JOURNAL ARTICLE

ESG-Driven Corporate Clustering and Stock Market Efficiency.

  • Published In: Journal of Financial Data Science, 2026, v. 8, n. 1. P. 99 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Choi, Jaehyung; Kim, Young Shin; Kim, Hyangju 3 of 3

Abstract

We test the return predictability of environmental, social, and governance (ESG) scores by adopting the k-means clustering algorithm in ranking portfolio construction. The performance of the ESG score–based long–short portfolios indicates that, within the S&P 500 universe, firms with lower ESG scores outperform those with higher ESG scores. Moreover, the use of the machine learning–based clustering approach enhances the performance of these zero-cost portfolios compared with the traditional ranking method that relies on simply ordered and equally sized ranking buckets. Factor analysis further supports the robustness of the ESG-based portfolio outperformance, even after controlling for risk factor exposures. In addition, the factor analysis suggests the presence of potential underlying drivers contributing to this outperformance. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Financial Data Science. 2026/01, Vol. 8, Issue 1, p99
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2026
  • ISSN:2640-3943
  • DOI:10.3905/jfds.2025.1.206
  • Accession Number:191616208
  • Copyright Statement:Copyright of Journal of Financial Data Science is the property of With Intelligence Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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