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

ArXiv ID: 2401.03737 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Large Language Models, GPT-4, Stock Selection, Chain of Thought, Alpha Generation, Equity

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper uses relatively straightforward mathematical concepts for an LLM-based framework, but provides detailed backtesting on a specific dataset (S&P 100 over 15 months) with reported performance metrics (alpha, cumulative returns, risk profile).

  flowchart TD
    A["<b>Research Goal</b><br/>Can LLMs Beat Wall Street?<br/>AI in Stock Selection"] --> B["<b>Framework: MarketSenseAI</b><br/>GPT-4 + Chain of Thought<br/>+ In-Context Learning"]
    B --> C["<b>Data Integration</b><br/>Market Trends, News,<br/>Fundamentals, Macro Factors"]
    C --> D["<b>AI Processing</b><br/>Expert-like Reasoning<br/>Signal Generation & Evaluation"]
    D --> E["<b>Validation</b><br/>S&P 100 Stocks<br/>15-Month Backtest"]
    E --> F["<b>Key Findings</b><br/>72% Cumulative Return<br/>10-30% Excess Alpha<br/>Controlled Risk"]
    style A fill:#e1f5fe
    style F fill:#e8f5e8