An Empirical Study of Multi-Objective Algorithms for Stock Ranking

Genetic Programming Theory and Practice, 2007

16 Pages Posted: 26 Jun 2007

See all articles by Ying L. Becker

Ying L. Becker

IBS

Harold Fox

Massachusetts Institute of Technology (MIT)

Peng Fei

State Street Global Advisors

Abstract

Quantitative models for stock selection and portfolio management face the challenge of determining the most efficacious factors, and how they interact, from large amounts of financial data. Genetic programming using "simple objective" fitness functions has been shown to be an effective technique for selecting factors and constructing multi-factor models for ranking stocks, but the resulted models can be somewhat unbalanced in satisfying the multiple objectives that portfolio managers seek: large excess returns that are consistent across time and the cross-sectional dimensions of the investment universe. In this study, we implement and evaluate three multi-objective algorithms to simultaneously optimize the information ratio, information coefficient, and intra-fractile hit rate of a portfolio. These algorithms - the constrained fitness function, sequential algorithm, and parallel algorithm - take widely different approaches to combine these different portfolio metrics. The results show that the multi-objective algorithms do produce well-balanced portfolio performance, with the constrained fitness function performing much better than the sequential and parallel multi-objective algorithms. Moreover, this algorithm generalizes to the held-out test data set much better than any of the single fitness algorithms.

Keywords: genetic programming, multi-objective algorithm, equity market, stock selection, quantitative asset management

JEL Classification: C63

Suggested Citation

Becker, Ying L. and Fox, Harold and Fei, Peng, An Empirical Study of Multi-Objective Algorithms for Stock Ranking. Genetic Programming Theory and Practice, 2007, Available at SSRN: https://ssrn.com/abstract=996484

Ying L. Becker (Contact Author)

IBS ( email )

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Harold Fox

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
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Cambridge, MA 02139-4307
United States

Peng Fei

State Street Global Advisors ( email )

One Lincoln St.
Boston, MA 02111
United States
617-664-1219 (Phone)

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