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Subset Optimization for Asset Allocation

Gillen, Benjamin J. (2016) Subset Optimization for Asset Allocation. Social Science Working Paper, 1421. California Institute of Technology , Pasadena, CA.

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Subset optimization provides a new algorithm for asset allocation that's particularly useful in settings with many securities and short return histories. Rather than optimizing weights for all N securities jointly, subset optimization constructs Complete Subset Portfolios (CSPs) that naively aggregate many "Subset Portfolios," each optimizing weights over a subset of only ^N randomly selected securities. With known means and variances, the complete subset efficient frontier for different subset sizes characterizes CSPs' utility loss due to satisficing, which generally decreases with ^N . In finite samples, the bound on CSPs' expected out-of-sample performance loss due to sampling error generally increases with ^N . By balancing this tradeoff, CSPs' expected out-of-sample performance dominates both the 1=N rule and sample-based optimization. Simulation and backtest experiments illustrate CSPs' robust performance against existing asset allocation strategies.

Item Type:Report or Paper (Working Paper)
Additional Information:I'm grateful for helpful commentary and suggestions from Yacine Ait-Sahalia, Khai Chiong, John Cochrane, Jaksa Cvitanic, Michael Ewens, Joseph Gerakos, Harry Markowitz, Tatiana Mayskaya, Nicolas Polson, Richard Roll, Alberto Rossi, Allan Timmermann, Ken Whinston, as well as seminar participants at Caltech.
Group:Social Science Working Papers
Series Name:Social Science Working Paper
Issue or Number:1421
Record Number:CaltechAUTHORS:20170725-111618998
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79336
Deposited By: Hanna Storlie
Deposited On:25 Jul 2017 20:51
Last Modified:03 Oct 2019 18:18

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