Working Paper: NBER ID: w13165
Authors: Jessica A. Wachter; Missaka Warusawitharana
Abstract: Are excess returns predictable and if so, what does this mean for investors? Previous literature has tended toward two polar viewpoints: that predictability is useful only if the statistical evidence for it is incontrovertible, or that predictability should affect portfolio choice, even if the evidence is weak according to conventional measures. This paper models an intermediate view: that both data and theory are useful for decision-making. We investigate optimal portfolio choice for an investor who is skeptical about the amount of predictability in the data. Skepticism is modeled as an informative prior over the R^2 of the predictive regression. We find that the evidence is sufficient to convince even an investor with a highly skeptical prior to vary his portfolio on the basis of the dividend-price ratio and the yield spread. The resulting weights are less volatile and deliver superior out-of-sample performance as compared to the weights implied by an entirely model-based or data-based view.
Keywords: predictable returns; asset allocation; market timing; Bayesian methods
JEL Codes: C11; C32; G11
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Skeptical prior (C46) | Portfolio adjustment (G11) |
| Skeptical prior (C46) | Perceived predictability of returns (G17) |
| Perceived predictability of returns (G17) | Portfolio adjustment (G11) |
| Dividend-price ratio (G35) | Portfolio weights (G11) |
| Yield spread (E43) | Portfolio weights (G11) |
| Skeptical prior (C46) | Portfolio weights (G11) |