Bloated Disclosures: Can ChatGPT Help Investors Process Information?
Not on the map. Why?
Abstract (arXiv)
Generative AI tools such as ChatGPT can fundamentally change the way investors process information. We probe the economic usefulness of these tools in summarizing complex corporate disclosures using the stock market as a laboratory. The unconstrained summaries are remarkably shorter compared to the originals, whereas their information content is amplified. When a document has a positive (negative) sentiment, its summary becomes more positive (negative). Importantly, the summaries are more effective at explaining stock market reactions to the disclosed information. Motivated by these findings, we propose a measure of information ``bloat." We show that bloated disclosure is associated with adverse capital market consequences, such as lower price efficiency and higher information asymmetry. Finally, we show that the model is effective at constructing targeted summaries that identify firms' (non-)financial performance. Collectively, our results indicate that generative AI adds considerable value for investors with information processing constraints.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 5 of its 49 references that are in the map. First posted on arXiv 17 Jun 2023.
Builds on
5 of 49 references.
References · 5
- 2017Attention Is All You Needcited by 60,361
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
- 2022News Summarization and Evaluation in the Era of GPT-3cited by 327
- 2023Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarizationcited by 122
Built on by
Citing papers per year: 2023: 2; 2024: 5; 2025: 2.
9 papers.
- 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 90
- 2024Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selectioncited by 29
- 2023ESGReveal: An LLM-based approach for extracting structured data from ESG reportscited by 15
- 2024Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformercited by 6
- 2024A Scoping Review of ChatGPT Research in Accounting and Financecited by 6
- 2025Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysiscited by 6
- 2025Bridging Language Models and Financial Analysiscited by 4
- 2023From Transcripts to Insights: Uncovering Corporate Risks Using Generative AIcited by 3
- 2024Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigmscited by 1
- 2025Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis5 Jun 2025
- 2025Bridging Language Models and Financial Analysis14 Mar 2025
- 2024A Scoping Review of ChatGPT Research in Accounting and Finance7 Dec 2024
- 2024Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms19 Oct 2024
- 2024Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer30 Aug 2024
- 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges15 Jun 2024
- 2024Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection8 Jan 2024
- 2023ESGReveal: An LLM-based approach for extracting structured data from ESG reports25 Dec 2023
- 2023From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI26 Oct 2023