AI-Powered (Finance) Scholarship
The Scale and Scope of AI-Generated Research Our study begins by mining over 30,000 potential stock return predictor signals from accounting data. These signals are constructed using various combinations of financial statement items from the COMPUSTAT database, representing a comprehensive universe of accounting-based return predictors. We identify 96 signals that demonstrate robust predictive power for […]
Robert Novy-Marx is the Lori and Alan S. Zekelman Distinguished Professor of Business Administration at Simon Business School, University of Rochester, and Mihail Z. Velikov is an Assistant Professor of Finance at Smeal College of Business at Penn State University. This post is based on their recent paper.
The Scale and Scope of AI-Generated Research
Our study begins by mining over 30,000 potential stock return predictor signals from accounting data. These signals are constructed using various combinations of financial statement items from the COMPUSTAT database, representing a comprehensive universe of accounting-based return predictors. We identify 96 signals that demonstrate robust predictive power for stock returns using the Novy-Marx and Velikov (2023) “Assaying Anomalies” protocol. This validation process involves multiple stages of increasingly stringent criteria, including tests for statistical significance, robustness to different portfolio construction methodologies, and controls for 200+ other known stock return predictors.
For each of these validated signals, we use state-of-the-art Large Language Models (LLMs) and “template reports” generated by the “Assaying Anomalies” protocol to programmatically generate three distinct versions of complete academic papers. Each version contains different theoretical justifications while maintaining consistency with the empirical findings. This approach allows us to explore how AI can generate multiple plausible explanations for the same empirical phenomena, mimicking a common practice in academic finance where researchers often develop hypothesis after discovering empirical patterns, a practice known as HARKing (Hypothesizing After Results are Known).
