JSAI2020
Jun 9 - Jun 12, 2020Virtual Meetings
JSAI2020
Jun 9 - Jun 12, 2020Virtual Meetings
[1D3-GS-13-02]ESG factor investing of news text data using Word2Vec
〇Shogo Akiyama1, Junichi Eguchi2, Tomoya Suzuki1,2(1. Graduate School of Ibaraki University, 2. Daiwa Asset Management)
Keywords:
Finance,Text Mining,Reinforcement Learning
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 companiesdue 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.