Investment stock portfolio with multi-stage genetic algorithm optimization

Portfolio optimization problem decides the percentage of the overall portfolio value allocated to each portfolio component with specified risk-retum characteristics. A multi-stage stochastic optimization manages portfolio in constantly changing financial markets by periodically rebalancing the asset portfolio to achieve return maximization and/or risk minimization. This paper presents a decision-making process that incorporates Genetic Algorithms into multi-stage portfolio optimization system. The objective function is to maximize one's economic utility or end-of-period wealth. The performance of our system is demonstrated by optimizing the allocation of cash and various stocks in Shenzhen market of China. Experiments are conducted to compare performance of the portfolios optimized by different objective functions in terms of expected return and standard derivation.