Forecasting Stock Returns under Economic Constraints

Davide Pettenuzzo (), Allan Timmermann () and Rossen Valkanov
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Allan Timmermann: University of California, San Diego

No 57, Working Papers from Brandeis University, Department of Economics and International Business School

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 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.

Keywords: Economic constraints; Sharpe ratio, Equity premium predictions; Bayesian analysis (search for similar items in EconPapers)
JEL-codes: C11 C22 G11 G12 (search for similar items in EconPapers)
Pages: 59 pages
Date: 2013-05
New Economics Papers: this item is included in nep-fmk and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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http://www.brandeis.edu/economics/RePEc/brd/doc/Brandeis_WP57.pdf First version, 2013 (application/pdf)

Related works:
Journal Article: Forecasting stock returns under economic constraints (2014)
Working Paper: Forecasting Stock Returns under Economic Constraints (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:brd:wpaper:57

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