Enhancing Business Sustainability through Stock Analysis: A Machine Learning Approach

Abstract

This study explores the integration of machine learning methodologies in stock analysis to enhance the understanding of the relationship between sustainable business practices and financial performance. Against the backdrop of a shifting investment landscape that emphasizes responsible and informed decision-making, our research addresses the need for innovative approaches in evaluating stocks within a sustainability framework. Leveraging a combination of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and linear regression, we systematically analyze a dataset comprising sustainability metrics and stock performance. The DBSCAN clustering identifies distinct groups of stocks based on sustainability profiles, offering novel insights into market segmentation. Concurrently, linear regression models quantitatively reveal the impact of sustainability metrics on stock outcomes. The results affirm the significance of sustainability considerations in investment decisions, presenting a compelling case for the adoption of machine learning techniques in responsible investing strategies.