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Characteristics-based reversals: Exploiting the gap between predicted and realized returns

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  • Ko, Seongdeok

Abstract

This paper investigates whether discrepancies between predicted and realized return deciles — based on cross-sectional firm characteristics — contain information about future returns. Using 94 characteristics, we classify each stock into a predicted decile and compare it to its realized decile each month. A long-short strategy that exploits these deviations yields high raw returns. However, the performance is primarily driven by small, illiquid, high-volatility stocks, leading to substantial transaction costs that limit practical implementation.

Suggested Citation

  • Ko, Seongdeok, 2025. "Characteristics-based reversals: Exploiting the gap between predicted and realized returns," Finance Research Letters, Elsevier, vol. 85(PC).
    • Handle: RePEc:eee:finlet:v:85:y:2025:i:pc:s1544612325013388
    DOI: 10.1016/j.frl.2025.108081
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    References listed on IDEAS

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    1. Lewellen, Jonathan, 2015. "The Cross-section of Expected Stock Returns," Critical Finance Review, now publishers, vol. 4(1), pages 1-44, June.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
    4. Juhani T Linnainmaa & Michael R Roberts, 2018. "The History of the Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2606-2649.
    5. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    6. Duffie, Darrell & Garleanu, Nicolae & Pedersen, Lasse Heje, 2002. "Securities lending, shorting, and pricing," Journal of Financial Economics, Elsevier, vol. 66(2-3), pages 307-339.
    7. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    8. Jeffrey Pontiff, 1996. "Costly Arbitrage: Evidence from Closed-End Funds," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(4), pages 1135-1151.
    9. Doron Avramov & Tarun Chordia & Amit Goyal, 2006. "Liquidity and Autocorrelations in Individual Stock Returns," Journal of Finance, American Finance Association, vol. 61(5), pages 2365-2394, October.
    10. Pontiff, Jeffrey, 2006. "Costly arbitrage and the myth of idiosyncratic risk," Journal of Accounting and Economics, Elsevier, vol. 42(1-2), pages 35-52, October.
    11. Eugene F. Fama & Kenneth R. French, 2008. "Dissecting Anomalies," Journal of Finance, American Finance Association, vol. 63(4), pages 1653-1678, August.
    12. Soohun Kim & Robert A Korajczyk & Andreas Neuhierl & Wei JiangEditor, 2021. "Arbitrage Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 2813-2856.
    13. Anh Duy Nguyen, 2020. "Alternative reversal variable," Post-Print hal-02388743, HAL.
    14. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    15. Shumway, Tyler, 1997. "The Delisting Bias in CRSP Data," Journal of Finance, American Finance Association, vol. 52(1), pages 327-340, March.
    16. Nguyen, Anh Duy, 2020. "Alternative reversal variable," Finance Research Letters, Elsevier, vol. 33(C).
    17. Zhi Da & Qianqiu Liu & Ernst Schaumburg, 2014. "A Closer Look at the Short-Term Return Reversal," Management Science, INFORMS, vol. 60(3), pages 658-674, March.
    18. Wahal, Sunil & Yavuz, M. Deniz, 2013. "Style investing, comovement and return predictability," Journal of Financial Economics, Elsevier, vol. 107(1), pages 136-154.
    19. Andrew Detzel & Robert Novy‐Marx & Mihail Velikov, 2023. "Model Comparison with Transaction Costs," Journal of Finance, American Finance Association, vol. 78(3), pages 1743-1775, June.
    20. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
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