ESG 등급의 시퀀스 특성이 기업의 재무적 성과에 미치는 영향: OMA와 CatBoost 기법을 중심으로
The Impact of ESG Rating Sequences on Corporate Financial Performance: An Analysis Using OMA and CatBoost Algorithm
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초록

Most prior studies investigating the relationship between Environmental, Social, and Governance (ESG) performance and financial outcomes have concentrated primarily on short-term effects. However, stakeholders may evaluate the authenticity and sustainability of a firm’s ESG management based on the long-term trajectory of its performance. This study examines whether sequence patterns of ESG performance are significantly associated with variations in firms' Return on Equity (ROE). Utilizing data from Korean firms spanning 2019 to 2023, we constructed sequences of both overall and dimension-specific ESG ratings. We then applied Optimal Matching Analysis (OMA) to classify ESG performance into three distinct patterns: upward, stable, and downward. Subsequently, we investigated the relationship between these sequence patterns and ROE growth rates using a CatBoost classification model combined with SHapley Additive exPlanations (SHAP) analysis. The results indicate that firms with consistently improving ESG ratings exhibit higher ROE growth compared to those with stable or declining ratings. Among the three ESG dimensions, the sequence patterns of the environmental (E) dimension emerged as the most influential determinant. These findings suggest that not only the absolute level of ESG performance but also its long-term trajectory has a significant impact on financial outcomes.

키워드

ESG재무성과시퀀스머신러닝SHAP분석ESGFinancial PerformanceSequenceMachine LearningSHAP Analysis
제목
ESG 등급의 시퀀스 특성이 기업의 재무적 성과에 미치는 영향: OMA와 CatBoost 기법을 중심으로
제목 (타언어)
The Impact of ESG Rating Sequences on Corporate Financial Performance: An Analysis Using OMA and CatBoost Algorithm
저자
오소욱정동일
DOI
10.17287/kmr.2025.54.6.1803
발행일
2025-12
유형
Y
저널명
경영학연구
54
6
페이지
1803 ~ 1824