ESG Scoring System Construction: Portfolio Investment Based on Machine Learning

Abstract

The majority of existing ESG rating systems in the Chinese market are based on categorical classification ratings, and as a result of the voluntary disclosure system, rating data provided by rating organizations is occasionally absent or delayed. This article employs natural language processing (NLP) to extract keywords such as green, clean, renewable, poverty alleviation, and moral from the financial reports of CSI 300 constituent companies, and then counts their corresponding frequencies in order to construct percentage ESG ratings that address the discontinuity, imprecision, and time lag inherent in the original ratings. This article employs a self-normalized neural network (SNN) to develop a multi factor model based on the suggested ESG ratings and then conducts sector neutral hierarchical back-testing to compare the proposed rating to the traditional ratings. The results indicate that the model generated using the ESG ratings developed in this research yields a higher rate of return than the model built using traditional ESG ratings, and the model constructed without an ESG factor. This may be because deriving ESG ratings directly from financial statements eliminates the risk of corporate falsification or whitewashing of accounts. This work adds to the body of knowledge by proposing a novel approach to constructing an ESG scoring system and incorporating it into portfolio investments to maximize returns.