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Activity Number: 707
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #320530 View Presentation
Title: Sparse Mean-Variance Portfolios: A Penalized Utility Approach
Author(s): David Puelz* and Carlos Carvalho and P. Richard Hahn
Companies: The University of Texas and The University of Texas and The University of Chicago Booth School of Business
Keywords: decoupling shrinkage and selection ; variable selection ; portfolio optimization ; statistical uncertainty and selection

This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset returns and parameter estimates. Our loss function used for selection is constructed by integrating over both dimensions of uncertainty and our optimization is structured as a LASSO minimization with penalized weights. The foundations of this paper are adapted from the decoupled shrinkage and selection (DSS) procedure of Hahn and Carvalho (2015) where statistical inference and the investor's preference for simplicity are separated.

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