Paper: arXiv 2404.05101

Abstract

This paper introduces StockGPT, an autoregressive ``number’’ model trained and tested on 70 million daily U.S.\ stock returns over nearly 100 years. Treating each return series as a sequence of tokens, StockGPT automatically learns the hidden patterns predictive of future returns via its attention mechanism. On a held-out test sample from 2001 to 2023, daily and monthly rebalanced long-short portfolios formed from StockGPT predictions yield strong performance. The StockGPT-based portfolios span momentum and long-/short-term reversals, eliminating the need for manually crafted price-based strategies, and yield highly significant alphas against leading stock market factors, suggesting a novel AI pricing effect. This highlights the immense promise of generative AI in surpassing human in making complex financial investment decisions.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced generative AI/transformer architectures with attention mechanisms (high math complexity) and demonstrates rigorous out-of-sample backtesting across 23 years with 70M data points, detailed portfolio construction, and statistical significance testing (high empirical rigor).

Research Flowchart

  flowchart TD
  A["Research Goal: Can Generative AI<br>surpass human stock prediction?"] --> B["Methodology: Autoregressive<br>Attention-Based Model"]
  B --> C["Input: 70M Daily US Stock<br>Returns (1926-2023)"]
  C --> D["Process: Train StockGPT to<br>predict next return tokens"]
  D --> E["Output: Daily/Monthly<br>Long-Short Portfolios"]
  E --> F["Key Findings:<br>Strong Out-of-Sample Returns<br>Significant Alphas vs. Factors<br>Novel AI Pricing Effect"]