Do past ESG scores efficiently predict future ESG performance?

Dilvin Taskin, Görkem Sariyer, Ece Acar and Efe Caglar Cagli

Research in International Business and Finance, 2025, vol. 74, issue C

Abstract: Given the effects of Environmental, Social, and Governance (ESG) scores on financial performance and stock returns, the prediction of future ESG scores is highly crucial. ESG scores are calculated using an enormous number of variables related to the sustainability practices of firms; thus, it is impractical for investors to come up with predictions of ESG performance. This paper aims to fill this gap by using only the past score-based and rating-based ESG performance as the determinant of future ESG performance using four machine learning-based algorithms; decision tree (DT), random-forest (RF), k-nearest neighbor (KNN), and logistic regression (LR). The proposed model is validated in BIST sustainability index companies. The results suggest that past ESG grade-based and numerical scores can be used as a determinant of future ESG performance. The results prove that a simple indicator could serve to predict future ESG scores rather than complex data alternatives. Using data from BIST sustainability index companies in Turkey, the findings demonstrate that past ESG grades and scores are reliable predictors of future ESG performance, offering a simple yet effective alternative to complex data-driven methods. This study not only contributes to advancing sustainable finance practices but also provides practical tools for emerging markets like Turkey to align corporate strategies with global sustainability standards. The methodological contributions also have broader relevance for international financial markets.

Keywords: ESG score prediction; Machine learning algorithms; Decision tree; Random forest; K-nearest neighbor; Logistic regression (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0275531924004999
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:74:y:2025:i:c:s0275531924004999

DOI: 10.1016/j.ribaf.2024.102706

Access Statistics for this article

Research in International Business and Finance is currently edited by T. Lagoarde Segot

More articles in Research in International Business and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().