From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI

Alex Kim, Maximilian Muhn, Valeri Nikolaev
10/26/2023
econ.GNcs.AIcs.CL

No confident code implementation found for this paper.

Abstract

We explore the value of generative AI tools, such as ChatGPT, in helping investors uncover dimensions of corporate risk. We develop and validate firm-level measures of risk exposure to political, climate, and AI-related risks. Using the GPT 3.5 model to generate risk summaries and assessments from the context provided by earnings call transcripts, we show that GPT-based measures possess significant information content and outperform the existing risk measures in predicting (abnormal) firm-level volatility and firms' choices such as investment and innovation. Importantly, information in risk assessments dominates that in risk summaries, establishing the value of general AI knowledge. We also find that generative AI is effective at detecting emerging risks, such as AI risk, which has soared in recent quarters. Our measures perform well both within and outside the GPT's training window and are priced in equity markets. Taken together, an AI-based approach to risk measurement provides useful insights to users of corporate disclosures at a low cost.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators

84% match2023

Bloated Disclosures: Can ChatGPT Help Investors Process Information?

81% match2023

Anticipatory Thinking Challenges in Open Worlds: Risk Management

81% match2023

AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies

80% match2024

Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modeling

79% match2025

The Risk-Adjusted Intelligence Dividend: A Quantitative Framework for Measuring AI Return on Investment Integrating ISO 42001 and Regulatory Exposure

79% match2025

Cite this paper

@article{kim2023from,
  title  = {From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI},
  author = {Alex Kim and Maximilian Muhn and Valeri Nikolaev},
  year   = {2023},
  eprint = {2310.17721},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.17721v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.17721.svg)](https://codeofpaper.com/paper/2310.17721)

Discussion