A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30

2026-07-15

AI Summary

This study addresses the limited research on stock market forecasting in emerging economies by focusing on short- and long-term predictions of Egypt’s EGX30 index. A systematic evaluation of KNN, Random Forest, XGBoost, LSTM, and GRU models—augmented with ensemble learning—is conducted using RMSE, MAPE, and R² metrics. Results indicate that XGBoost achieves the best performance for one-day-ahead forecasts, while GRU excels in predictions spanning one week to two months. Notably, the ensemble approach improves two-month forecast accuracy by nearly fivefold, and KNN demonstrates unexpected strength in long-term prediction. These findings offer data-driven support for investment decision-making in Middle Eastern emerging markets and highlight substantial variations in model performance across different forecasting horizons.

Abstract

This study concentrates on predicting stock prices in the Egyptian market, focusing on the EGX30, an influential financial hub in the Middle East. While most research focuses on global stocks, there's a growing need to understand stock trends in developing countries like Egypt. The study compares different machine learning models for forecasting EGX30 trends, covering short and long-term predictions. Using historical EGX30 data, including metrics like root mean squared error, Mean Absolute Percentage Error, and coefficient of determination, models like K-Nearest Neighbours, random forest, extreme gradient boosting, long short-term memory networks, and gated recurrent unit networks were evaluated. The goal is to determine the most effective models for EGX30 prediction, considering Egypt's unique market dynamics. Insights from this study aid investors in making informed decisions. Results show that the Gated Recurrent Unit (GRU) outperformed the other models in the one-week, one-month, and two-months while the eXtreme Gradient Boosting (XGBoost) model outperformed others in the one-day predictions, highlighting their usefulness in predictive analysis for financial markets. The study also showed the importance of using the ensemble techniques, especially in the long-term predictions which proved better results reaching 5 times the GRU in the two-month predictions. Additionally, the study notes the surprisingly good performance of K-Nearest Neighbours (KNN) on long-term predictions, suggesting its enduring relevance and potential for future applications in the fintech domains.

Problem

• Egyptian Stock Market
• EGX30
• Stock Price Forecasting
• Machine Learning
• Emerging Markets

Innovation

• Gated Recurrent Unit
• eXtreme Gradient Boosting
• ensemble techniques
• stock market forecasting
• emerging markets