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

Author

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  • Fan, Xiaoqian
  • Yuan, Ying
  • Zhuang, Xintian
  • Jin, Xiu

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.

Suggested Citation

  • Fan, Xiaoqian & Yuan, Ying & Zhuang, Xintian & Jin, Xiu, 2017. "Long memory of abnormal investor attention and the cross-correlations between abnormal investor attention and trading volume, volatility respectively," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 323-333.
    • Handle: RePEc:eee:phsmap:v:469:y:2017:i:c:p:323-333
    DOI: 10.1016/j.physa.2016.11.009
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    6. Hua Wu & Taiwen Feng & Wenbo Jiang & Ting Kong, 2022. "Environmental Penalties, Investor Attention and Stock Market Reaction: Moderating Roles of Air Pollution and Industry Saliency," IJERPH, MDPI, vol. 19(5), pages 1-27, February.

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