Authors - Kapil Mohan, Ritu Chauhan, Harleen Kaur Abstract - ESG (Environment, Social and Governance) rating in today’s financial world is becoming a good indicator for investors in decision making and risk analysis. There has been stress on E and S in the recent past as Governments and Regulators stress these parameters and benefits to those who are working towards this improvement rating. The rating is a clear indicator of sustainability and promising business and thus is gaining popularity. The analytics firms have combined this indicator and have come up with this calculation using certain scientific and mathematical models from the published data and/or requested data that are provided exclusively to do this calculation for the indicator. These ratings are published annually by analytics firms like Sustain analytics and Bloomberg ESG data service for global but limited firms. This study’s focus is to fit financial data of firms on machine learning models and predict ESG rating with changing market fundamentals and firm’s business value indicators. The result can be com-pared to passed ratings, category averages, deviation and outliers which can benefit venture capitalists and investors to refine their investment strategies. The re-search also captures and compares this output and suggests the approach that best suits this problem by building an architecture that can update the model and can predict live data from the market.
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