Investment portfolio through evolutionary algorithm

Abstract

This dissertation investigates the application of evolutionary algorithms—specifically the Genetic Algorithm (GA) and Simulated Annealing (SA)—for portfolio optimisation within the S&P 500, addressing the limitations of traditional models. The methodology uses Gower’s distance to handle mixed numerical and categorical data, allowing for the construction of factor-aligned portfolios based on Growth, Value, and Quality dimensions. The primary optimisation objective is to maximise the Sortino Ratio, focusing on downside-risk-adjusted returns. The methodology employs composite feature engineering to rank stocks across Growth, Value, and Quality dimensions, followed by distance-based clustering and anomaly detection to reveal market structures. GA and AGA are then applied with objective functions designed to maximise the Sortino Ratio, which emphasises downside-risk-adjusted returns. Hyperparameters such as population size, mutation rate, crossover probability, and annealing temperature schedules are tuned to balance exploration and exploitation. Empirical evaluation demonstrates that both GA and AGA generate highly diversified portfolios with competitive performance. The GA-optimised maximum Sortino portfolio achieved an annualised return of 18.95%, a Sortino Ratio of 1.0999, and a beta of 0.98, indicating strong returns with reduced downside risk relative to the market. Comparative analysis reveals that AGA converges faster and achieves marginally superior downside protection, validating its advantage in complex search landscapes. Complementary tools such as similarity maps, hierarchical clustering, and a diversification recommender further enhance interpretability and practical applicability. The results underscore the potential of evolutionary algorithms to construct robust, risk-aware investment portfolios that go beyond linear optimisation frameworks. By combining factor-based insights with evolutionary optimisation, this work contributes to the growing literature on computational finance and demonstrates actionable applications for institutional and retail portfolio managers.