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Research on the effect of firm-specific investor sentiment on the idiosyncratic volatility anomaly: Evidence from the Chinese market

Author

Listed:
  • Haozhi Chen
  • Yue Zhang

    (SAFTI - Shenzhen Audencia Financial Technology Institute)

Abstract

This paper evaluates how investor sentiment contributes to explaining the idiosyncratic volatility (IVOL) anomaly from a firm-level perspective in the Chinese stock market. This study employs the principal component analysis method to construct firm-specific investor sentiment (FSIS) and analyzes the effects of investor sentiment on the IVOL anomaly. In contrast to market-level sentiment, FSIS, as a varying cross-sectional sentiment indicator, can contribute to interpreting the idiosyncratic volatility anomaly from a micro- and portfolio-level viewpoint. According to our empirical investigation, stocks with higher FSIS have lower IVOL returns, i.e., a significant positive correlation exists between FSIS and IVOL. Additionally, our research findings also show that the rising (falling) FSIS effectively strengthens (weakens) the idiosyncratic volatility anomaly, indicating that variations in investor sentiment substantially impact earnings anomalies. In the study of the persistent impact of FSIS, we find that portfolios with the highest FSIS have longer-term negative IVOL premium returns than portfolios with medium and low FSIS. Finally, our research serves as a reminder to regulators and investors that risk can be minimized by avoiding exposure to stocks with high investor sentiment.

Suggested Citation

  • Haozhi Chen & Yue Zhang, 2023. "Research on the effect of firm-specific investor sentiment on the idiosyncratic volatility anomaly: Evidence from the Chinese market," Post-Print hal-04232650, HAL.
  • Handle: RePEc:hal:journl:hal-04232650
    DOI: 10.1016/j.pacfin.2023.102114
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    Cited by:

    1. Liu, Peng & Chen, Yaru & Mu, Yan, 2024. "The impact of climate risk aversion on agribusiness share price volatility," Finance Research Letters, Elsevier, vol. 61(C).

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