Portfolio Optimization: Maximizing Investor Satisfaction through Bayesian Belief Networks and ESG Factors

Abstract

In this thesis, we aim at discovering a portfolio structure that fulfills investor satisfaction in both financial and sustainability performance. To develop an integrated model, we employ Bayesian Belief Networks (BBNs) due to their capability to capture and visualize complex, non-linear relationships between variables. We select financial and ESG factors grounded in academic research and the methodologies developed by Morningstar and the London Stock Exchange Group, two leading institutions in investment research and management services. Our findings indicate that portfolios with a high emphasis on ESG factors show significant improvements in investor utility, particularly in high-volatility scenarios. Our study underscores the effectiveness of ESG integration into the portfolio optimization process, which not only maximizes investor utility but also aligns with the growing trend of sustainability-driven investments in financial markets. Key words: Bayesian Belief Networks, Investor satisfaction, Financial factors, ESG factors, Portfolio optimization