IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v68y2023ics1062940823001043.html
   My bibliography  Save this article

Cognitive biases, downside risk shocks, and stock expected returns

Author

Listed:
  • Li, Si
  • He, Fangyi
  • Shi, Fangquan

Abstract

This paper finds that the pricing effect of past stock downside risks in stock markets is greatly influenced by two cognitive biases: the representativeness heuristic bias and the conservatism bias. The two cognitive biases can cause investors to misreact to past downside risks of stocks, resulting in abnormal returns. Using the pseudo-Bayesian model, we theoretically describe how investors' incorrect belief updates, influenced by two cognitive biases regarding downside risks of a stock, affect future stock returns under four scenarios. Our empirical analysis confirms that biased beliefs lead to a positive correlation between short-term downside risk shocks and future stock returns, while a negative correlation exists between long-term downside risk shocks and future stock returns. This phenomenon is prevalent in the Chinese A-share market, even after controlling for several commonly used firm characteristics. Similar results are also observed in the US stock market. Furthermore, more active retail investors and low investor sentiments can strengthen the anomalous relation.

Suggested Citation

  • Li, Si & He, Fangyi & Shi, Fangquan, 2023. "Cognitive biases, downside risk shocks, and stock expected returns," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:ecofin:v:68:y:2023:i:c:s1062940823001043
    DOI: 10.1016/j.najef.2023.101981
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940823001043
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.najef.2023.101981?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guo, Xu & McAleer, Michael & Wong, Wing-Keung & Zhu, Lixing, 2017. "A Bayesian approach to excess volatility, short-term underreaction and long-term overreaction during financial crises," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 346-358.
    2. Kent Daniel & David Hirshleifer & Lin Sun, 2020. "Short- and Long-Horizon Behavioral Factors," The Review of Financial Studies, Society for Financial Studies, vol. 33(4), pages 1673-1736.
    3. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    4. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    5. Meir Statman & Steven Thorley & Keith Vorkink, 2006. "Investor Overconfidence and Trading Volume," The Review of Financial Studies, Society for Financial Studies, vol. 19(4), pages 1531-1565.
    6. Lam, Kin & Liu, Taisheng & Wong, Wing-Keung, 2010. "A pseudo-Bayesian model in financial decision making with implications to market volatility, under- and overreaction," European Journal of Operational Research, Elsevier, vol. 203(1), pages 166-175, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Zijun, 2021. "The high volume return premium and economic fundamentals," Journal of Financial Economics, Elsevier, vol. 140(1), pages 325-345.
    2. Yang, Baochen & Ye, Tao & Ma, Yao, 2022. "Financing anomaly, mispricing and cross-sectional return predictability," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 579-598.
    3. Jeffrey J. Coulton & Tami Dinh & Andrew B. Jackson & Tom Smith, 2016. "The impact of sentiment on price discovery," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 56(3), pages 669-694, September.
    4. Ahmad, Fawad & Oriani, Raffaele, 2022. "Investor attention, information acquisition, and value premium: A mispricing perspective," International Review of Financial Analysis, Elsevier, vol. 79(C).
    5. Cheng, Feiyang & Chiao, Chaoshin & Wang, Chunfeng & Fang, Zhenming & Yao, Shouyu, 2021. "Does retail investor attention improve stock liquidity? A dynamic perspective," Economic Modelling, Elsevier, vol. 94(C), pages 170-183.
    6. Tai-Yuen Hon & Massoud Moslehpour & Kai-Yin Woo, 2021. "Review on Behavioral Finance with Empirical Evidence," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(4), pages 15-41, December.
    7. Khasawneh, Maher & McMillan, David G. & Kambouroudis, Dimos, 2024. "Left-tail risk and UK stock return predictability: Underreaction, overreaction, and arbitrage difficulties," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    8. Yin, Libo & Yang, Zhichen, 2022. "The profitability effect: Insight from a dynamic perspective," International Review of Financial Analysis, Elsevier, vol. 80(C).
    9. Liu, Xufeng & Wan, Die, 2022. "Asymmetric positive feedback trading and stock pricing in China," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    10. Constantinos Antoniou & John A. Doukas & Avanidhar Subrahmanyam, 2016. "Investor Sentiment, Beta, and the Cost of Equity Capital," Management Science, INFORMS, vol. 62(2), pages 347-367, February.
    11. Turan G. Bali & Robert F. Engle & Yi Tang, 2017. "Dynamic Conditional Beta Is Alive and Well in the Cross Section of Daily Stock Returns," Management Science, INFORMS, vol. 63(11), pages 3760-3779, November.
    12. Chan, Kalok & Covrig, Vicentiu, 2012. "What determines mutual fund trading in foreign stocks?," Journal of International Money and Finance, Elsevier, vol. 31(4), pages 793-817.
    13. Chang, Xiaochen & Guo, Songlin & Huang, Junkai, 2022. "Kidnapped mutual funds: Irrational preference of naive investors and fund incentive distortion," International Review of Financial Analysis, Elsevier, vol. 83(C).
    14. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    15. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    16. Lin, Mei-Chen & Wu, Chu-Hua & Chiang, Ming-Ti, 2014. "Investor attention and information diffusion from analyst coverage," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 235-246.
    17. Saad, Mohsen & Samet, Anis, 2020. "Collectivism and commonality in liquidity," Journal of Business Research, Elsevier, vol. 116(C), pages 137-162.
    18. Gow-Cheng Huang & Kartono Liano & Ming-Shiun Pan, 2015. "The effects of stock splits on stock liquidity," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(1), pages 119-135, January.
    19. Wang, Zijun & Qian, Yan & Wang, Shiwen, 2018. "Dynamic trading volume and stock return relation: Does it hold out of sample?," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 195-210.
    20. Lin, Chaonan & Chen, Hong-Yi & Ko, Kuan-Cheng & Yang, Nien-Tzu, 2021. "Time-dependent lottery preference and the cross-section of stock returns," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 272-294.

    More about this item

    Keywords

    Representativeness Heuristic Bias; Conservatism Bias; Pseudo-Bayesian Model; Downside Risk Shock; Stock Expected Return;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecofin:v:68:y:2023:i:c:s1062940823001043. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.