IDEAS home Printed from https://ideas.repec.org/a/cup/jfinqa/v52y2017i02p401-425_00.html
   My bibliography  Save this article

Upper Bounds on Return Predictability

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0022109017000096/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    3. Neely, Christopher J., 2022. "How persistent are unconventional monetary policy effects?," Journal of International Money and Finance, Elsevier, vol. 126(C).
    4. 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.
    5. Fletcher, Jonathan, 2018. "Bayesian tests of global factor models," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 279-289.
    6. 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.
    7. 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.
    8. Ruan, Xinfeng & Zhang, Jin E., 2018. "Risk-neutral moments in the crude oil market," Energy Economics, Elsevier, vol. 72(C), pages 583-600.
    9. Fletcher, Jonathan, 2021. "Evaluating the performance of U.S. international equity closed-end funds," Journal of Multinational Financial Management, Elsevier, vol. 60(C).
    10. Hjalmarsson, Erik, 2018. "Maximal predictability under long-term mean reversion," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 269-282.
    11. Potì, Valerio, 2018. "A new tight and general bound on return predictability," Economics Letters, Elsevier, vol. 162(C), pages 140-145.
    12. Lioui, Abraham & Poncet, Patrice, 2019. "Long horizon predictability: An asset allocation perspective," European Journal of Operational Research, Elsevier, vol. 278(3), pages 961-975.
    13. 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).
    14. 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.
    15. 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.

    More about this item

    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:cup:jfinqa:v:52:y:2017:i:02:p:401-425_00. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/jfq .

    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.