A Study of Approach for Investors to Identify Risks and Opportunities Based On Machine Learning Algorithm

Abstract

ESG wizard is an important tool in the evolving Fintech space. With the growing number of investors investing in assets based on overall aspects of the organization, our model ESG wiz would help users with their investment strategies to research and make investments based on Environmental, Social and Governance aspects of the Organizations. GDELT APIs facilitate the retrieval of ESG data by giving us the ability to view this data with multiple perspectives. Next, a well-designed Machine learning model today gives users access at various levels, allows them to ask many questions, rapidly provides accurate and meaningful answers, and enables active delivery of information. The different components of the Machine learning model are- reading the data, training the models, testing the models with the data, performing analysis by selecting the best model, and testing for accuracy. This paper also reflects on various studies done by Harvard and in several conferences and white papers. This suggests higher ESG performance greater the financial potential in the organizations. Lot of studies in various findings suggests a positive link between ESG and the financial performance of an organization. In this paper, there has been a discussion of the performance of every active aspect which creates complex tasks easily by taking less time. This is the primary task of machine learning. Machine learning is a subfield of AI that works in the development of the model, mainly the statistical models that give us some predictive outcomes. We have proposed a way to study the ESG performance of the organizations based on various data points available and also help investors to identify their competitors.

Keywords: ESG, Machine Learning, Algorithms, Prediction, Supervised, reinforcement, model, AI, network, training, investment, uncertainty, data, tree, decision, labeled, profit, opportunity, possibility, accuracy, risk