From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866913745271259136 |
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| author | Kim, Alex Muhn, Maximilian Nikolaev, Valeri |
| author_facet | Kim, Alex Muhn, Maximilian Nikolaev, Valeri |
| contents | We explore the value of generative AI tools, such as ChatGPT, in helping investors uncover dimensions of corporate risk. We develop and validate firm-level measures of risk exposure to political, climate, and AI-related risks. 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. Importantly, information in risk assessments dominates that in risk summaries, establishing the value of general AI knowledge. We also find that generative AI is effective at detecting emerging risks, such as AI risk, which has soared in recent quarters. Our measures perform well both within and outside the GPT's training window and are priced in equity markets. Taken together, an AI-based approach to risk measurement provides useful insights to users of corporate disclosures at a low cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17721 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI Kim, Alex Muhn, Maximilian Nikolaev, Valeri General Economics Economics Artificial Intelligence Computation and Language We explore the value of generative AI tools, such as ChatGPT, in helping investors uncover dimensions of corporate risk. We develop and validate firm-level measures of risk exposure to political, climate, and AI-related risks. 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. Importantly, information in risk assessments dominates that in risk summaries, establishing the value of general AI knowledge. We also find that generative AI is effective at detecting emerging risks, such as AI risk, which has soared in recent quarters. Our measures perform well both within and outside the GPT's training window and are priced in equity markets. Taken together, an AI-based approach to risk measurement provides useful insights to users of corporate disclosures at a low cost. |
| title | From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI |
| topic | General Economics Economics Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2310.17721 |