Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting

TL;DR

This paper introduces an adaptive Mixture of Experts framework combining RNNs and linear models for stock forecasting, effectively handling different volatility regimes and outperforming traditional models.

Contribution

It presents a novel adaptive MoE model with a volatility-aware gating mechanism for improved stock prediction across market regimes.

Findings

Up to 33% MSE improvement for volatile stocks

Up to 28% MSE improvement for stable stocks

Effective adaptation to market volatility regimes

Abstract

This study develops and empirically validates a Mixture of Experts (MoE) framework for stock price prediction across heterogeneous volatility regimes using real market data. The proposed model combines a Recurrent Neural Network (RNN) optimized for high-volatility stocks with a linear regression model tailored to stable equities. A volatility-aware gating mechanism dynamically weights the contributions of each expert based on asset classification. Using a dataset of 30 publicly traded U.S. stocks spanning diverse sectors, the MoE approach consistently outperforms both standalone models. Specifically, it achieves up to 33% improvement in MSE for volatile assets and 28% for stable assets relative to their respective baselines. Stratified evaluation across volatility classes demonstrates the model's ability to adapt complexity to underlying market dynamics. These results confirm that no single model suffices across market regimes and highlight the advantage of adaptive architectures in financial prediction. Future work should explore real-time gate learning, dynamic volatility segmentation, and applications to portfolio optimization.