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Can ChatGPT improve investment decisions? From a portfolio management perspective

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  • Ko, Hyungjin
  • Lee, Jaewook

Abstract

We examine ChatGPT, a prominent Large Language Model (LLM), in supporting portfolio management with a focus on asset selection and diversification through quantitative methods. We use ChatGPT to select assets from various asset classes and evaluate the diversification effect of its selections. Our results suggest that ChatGPT’s selections are statistically significantly better in diversity index than randomly selected assets. We also construct portfolios based on ChatGPT’s selections and find that they outperform portfolios built on randomly selected assets. Overall, our study contributes to a better understanding of the role of LLMs like ChatGPT as potential assistants for portfolio managers.

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  • Ko, Hyungjin & Lee, Jaewook, 2024. "Can ChatGPT improve investment decisions? From a portfolio management perspective," Finance Research Letters, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:finlet:v:64:y:2024:i:c:s154461232400463x
    DOI: 10.1016/j.frl.2024.105433
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    1. Pawe{l} Niszczota & Sami Abbas, 2023. "GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice," Papers 2309.00649, arXiv.org, revised Sep 2024.
    2. Seongwan Park & Seungju Lee & Yunyoung Lee & Hyungjin Ko & Bumho Son & Jaewook Lee & Huisu Jang, 2023. "Price co-movements in decentralized financial markets," Applied Economics Letters, Taylor & Francis Journals, vol. 30(21), pages 3075-3082, December.
    3. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    4. Ko, Hyungjin & Byun, Junyoung & Lee, Jaewook, 2023. "A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    5. Ko, Hyungjin & Lee, Seungyun & Lee, Jaewook, 2024. "Sequence and longevity risks of South Korean retirees: Insights and potential remedies," Pacific-Basin Finance Journal, Elsevier, vol. 83(C).
    6. Ko, Hyungjin & Son, Bumho & Lee, Jaewook, 2024. "Portfolio insurance strategy in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 67(PA).
    7. 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.
    8. Ko, Hyungjin & Son, Bumho & Lee, Jaewook, 2024. "A novel integration of the Fama–French and Black–Litterman models to enhance portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    9. Suleyman Basak & Georgy Chabakauri, 2010. "Dynamic Mean-Variance Asset Allocation," The Review of Financial Studies, Society for Financial Studies, vol. 23(8), pages 2970-3016, August.
    10. Dowling, Michael & Lucey, Brian, 2023. "ChatGPT for (Finance) research: The Bananarama Conjecture," Finance Research Letters, Elsevier, vol. 53(C).
    11. Shapira, Zur & Venezia, Itzhak, 2001. "Patterns of behavior of professionally managed and independent investors," Journal of Banking & Finance, Elsevier, vol. 25(8), pages 1573-1587, August.
    12. John H. Cochrane, 2014. "A Mean-Variance Benchmark for Intertemporal Portfolio Theory," Journal of Finance, American Finance Association, vol. 69(1), pages 1-49, February.
    13. Brad M. Barber & Terrance Odean, 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors," Journal of Finance, American Finance Association, vol. 55(2), pages 773-806, April.
    14. Hyungjin Ko & Jaewook Lee & Junyoung Byun & Bumho Son & Saerom Park, 2019. "Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis," Sustainability, MDPI, vol. 11(12), pages 1-24, June.
    15. Avramov, Doron & Cheng, Si & Lioui, Abraham & Tarelli, Andrea, 2022. "Sustainable investing with ESG rating uncertainty," Journal of Financial Economics, Elsevier, vol. 145(2), pages 642-664.
    16. Pelster, Matthias & Val, Joel, 2024. "Can ChatGPT assist in picking stocks?," Finance Research Letters, Elsevier, vol. 59(C).
    17. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    18. Oehler, Andreas & Horn, Matthias, 2024. "Does ChatGPT provide better advice than robo-advisors?," Finance Research Letters, Elsevier, vol. 60(C).
    19. Oleksandr Romanko & Akhilesh Narayan & Roy H. Kwon, 2023. "ChatGPT-based Investment Portfolio Selection," Papers 2308.06260, arXiv.org.
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    3. Xuewen Han & Neng Wang & Shangkun Che & Hongyang Yang & Kunpeng Zhang & Sean Xin Xu, 2024. "Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research," Papers 2411.04788, arXiv.org.
    4. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).

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