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Betting against beta with intraday and overnight signals

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  • Insana, Alessandra

Abstract

The abnormal returns of the Betting Against Beta (BAB) strategy have attracted much interest among researchers and practitioners. Based on a market anomaly related to the Capital Asset Pricing Model, this strategy uses daily beta as a signal for portfolio construction. However, recent literature shows how some financial quantities, including beta, change between trading and non-trading periods. For this reason, we decided to compare the performance of the original BAB strategy with two BAB variants, where the signal for portfolio construction is given by intraday and overnight beta, respectively. Despite all strategies exhibiting positive cumulative returns, using the intraday beta signal leads to significantly higher performances. Further analyses show that the abnormal intraday BAB returns are mainly due to nano and micro-cap stocks which tend to outperform large-cap stocks, as well known from the literature.

Suggested Citation

  • Insana, Alessandra, 2023. "Betting against beta with intraday and overnight signals," International Review of Financial Analysis, Elsevier, vol. 86(C).
    • Handle: RePEc:eee:finana:v:86:y:2023:i:c:s1057521923000583
    DOI: 10.1016/j.irfa.2023.102542
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    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. De Santis, Giorgio & Gerard, Bruno, 1997. "International Asset Pricing and Portfolio Diversification with Time-Varying Risk," Journal of Finance, American Finance Association, vol. 52(5), pages 1881-1912, December.
    3. Lou, Dong & Polk, Christopher & Skouras, Spyros, 2019. "A tug of war: Overnight versus intraday expected returns," Journal of Financial Economics, Elsevier, vol. 134(1), pages 192-213.
    4. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    5. Asness, Cliff & Frazzini, Andrea & Gormsen, Niels Joachim & Pedersen, Lasse Heje, 2020. "Betting against correlation: Testing theories of the low-risk effect," Journal of Financial Economics, Elsevier, vol. 135(3), pages 629-652.
    6. Frazzini, Andrea & Pedersen, Lasse Heje, 2014. "Betting against beta," Journal of Financial Economics, Elsevier, vol. 111(1), pages 1-25.
    7. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    8. Liu, Jianan & Stambaugh, Robert F. & Yuan, Yu, 2018. "Absolving beta of volatility’s effects," Journal of Financial Economics, Elsevier, vol. 128(1), pages 1-15.
    9. Randolph B. Cohen & Christopher Polk & Tuomo Vuolteenaho, 2005. "Money Illusion in the Stock Market: The Modigliani-Cohn Hypothesis," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(2), pages 639-668.
    10. Ma, Tianyi & Tee, Kai-Hong & Li, Baibing, 2022. "Timing the volatility risk of beta anomaly: Evidence from hedge fund strategies," International Review of Financial Analysis, Elsevier, vol. 81(C).
    11. Qin, Jie, 2020. "Regret-based capital asset pricing model," Journal of Banking & Finance, Elsevier, vol. 114(C).
    12. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    13. Vasicek, Oldrich A, 1973. "A Note on Using Cross-Sectional Information in Bayesian Estimation of Security Betas," Journal of Finance, American Finance Association, vol. 28(5), pages 1233-1239, December.
    14. Hendershott, Terrence & Livdan, Dmitry & Rösch, Dominik, 2020. "Asset pricing: A tale of night and day," Journal of Financial Economics, Elsevier, vol. 138(3), pages 635-662.
    15. 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.
    16. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    17. Novy-Marx, Robert & Velikov, Mihail, 2022. "Betting against betting against beta," Journal of Financial Economics, Elsevier, vol. 143(1), pages 80-106.
    18. Bali, Turan G. & Brown, Stephen J. & Murray, Scott & Tang, Yi, 2017. "A Lottery-Demand-Based Explanation of the Beta Anomaly," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(6), pages 2369-2397, December.
    19. Insana, Alessandra, 2022. "Does systematic risk change when markets close? An analysis using stocks’ beta," Economic Modelling, Elsevier, vol. 109(C).
    20. Ham, Hyuna & Ryu, Doojin & Webb, Robert I., 2022. "The effects of overnight events on daytime trading sessions," International Review of Financial Analysis, Elsevier, vol. 83(C).
    21. Black, Fischer, 1972. "Capital Market Equilibrium with Restricted Borrowing," The Journal of Business, University of Chicago Press, vol. 45(3), pages 444-455, July.
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    More about this item

    Keywords

    Beta; Beta overnight; Beta intraday; Betting against beta; Anomalies;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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