Stock market prediction has remained an extremely challenging problem for many decades owing to its inherent high volatility and low information noisy ratio. Existing solutions based on machine learning or deep learning demonstrate superior performance by employing a single model trained on the entire stock dataset to generate predictions across all types of stocks. However, due to the significant variations in stock styles and market trends, a single end-to-end model struggles to fully capture the differences in these stylized stock features, leading to relatively inaccurate predictions for all types of stocks. In this paper, we present MIGA, a novel Mixture of Expert with Group Aggregation framework designed to generate specialized predictions for stocks with different styles by dynamically switching between distinct style experts. To promote collaboration among different experts in MIGA, we propose a novel inner group attention architecture, enabling experts within the same group to share information and thereby enhancing the overall performance of all experts. As a result, MIGA significantly outperforms other end-to-end models on three Chinese Stock Index benchmarks including CSI300, CSI500, and CSI1000. Notably, MIGA-Conv reaches 24 % excess annual return on CSI300 benchmark, surpassing the previous state-of-the-art model by 8% absolute. Furthermore, we conduct a comprehensive analysis of mixture of experts for stock market prediction, providing valuable insights for future research.

翻译：股市预测因其固有的高波动性和低信息噪声比，数十年来一直是一个极具挑战性的问题。现有的基于机器学习或深度学习的解决方案，通常采用在全部股票数据集上训练的单一模型来对所有类型股票进行预测，并展现出优越性能。然而，由于股票风格与市场趋势存在显著差异，单一的端到端模型难以充分捕捉这些风格化股票特征间的区别，导致对所有类型股票的预测均相对不准确。本文提出MIGA，一种新颖的基于分组聚合的专家混合框架，旨在通过在不同风格专家间动态切换，为不同风格的股票生成专业化预测。为促进MIGA中不同专家间的协作，我们提出一种新颖的组内注意力架构，使同一组内的专家能够共享信息，从而提升所有专家的整体性能。实验结果表明，在包括沪深300、中证500和中证1000在内的三个中国股票指数基准上，MIGA显著优于其他端到端模型。值得注意的是，MIGA-Conv在沪深300基准上实现了24%的超额年化收益，绝对收益较先前最优模型提升了8%。此外，我们对专家混合模型在股市预测中的应用进行了全面分析，为未来研究提供了有价值的见解。