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Aggregate Investor Confidence in the Stock Market

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  • Chris Meier

Abstract

Overconfidence is one of the most robust findings in the field of behavioral finance, and is associated with excessive trading and risk taking among market participants. Assessment of the level of confidence of individuals in their abilities and skills is well documented. However, the literature lacks an aggregate measure of investor confidence, with this required to test its implications on a macro level. The author introduces a simple measure of aggregate investor confidence by adopting a formal model of overconfidence. The applications of the measure suggest that, in aggregate, higher trading activity occurs when investor confidence soars, particularly for smaller stocks. Subsequently, the effect partially reverses, implying a correction to an initial overreaction. The newly introduced investor confidence index possesses better ability to predict trading activity than past returns, as used in prior studies. Additionally, investors tend to have a higher risk appetite when confident, as shown by increased investment in small stocks with higher risk.

Suggested Citation

  • Chris Meier, 2018. "Aggregate Investor Confidence in the Stock Market," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 19(4), pages 421-433, October.
    • Handle: RePEc:taf:hbhfxx:v:19:y:2018:i:4:p:421-433
    DOI: 10.1080/15427560.2018.1406942
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    Citations

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    Cited by:

    1. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    2. Chen, Rongda & Wu, Ling & Jin, Chenglu & Wang, Shengnan, 2021. "Unintended investor sentiment on bank financial products: Evidence from China," Emerging Markets Review, Elsevier, vol. 49(C).
    3. Huang, Wenli & Zhou, Fengbo & Yu, Chenkang & Hu, Yue & Zhang, Hong & Xu, Yueling, 2023. "Momentum effect and contrarian effect in China's A-share market, under registration-based system," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
    4. Chen, Rongda & Yu, Jingjing & Jin, Chenglu & Bao, Weiwei, 2019. "Internet finance investor sentiment and return comovement," Pacific-Basin Finance Journal, Elsevier, vol. 56(C), pages 151-161.
    5. Jitender Kumar & Neha Prince, 2022. "Overconfidence bias in the Indian stock market in diverse market situations: an empirical study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3031-3047, December.
    6. Mohammed H. Alamoudi & Omer A. Bafail, 2022. "BWM—RAPS Approach for Evaluating and Ranking Banking Sector Companies Based on Their Financial Indicators in the Saudi Stock Market," JRFM, MDPI, vol. 15(10), pages 1-20, October.
    7. Wang, Gaoshan & Yu, Guangjin & Shen, Xiaohong, 2021. "The effect of online environmental news on green industry stocks: The mediating role of investor sentiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    8. Zhou, Xinxing & Gao, Yan & Wang, Ping & Zhu, Bangzhu, 2022. "Examining the overconfidence and overreaction in China’s carbon markets," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 472-489.
    9. Ho, Kung-Cheng & Yang, Lu & Luo, Sijia, 2022. "Information disclosure ratings and continuing overreaction: Evidence from the Chinese capital market," Journal of Business Research, Elsevier, vol. 140(C), pages 638-656.
    10. Sayyed Sadaqat Hussain Shah & Xia Xinping & Muhammad Asif Khan & Sinan Abdullah Harjan, 2018. "Investor and Manager Overconfidence Bias and Firm Value: Micro-Level Evidence from the Pakistan Equity Market," International Journal of Economics and Financial Issues, Econjournals, vol. 8(5), pages 190-199.

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