StockGPT: A GenAI Model for Stock Prediction and Trading
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.
Keywords
Cite
@article{arxiv.2404.05101,
title = {StockGPT: A GenAI Model for Stock Prediction and Trading},
author = {Dat Mai},
journal= {arXiv preprint arXiv:2404.05101},
year = {2024}
}
Comments
26 pages, 3 figures, 8 tables