An Effective Decision-Based Genetic Algorithm Approach to Multiobjective Portfolio Optimization Problem

Multiobjective portfolio optimization problem is the portfolio process of the highest expected return among the various financial commodities of the capital market to meet the expected return objectives. And one of the most important and common management issues lies in determining the best portfolio out of a given set of investment proposals. As we know, modern portfolio theory provides a well-developed paradigm to form a portfolio with the highest expected return for a given level of risk tolerance. However, for making the profit via the limited available capital, allocating the money to construct a portfolio is a challenge to be dealt with. In the capital market, there are thousands of financial commodities. Depending on the characteristics of the commodity, the risk and return of the investment are dissimilar. And the risk and return should also be simultaneously considered in practice of stock market. Hence, portfolio optimization is a complex multiobjective problem of multistage decision-based. In this paper, the multistage decision-based genetic algorithm is proposed for the multiobjective portfolio optimization problem. On the basis of the illustrative example, we can show the effectiveness of the proposed algorithm is validated for solving this problem.