A Genetic Algorithm Approach for Portfolio Optimization

For many investors, investing in the capital market can be quite challenging. It involves the task of accurately selecting and allocating funds to different shares in order to create an optimal portfolio. The problem of determining the ideal combination and proportion of shares within the portfolio can be addressed through the utilization of a genetic algorithm method approach. Portfolio optimization is the process of strategically selecting the most advantageous combination of investments that offers the highest potential return for a given level of risk undertaken by investors an optimal portfolio is one that maximizes the Sharpe ratio, which measures the potential return an investment can yield in relation to the risk assumed by investors. This study explores the application of genetic algorithms, a class of optimization algorithms, to enhance portfolio optimization. Genetic algorithms offer a powerful approach to determining the most favorable investment portfolios based on various criteria, including profit maximization, risk reduction, and management of asset correlations. The research critically highlights the distinct advantages of utilizing genetic algorithms over conventional techniques for portfolio optimization. Moreover, it introduces a comprehensive method to assess the effectiveness of genetic algorithms in portfolio optimization.