Working Paper: CEPR ID: DP9377
Authors: Davide Pettenuzzo; Allan G. Timmermann; Rossen Valkanov
Abstract: We propose a new approach to imposing economic constraints on time-series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two types of constraints: Non-negative equity premia and bounds on the conditional Sharpe ratio, the latter of which incorporates timevarying volatility in the predictive regression framework. Empirically, we find that economic constraints systematically reduce uncertainty about model parameters, reduce the risk of selecting a poor forecasting model, and improve both statistical and economic measures of out-of-sample forecast performance. The Sharpe ratio constraint, in particular, results in considerable economic gains.
Keywords: Bayesian analysis; Economic constraints; Sharpe ratio; Stock return predictability
JEL Codes: C11; C22; G11; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| economic constraints (D10) | reduced uncertainty about model parameters (C51) |
| reduced uncertainty about model parameters (C51) | improved predictive accuracy of equity premium forecasts (G17) |
| Sharpe ratio constraint (G11) | enhanced economic performance in portfolio allocation (G11) |
| economic constraints (D10) | improved forecasts over time (C53) |