Working Paper: NBER ID: w12814
Authors: Lubos Pastor; Robert F. Stambaugh
Abstract: The standard regression approach to modeling return predictability seems too restrictive in one way but too lax in another. A predictive regression models expected returns as an exact linear function of a given set of predictors but does not exploit the likely economic property that innovations in expected returns are negatively correlated with unexpected returns. We develop an alternative framework - a predictive system - that accommodates imperfect predictors and beliefs about that negative correlation. In this framework, the predictive ability of imperfect predictors is supplemented by information in lagged returns as well as lags of the predictors. Compared to predictive regressions, predictive systems deliver different and substantially more precise estimates of expected returns as well as different assessments of a given predictor's usefulness.
Keywords: Predictive Regression; Expected Returns; Imperfect Predictors; Bayesian Methods
JEL Codes: G1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| innovations in expected returns (G17) | unexpected returns (C59) |
| expected return (G17) | unexpected return (Y60) |
| predictors (C29) | expected return (G17) |
| lagged predictors (C29) | expected return (G17) |
| lagged returns (G17) | expected return (G17) |
| expected return (G17) | precision of estimates (C13) |
| prior beliefs (D80) | expected return (G17) |