MIGA: Mixture-of-Experts with Group Aggregation for Stock Market Prediction
Keywords: mixture-of-expert, stock market prediction
Abstract: 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 of 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.
Primary Area: learning on time series and dynamical systems
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Submission Number: 10698
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