MIGA：混合专家组聚合在股市预测中的应用

MIGA: Mixture-of-Experts with Group Aggregation for Stock Market Prediction

October 3, 2024

作者: Zhaojian Yu, Yinghao Wu, Genesis Wang, Heming Weng

cs.AI

摘要

股市预测长期以来一直是一个极具挑战性的问题，这是因为股市固有的高波动性和低信息噪声比。基于机器学习或深度学习的现有解决方案通过采用单一模型在整个股票数据集上训练，以生成各类股票的预测，展现出卓越的性能。然而，由于股票风格和市场趋势存在显著变化，单一端到端模型难以完全捕捉这些风格化股票特征的差异，导致对所有类型股票的预测相对不准确。本文提出了MIGA，一种新颖的专家混合与组聚合框架，旨在通过动态切换不同风格专家，为不同风格的股票生成专业化预测。为促进MIGA中不同专家之间的合作，我们提出了一种新颖的内部组关注架构，使同一组内的专家共享信息，从而提升所有专家的整体性能。结果表明，MIGA在包括沪深300指数、中证500指数和中证1000指数在内的三个中国股指基准上明显优于其他端到端模型。值得注意的是，MIGA-Conv在沪深300指数基准上达到了24%的超额年回报，超过了之前最先进模型8%的绝对值。此外，我们对股市预测中的专家混合进行了全面分析，为未来研究提供了宝贵的见解。

English

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.