Assessing the impact of Machine Learning Algorithms on Portfolio Optimization and Risk Management in the Era of Sustainable Investing

Abstract

The contemporary financial landscape is rapidly evolving, with sustainable investing emerging as a pivotal dimension. Against this backdrop, this research aimed to assess the impact of machine learning algorithms on portfolio optimization and risk management in the era of sustainable investing. Utilizing data from 90 FTSE100 companies that possess an ESG score, multiple machine learning models were employed to understand their efficacy. The Lasso regression model emerged as the top performer, underscoring the potential of machine learning in this domain. A conspicuous gap was identified in the literature, highlighting a limited implementation of machine learning techniques in the financial sector despite their proven capabilities. In an era characterized by the proliferation of big data, the rise of sustainable investing, and unparalleled computational power, relying solely on traditional methods becomes impractical. As manual calculations become increasingly untenable, the seamless integration of machine learning becomes imperative. However, while the preliminary findings are promising, further studies are essential to navigate potential pitfalls and to refine the application of these algorithms in real-world financial settings.