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How good are LLMs in risk profiling?

Author

Listed:
  • Thorsten Hens

    (Department of Finance, University of Zurich, Department of Finance, Norwegian School of Economics, NHH, Institute of Economic Research, Kyoto University)

  • Trine Nordlie

    (Department of Finance, Norwegian School of Economics, NHH, Bergen)

Abstract

This study compares OpenAI's ChatGPT-4 and Google's Bard with bank experts in determining investors'risk profiles. We find that for half of the client cases used, there are no statistically significant differences in the risk profiles. Moreover, the economic relevance of the differences is small.

Suggested Citation

  • Thorsten Hens & Trine Nordlie, 2024. "How good are LLMs in risk profiling?," KIER Working Papers 1103, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:1103
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    References listed on IDEAS

    as
    1. Graeme D. Ruxton, 2006. "The unequal variance t-test is an underused alternative to Student's t-test and the Mann--Whitney U test," Behavioral Ecology, International Society for Behavioral Ecology, vol. 17(4), pages 688-690, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Large Language Models; ChatGPT; Bard; Risk Profiling;
    All these keywords.

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

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