Stock Prediction by Signal Decomposition-Driven Multivariate Feature Extractor and Executor-Based Mixture of Experts

Stock market prediction is a complex and dynamic area of fintech, involving the assessment of risk, projecting returns, and analyzing trends. Researchers have demonstrated that signal decomposition can facilitate all of these tasks by untangling intricate data streams. This paper presents a novel approach to predicting short-term stock market returns. The proposed scheme integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and the Mixture of Experts (MoE) method within an ensemble learning framework. Decomposed signals are encoded into embeddings via attention mechanisms to facilitate feature extraction, and an innovative training target tailored for the unique MoE architecture was shown to significantly boost prediction accuracy. In simulations using two real-world stock markets, the proposed method outperformed existing state-of-the-art methods on all relevant metrics.