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- W2121610132 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. 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." @default.
- W2121610132 created "2016-06-24" @default.
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- W2121610132 date "2012-01-01" @default.
- W2121610132 modified "2023-10-16" @default.
- W2121610132 title "Sequential Learning, Predictive Regressions, and Optimal Portfolio Returns" @default.
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- W2121610132 doi "https://doi.org/10.2139/ssrn.1108905" @default.
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