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Forecasting Stock Returns under Economic Constraints

  • Davide Pettenuzzo

    ()

    (Economics Department, Brandeis University)

  • Allan Timmermann

    ()

    (University of California, San Diego)

  • Rossen Valkanov

    ()

    (University of California, San Diego)

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 Önd 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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File URL: http://www.brandeis.edu/departments/economics/RePEc/brd/doc/Brandeis_WP57.pdf
File Function: First version, 2013
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Paper provided by Brandeis University, Department of Economics and International Businesss School in its series Working Papers with number 57.

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Length: 59 pages
Date of creation: May 2013
Date of revision:
Handle: RePEc:brd:wpaper:57
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Web page: http://www.brandeis.edu/departments/economics/

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