# Generative AI for Stock Selection

**Authors:** Keywan Christian Rasekhschaffe  
**arXiv:** [2602.00196](https://arxiv.org/abs/2602.00196)  
**Date:** 2026-01-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2602.00196v1/

## Key points

- Study explores potential of generative artificial intelligence (AI) in automating feature discovery within U.S. equities
- Research utilizes large language models to extract economically motivated features from analyst reports, options data, and price-volume information
- Synthesized features used as inputs for a tabular machine-learning model to predict short-horizon returns
- AI-generated features consistently perform competitively compared to established baselines, with Sharpe ratio improvements ranging from 14% to 91%
- Quality of retrieval crucial for favorable outcomes; better knowledge bases enhance results significantly
- Signals generated by AI show weak correlation with traditional features, suggesting potential for effective combination of both types of inputs
- Generative AI can enhance feature discovery processes when retrieval quality is carefully managed
- Technology produces interpretable signals and reduces manual engineering efforts, promising advancement in stock selection strategies in financial markets

## Summary

The study "Generative AI for Stock Selection" by Keywan Christian Rasekhschaffe explores the potential of generative artificial intelligence (AI) in automating feature discovery within U.S. equities. The research utilizes large language models to extract economically motivated features from analyst reports, options data, and price-volume information through retrieval-augmented generation and structured/programmatic prompting. These synthesized features are then used as inputs for a tabular machine-learning model to predict short-horizon returns. Results from various datasets show that the AI-generated features consistently perform competitively compared to established baselines, with Sharpe ratio improvements ranging from 14% to 91%. The quality of retrieval is crucial in achieving favorable outcomes, with better knowledge bases significantly enhancing results. Interestingly, the signals generated by AI show weak correlation with traditional features, suggesting potential for combining both types of inputs effectively. Overall, this study demonstrates that generative AI can enhance feature discovery processes when retrieval quality is carefully managed. By producing interpretable signals and reducing manual engineering efforts, this technology holds promise for advancing stock selection strategies in financial markets.