An Empirical Study of Multi-Objective Algorithms for Stock Ranking

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.