The Role of Learning in Dynamic Portfolio Decisions This paper analyzes the effect of uncertainty about the mean return on the risky asset on the portfolio decisions of an investor who has a long investment horizon. Building on the earlier work of Detemple (1986), Dothan and Feldman (1986), and Gennotte (1986), it is shown that the possibility of future learning about the mean return on the risky asset induces the investor to take a larger or smaller position in the risky asset than she would if there were no learning, the direction of the effect depending on whether the investor is more or less risk tolerant than the logarithmic investor whose portfolio decisions are unaffected by the possibility of future learning. Numerical calculations show that uncertainty about the mean return on the market portfolio has a significant effect on the portfolio decision of an investor with a 20 year horizon if her assessment of the market risk premium is based solely on the Ibbotson and Sinquefield ...

The optimal portfolio strategy is developed for an investor who has detected an asset pricing anomaly but is not certain that the anomaly is genuine rather than merely apparent. The analysis takes account of the fact that the parameters of both the underlying asset pricing model and the anomalous returns are estimated rather than known. The value that an investor would place on the ability to invest to exploit the apparent anomaly is also derived and illustrative calculations are presented for the Fama-French SMB and HML portfolios, whose returns are anomalous relative to the CAPM. An asset pricing anomaly is a statistically significant difference between the realized average returns associated with certain characteristics of securities, or on portfolios of securities formed on the basis of those characteristics, and the returns that are predicted by a particular asset pricing model. What is anomalous with respect to one model may be consistent with the predictions of other asset pricing models. For example, an excess return associated with a security’s dividend yield is anomalous with respect to the basic Capital Asset Pricing Model but is consistent with extensions that incorporate investor taxes. Some anomalies are inconsistent with any known rational asset pricing model; they appear to represent “money left on the table”; such examples include the

The focus of our paper is on the implications of model uncertainty for the crosssectional properties of returns. We perform our analysis in a tractable single-period mean-variance framework. We show that there is an uncertainty premium in equilibrium expected returns on financial assets and study how the premium varies across the assets. In particular, the cross-sectional distribution of expected returns can be formally described by a two-factor model, where expected returns are derived as compensation for the asset’s marginal contribution to the equilibrium risk and uncertainty of the market portfolio. Thus, the standard result that expected returns are related only to systematic, and not diversifiable risk, carries over to economies with model uncertainty as well. Our two-factor pricing model also illustrates that model uncertainty in financial markets may be distinguished from risk, addressing some of the observational equivalence issues raised in the literature.

We study an investor’s optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor’s aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. This effect is large for high and low values of the predictive variable. Unlike in the Bayesian framework, the hedging demand against model uncertainty may cause the investor’s stock allocations to first decrease sharply and then increase with his prior probability of the IID model, even when the expected stock return under the IID model is lower than under the predictability model. Adopting suboptimal investment strategies by ignoring model uncertainty can lead to sizable welfare costs.

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What percentage of their portfolio should investors allocate to alternative investment vehicles? The only available answers to the above question are set in a static meanvariance framework, with no explicit accounting for uncertainty on the active manager’s ability to generate abnormal return. In this paper we consider the problem of an investor who can choose between the riskfree security and two risky securities: a passive fund that tracks the market and a hedge fund. The hedge fund might o¤er a positive abnormal expected return or alpha (excess risk-adjusted expected return). The investor has power utility and is uncertain about both the expected return of the index and the alpha of the hedge fund, but upgrades beliefs in a Bayesian way. We derive analytic expressions for the optimal investment policy of the investor and calibrate our model to a database of hedge funds. Our results have important implications for investors who consider including alternative investment vehicles in their portfolios. In particular, they suggest that low beta hedge funds may serve as natural substitutes for a signi…cant portion of an investor risk-free asset holdings.