title: MIGA: Mixture-of-Experts with Group Aggregation for Stock Market
    Prediction
summary: 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.
paper_authors._id: 67037d0c5ce58dd0c356ebce
paper_authors.name: Zhaojian Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-07T07:26:26.903Z
paper_authors._id: 646f3443c261dc413383b8a4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/646f3443c261dc413383b8a4/7R2XX3VqQ6DmgQoMU7Un4.jpeg
paper_authors.fullname: Zhaojian Yu
paper_authors.type: user
paper_authors.user: zjy2001
paper_authors._id: 67037d0c5ce58dd0c356ebcf
paper_authors.name: Yinghao Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-07T08:53:52.440Z
paper_authors._id: 650ed552dc509ae7d7bb1ccc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/650ed552dc509ae7d7bb1ccc/CPR6gaIjPfnBGfr-D_rpO.jpeg
paper_authors.fullname: Yinghao Wu
paper_authors.type: user
paper_authors.user: yh1567
paper_authors._id: 67037d0c5ce58dd0c356ebd0
paper_authors.name: Genesis Wang
paper_authors._id: 67037d0c5ce58dd0c356ebd1
paper_authors.name: Heming Weng
paper_title: MIGA: Mixture-of-Experts with Group Aggregation for Stock Market
    Prediction
paper_summary: 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.
paper_ai_summary: MIGA, a novel Mixture of Experts framework with Group Aggregation and inner group attention, improves stock market prediction by specializing in different stock styles and outperforms existing models on Chinese stock index benchmarks.
paper_ai_keywords: Mixture of Experts Group Aggregation inner group attention stock market prediction stock styles market trends excess annual return