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Time-Varying Sharpe Ratios and Market Timing

Author

Listed:
  • Yi Tang

    (School of Business, Fordham University, Bronx, NY 10458, United States)

  • Robert F. Whitelaw

    (Stern School of Business, NYU, 44 W. 4th St., Suite 9-190, New York, NY 10012, USA;
    National Bureau of Economic Research, USA)

Abstract

This paper documents predictable time-variation in stock market Sharpe ratios. Predetermined financial variables are used to estimate both the conditional mean and volatility of equity returns, and these moments are combined to estimate the conditional Sharpe ratio, or the Sharpe ratio is estimated directly as a linear function of these same variables. In sample, estimated conditional Sharpe ratios show substantial time-variation that coincides with the phases of the business cycle. Generally, Sharpe ratios are low at the peak of the cycle and high at the trough. In an out-of-sample analysis, using 10-year rolling regressions, relatively naive market-timing strategies that exploit this predictability can identify periods with Sharpe ratios more than 45% larger than the full sample value. In spite of the well-known predictability of volatility and the more controversial forecastability of returns, it is the latter factor that accounts primarily for both the in-sample and out-of-sample results.

Suggested Citation

  • Yi Tang & Robert F. Whitelaw, 2011. "Time-Varying Sharpe Ratios and Market Timing," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 1(03), pages 465-493.
    • Handle: RePEc:wsi:qjfxxx:v:01:y:2011:i:03:n:s2010139211000122
    DOI: 10.1142/S2010139211000122
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    Citations

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    Cited by:

    1. Juan Carlos Escanciano & Juan Carlos Pardo-Fernández & Ingrid Van Keilegom, 2017. "Semiparametric Estimation of Risk–Return Relationships," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 40-52, January.
    2. William N. Goetzmann & Dasol Kim, 2018. "Negative bubbles: What happens after a crash," European Financial Management, European Financial Management Association, vol. 24(2), pages 171-191, March.
    3. Robert Goldberg, 2015. "A methodology for computing and comparing implied equity and corporate-debt Sharpe Ratios," Review of Quantitative Finance and Accounting, Springer, vol. 44(4), pages 733-754, May.
    4. Xiaoyi Shen & Albert K. Tsui & Zhaoyong Zhang, 2019. "Volatility Timing in CPF Investment Funds in Singapore: Do They Outperform Non-CPF Funds?," Risks, MDPI, vol. 7(4), pages 1-16, October.
    5. Jones Paul M. & O’Steen Haley, 2018. "Time-varying correlations and Sharpe ratios during quantitative easing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(1), pages 1-11, February.
    6. Agostino Capponi & Sveinn Olafsson & Thaleia Zariphopoulou, 2019. "Personalized Robo-Advising: Enhancing Investment through Client Interaction," Papers 1911.01391, arXiv.org, revised Nov 2020.
    7. Francisco Peñaranda & Liuren Wu, 2022. "Targets, Predictability, and Performance," Management Science, INFORMS, vol. 68(2), pages 1537-1555, February.
    8. Agostino Capponi & Sveinn Ólafsson & Thaleia Zariphopoulou, 2022. "Personalized Robo-Advising: Enhancing Investment Through Client Interaction," Management Science, INFORMS, vol. 68(4), pages 2485-2512, April.
    9. Alan Moreira & Tyler Muir, 2016. "Volatility Managed Portfolios," NBER Working Papers 22208, National Bureau of Economic Research, Inc.
    10. Maio, Paulo & Santa-Clara, Pedro, 2012. "Multifactor models and their consistency with the ICAPM," Journal of Financial Economics, Elsevier, vol. 106(3), pages 586-613.
    11. Yong Shi & Wei Dai & Wen Long & Bo Li, 2021. "Deep Kernel Gaussian Process Based Financial Market Predictions," Papers 2105.12293, arXiv.org.

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