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

ArXiv ID: 2508.02686 “View on arXiv”

Authors: Diego Vallarino

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.

Keywords: Stock Price Prediction, Mixture of Experts (MoE), Volatility Regimes, Recurrent Neural Network (RNN), Adaptive Modeling

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper uses relatively straightforward mathematical frameworks (Mixture of Experts, LSTM equations, GARCH) without deep derivations, placing it on the lower end of math complexity. However, it demonstrates strong empirical rigor through backtesting on real market data (30 U.S. stocks), explicit performance metrics (MSE improvements), and a commitment to reproducibility via a GitHub repository.

  flowchart TD
    A["Research Goal: Adaptive Stock Forecasting<br>for Volatile vs. Stable Regimes"] --> B["Methodology: Mixture of Experts (MoE)<br>Volatility-Aware Gating"]
    B --> C["Data: 30 U.S. Stocks<br>Diverse Sectors"]
    C --> D{"Dynamic Classification:<br>Volatility Regime?"}
    D -- High Volatility --> E["RNN Expert<br>Deep Temporal Patterns"]
    D -- Stable/Low Volatility --> F["Linear Regression Expert<br>Baseline Simplicity"]
    E --> G["Output: Weighted Prediction"]
    F --> G
    G --> H["Findings: Adaptive MoE Outperforms Baselines<br>+33% MSE (Volatile), +28% MSE (Stable)"]
```mermaid
flowchart TD
    A["Research Goal: Adaptive Stock Forecasting<br>for Volatile vs. Stable Regimes"] --> B["Methodology: Mixture of Experts (MoE)<br>Volatility-Aware Gating"]
    B --> C["Data: 30 U.S. Stocks<br>Diverse Sectors"]
    C --> D{"Dynamic Classification:<br>Volatility Regime?"}
    D -- High Volatility --> E["RNN Expert<br>Deep Temporal Patterns"]
    D -- Stable/Low Volatility --> F["Linear Regression Expert<br>Baseline Simplicity"]
    E --> G["Output: Weighted Prediction"]
    F --> G
    G --> H["Findings: Adaptive MoE Outperforms Baselines<br>+33% MSE (Volatile), +28% MSE (Stable)"]