# ChatGPT-based Investment Portfolio Selection

arXiv: https://arxiv.org/abs/2308.06260 · HTML: https://mlsanity.com/paper/2308.06260/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)

## Built on by (most cited first, 8 of 8)

- [AI Hallucinations: A Misnomer Worth Clarifying](https://mlsanity.com/paper/2401.06796/) (2024)
- [Large Language Models for Combinatorial Optimization: A Systematic Review](https://mlsanity.com/paper/2507.03637/) (2025)
- [Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization](https://mlsanity.com/paper/2502.00828/) (2025)
- [Beyond the Reported Cutoff: Where Large Language Models Fall Short on Financial Knowledge](https://mlsanity.com/paper/2504.00042/) (2025)
- [A Scoping Review of ChatGPT Research in Accounting and Finance](https://mlsanity.com/paper/2412.05731/) (2024)
- [Integrating Large Language Models in Financial Investments and Market Analysis: A Survey](https://mlsanity.com/paper/2507.01990/) (2025)
- [Refined and Segmented Price Sentiment Indices from Survey Comments](https://mlsanity.com/paper/2411.09937/) (2024)
- [Generative AI-enhanced Sector-based Investment Portfolio Construction](https://mlsanity.com/paper/2512.24526/) (2025)

## Abstract (arXiv)

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.