Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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Investors varywith respect to their expected return and aversion to associated risk, and hence also vary in their performance expectations of the stock market portfolios they hold. In this work we present an empirical study of the use of the Multiobjective genetic programming (MOGP) technique for a real world problem of portfolio optimisation on UK FTSE-100 stocks. The MOGP evolves a nonlinear factor model of technical factors for asset ranking, and provides visual insight into the risk return trade-off involved in discovering an approximation to the risk-return efficient frontier of a portfolio optimization problem. We provide preliminary analysis of the set of factors chosen in the evolved solutions. We find evidence for the effect on risk-adjusted returns of firm size, return on equity and cash yield (and little or no evidence for the book to market ratio).