ChatGPT-Based Investment Portfolio Selection

Oleksandr Romanko (), Akhilesh Narayan () and Roy H. Kwon ()
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Oleksandr Romanko: University of Toronto
Akhilesh Narayan: Indian Institute of Technology Bombay
Roy H. Kwon: University of Toronto

SN Operations Research Forum, 2023, vol. 4, issue 4, 1-27

Abstract: Abstract In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model “hallucinations,” necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future.

Keywords: Portfolio optimization; Investment management; Generative AI; ChatGPT (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43069-023-00277-6

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