2024/10/04 by Diego Vallarino, Vallarino, Diego · 1 citation
Decision Sciences · #Computational Finance (q-fin.CP) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2410.07234
openalex publication_date 2024/10/04 · openalex created_date 2024/10/13 · openalex updated_date 2026/07/28
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.