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Evaluating climate-risk language in 10-K filings
: a ClimateBERT-driven study of firm valuation

  • Anais Weghorst (Student)

Student thesis: Master's Thesis

Abstract

This thesis presents a transformer-based approach to quantify and price corporate climate-risk disclosures. By utilizing ClimateBERT, I classify Item 1A risk-factor text in S&P 500 10-K filings (200532024) to generate annual transition, physical, and general climate-risk scores for 8,001 firm-year observations. Fixed-effects regressions link these lagged scores to Tobin’s Q, revealing a significant negative valuation effect for transition-risk language, especially in high-exposure sectors (Utilities; Transportation & Warehousing), while physical-risk impacts primarily arise within the same industries. By combining advanced NLP with rigorous panel econometrics, this study provides detailed, sector-sensitive metrics that illuminate how investors value different aspects of corporate climate-risk disclosure.
Date of Award8 Jul 2025
Original languageEnglish
Awarding Institution
  • Universidade Católica Portuguesa
SupervisorJulien Fouquau (Supervisor)

UN SDGs

This student thesis contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth
  2. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production
  3. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Climate risk
  • NLP
  • ClimateBERT
  • 10-K
  • S&P 500
  • Firm valuation

Designation

  • Mestrado em Finanças (mestrado internacional)

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