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Bloated Disclosures: Can ChatGPT Help Investors Process Information?

Alex Kim, Maximilian Muhn and Valeri Nikolaev

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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.

Date: 2023-06, Revised 2024-02
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-fmk and nep-mst
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