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Harnessing Generative AI for Economic Insights

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  • Manish Jha
  • Jialin Qian
  • Michael Weber
  • Baozhong Yang

Abstract

We use generative AI to extract managerial expectations about their economic outlook from over 120,000 corporate conference call transcripts. The overall measure, AI Economy Score, robustly predicts future economic indicators such as GDP growth, production, and employment, both in the short term and to 10 quarters. This predictive power is incremental to that of existing measures, including survey forecasts. Moreover, industry and firm-level measures provide valuable information about sector-specific and individual firm activities. Our findings suggest that managerial expectations carry unique insights about economic activities, with implications for both macroeconomic and microeconomic decision-making.

Suggested Citation

  • Manish Jha & Jialin Qian & Michael Weber & Baozhong Yang, 2024. "Harnessing Generative AI for Economic Insights," Papers 2410.03897, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2410.03897
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    References listed on IDEAS

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    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Olivier Coibion & Yuriy Gorodnichenko & Saten Kumar, 2018. "How Do Firms Form Their Expectations? New Survey Evidence," American Economic Review, American Economic Association, vol. 108(9), pages 2671-2713, September.
    3. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    4. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023. "Bloated Disclosures: Can ChatGPT Help Investors Process Information?," Papers 2306.10224, arXiv.org, revised Feb 2024.
    5. Olivier J. Blanchard & Jean-Paul L'Huillier & Guido Lorenzoni, 2013. "News, Noise, and Fluctuations: An Empirical Exploration," American Economic Review, American Economic Association, vol. 103(7), pages 3045-3070, December.
    6. Barsky, Robert B. & Sims, Eric R., 2011. "News shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 58(3), pages 273-289.
    7. Olivier Coibion & Yuriy Gorodnichenko & Tiziano Ropele, 2020. "Inflation Expectations and Firm Decisions: New Causal Evidence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(1), pages 165-219.
    8. Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
    9. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2024.
    10. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    11. Ryan Chahrour & Kyle Jurado, 2018. "News or Noise? The Missing Link," American Economic Review, American Economic Association, vol. 108(7), pages 1702-1736, July.
    12. Olivier Coibion & Yuriy Gorodnichenko & Michael Weber, 2022. "Monetary Policy Communications and Their Effects on Household Inflation Expectations," Journal of Political Economy, University of Chicago Press, vol. 130(6), pages 1537-1584.
    13. Michael Weber & Francesco D'Acunto & Yuriy Gorodnichenko & Olivier Coibion, 2022. "The Subjective Inflation Expectations of Households and Firms: Measurement, Determinants, and Implications," Journal of Economic Perspectives, American Economic Association, vol. 36(3), pages 157-184, Summer.
    14. Schlingemann, Frederik P. & Stulz, René M., 2022. "Have exchange-listed firms become less important for the economy?," Journal of Financial Economics, Elsevier, vol. 143(2), pages 927-958.
    15. Jules H. van Binsbergen & Svetlana Bryzgalova & Mayukh Mukhopadhyay & Varun Sharma, 2024. "(Almost) 200 Years of News-Based Economic Sentiment," NBER Working Papers 32026, National Bureau of Economic Research, Inc.
    16. Graham, John R. & Harvey, Campbell R., 2001. "The theory and practice of corporate finance: evidence from the field," Journal of Financial Economics, Elsevier, vol. 60(2-3), pages 187-243, May.
    17. Shumiao Ouyang & Hayong Yun & Xingjian Zheng, 2024. "How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences of LLMs," Papers 2406.01168, arXiv.org, revised Aug 2024.
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