Sequential Learning, Predictability, and Optimal Portfolio Returns

Michael Slater Johannes · Arthur G. Korteweg · Nicholas Polson

234 citations0/0 files storedThe Journal of Finance

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Financial Markets and Investment Strategies Financial Risk and Volatility Modeling Risk and Portfolio Optimization

Abstract

ABSTRACT 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.

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References

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