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Upper Bounds on Return Predictability

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  • Huang, Dashan
  • Zhou, Guofu

Abstract

Can the degree of predictability found in data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R 2 of predictive regressions. Using data on the market portfolio and component portfolios, we find that the empirical R 2s are significantly greater than the theoretical upper bounds. Our results suggest that the most promising direction for future research should aim to identify new state variables that are highly correlated with stock returns instead of seeking more elaborate stochastic discount factors.

Suggested Citation

  • Huang, Dashan & Zhou, Guofu, 2017. "Upper Bounds on Return Predictability," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(2), pages 401-425, April.
    • Handle: RePEc:cup:jfinqa:v:52:y:2017:i:02:p:401-425_00
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    Cited by:

    1. Ashby, M. & Linton, O. B., 2022. "Do Consumption-based Asset Pricing Models Explain Own-history Predictability in Stock Market Returns?," Cambridge Working Papers in Economics 2259, Faculty of Economics, University of Cambridge.
    2. Neely, Christopher J., 2022. "How persistent are unconventional monetary policy effects?," Journal of International Money and Finance, Elsevier, vol. 126(C).
    3. Ashby, M. & Linton, O. B., 2022. "Do Consumption-based Asset Pricing Models Explain Own-history Predictability in Stock Market Returns?," Janeway Institute Working Papers 2226, Faculty of Economics, University of Cambridge.
    4. Fletcher, Jonathan, 2021. "Evaluating the performance of U.S. international equity closed-end funds," Journal of Multinational Financial Management, Elsevier, vol. 60(C).
    5. Fletcher, Jonathan, 2018. "Bayesian tests of global factor models," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 279-289.
    6. Hjalmarsson, Erik, 2018. "Maximal predictability under long-term mean reversion," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 269-282.
    7. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    8. Potì, Valerio, 2018. "A new tight and general bound on return predictability," Economics Letters, Elsevier, vol. 162(C), pages 140-145.
    9. Fletcher, Jonathan, 2019. "Model comparison tests of linear factor models in U.K. stock returns," Finance Research Letters, Elsevier, vol. 28(C), pages 281-291.
    10. Lioui, Abraham & Poncet, Patrice, 2019. "Long horizon predictability: An asset allocation perspective," European Journal of Operational Research, Elsevier, vol. 278(3), pages 961-975.
    11. Lin Liu & Qiguang Chen, 2020. "How to compare market efficiency? The Sharpe ratio based on the ARMA-GARCH forecast," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-21, December.
    12. Potì, Valerio & Levich, Richard & Conlon, Thomas, 2020. "Predictability and pricing efficiency in forward and spot, developed and emerging currency markets," Journal of International Money and Finance, Elsevier, vol. 107(C).
    13. Yaojie Zhang & Feng Ma & Chao Liang & Yi Zhang, 2021. "Good variance, bad variance, and stock return predictability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4410-4423, July.
    14. Levich, Richard & Conlon, Thomas & Potì, Valerio, 2019. "Measuring excess-predictability of asset returns and market efficiency over time," Economics Letters, Elsevier, vol. 175(C), pages 92-96.
    15. Ruan, Xinfeng & Zhang, Jin E., 2018. "Risk-neutral moments in the crude oil market," Energy Economics, Elsevier, vol. 72(C), pages 583-600.

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