Alex Kim, Maximilian Muhn, and Valeri Nikolaev (2023)

Chicago Booth Working Paper, Fama-Miller Center Working Paper, Becker Friedman Institute for Economics Working Paper

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We probe the economic usefulness of generative AI in summarizing complex corporate disclosures. The summaries are more effective at explaining stock market reactions to the disclosed information. We show that bloated disclosure is associated with adverse capital market consequences.

Alex Kim, Maximilian Muhn, and Valeri Nikolaev (2023)

Chicago Booth Working Paper, Fama-Miller Center Working Paper, Becker Friedman Institute for Economics Working Paper

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We explore the value of generative AI tools in helping investors uncover dimensions of firm-level corporate risk. 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.

Alex Kim, Maximilian Muhn, and Valeri Nikolaev (2024)

Chicago Booth Working Paper, Fama-Miller Center Working Paper, Becker Friedman Institute for Economics Working Paper

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We investigate whether an LLM can successfully perform financial statement analysis in a way similar to a professional human analyst. We provide standardized and anonymous financial statements to GPT4 and instruct the model to analyze them to determine the direction of future earnings. Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle. Furthermore, we find that the prediction accuracy of the LLM is on par with the performance of a narrowly trained state-of-the-art ML model.