기업의 재무 정보를 활용한 ESG 등급 예측: 머신러닝과 설명 가능한 인공지능의 융합
ESG Rating Prediction Using Corporate Financial Data: Integration of Machine Learning and Explainable AI
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
최근 전 세계적으로 기업의 지속 가능한 경영이 중요한 이슈로 부상하면서 ESG 경영의 중요성이 강조되고 있다. 본 연구는 재무 데이터를 활용한 ESG 등급 예측 모형을 개발하여 기업들이 효율적이고 객관적으로 ESG 경영을 수행할 수 있도록 지원하고자 한다. 이를 위해 다양한 머신러닝 기법을 적용하여 한국 상장 기업의 재무 데이터를 바탕으로 ESG 등급을 예측하고, 그 성과를 비교 분석하였다. 연구 결과, XGBoost 모델이 가장 높은 예측 성능을 보였으며, 이를 통해 ESG 등급을 보다정확하게 예측할 수 있음을 확인하였다. 또한, SHAP 기법을 적용하여 ESG 등급에 영향을 미치는 주요 요인들을 분석함으로써 예측 결과의 해석 가능성을 높였다. 본 연구는 ESG 등급 예측에 있어 정량적 접근의 유용성을 입증하였으며, 기업들이 ESG 경영 전략을 보다 효과적으로 수립하는 데 기여할 수 있을 것으로 기대된다. 나아가, 이 연구는 투자자들에게도 신뢰성 있는 ESG 관련 정보를 제공함으로써 ESG 경영이 강화될 수 있는 중요한 기초 자료를 제공할 수 있다.
Abstract
As sustainable management has become a significant global issue, the importance of ESG (Environmental, Social, and Governance) management is increasingly emphasized. This study aims to support companies in efficiently and objectively implementing ESG management by developing an ESG rating prediction model based on financial data. Various machine learning techniques were applied to predict the ESG ratings of Korean listed companies using their financial data, and the performance of these models was comparatively analyzed. The results indicate that the XGBoost model exhibited the highest predictive performance, confirming its ability to accurately forecast ESG ratings. Additionally, by applying the SHAP method, the study analyzed the key factors influencing ESG ratings, thereby enhancing the interpretability of the prediction results. This study demonstrates the usefulness of a quantitative approach to ESG rating prediction and is expected to assist companies in more effectively formulating ESG management strategies. Furthermore, the research provides reliable ESG-related information to investors, offering a crucial foundation for strengthening ESG management.
참고문헌 (29)
- I-Y. Cho, "A study on corporate ESG environmental ratings and financial performance", Logos Management Review, 21(2), pp.269~290 (2023)
- C. LINWEI, "The impact of corporate ESG activities on corporate image : Focusing on process fairness perception and consumer knowledge", pp.2023
- H-J. Han, "The impact of CSR strategy on firm market value : Based on stakeholder theory and shareholder theory", Korean Journal of Marketing, 38(1), pp.49~74 (2023)
- K-H. Heo, "An analysis on the impact of intangible asset investment on the firm value of family businesses", Korea Journal of Business Administration, 55(3), pp.229~236 (2020)
- J. Martto, "ESG strategies in the oil and gas industry from the maritime & logistics perspective-opportunities & risks", Abu Dhabi International Petroleum Exhibition and Conference (2023)
- C. Gordon, "The implications of corporate social responsibility for investors: An overview and evaluation of the existing CSR literature", Available at SSRN 2481877, ~ (2014)
- H-B. Jung, "A case study of ESG practices in supply chain : Focusing on listed domestic companies", Journal of the Korean Production and Operations Management Society, 34(3), pp.309~333 (2023)
- P. Xiaoyan, "Artificial intelligence-driven multi-energy optimization : Promoting green transition of rural energy planning and sustainable energy economy", Sustainability, 16(10), pp.4111~ (2024)
- L. Peide, "ESG and financial performance : A qualitative comparative analysis in China's new energy companies", Journal of Cleaner Production, 379, pp.134721~ (2022)
- G-K. Bae, "The impact of ESG on brand trust and word of mouth in food and beverage companies: Focusing on jeju island tourists", Sustainability, 15(3), pp.2348~ (2023)
- S. Mauro, "Socially responsible investment strategies for the transition towards sustainable development : The importance of integrating and communicating ESG", The TQM Journal, 33(7), pp.39~56 (2021)
- K. Mariusz, "corporate governance(ESG)and reputational risk in integrated reports", Corporate Social Responsibility and Environmental Management, 28(4), pp.1270~1284 (2021)
- B-S. Kim, "Analysis of the impact of a company's past financial factors on company's ESG rating", Management & Information Systems Review, 41(2), pp.113~127 (2022)
- B-S. Kim, "An exploratory study on the lead-lag relationship between firms’ ESG efforts and their financial performance", Journal of the Korean Production and Operations Management Society, 27(4), pp.513~538 (2016)
- C-H. Hae, "The trend of studies using non-financial measures in the field of accounting and ESG issue", Korean Accounting Journal, 30(2), pp.235~276 (2021)
- G. Friede, "ESG and financial performance: Aggregated evidence from more than 2000 empirical studies", Journal of sustainable finance & investment, 5(4), pp.210~233 (2000)
- C. Gordon, "From the stockholder to the stakeholder: How sustainability can drive financial outperformance", Available at SSRN 2508281, ~ (2015)
- G. Spellman, "ESG matters", ISS EVA, 3~4 (2019)
- G-M. Kim, "The impact of ESG ratings on credit ratings, cost of debt, and firm value", Proceedings of the conference on Journal of Industrial Economics and Business, pp.11~19 (2021)
- G-H. Oh, "The effects of ethical management to firm value and financial performances in Korea", Journal of Knowledge Studies, 10(3), pp.3~27 (2012)
- J-E. Lee, "A study on relationship between corporate values and corporate governance, Social and Environmental Evaluation Index", pp.2013
- Z. Yang, "Probabilistic neural networks in bankruptcy prediction", Journal of Business Research, 44(2), pp.67~74 (1999)
- C. Arindam, "Fuzzy support vector machine for bankruptcy prediction", Applied Soft Computing, 11(2), pp.2472~2486 (2011)
- T-I. Kim, "A dynamic model for the prediction of ocean shipping company failure", pp.2014
- B. Flavio, "Machine learning models and bankruptcy prediction", Expert Systems with Applications, 83, pp.405~417 (2017)
- S-W. Lee, "Corporate credit rating prediction model using key financial variables and their growth rates", pp.2016
- S-J. Kim, "Application of random forests to corporate credit rating prediction", Journal of Industrial Innovation, 32(1), pp.187~211 (2016)
- W. Oyindamola, "Towards human-centred explainable ai: A systematic literature review", Master, pp.2021