MIGA：具有群組聚合的專家混合模型，用於股市預測

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

股市預測因其固有的高波動性和低信息噪聲比而成為一個極具挑戰性的問題，數十年來一直如此。基於機器學習或深度學習的現有解決方案通過使用在整個股票數據集上訓練的單一模型展現出優越的性能，以生成各種類型股票的預測。然而，由於股票風格和市場趨勢存在顯著變化，單一端到端模型難以完全捕捉這些風格化股票特徵的差異，導致對所有類型股票的預測相對不準確。本文提出了一種新穎的MIGA（Mixture of Expert with Group Aggregation）框架，旨在通過動態在不同風格專家之間切換，為具有不同風格的股票生成專業預測。為促進MIGA中不同專家之間的合作，我們提出了一種新穎的內部組關注架構，使同一組中的專家共享信息，從而提高所有專家的整體性能。結果，MIGA在包括CSI300、CSI500和CSI1000在內的三個中國股票指數基準上明顯優於其他端到端模型。值得注意的是，MIGA-Conv在CSI300基準上達到24％的超額年回報，超越先前的最先進模型8個百分點。此外，我們對股市預測的專家混合進行了全面分析，為未來研究提供了寶貴的見解。

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.