An Efficient Predicative Approach of ESG Invest Scoring Using Random Forest Algorithm

Dingwen Si

doi:10.54691/bcpbm.v45i.4950

Authors

Dingwen Si

DOI:

https://doi.org/10.54691/bcpbm.v45i.4950

Keywords:

Machine learning, ESG core Prediction, Random Forest, Data analysis, Firm performance.

Abstract

Environmental, social, and governance (ESG) factors are considered while making business and investment choices. Human capital and climate change are causing firms to re-evaluate their focus away from conventional financial gains. Investors are drawn to socially responsible investments due to a shift in global attitudes toward sustainability and the availability of environmental, social, and governance (ESG) indicators. The strategic value of ESG measures has been researched extensively for private organisations, but less attention has been paid to public corporations. The use of quantitative methodologies for improving and standardising ESG grading, as well as for building ESG portfolios, is neglected, despite the fact that ESG-driven portfolios currently represent a significant and rising share of global assets under management. Deep learning is used to develop an ESG investment score prediction algorithm in this article. The ESG score is analysed and predicted using a random forest learning algorithm in the suggested system.

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