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What is the Chance that the Equity Premium Varies over Time? Evidence from Regressions on the Dividend-Price Ratio

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  • Jessica A. Wachter
  • Missaka Warusawitharana

Abstract

We examine the evidence on excess stock return predictability in a Bayesian setting in which the investor faces uncertainty about both the existence and strength of predictability. When we apply our methods to the dividend-price ratio, we find that even investors who are quite skeptical about the existence of predictability sharply modify their views in favor of predictability when confronted by the historical time series of returns and predictor variables. Correctly taking into account the stochastic properties of the regressor has a dramatic impact on inference, particularly over the 2000-2005 period.

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  • Jessica A. Wachter & Missaka Warusawitharana, 2011. "What is the Chance that the Equity Premium Varies over Time? Evidence from Regressions on the Dividend-Price Ratio," NBER Working Papers 17334, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:17334
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    Citations

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    Cited by:

    1. Avdis, Efstathios & Wachter, Jessica A., 2017. "Maximum likelihood estimation of the equity premium," Journal of Financial Economics, Elsevier, vol. 125(3), pages 589-609.
    2. Mathias S. Kruttli, 2016. "From Which Consumption-Based Asset Pricing Models Can Investors Profit? Evidence from Model-Based Priors," Finance and Economics Discussion Series 2016-027, Board of Governors of the Federal Reserve System (U.S.).
    3. Koijen, R.S.J., 2008. "Essays on asset pricing," Other publications TiSEM 75662994-29dc-4a83-a3ff-9, Tilburg University, School of Economics and Management.
    4. Hongye Guo & Jessica A. Wachter, 2019. ""Superstitious" Investors," NBER Working Papers 25603, National Bureau of Economic Research, Inc.
    5. Liu, Yan, 2021. "Index option returns and generalized entropy bounds," Journal of Financial Economics, Elsevier, vol. 139(3), pages 1015-1036.
    6. Davide Pettenuzzo & Allan G. Timmermann & Rossen I. Valkanov, 2008. "Return Predictability under Equilibrium Constraints on the Equity Premium," Working Papers 37, Brandeis University, Department of Economics and International Business School.
    7. Michael Johannes & Arthur Korteweg & Nicholas Polson, 2014. "Sequential Learning, Predictability, and Optimal Portfolio Returns," Journal of Finance, American Finance Association, vol. 69(2), pages 611-644, April.
    8. Nazliben, K. Korhan & Rodríguez, Juan Carlos, 2018. "Permanent shocks, signal extraction, and portfolio selection," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 47-68.
    9. Jessica A. Wachter, 2010. "Asset Allocation," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 175-206, December.
    10. Oleg Rytchkov, 2012. "Filtering Out Expected Dividends and Expected Returns," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 1-56.
    11. Byrne, Joseph & Fu, Rong, 2016. "Stock Return Prediction with Fully Flexible Models and Coefficients," MPRA Paper 75366, University Library of Munich, Germany.
    12. Efstathios Avdis & Jessica A. Wachter, 2013. "Maximum likelihood estimation of the equity premium," NBER Working Papers 19684, National Bureau of Economic Research, Inc.
    13. Doron Avramov & Si Cheng & Lior Metzker & Stefan Voigt, 2023. "Integrating Factor Models," Journal of Finance, American Finance Association, vol. 78(3), pages 1593-1646, June.
    14. Missaka Warusawitharana, 2011. "The expected real return to equity," Finance and Economics Discussion Series 2011-14, Board of Governors of the Federal Reserve System (U.S.).
    15. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
    16. Simon Oh & Jessica A. Wachter, 2018. "Cross-sectional Skewness," NBER Working Papers 25113, National Bureau of Economic Research, Inc.
    17. Jing Tian & Qing Zhou, 2018. "Improving equity premium forecasts by incorporating structural break uncertainty," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 619-656, November.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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