# StockGPT: A GenAI Model for Stock Prediction and Trading

arXiv: https://arxiv.org/abs/2404.05101 · HTML: https://mlsanity.com/paper/2404.05101/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://mlsanity.com/paper/2304.07619/) (2023)
- [ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation](https://mlsanity.com/paper/2306.09968/) (2023)
- [The Social Impact of Generative AI: An Analysis on ChatGPT](https://mlsanity.com/paper/2403.04667/) (2024)

## Built on by (most cited first, 11 of 11)

- [Large Language Model Agent in Financial Trading: A Survey](https://mlsanity.com/paper/2408.06361/) (2024)
- [Opportunities and Challenges of Generative-AI in Finance](https://mlsanity.com/paper/2410.15653/) (2024)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective](https://mlsanity.com/paper/2605.05211/) (2026)
- [The Gaining Paths to Investment Success: Information-Driven LLM Graph Reasoning for Venture Capital Prediction](https://mlsanity.com/paper/2512.23489/) (2025)
- [Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading](https://mlsanity.com/paper/2605.01954/) (2026)
- [Large-scale portfolio optimization with variational neural annealing](https://mlsanity.com/paper/2507.07159/) (2025)
- [BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation](https://mlsanity.com/paper/2601.06401/) (2026)
- [UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos](https://mlsanity.com/paper/2601.22162/) (2026)
- [Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems](https://mlsanity.com/paper/2606.08285/) (2026)
- [VAIOM: Continuous-Input, Discrete-Output Decoder-Only Financial Sequence Modeling](https://mlsanity.com/paper/2607.13929/) (2026)

## Abstract (arXiv)

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.