ChatGPT and Perception Biases in Investments: An Experimental Study

54 Pages Posted: 17 Apr 2024

See all articles by Anastassia Fedyk

Anastassia Fedyk

University of California, Berkeley - Haas School of Business

Ali Kakhbod

University of California, Berkeley

Peiyao Li

University of California, Berkeley - Haas School of Business

Ulrike Malmendier

University of California, Berkeley - Department of Economics; University of California, Berkeley - Haas School of Business; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR); Institute for the Study of Labor (IZA)

Date Written: April 8, 2024

Abstract

Applications of artificial intelligence (AI) in finance have been met with concerns about algorithmic bias, following issues observed in domains such as medical treatment and lending. We ask whether AI models accurately capture investment preferences across demographics. We elicit investment preferences from over 1,200 survey participants and compare the data directly to investment ratings generated by OpenAI’s ChatGPT (GPT4). We find that ChatGPT predicts investment preferences with high accuracy across demographics. Specifically, ChatGPT correctly predicts that women rate stocks lower than men, older individuals prefer holding cash, and higher incomes are associated with higher ratings for stocks and bonds. Moreover, free-form responses from ChatGPT focus on the same aspects as human free-form responses. Most common themes in both responses are “risk" and “return," and "knowledge" and "experience" play an important role for stock market participation. One difference is that ChatGPT responses are almost always transitive, whereas human responses are more prone to violating transitivity, especially when expressing indifference. Overall, the use of AI in finance offers a promising direction for augmenting human surveys in preference elicitation, with important applications for areas such as robo-advsing.

Keywords: Investment preferences, Large language models, Behavioral biases, Experimental economics, Financial surveys, Generative AI.

JEL Classification: C1, G10, G11, G12.

Suggested Citation

Fedyk, Anastassia and Kakhbod, Ali and Li, Peiyao and Malmendier, Ulrike, ChatGPT and Perception Biases in Investments: An Experimental Study (April 8, 2024). Available at SSRN: https://ssrn.com/abstract=4787249 or http://dx.doi.org/10.2139/ssrn.4787249

Anastassia Fedyk

University of California, Berkeley - Haas School of Business ( email )

545 Student Services Building, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States

Ali Kakhbod (Contact Author)

University of California, Berkeley ( email )

Haas School of Business
2220 Piedmont Ave
Berkeley, CA 94720
United States

Peiyao Li

University of California, Berkeley - Haas School of Business ( email )

545 Student Services Building, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States

Ulrike Malmendier

University of California, Berkeley - Department of Economics ( email )

549 Evans Hall #3880
Berkeley, CA 94720-3880
United States
(510) 642-8724 (Phone)
(510) 642-6615 (Fax)

HOME PAGE: http://www.econ.berkeley.edu/~ulrike/

University of California, Berkeley - Haas School of Business ( email )

545 Student Services Building, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Institute for the Study of Labor (IZA)

P.O. Box 7240
Bonn, D-53072
Germany

HOME PAGE: http://www.iza.org/en/webcontent/personnel/photos/index_html?key=918

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