Lin, Aijing
2016-01-01
Empirical mode decomposition (EMD) is a data-driven signal analysis method for nonlinear and nonstationary data. Since it is intuitive, direct, posterior and adaptive, EMD is widely applied to various fields of study. In this paper, EMD and ensemble empirical mode decomposition (EEMD), a modified method of EMD, are applied to financial time series. Through analyzing the intrinsic mode functions (IMFs) of EMD and EEMD, we find EEMD method performs better on the orthogonality of IMFs than EMD. With clustering the ordered frequencies of IMFs, the IMFs obtained from EEMD method are grouped into high-, medium-, and low-frequency components, representing the short-, medium-, and long-term volatilities of the index sequences, respectively. With the cross-correlation analysis of DCCA cross-correlation coefficient, our findings allow us to gain further and detailed insight into the cross-correlations of stock markets.
Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets
NASA Astrophysics Data System (ADS)
Eom, Cheoljun; Choi, Sunghoon; Oh, Gabjin; Jung, Woo-Sung
2008-07-01
We empirically investigated the relationships between the degree of efficiency and the predictability in financial time-series data. The Hurst exponent was used as the measurement of the degree of efficiency, and the hit rate calculated from the nearest-neighbor prediction method was used for the prediction of the directions of future price changes. We used 60 market indexes of various countries. We empirically discovered that the relationship between the degree of efficiency (the Hurst exponent) and the predictability (the hit rate) is strongly positive. That is, a market index with a higher Hurst exponent tends to have a higher hit rate. These results suggested that the Hurst exponent is useful for predicting future price changes. Furthermore, we also discovered that the Hurst exponent and the hit rate are useful as standards that can distinguish emerging capital markets from mature capital markets.
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
Predictive fuzzy reasoning method for time series stock market data mining
NASA Astrophysics Data System (ADS)
Khokhar, Rashid H.; Md Sap, Mohd Noor
2005-03-01
Data mining is able to uncover hidden patterns and predict future trends and behaviors in financial markets. In this research we approach quantitative time series stock selection as a data mining problem. We present another modification of extraction of weighted fuzzy production rules (WFPRs) from fuzzy decision tree by using proposed similarity-based fuzzy reasoning method called predictive reasoning (PR) method. In proposed predictive reasoning method weight parameter can be assigned to each proposition in the antecedent of a fuzzy production rule (FPR) and certainty factor (CF) to each rule. Certainty factors are calculated by using some important variables like effect of other companies, effect of other local stock market, effect of overall world situation, and effect of political situation from stock market. The predictive FDT has been tested using three data sets including KLSE, NYSE and LSE. The experimental results show that WFPRs rules have high learning accuracy and also better predictive accuracy of stock market time series data.
NASA Astrophysics Data System (ADS)
Dionisio, A.; Menezes, R.; Mendes, D. A.
2006-03-01
In recent years there has been a closer interrelationship between several scientific areas trying to obtain a more realistic and rich explanation of the natural and social phenomena. Among these it should be emphasized the increasing interrelationship between physics and financial theory. In this field the analysis of uncertainty, which is crucial in financial analysis, can be made using measures of physics statistics and information theory, namely the Shannon entropy. One advantage of this approach is that the entropy is a more general measure than the variance, since it accounts for higher order moments of a probability distribution function. An empirical application was made using data collected from the Portuguese Stock Market.
A new indicator of imminent occurrence of drawdown in the stock market
NASA Astrophysics Data System (ADS)
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2009-09-01
The crashes in financial markets have caught the attention of many researchers since 1929 and several mathematical models have been proposed to try to forecast the occurrence of these events. The main idea in this work is to use a wavelet transform to detect imminent abrupt changes in a financial time series, which may be eventually related to the possibility of a crash. Case studies are conducted using wavelet approaches with data covering pre-crash and post-crash 1929, as well as more recent Hang Seng and IBOVESPA data. The financial crisis of 2008 also is analyzed using this method. These time series provide useful insights into the behavior of wavelet coefficients under the possibility of short term crashes in stock market. However, it is not a trivial task to infer an imminent drawdown by simply examining the pattern of the wavelet transform coefficients. Hence, an index (a real number between 0 and 1) is proposed to aggregate the information provided by the wavelet coefficients. The new index presented good capability of monitoring crashes and drawdown with small error margins, at least in the studied cases.
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2013-10-22
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Application of the Beck model to stock markets: Value-at-Risk and portfolio risk assessment
NASA Astrophysics Data System (ADS)
Kozaki, M.; Sato, A.-H.
2008-02-01
We apply the Beck model, developed for turbulent systems that exhibit scaling properties, to stock markets. Our study reveals that the Beck model elucidates the properties of stock market returns and is applicable to practical use such as the Value-at-Risk estimation and the portfolio analysis. We perform empirical analysis with daily/intraday data of the S&P500 index return and find that the volatility fluctuation of real markets is well-consistent with the assumptions of the Beck model: The volatility fluctuates at a much larger time scale than the return itself and the inverse of variance, or “inverse temperature”, β obeys Γ-distribution. As predicted by the Beck model, the distribution of returns is well-fitted by q-Gaussian distribution of Tsallis statistics. The evaluation method of Value-at-Risk (VaR), one of the most significant indicators in risk management, is studied for q-Gaussian distribution. Our proposed method enables the VaR evaluation in consideration of tail risk, which is underestimated by the variance-covariance method. A framework of portfolio risk assessment under the existence of tail risk is considered. We propose a multi-asset model with a single volatility fluctuation shared by all assets, named the single β model, and empirically examine the agreement between the model and an imaginary portfolio with Dow Jones indices. It turns out that the single β model gives good approximation to portfolios composed of the assets with non-Gaussian and correlated returns.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Huang, Shupei; An, Haizhong; Gao, Xiangyun; Huang, Xuan
2015-09-01
The aim of this research is to investigate the multiscale dynamic linkages between crude oil price and the stock market in China at the sector level. First, the Haar à trous wavelet transform is implemented to extract multiscale information from the original time series. Furthermore, we incorporate the vector autoregression model to estimate the dynamic relationship pairing the Brent oil price and each sector stock index at each scale. There is a strong evidence showing that there are bidirectional Granger causality relationships between most of the sector stock indices and the crude oil price in the short, medium and long terms, except for those in the health, utility and consumption sectors. In fact, the impacts of the crude oil price shocks vary for different sectors over different time horizons. More precisely, the energy, information, material and telecommunication sector stock indices respond to crude oil price shocks negatively in the short run and positively in the medium and long runs, terms whereas the finance sector responds positively over all three time horizons. Moreover, the Brent oil price shocks have a stronger influence on the stock indices of sectors other than the health, optional and utility sectors in the medium and long terms than in the short term. The results obtained suggest implication of this paper as that the investment and policymaking decisions made during different time horizons should be based on the information gathered from each corresponding time scale.
Entropy: A new measure of stock market volatility?
NASA Astrophysics Data System (ADS)
Bentes, Sonia R.; Menezes, Rui
2012-11-01
When uncertainty dominates understanding stock market volatility is vital. There are a number of reasons for that. On one hand, substantial changes in volatility of financial market returns are capable of having significant negative effects on risk averse investors. In addition, such changes can also impact on consumption patterns, corporate capital investment decisions and macroeconomic variables. Arguably, volatility is one of the most important concepts in the whole finance theory. In the traditional approach this phenomenon has been addressed based on the concept of standard-deviation (or variance) from which all the famous ARCH type models - Autoregressive Conditional Heteroskedasticity Models- depart. In this context, volatility is often used to describe dispersion from an expected value, price or model. The variability of traded prices from their sample mean is only an example. Although as a measure of uncertainty and risk standard-deviation is very popular since it is simple and easy to calculate it has long been recognized that it is not fully satisfactory. The main reason for that lies in the fact that it is severely affected by extreme values. This may suggest that this is not a closed issue. Bearing on the above we might conclude that many other questions might arise while addressing this subject. One of outstanding importance, from which more sophisticated analysis can be carried out, is how to evaluate volatility, after all? If the standard-deviation has some drawbacks shall we still rely on it? Shall we look for an alternative measure? In searching for this shall we consider the insight of other domains of knowledge? In this paper we specifically address if the concept of entropy, originally developed in physics by Clausius in the XIX century, which can constitute an effective alternative. Basically, what we try to understand is, which are the potentialities of entropy compared to the standard deviation. But why entropy? The answer lies on the fact
HMO behavior and stock market valuation: what does Wall Street reward and punish?
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. PMID:11669294
The Dow is Killing Me: Risky Health Behaviors and the Stock Market.
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. PMID:24803424