Portfolio Strategy Optimizing Model for Risk Management Utilizing Evolutionary Computation

SUMMARY This paper proposes a new optimizing system for stock portfolios that uses evolutionary computation techniques to derive a highly suitable combination and investment ratio of brands as well as an appropriate trading-strategy tree. Accurately predicting price trends in the stock market is a difficult task to achieve with the result that investors often suffer great losses. Because stock portfolios are thought to be a valid means of avoiding such risks in terms of financial engineering, they have the effect of reducing risk by diversifying investment into several different brands. Based on this, it was attempted to determine an optimal combination of brands that constitute a portfolio and to derive the investment ratio using a multiobjective genetic algorithm, and also to optimize a trading strategy tree using genetic programming. When a performance evaluation was carried out, the system was found to generally obtain the operative results by making it possible to obtain stable profits using a combination of low risk brands. The system was also able to realize low risk investments in all test periods.