Title: StockGPT: A GenAI Model for Stock Prediction and Trading

Abstract: This paper introduces StockGPT, an autoregressive "number" model pretrained directly on the history of daily U.S. stock returns. Treating each return series as a sequence of tokens, the model excels at understanding and predicting the highly intricate stock return dynamics. Instead of relying on handcrafted trading patterns using historical stock prices, StockGPT automatically learns the hidden representations predictive of future returns via its attention mechanism. On a held-out test sample from 2001 to 2023, a daily rebalanced long-short portfolio formed from StockGPT predictions earns an annual return of 119% with a Sharpe ratio of 6.5. The StockGPT-based portfolio completely explains away momentum and long-/short-term reversals, eliminating the need for manually crafted price-based strategies and also encompasses most leading stock market factors. This highlights the immense promise of generative AI in surpassing human in making complex financial investment decisions and illustrates the efficacy of the attention mechanism of large language models when applied to a completely different domain.