scholarly journals Dynamics of Correlation Structure in Stock Market

Entropy ◽  
2014 ◽  
Vol 16 (1) ◽  
pp. 455-470 ◽  
Author(s):  
Maman Djauhari ◽  
Siew Gan
Keyword(s):  
Stock Market ◽  
Correlation Structure

scholarly journals Correlation structure networks of stock market during terrorism: evidence from Pakistan

2021 ◽  
Vol 1 (2) ◽  
pp. 117-140
Author(s):  
Bilal Ahmed Memon ◽  
◽  
Hongxing Yao ◽  
Keyword(s):  
Stock Market ◽  
Correlation Structure

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

2017 ◽  
Vol 16 (02) ◽  
pp. 1750018 ◽  
Author(s):  
Rui-Qi Han ◽  
Wen-Jie Xie ◽  
Xiong Xiong ◽  
Wei Zhang ◽  
Wei-Xing Zhou
Keyword(s):  
Stock Market ◽  
Random Matrix ◽  
Correlation Matrix ◽  
Partial Correlation ◽  
Correlation Structure ◽  
Matrix Analysis ◽  
Chinese Stock Market ◽  
Average Correlation ◽  
Largest Eigenvalue ◽  
Largest Eigenvalues

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

Predicting Stock Market Performance

2020 ◽  
pp. 1-10
Author(s):  
Thomas Plieger ◽  
Thomas Grünhage ◽  
Éilish Duke ◽  
Martin Reuter
Keyword(s):  
Stock Market ◽  
Risk Taking ◽  
Life Stress ◽  
Price Volatility ◽  
Individual Characteristics ◽  
Self Report ◽  
Financial Behavior ◽  
Market Simulation ◽  
Stock Market Performance ◽  
Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

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