Paper: SSRN 3508497

Abstract

This paper conducts a textual analysis of earnings call transcripts to quantify climate risk exposure at the firm level. We construct dictionaries that measure

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The research focuses on textual analysis and dictionary construction with relatively basic statistical measures, placing it in low-to-moderate math complexity. However, the use of earnings call transcripts, firm-level quantification, and likely implementation of text mining tools suggests a data-heavy, backtest-ready approach suited for practical trading or risk management.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Quantify firm-level climate risk"] --> B["Data Source<br>Earnings Call Transcripts"]
  B --> C["Methodology<br>Textual Analysis & Dictionary Construction"]
  C --> D["Computational Process<br>Measure Risk Exposure Scores"]
  D --> E{"Key Outcomes"}
  E --> F["Climate Risk Quantified<br>at Firm Level"]
  E --> G["Discriminates between<br>Physical & Transition Risks"]