Quantifying Financial Disparities in Corporate Sustainability: Predictive Modeling using Support Vector Machines and Regression Analysis – American Journal of Student Research

American Journal of Student Research

Quantifying Financial Disparities in Corporate Sustainability: Predictive Modeling using Support Vector Machines and Regression Analysis

Publication Date : Apr-03-2026

DOI: 10.70251/HYJR2348.42296306


Author(s) :

Aarush Arun.


Volume/Issue :
Volume 4
,
Issue 2
(Apr - 2026)



Abstract :

As the world becomes increasingly aware of climate change and recognizes the need for environmental, social, and governance (ESG) consciousness, it has become necessary to balance economic and private sector growth with ecological sustainability. Hence, many efforts have been made to categorize businesses by their sustainability performance, aiming to incentivize sustainable corporate practices. Yet, despite this, it is actively debated whether being a sustainable company is beneficial to companies in terms of profit and economic growth. This paper examines the structural differences and associations between being a corporate sustainability leader and key financial metrics, including stock performance, profit, and growth. This study utilizes the Corporate Knights Global 100, a ranking of the world’s 100 most sustainable public companies with revenue exceeding 1 billion USD. By employing rigorous nonparametric statistical testing, subsequent False Discovery Rate corrections, and predictive modeling using Support Vector Machines, this study compares these highly ranked firms against their unranked peers. Despite potentially strengthening a company’s brand image by striving to be at the forefront of corporate sustainability, findings suggest that ranked companies are generally associated with inferior financial performance compared to their unranked peers, raising questions about the potential trade-offs of ESG leadership.