Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection
Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection
This research paper introduces MarketSenseAI, an innovative framework that leverages GPT-4's advanced reasoning capabilities for stock selection in financial markets. The framework integrates Chain of Thought and In-Context Learning techniques to analyze diverse data sources including market trends, news, fundamentals, and macroeconomic factors, effectively emulating expert investment decision-making. A distinctive aspect of this work is the dual use of GPT-4 both as a predictive mechanism and as a signal evaluator, with findings revealing that AI-generated explanations significantly enhance 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% while maintaining a risk profile comparable to the broader market. The paper's findings highlight the transformative potential of Large Language Models in financial decision-making, marking a significant advancement in integrating generative AI into financial analytics and investment strategies. The research underscores how AI-generated explanations contribute to both performance outcomes and practical acceptance of automated investment signals.
Highlights
- 1Introduces MarketSenseAI, a novel framework using GPT-4 for stock selection
- 2Integrates Chain of Thought and In-Context Learning to analyze diverse financial data
- 3Employs GPT-4 both as a predictive mechanism and signal evaluator
- 4Demonstrates exceptional performance with excess alpha of 10-30% and up to 72% cumulative return
- 5Highlights the transformative potential of LLMs in financial decision-making
Methods
- MGPT-4-based framework with advanced reasoning capabilities
- MChain of Thought and In-Context Learning techniques
- MAnalysis of market trends, news, fundamentals, and macroeconomic factors
- MEmpirical testing on S&P 100 stocks over 15-month period
Results
- RMarketSenseAI delivered excess alpha of 10% to 30%
- RAchieved cumulative return of up to 72% over the testing period
- RMaintained risk profile comparable to the broader market
- RAI-generated explanations significantly improved signal accuracy, reliability and acceptance
- RFramework generated actionable and interpretable investment signals
Analyze Paper
Generate insights from "Can Large Language Models Beat Wall Street? Unveiling the Po...".