Variable Selection for Portfolio Choice

Authors

  • Yacine AÏT-SAHALI,

  • Michael W. Brandt

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    • Aїt-Sahalia is with the Department of Economics, Princeton University, and NBER. Brandt is with the Wharton School, University of Pennsylvania, and NBER. We thank John Campbell, George Constantinides, David Chapman, Frank Diebold, Han Hong, Paul Pfleiderer, Jessica Wachter, and especially Anthony Lynch for their comments and suggestions. We also thank seminar participants at the 2001 Annual Meeting of the American Finance Association, Columbia University, Harvard University, MIT, NYU, Princeton University, Stanford University, and the University of Chicago. This research was conducted during the first author's tenure as an Alfred P. Sloan Research Fellow. Financial support from the NSF under Grant SBR-9996023, the Bendheim Center for Finance at Princeton University, and the Rodney White Center at the Wharton School is gratefully acknowledged.

Abstract

We study asset allocation when the conditional moments of returns are partly predictable. Rather than first model the return distribution and subsequently characterize the portfolio choice, we determine directly the dependence of the optimal portfolio weights on the predictive variables. We combine the predictors into a single index that best captures time variations in investment opportunities. This index helps investors determine which economic variables they should track and, more importantly, in what combination. We consider investors with both expected utility (mean variance and CRRA) and nonexpected utility (ambiguity aversion and prospect theory) objectives and characterize their market timing, horizon effects, and hedging demands.

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