# Random matrix approach to the dynamics of stock inventory variations

W. -X. Zhou, G. -H. Mu and J. Kert\'esz,
W. -X. Zhou: ECUST
G. -H. Mu: ECUST
J. Kert\'esz,: BME

Papers from arXiv.org

Abstract: We study the cross-correlation matrix $C_{ij}$ of inventory variations of the most active individual and institutional investors in an emerging market to understand the dynamics of inventory variations. We find that the distribution of cross-correlation coefficient $C_{ij}$ has a power-law form in the bulk followed by exponential tails and there are more positive coefficients than negative ones. In addition, it is more possible that two individuals or two institutions have stronger inventory variation correlation than one individual and one institution. We find that the largest and the second largest eigenvalues ($\lambda_1$ and $\lambda_2$) of the correlation matrix cannot be explained by the random matrix theory and the projection of inventory variations on the first eigenvector $u(\lambda_1)$ are linearly correlated with stock returns, where individual investors play a dominating role. The investors are classified into three categories based on the cross-correlation coefficients $C_{VR}$ between inventory variations and stock returns. Half individuals are reversing investors who exhibit evident buy and sell herding behaviors, while 6% individuals are trending investors. For institutions, only 10% and 8% investors are trending and reversing investors. A strong Granger causality is unveiled from stock returns to inventory variations, which means that a large proportion of individuals hold the reversing trading strategy and a small part of individuals hold the trending strategy. Comparing with the case of Spanish market, Chinese investors exhibit common and market-specific behaviors. Our empirical findings have scientific significance in the understanding of investors' trading behaviors and in the construction of agent-based models for stock markets.

Date: 2012-01
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Published in New Journal of Physics 14 (9), 093025 (2012)

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