How good are LLMs in risk profiling?
Thorsten Hens (thorsten.hens@df.uzh.ch) and
Trine Nordlie (trine.nordlie@student.nhh.no)
Additional contact information
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
No 1103, KIER Working Papers from Kyoto University, Institute of Economic Research
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.
Keywords: Large Language Models; ChatGPT; Bard; Risk Profiling (search for similar items in EconPapers)
JEL-codes: D14 D8 D81 G51 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2024-04
New Economics Papers: this item is included in nep-ain, nep-cmp and nep-rmg
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.kier.kyoto-u.ac.jp/wp/wp-content/uploads/2024/04/DP1103.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:kyo:wpaper:1103
Access Statistics for this paper
More papers in KIER Working Papers from Kyoto University, Institute of Economic Research Contact information at EDIRC.
Bibliographic data for series maintained by Makoto Watanabe (watanabe.makoto.2d@kyoto-u.ac.jp).