Forecasting Stock Returns under Economic Constraints

Allan Timmermann, Davide Pettenuzzo () and Rossen Valkanov

No 9377, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 (search for similar items in EconPapers)
JEL-codes: C11 C22 G11 G12 (search for similar items in EconPapers)
Date: 2013-03
New Economics Papers: this item is included in nep-ecm and nep-for
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Citations: View citations in EconPapers (7)

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Journal Article: Forecasting stock returns under economic constraints (2014)
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