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.

本文介绍了 StockGPT，一种在美国股票每日收益历史数据上直接预训练的自回归 ' 数值 ' 模型。通过将每个收益系列视为标记序列，该模型在理解和预测高度复杂的股票收益动态方面表现出色。利用注意机制，StockGPT 自动学习预测未来收益的隐藏表示。在 2001 年至 2023 年的测试样本中，基于 StockGPT 预测的每日重新平衡的多空投资组合年收益为 119%，夏普比率为 6.5。基于 StockGPT 的投资组合完全解释了动量和长期 / 短期逆转现象，消除了手动构建基于价格的策略的需要，并涵盖了大多数领先的股市因素。这凸显了生成型人工智能在超越人类进行复杂金融投资决策方面的巨大潜力，并证明了大型语言模型的注意机制在完全不同的领域中的有效性。

StockGPT：用于股票预测和交易的 GenAI 模型

StockGPT: A GenAI Model for Stock Prediction and Trading

We propose a novel probabilistic model to facilitate the learning of
multivariate tail dependence of multiple financial assets. Our method allows
one to construct from known random vectors, e.g., standard normal,
sophisticated joint heavy-tailed random vectors featuring not only distinct
marginal tail heaviness, but also flexible tail dependence structure. The
novelty lies in that pairwise tail dependence between any two dimensions is
modeled separately from their correlation, and can vary respectively according
to its own parameter rather than the correlation parameter, which is an
essential advantage over many commonly used methods such as multivariate $t$ or
elliptical distribution. It is also intuitive to interpret, easy to track, and
simple to sample comparing to the copula approach. We show its flexible tail
dependence structure through simulation. Coupled with a GARCH model to
eliminate serial dependence of each individual asset return series, we use this
novel method to model and forecast multivariate conditional distribution of
stock returns, and obtain notable performance improvements in multi-dimensional
coverage tests. Besides, our empirical finding about the asymmetry of tails of
the idiosyncratic component as well as the market component is interesting and
worth to be well studied in the future.

本文提出一种新颖的概率模型，用于学习多元金融资产的尾部依赖，其方法可以构建新的随机向量，并且相较于常见的方法，具有更灵活、直观、易于追踪和简单抽样等优势，结合 GARCH 模型进行预测和检验，发现资产收益率的尾部具有不对称性，对此进行了初步研究。

金融资产之间极值依赖的截面学习

Cross-sectional Learning of Extremal Dependence among Financial Assets

Many studies have been undertaken by using machine learning techniques,
including neural networks, to predict stock returns. Recently, a method known
as deep learning, which achieves high performance mainly in image recognition
and speech recognition, has attracted attention in the machine learning field.
This paper implements deep learning to predict one-month-ahead stock returns in
the cross-section in the Japanese stock market and investigates the performance
of the method. Our results show that deep neural networks generally outperform
shallow neural networks, and the best networks also outperform representative
machine learning models. These results indicate that deep learning shows
promise as a skillful machine learning method to predict stock returns in the
cross-section.

本研究利用深度学习方法预测日本股市横截面中一个月的股票回报，并发现深度神经网络通常优于浅层神经网络，而且最佳网络也优于代表性的机器学习模型，这表明深度学习在预测横截面股票回报方面表现良好。