# Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection

arXiv: https://arxiv.org/abs/2401.03737 · HTML: https://mlsanity.com/paper/2401.03737/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 67 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks](https://mlsanity.com/paper/2305.05862/) (2023)
- [ChatGPT Informed Graph Neural Network for Stock Movement Prediction](https://mlsanity.com/paper/2306.03763/) (2023)
- [Transforming Sentiment Analysis in the Financial Domain with ChatGPT](https://mlsanity.com/paper/2308.07935/) (2023)
- [Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams](https://mlsanity.com/paper/2310.08678/) (2023)
- [Bloated Disclosures: Can ChatGPT Help Investors Process Information?](https://mlsanity.com/paper/2306.10224/) (2023)
- [Fusion-Eval: Integrating Assistant Evaluators with LLMs](https://mlsanity.com/paper/2311.09204/) (2023)

## Built on by (most cited first, 29 of 29)

- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [Position: What Can Large Language Models Tell Us about Time Series Analysis](https://mlsanity.com/paper/2402.02713/) (2024)
- [XAI for All: Can Large Language Models Simplify Explainable AI?](https://mlsanity.com/paper/2401.13110/) (2024)
- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.11420/) (2025)
- [AI in Investment Analysis: LLMs for Equity Stock Ratings](https://mlsanity.com/paper/2411.00856/) (2024)
- [ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism](https://mlsanity.com/paper/2508.00554/) (2025)
- [Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs](https://mlsanity.com/paper/2402.12713/) (2024)
- [Opportunities and Challenges of Generative-AI in Finance](https://mlsanity.com/paper/2410.15653/) (2024)
- [Exposing Product Bias in LLM Investment Recommendation](https://mlsanity.com/paper/2503.08750/) (2025)
- [Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning](https://mlsanity.com/paper/2507.16802/) (2025)
- [Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents](https://mlsanity.com/paper/2504.05527/) (2025)
- [Integrating Large Language Models in Financial Investments and Market Analysis: A Survey](https://mlsanity.com/paper/2507.01990/) (2025)
- [Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges](https://mlsanity.com/paper/2507.18577/) (2025)
- [FairyLandAI: Personalized Fairy Tales utilizing ChatGPT and DALLE-3](https://mlsanity.com/paper/2407.09467/) (2024)
- [Trust & Safety of LLMs and LLMs in Trust & Safety](https://mlsanity.com/paper/2412.02113/) (2024)
- [MASCA: LLM based-Multi Agents System for Credit Assessment](https://mlsanity.com/paper/2507.22758/) (2025)
- [Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation](https://mlsanity.com/paper/2502.17011/) (2025)
- [FISCAL: Financial Synthetic Claim-document Augmented Learning for Efficient Fact-Checking](https://mlsanity.com/paper/2511.19671/) (2025)
- [Towards Conversational AI for Human-Machine Collaborative MLOps](https://mlsanity.com/paper/2504.12477/) (2025)
- [HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning](https://mlsanity.com/paper/2509.04303/) (2025)
- [Signal or Noise in Multi-Agent LLM-based Stock Recommendations?](https://mlsanity.com/paper/2604.17327/) (2026)
- [Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading](https://mlsanity.com/paper/2605.01954/) (2026)
- [People Are Highly Cooperative with Large Language Models, Especially When Communication Is Possible or Following Human Interaction](https://mlsanity.com/paper/2507.18639/) (2025)
- [Strategic Innovation Management in the Age of Large Language Models Market Intelligence, Adaptive R&D, and Ethical Governance](https://mlsanity.com/paper/2511.14709/) (2025)
- [An Organization-Scoped LLM Agent Runtime Architecture for Regulated Cybersecurity Operations](https://mlsanity.com/paper/2605.30604/) (2026)
- [SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes](https://mlsanity.com/paper/2609.19705/) (2026)
- [Trustworthy FinAInce: Unpacking How AI-Mediated Financial Advice is Judged](https://mlsanity.com/paper/2609.20989/) (2026)

## Abstract (arXiv)

This paper introduces MarketSenseAI, an innovative framework leveraging GPT-4's advanced reasoning for selecting stocks in financial markets. By integrating Chain of Thought and In-Context Learning, MarketSenseAI analyzes diverse data sources, including market trends, news, fundamentals, and macroeconomic factors, to emulate expert investment decision-making. The development, implementation, and validation of the framework are elaborately discussed, underscoring its capability to generate actionable and interpretable investment signals. A notable feature of this work is employing GPT-4 both as a predictive mechanism and signal evaluator, revealing the significant impact of the AI-generated explanations on signal accuracy, reliability and acceptance. Through empirical testing on the competitive S&P 100 stocks over a 15-month period, MarketSenseAI demonstrated exceptional performance, delivering excess alpha of 10% to 30% and achieving a cumulative return of up to 72% over the period, while maintaining a risk profile comparable to the broader market. Our findings highlight the transformative potential of Large Language Models in financial decision-making, marking a significant leap in integrating generative AI into financial analytics and investment strategies.