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.
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I thank for Russell Cooper, David Kendrick, Douglas Dacy for their helpful comments.
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Yang, X. Improving Portfolio Efficiency: A Genetic Algorithm Approach. Comput Econ 28, 1–14 (2006). https://doi.org/10.1007/s10614-006-9021-y
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DOI: https://doi.org/10.1007/s10614-006-9021-y