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Abstract

Corporate ethics, sustainability, and social responsibility are essential to the future of finance. Businesses are releasing more information on their environmental, social, and governance (ESG) performance to stay competitive. In general, there is a negative correlation between volatility and higher ESG ratings as well as value and profitability. Investors are irritated by the lack of standardized ESG data, albeit as a result of the quick expansion of ESG investments, which has increased the amount of ESG data and rating providers. There are several measures and parameters offered by ESG rating providers, which can cause issuers to become exhausted from reporting on sustainability concerns. Conversely, the complexity of various ESG measures makes it difficult to make direct comparisons between businesses, especially when comparing them in different sectors. Emerging artificial intelligence capabilities can close the ESG disclosure gap and offer new perspectives on the ESG data that is currently available, which could help address these issues. The objective of this project was to automatically extract and analyze company ESG initiatives and keywords from ESG reports, as well as news and articles from other parties, using the Tunicate Swarm Optimized Dynamic Support Vector Machine (TSO-DSVM). With this method, an internal ESG score that is more accurate and understandable could be generated. The relationship between this internal ESG score and market risk in inclusive ESG investing portfolios will be evaluated by integrating it into the study. The proposed method has performed well than the existing methods with an F1-score of 89.44%, recall of 88.65%, precision of 90.75%, and an accuracy of 92.54%. The findings suggest that decreased value at risk is favorably associated with an increased internal ESG score, which improves risk management tactics for investors in the ESG sector.

Keywords

risk management ESG investment portfolios financial markets Tunicate Swarm Optimized Dynamic Support Vector Machine (TSO-DSVM)

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Enhancing risk management in inclusive ESG investment portfolios in financial markets through big data analysis

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