Correlation Pitfalls with ChatGPT: Would You Fall for Them?

Marius Hofert ()
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Marius Hofert: Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China

Risks, 2023, vol. 11, issue 7, 1-17

Abstract: This paper presents an intellectual exchange with ChatGPT, an artificial intelligence chatbot, about correlation pitfalls in risk management. The exchange takes place in the form of a conversation that provides ChatGPT with context. The purpose of this conversation is to evaluate ChatGPT’s understanding of correlation pitfalls, to offer readers an engaging alternative for learning about them, but also to identify related risks. Our findings indicate that ChatGPT possesses solid knowledge of basic and mostly non-technical aspects of the topic, but falls short in terms of the mathematical rigor needed to avoid certain pitfalls or completely comprehend the underlying concepts. Nonetheless, we suggest ways in which ChatGPT can be utilized to enhance one’s own learning process.

Keywords: ChatGPT; correlation; dependence; pitfalls (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:11:y:2023:i:7:p:115-:d:1176469

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