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Baidu index and predictability of Chinese stock returns

Author

Listed:
  • Dehua Shen

    (Tianjin University
    Tianjin University)

  • Yongjie Zhang

    (Tianjin University
    Key Laboratory of Computation and Analytics of Complex Management Systems (CACMS))

  • Xiong Xiong

    (Tianjin University
    Tianjin University)

  • Wei Zhang

    (Tianjin University
    Key Laboratory of Computation and Analytics of Complex Management Systems (CACMS))

Abstract

A number of studies have investigated the predictability of Chinese stock returns with economic variables. Given the newly emerged dataset from the Internet, this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns. The empirical results show that 1) the Search Frequency of Baidu Index (SFBI) can predict next day’s price changes; 2) the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks; 3) the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs. These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.

Suggested Citation

  • Dehua Shen & Yongjie Zhang & Xiong Xiong & Wei Zhang, 2017. "Baidu index and predictability of Chinese stock returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-8, December.
  • Handle: RePEc:spr:fininn:v:3:y:2017:i:1:d:10.1186_s40854-017-0053-1
    DOI: 10.1186/s40854-017-0053-1
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    Cited by:

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    18. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    19. Zuochao Zhang & Yongjie Zhang & Dehua Shen & Wei Zhang, 2018. "The Dynamic Cross-Correlations between Mass Media News, New Media News, and Stock Returns," Complexity, Hindawi, vol. 2018, pages 1-11, February.
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    22. Yongheng Wang & Xiaozan Zhang & Zengwang Wang, 2018. "A Proactive Decision Support System for Online Event Streams," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1891-1913, November.

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