ESG factor investing using news text data

Abstract

ESG investment is getting popular as an investing method that evaluates environmental, social and governance (ESG) efforts of each company. However, although the disclosure of information on ESG is already common, it is difficult to objectively evaluate ESG efforts of companies due to the ambiguity in the definition of ESG words: environment, society, and governance. For this reason, we applied Word2Vec to extract similar words to each ESG word, and evaluated ESG efforts of companies from the viewpoint of these extracted words. As a result, we confirmed that the companies with higher ESG score can make a more profitable portfolio than those with lower ESG score, and this ESG score can be useful for factor investing.