Genetic algorithm for intelligent portfolio selection: A data-driven approach to asset allocation

This study examines the efficacy of Genetic Algorithms (GAs) in portfolio optimization by contrasting their results with those of more conventional techniques such as Mean-Variance Optimization (MVO) and Modern Portfolio Theory (MPT). The objective is to compare the ability of GAs to optimize risk-adjusted returns while ensuring diversification and flexibility in changing financial environments. A GA-based portfolio selection model is built and tested using ten years of actual financial data, employing selection, crossover, and mutation operations to attain maximum asset allocation. Its performance is evaluated against key financial metrics such as the Sharpe Ratio, portfolio return, and risk (standard deviation), in comparison with MPT, Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The results indicate that GAs consistently outperform traditional methods in risk-adjusted returns, yield higher Sharpe Ratios, promote better diversification, and result in lower portfolio volatility. Sensitivity analysis demonstrates that GAs are robust across different parameter values. The pragmatic implications of this study are relevant for portfolio managers and algorithmic traders seeking improved risk-return trade-offs. Additionally, the research contributes to the literature by highlighting the superiority of GAs over traditional approaches and provides directions for future research into hybrid AI-driven portfolio optimization techniques.