Sequential Learning, Predictability, and Optimal Portfolio Returns

doi:10.1111/jofi.12121

Sequential Learning, Predictability, and Optimal Portfolio Returns

2014-03-17

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DOI：10.1111/jofi.12121

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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.

Keywords:

STOCK RETURNS

PREDICTIVE REGRESSIONS

STOCHASTIC VOLATILITY

ECONOMIC VALUE

INFERENCE

PRICES

CHOICE

Sequential Learning, Predictability, and Optimal Portfolio Returns

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