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AI-derived ESG narrative voice and the conditional evaluation of corporate governance

Author

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  • Chang, Yen Wen
  • Hussain, Hafezali Iqbal
  • Al-Jaifi, Hamdan

Abstract

This study investigates the associations between AI-derived narrative tone from Sustainability Reporting (SR) and structural Corporate Governance (CG) parameters in firm valuation. This study develops an automated SR Sentiment (SRS) score and compares it with Bloomberg’s CG Score (CGScore) to evaluate narrative intensity. Regressions with Driscoll-Kraay standard errors indicate that SRS and CGScore are positively associated with environmental performance (EScore), with SRS showing a larger estimated coefficient. While CGScore indicates a negative conditional coefficient, SRS remains positively associated with firm performance in joint valuation models (Tobin’s Q and MBV). These results reveal that the sample’s structural governance indicators and the degree of narrative disclosure are evaluated differently. An efficient structure for investigating ESG-related reporting patterns in developing capital markets with various types of institutional contents in the ASEAN region.

Suggested Citation

  • Chang, Yen Wen & Hussain, Hafezali Iqbal & Al-Jaifi, Hamdan, 2026. "AI-derived ESG narrative voice and the conditional evaluation of corporate governance," Finance Research Letters, Elsevier, vol. 102(C).
    • Handle: RePEc:eee:finlet:v:102:y:2026:i:c:s1544612326005465
    DOI: 10.1016/j.frl.2026.110017
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