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Forecasting Stock Returns under Economic Constraints

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  • Pettenuzzo, Davide
  • Timmermann, Allan G
  • Valkanov, Rossen

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.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 9377.

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Date of creation: Mar 2013
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Handle: RePEc:cpr:ceprdp:9377

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Keywords: Bayesian analysis; Economic constraints; Sharpe Ratio; Stock return predictability;

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