Portfolio Optimization Using Evolutionary Algorithms

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsPortfolio optimization is a widely studied field in modern finance. It involves finding the optimal balance between two contradictory objectives, the risk and the return. As the number of assets rises, the complexity in portfolios increases considerably, making it a computational challenge. This report explores the application of the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and Genetic Algorithm (GA) in the field of portfolio optimization. MOEA/D and GA have proven to be effective at finding portfolios. However, it remains unclear how they perform when compared to traditional approaches used in finance. To achieve this, a framework for portfolio optimization is proposed, using MOEA/D, and GA separately as optimization algorithms and Capital Asset Pricing Model (CAPM) and Mean-Variance Model as methods to evaluate portfolios. The proposed framework is able to produce weighted portfolios successfully. These generated portfolios were evaluated using a simulation with subsequent (unseen) prices of the assets included in the portfolio. The simulation was compared with well known portfolios in the same market and other market benchmarks (Security Market Line and Market Portfolio). The results obtained in this investigation exceeded expectation by creating portfolios that perform better than the market. CAPM and Mean-Variance Model, although they fail to model all the variables that affect the stock market, provide a simple valuation for assets and portfolios. MOEA/D using Differential Evolution operators and the CAPM model produced the best portfolios in this research. Work can still be done to accommodate more variables that can affect markets and portfolios, such as taxes, investment horizon and costs for transactions