Improving Portfolio Efficiency: A Genetic Algorithm Approach

Authors:
Xiaolou Yang
Affiliations:
The School of Business, Humboldt State University, Humboldt, USA
Venue:
Computational Economics
Year:
2006

Citing 6
Cited 6

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Abstract

In this paper, I present a decision-making process that incorporates a Genetic Algorithm (GA) into a state dependent dynamic portfolio optimization system. A GA is a probabilistic search approach and thus can serve as a stochastic problem solving technique. A Genetic Algorithm solves the model by forward-looking and backward-induction, which incorporates both historical information and future uncertainty when estimating the asset returns. It significantly improves the accuracy of expected return estimation and thus improves the overall portfolio efficiency over the classical mean-variance method. In addition a GA could handle a large variety of future uncertainties, which overcome the computational difficulties in the traditional Bayesian approach.