Computational Economics

, 28:1

Improving Portfolio Efficiency: A Genetic Algorithm Approach

Article

DOI: 10.1007/s10614-006-9021-y

Cite this article as:
Yang, X. Comput Econ (2006) 28: 1. doi:10.1007/s10614-006-9021-y

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.

Keywords

genetic algorithmestimation riskmean-variance analysisBayesian approach

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.The School of BusinessHumboldt State UniversityHumboldtUSA