🤖 AI Summary
This study investigates how generative AI (e.g., ChatGPT) mitigates the adverse impact of corporate “information clutter”—excessively lengthy, low signal-to-noise textual disclosures—on capital market efficiency. To address disclosure-induced information asymmetry and impaired price discovery, the authors first construct a novel, quantifiable measure of information clutter and propose an unconstrained generative summarization framework: ChatGPT generates performance-differentiated summaries (financial vs. non-financial), integrated with sentiment analysis and event-study methodology, and embedded within an information-efficiency econometric model. Empirical results show that AI-generated summaries significantly reduce text length, increase information density and sentiment polarity strength, enhance market sensitivity and explanatory power regarding disclosure events, and improve stock return synchronicity and pricing efficiency. The core contribution is the first empirical quantification of the economic cost of disclosure redundancy and rigorous validation of generative AI’s measurable value as an information intermediary.
📝 Abstract
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.
