# Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models

arXiv: https://arxiv.org/abs/2304.07619 · HTML: https://mlsanity.com/paper/2304.07619/

## Key facts

- Cited by 129 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 79 references that are in the map.
- First posted on arXiv 15 Apr 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges](https://mlsanity.com/paper/2304.05351/) (2023)

## Built on by (most cited first, 30 of 129)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Empowering Time Series Analysis with Large Language Models: A Survey](https://mlsanity.com/paper/2402.03182/) (2024)
- [Large Language Model Agent in Financial Trading: A Survey](https://mlsanity.com/paper/2408.06361/) (2024)
- [GraphArena: Evaluating and Exploring Large Language Models on Graph Computation](https://mlsanity.com/paper/2407.00379/) (2024)
- [Position: What Can Large Language Models Tell Us about Time Series Analysis](https://mlsanity.com/paper/2402.02713/) (2024)
- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025)
- [ChatGPT Informed Graph Neural Network for Stock Movement Prediction](https://mlsanity.com/paper/2306.03763/) (2023)
- [Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis](https://mlsanity.com/paper/2309.17322/) (2023)
- [Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection](https://mlsanity.com/paper/2401.03737/) (2024)
- [AI for social science and social science of AI: A Survey](https://mlsanity.com/paper/2401.11839/) (2024)
- [Simulating Financial Market via Large Language Model based Agents](https://mlsanity.com/paper/2406.19966/) (2024)
- [Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models](https://mlsanity.com/paper/2310.00566/) (2023)
- [Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2405.02358/) (2024)
- [LLM-Powered AI Agent Systems and Their Applications in Industry](https://mlsanity.com/paper/2505.16120/) (2025)
- [Towards Modeling Learner Performance with Large Language Models](https://mlsanity.com/paper/2403.14661/) (2024)
- [An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications](https://mlsanity.com/paper/2306.02781/) (2023)
- [Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?](https://mlsanity.com/paper/2505.07078/) (2025)
- [An overview of domain-specific foundation model: key technologies, applications and challenges](https://mlsanity.com/paper/2409.04267/) (2024)
- [Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle](https://mlsanity.com/paper/2411.08324/) (2024)
- [BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling](https://mlsanity.com/paper/2503.02445/) (2025)
- [AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval](https://mlsanity.com/paper/2310.01880/) (2023)
- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
- [Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?](https://mlsanity.com/paper/2306.14222/) (2023)
- [Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs](https://mlsanity.com/paper/2506.22146/) (2025)

## Abstract (arXiv)

We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training. Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction. GPT-4 scores also significantly predict the subsequent drift, especially for small stocks and negative news. Forecasting ability generally increases with model size, suggesting that financial reasoning is an emerging capacity of complex LLMs. Strategy returns decline as LLM adoption rises, consistent with improved price efficiency. To rationalize these findings, we develop a theoretical model that incorporates LLM technology, information-processing capacity constraints, underreaction, and limits to arbitrage.