Discovering variation financial performance of ESG scoring through big data analytics

Abstract

The newest innovation in the world of corporate reporting is environmental disclosure. In actuality, it is a technique that may more accurately depict a company’s ability to produce financial performance over time. Therefore, utilising the moderating influence of social and ethical behaviours, this article examines whether environmental disclosure (ED) done by companies listed on the ESG index affects their financial performance (FP). Both possibilities and problems for financial innovation are brought by the growth of big data (BD). In many respects, the development of BD has given comfort to individuals and civilizations. Additionally, there is a dearth of empirical research examining the impact of the BD on financial performance and market value. This suggests that although a weak environmental disclosure lowers financial performance, a good one raises it. Additionally, the study contends that social and ethical behaviours have a moderating influence on the relationship between environmental disclosure and the firm’s financial performance. The recently established double machine learning framework may handle interesting causal concerns, whereas the naïve use of machine learning typically fails in this situation. The most recent findings for categorising attitudes using classical classifiers like Naive Bayes and transfer learning models like CNN surpass the Naive Bayes and model classifiers, according to extensive research and tests.