Use of Genetic Algorithms for Optimal Investment Strategies
In this study, a genetic algorithm is used in the development of investment strategies that decide the optimum asset allocations which back up a portfolio of term insurance contracts and the re-balancing strategy that responds to the changing financial markets, such as change in interest rates and mortality experience. The objective function used allows us to accommodate three objectives that should be of interest to the management in insurance companies. The three objectives under consideration are, maximizing the total value of wealth at the end of the period, minimizing the variance of the total value of the wealth across the simulated interest rate scenarios and achieving consistent return on the portfolio from year to year. Experiments are conducted to compare the performance of the investment strategy proposed by the genetic algorithm to the standard duration matching strategy in terms of the different objectives under different situations.