Earticle

초록

영어

With the advent of the coronavirus 19 era, the importance of ESG in investment is increasingly emphasized. Moreover, with the increasing trend of ESG investment, there exists a tendency to analyze it through AI models. As the existing research about ESG could not explain the relationship between ESG and profit, our research aims to derive the correlation between them via statistical models. We got the dataset from the kaggle website, which involved numerous financial variables and ESG features. Multitudinous models from machine learning and deep learning were utilized for the prediction and the LGBM yielded 0.07 of MAE score, which was the lowest. Moreover, SHAP algorithm and R-squared score were applied in pursuit of better prediction. ESG variable was the third highest feature among variables in the given dataset and R-squared score released 0.99. Both methods proved the high positive relationship between the ESG variable and the net profit margin ratio. Though our research involved a critical limitation that we could not employ various formulas due to the restricted dataset, our research is worthwhile as we successfully derived the positive correlation more accurately compared to previous research.

목차

Abstract
Introduction
Background
Methods
Data Description
Result
Discussion
Principal Finding
Limitation
Conclusion
Reference

키워드

저자

• 최하영 [ 한양대학교 공과대학 정보시스템학과 ]
• 전민종 [ 한양대학교 공과대학 정보시스템학과 ]
• 주한선 [ 가톨릭대학교 인문대학 철학과 ]
• 이욱 [ 한양대학교 공과대학 정보시스템학과 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658