IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v108y2010i2p184-186.html
   My bibliography  Save this article

How much stock return predictability can we expect from an asset pricing model?

Author

Listed:
  • Zhou, Guofu

Abstract

We provide a new upper bound on the R-squared of a predictive regression of stock returns on predictable variables, tightening substantially Ross's (2005) bound. An empirical application illustrates that while Ross's bound is not binding, our bound does.

Suggested Citation

  • Zhou, Guofu, 2010. "How much stock return predictability can we expect from an asset pricing model?," Economics Letters, Elsevier, vol. 108(2), pages 184-186, August.
    • Handle: RePEc:eee:ecolet:v:108:y:2010:i:2:p:184-186
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-1765(10)00183-7
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Wayne E. Ferson & Campbell R. Harvey, 1999. "Conditioning Variables and the Cross Section of Stock Returns," Journal of Finance, American Finance Association, vol. 54(4), pages 1325-1360, August.
    3. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    4. Ferson, Wayne E & Harvey, Campbell R, 1991. "The Variation of Economic Risk Premiums," Journal of Political Economy, University of Chicago Press, vol. 99(2), pages 385-415, April.
    5. Fama, Eugene F. & Schwert, G. William, 1977. "Asset returns and inflation," Journal of Financial Economics, Elsevier, vol. 5(2), pages 115-146, November.
    6. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    7. Ľuboš Pástor & Robert F. Stambaugh, 2009. "Predictive Systems: Living with Imperfect Predictors," Journal of Finance, American Finance Association, vol. 64(4), pages 1583-1628, August.
    8. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    9. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    10. Doron Avramov, 2004. "Stock Return Predictability and Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 17(3), pages 699-738.
    11. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    12. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    13. Tu, Jun & Zhou, Guofu, 2004. "Data-generating process uncertainty: What difference does it make in portfolio decisions?," Journal of Financial Economics, Elsevier, vol. 72(2), pages 385-421, May.
    14. Kirby, Chris, 1998. "The Restrictions on Predictability Implied by Rational Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 11(2), pages 343-382.
    15. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    2. Hammerschmid, Regina & Lohre, Harald, 2018. "Regime shifts and stock return predictability," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 138-160.
    3. Baetje, Fabian & Menkhoff, Lukas, 2013. "Macro determinants of U.S. stock market risk premia in bull and bear markets," Hannover Economic Papers (HEP) dp-520, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. Baoqing Gan, 2020. "Does Social Media Sentiment Trump News?," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 5-2020.
    5. Cunha, Ronan & Pereira, Pedro L. Valls, 2015. "Automatic model selection for forecasting Brazilian stock returns," Textos para discussão 398, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2014. "Forecasting stock returns under economic constraints," Journal of Financial Economics, Elsevier, vol. 114(3), pages 517-553.
    7. 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).
    8. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    9. Tom Engsted & Stig V. Møller & Magnus Sander, 2013. "Bond return predictability in expansions and recessions," CREATES Research Papers 2013-13, Department of Economics and Business Economics, Aarhus University.
    10. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    11. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    12. Bätje, Fabian & Menkhoff, Lukas, 2016. "Predicting the equity premium via its components," VfS Annual Conference 2016 (Augsburg): Demographic Change 145789, Verein für Socialpolitik / German Economic Association.
    13. Li Liu & Zhiyuan Pan & Yudong Wang, 2022. "Shrinking return forecasts," The Financial Review, Eastern Finance Association, vol. 57(3), pages 641-661, August.
    14. Brennan, M.J. & Taylor, Alex P., 2023. "Expected returns and risk in the stock market," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 276-300.
    15. Buncic, Daniel & Tischhauser, Martin, 2017. "Macroeconomic factors and equity premium predictability," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 621-644.
    16. Hjalmarsson, Erik, 2018. "Maximal predictability under long-term mean reversion," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 269-282.
    17. Potì, Valerio, 2018. "A new tight and general bound on return predictability," Economics Letters, Elsevier, vol. 162(C), pages 140-145.
    18. Hai Lin & Daniel Quill & Henk Berkman, 2016. "Information diffusion and the predictability of New Zealand stock market returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 56(3), pages 749-785, September.
    19. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    20. Fletcher, Jonathan & Basu, Devraj, 2016. "An examination of the benefits of dynamic trading strategies in U.K. closed-end funds," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 109-118.
    21. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    2. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    3. 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.
    4. Ľuboš Pástor & Robert F. Stambaugh, 2009. "Predictive Systems: Living with Imperfect Predictors," Journal of Finance, American Finance Association, vol. 64(4), pages 1583-1628, August.
    5. Henkel, Sam James & Martin, J. Spencer & Nardari, Federico, 2011. "Time-varying short-horizon predictability," Journal of Financial Economics, Elsevier, vol. 99(3), pages 560-580, March.
    6. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
    7. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    8. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
    9. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
    10. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
    11. Bakshi, Gurdip & Panayotov, George & Skoulakis, Georgios, 2011. "Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios," Journal of Financial Economics, Elsevier, vol. 100(3), pages 475-495, June.
    12. repec:zbw:bofrdp:2016_029 is not listed on IDEAS
    13. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    14. Yufeng Han, 2010. "On the Economic Value of Return Predictability," Annals of Economics and Finance, Society for AEF, vol. 11(1), pages 1-33, May.
    15. Mingwei Sun & Paskalis Glabadanidis, 2022. "Can technical indicators predict the Chinese equity risk premium?," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 114-142, March.
    16. Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
    17. Baetje, Fabian & Menkhoff, Lukas, 2013. "Macro determinants of U.S. stock market risk premia in bull and bear markets," Hannover Economic Papers (HEP) dp-520, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    18. José Afonso Faias & Tiago Castel-Branco, 2018. "Out-Of-Sample Stock Return Prediction Using Higher-Order Moments," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 1-27, September.
    19. repec:zbw:bofrdp:2017_001 is not listed on IDEAS
    20. Schrimpf, Andreas, 2010. "International stock return predictability under model uncertainty," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1256-1282, November.
    21. Ferreira, Miguel A. & Santa-Clara, Pedro, 2011. "Forecasting stock market returns: The sum of the parts is more than the whole," Journal of Financial Economics, Elsevier, vol. 100(3), pages 514-537, June.
    22. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.

    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:eee:ecolet:v:108:y:2010:i:2:p:184-186. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.