Working Paper: CEPR ID: DP6076
Authors: Lubos Pstor; 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: expected stock return; predictability; predictive regression; predictive system; state space model
JEL Codes: G1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| predictive regression approach (C29) | expected returns (G17) |
| innovations in expected returns (G17) | unexpected returns (C59) |
| expected returns modeled using first-order autoregressive process (C51) | unexpected negative return (G19) |
| predictive system (C53) | expected returns (G17) |
| predictive system (C53) | variance in expected returns (G17) |