Sequential Learning, Predictability, and Optimal Portfolio Returns
Michael S. Johannes
Columbia Business School - Finance and Economics
Arthur G. Korteweg
Stanford Graduate School of Business
University of Chicago - Booth School of Business
March 18, 2013
Journal of Finance, Forthcoming
AFA 2009 San Francisco Meetings Paper
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. The key is that investors must incorporate an ensemble of important features into their optimal portfolio problem, including time-varying volatility, and time-varying expected returns driven by improved predictors such as measures of yield that include share repurchase and issuance in addition to cash payouts. Moreover, investors need to account for estimation risk when forming optimal portfolios. 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 study the learning problem of investors, documenting the sequential process of learning about parameters, state variables, and models as new data arrives.
Number of Pages in PDF File: 58
Keywords: Learning, predictability, optimal portfolio formationAccepted Paper Series
Date posted: March 25, 2008 ; Last revised: April 4, 2013
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