Johannes, Michael S.
[Verfasser:in]
;
Korteweg, Arthur G.
[Sonstige Person, Familie und Körperschaft];
Polson, Nick
[Sonstige Person, Familie und Körperschaft]
Sequential Learning, Predictability, and Optimal Portfolio Returns
Anmerkungen:
In: Journal of Finance, Forthcoming
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 18, 2013 erstellt
Beschreibung:
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