Sequential Learning, Predictability, and Optimal Portfolio Returns





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    • Johannes is at the Graduate School of Business, Columbia University. Korteweg is at the Graduate School of Business, Stanford University. Polson is at the Booth School of Business, University of Chicago. We thank Cam Harvey (the Editor); the Associate Editor; an anonymous referee; Martijn Cremers; Darrell Duffie; Wayne Ferson; Pulak Ghosh; Stefan Nagel; and seminar participants at Columbia, Duke, Rice, the University of North Carolina, USC Marshall, Yale School of Management, the University of Chicago, the 2012 Society of Quantitative Analysts Meeting, the 2009 AFA meetings, the 2008 Conference on Modeling and Forecasting Economic and Financial Time Series with State Space models at Sveriges Riksbank, the 2009 CREATES conference in Skagen, Denmark, the 2009 SOFiE conference in Lausanne, the 2009 CIREQ-CIRANO Financial Econometrics Conference in Montreal, and the 2009 Quantitative Methods in Finance Symposium at UT Austin for helpful comments. We thank Ravi Pillai for excellent computing support. All errors are our own.


This paper finds statistically and economically significant out-of-sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. Investors must account for estimation risk, and incorporate an ensemble of important features, including time-varying volatility, and time-varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability, and our results suggest that this is largely due to omitting time-varying volatility and estimation risk. We also document the sequential process of investors learning about parameters, state variables, and models as new data arrive.