A Dynamic Approach to Stock Price Prediction: Comparing RNN and Mixture of Experts Models Across Different Volatility Profiles

ArXiv ID: 2410.07234 “View on arXiv”

Authors: Unknown

Abstract

This study evaluates the effectiveness of a Mixture of Experts (MoE) model for stock price prediction by comparing it to a Recurrent Neural Network (RNN) and a linear regression model. The MoE framework combines an RNN for volatile stocks and a linear model for stable stocks, dynamically adjusting the weight of each model through a gating network. Results indicate that the MoE approach significantly improves predictive accuracy across different volatility profiles. The RNN effectively captures non-linear patterns for volatile companies but tends to overfit stable data, whereas the linear model performs well for predictable trends. The MoE model’s adaptability allows it to outperform each individual model, reducing errors such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). Future work should focus on enhancing the gating mechanism and validating the model with real-world datasets to optimize its practical applicability.

Keywords: Mixture of Experts, Recurrent Neural Network, Stock Price Prediction, Volatility Modelling, Gating Network, Equities

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats
  • Why: The paper introduces advanced ML concepts like Mixture of Experts and RNNs/LSTMs with explicit formula derivations, indicating moderate mathematical density. However, it relies on simulated data rather than real-world backtesting, lacks code/dataset references, and explicitly mentions future validation is needed, limiting its empirical readiness.
  flowchart TD
    A["Research Goal: Compare MoE, RNN, and Linear Regression<br>for Stock Price Prediction"] --> B["Input Data: Stocks with<br>Different Volatility Profiles"]
    B --> C["Methodology: Dynamic MoE Framework<br>Gate + Expert Model Selection"]
    C --> D{"Compute & Train Models"}
    D --> E["Expert 1: RNN Model<br>For Volatile Stocks"]
    D --> F["Expert 2: Linear Regression<br>For Stable Stocks"]
    E --> G["Key Outcomes: MoE outperforms<br>individual models (Lower MSE/MAE)"]
    F --> G