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Long memory of abnormal investor attention and the cross-correlations between abnormal investor attention and trading volume, volatility respectively

Xiaoqian Fan, Ying Yuan, Xintian Zhuang and Xiu Jin

Physica A: Statistical Mechanics and its Applications, 2017, vol. 469, issue C, 323-333

Abstract: Taking Baidu Index as a proxy for abnormal investor attention (AIA), the long memory property in the AIA of Shanghai Stock Exchange (SSE) 50 Index component stocks was empirically investigated using detrended fluctuation analysis (DFA) method. The results show that abnormal investor attention is power-law correlated with Hurst exponents between 0.64 and 0.98. Furthermore, the cross-correlations between abnormal investor attention and trading volume, volatility respectively are studied using detrended cross-correlation analysis (DCCA) and the DCCA cross-correlation coefficient (ρDCCA). The results suggest that there are positive correlations between AIA and trading volume, volatility respectively. In addition, the correlations for trading volume are in general higher than the ones for volatility. By carrying on rescaled range analysis (R/S) and rolling windows analysis, we find that the results mentioned above are effective and significant.

Keywords: Abnormal investor attention; DCCA; Trading volume; Volatility (search for similar items in EconPapers)
Date: 2017
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