Ensemble deep learning models for stock price forecasting: evidence from the Saudi markets

Investments are the foundation of financial markets, directing resources toward activities that generate future returns. Stock trading plays a central role in wealth creation, yet forecasting stock prices remains difficult due to the nonlinear and volatile nature of financial data. This study presents an ensemble deep learning framework for short-term stock price forecasting in the Saudi market. It addresses the limited use of advanced ensemble methods in emerging economies. The framework uses multivariate data from ten companies listed on the Tadawul All Share Index and compares two ensemble strategies: averaging and stacking with standalone models (Gated Recurrent Unit, Long Short-Term Memory, and Bidirectional Recurrent Neural Network). A total of 34 engineered technical indicators and a rolling-window setup were applied for five-day-ahead predictions. The results show that ensemble models consistently outperform individual architectures across all evaluation metrics. Stacking ensembles, especially those combining Gated Recurrent Unit and Long Short-Term Memory, achieved the highest accuracy. Averaging ensembles provided stable and efficient alternatives. Overall, the findings confirm the robustness and adaptability of ensemble methods for financial forecasting and offer practical insights for investors and policymakers seeking reliable, data-driven prediction tools.