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Correlation Pitfalls with ChatGPT: Would You Fall for Them?

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  • Marius Hofert

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

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.

Suggested Citation

  • Marius Hofert, 2023. "Correlation Pitfalls with ChatGPT: Would You Fall for Them?," Risks, MDPI, vol. 11(7), pages 1-17, June.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:7:p:115-:d:1176469
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    References listed on IDEAS

    as
    1. 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.
    2. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
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    Cited by:

    1. Marius Hofert, 2023. "Assessing ChatGPT’s Proficiency in Quantitative Risk Management," Risks, MDPI, vol. 11(9), pages 1-29, September.

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