Bloated Disclosures: Can ChatGPT Help Investors Process Information?
This addresses the problem of information overload for investors by demonstrating generative AI's value in summarizing disclosures, though it is incremental in applying existing AI tools to financial data.
The study investigated whether ChatGPT can help investors process complex corporate disclosures by summarizing them, finding that summaries are shorter, amplify sentiment, and better explain stock market reactions, while also proposing a measure of information bloat linked to adverse market outcomes.
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.