Complete Finance Research by LLMs?

Can large language models (LLMs) create financial research? In their December 2024 paper entitled “AI-Powered (Finance) Scholarship”, Robert Novy-Marx and Mihail Velikov describe a process for automatically generating academic finance papers using LLMs and demonstrates its efficacy by producing hundreds of complete papers on stock return predictability. Specifically, they:

1. Identify 31,460 potential stock return predictors from accounting variables and their differences.
2. Screen these potential signals for redundancy, data robustness and stock selection breadth to identify 17,074 candidates for validation.
3. Validate these signals via decile and quintile portfolio sorts and controls for multiple known stock return factors to select 183 promising (alpha) signals.
4. Apply a series of anomaly evaluation tools to isolate 96 economically meaningful and statistically reliable signals and generate a standardized report for each that details signal performance, including a comparison to over 200 other known anomalies.
5. Use state-of-the-art LLMs to generate three conventional academic papers for each effective signal (288 reports in total), including:
   • Creative signal names.
   • Abstract.
   • Introductions with motivation, hypothesis development, results summary and contribution.
   • Data and conclusion.
   • Citations to other relevant papers.

Based on results from this process, they conclude that: (more…)