Sequential Learning, Predictability, and Optimal Portfolio Returns
研究发现，投资者在构建最优投资组合时使用收益可预测模型能带来显著的经济收益，关键在于考虑时变波动和估计风险，并纳入股票回购与发行等指标。
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. 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.