MIGA: Mixture-of-Experts with Group Aggregation for Stock Market
  Prediction
Authors: name: Zhaojian Yu, name: Yinghao Wu, name: Genesis Wang, name: Heming Weng

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.