How can media attention reveal ESG improvement opportunities? A multi-algorithm machine learning-based approach for Taiwan’s electronics industry

Shu Ling Lin, Yu Rou Lin and Xiao Jin

The North American Journal of Economics and Finance, 2025, vol. 78, issue C

Abstract: The wave of discussions on ESG (environment, social, governance) issues widely suggest that ESG goals can benefit companies and provide corresponding advantages to investors. However, few studies consider the actual value that ESG performance can deliver, leading to overly high expectations regarding ESG investments (Cornell & Damodaran, 2020). Companies with high ESG expectations may overinvest in such initiatives. To counteract the potential biases this could introduce, ESG ratings agencies might discreetly adjust their weighting methods to ensure more accurate assessments. Owning to differing focal points among stakeholders, ESG scores lack persuasive reform suggestions for corporations to improve ESG actions, reducing corporate enthusiasm and confidence in ESG resource allocation. This study employs the Refinitiv news database and multi-algorithm machine learning methods to target the ESG scores of Taiwan-listed companies in the electronics industry. Neural networks (NN), support vector machine (SVM) learning, and random forest algorithms are used to construct a multi-algorithm machine learning-based approach to explore the predictive ability of media attention.

Keywords: RM-ESG; ESG comprehensive scores; Media attention; Machine learning; Integrating stacked generalization models (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:78:y:2025:i:c:s1062940825000713

DOI: 10.1016/j.najef.2025.102431

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