58 Pages Posted: 25 Mar 2008 Last revised: 4 Apr 2013
Date Written: March 18, 2013
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.
Keywords: Learning, predictability, optimal portfolio formation
Suggested Citation: Suggested Citation
Johannes, Michael S. and Korteweg, Arthur G. and Polson, Nick, Sequential Learning, Predictability, and Optimal Portfolio Returns (March 18, 2013). Journal of Finance, Forthcoming; AFA 2009 San Francisco Meetings Paper. Available at SSRN: https://ssrn.com/abstract=1108905 or http://dx.doi.org/10.2139/ssrn.1108905