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 · Source: Artificial Intelligence · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

A study conducted a comparative analysis of machine learning models for long and short-term forecasting of the Egyptian stock market, specifically the EGX30. It evaluated K-Nearest Neighbours (KNN), Random Forest, eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) networks using historical EGX30 data. The research found that GRU models outperformed others for one-week, one-month, and two-month predictions, while XGBoost was superior for one-day forecasts. Ensemble techniques significantly improved long-term predictions, reaching 5 times the GRU's performance in two-month forecasts. Notably, KNN showed surprisingly good performance on long-term predictions, suggesting its continued relevance in fintech applications.

Key takeaway

For investors and data scientists focused on emerging markets like Egypt, you should prioritize GRU models for multi-week to two-month stock predictions and XGBoost for daily forecasts. Incorporate ensemble techniques to significantly enhance long-term accuracy, potentially yielding 5x better results. Also, consider K-Nearest Neighbours for its surprising long-term predictive utility in financial market analysis.

Key insights

GRU and XGBoost models demonstrate superior performance in short and long-term EGX30 stock market forecasting, with ensemble methods enhancing long-term accuracy.

Principles

• Market dynamics influence model effectiveness.
• Ensemble methods boost long-term accuracy.
• Simpler models like KNN retain relevance.

Method

Evaluated KNN, Random Forest, XGBoost, LSTM, and GRU models using historical EGX30 data, assessing performance with RMSE, MAPE, and R² for one-day to two-month forecasts.

In practice

• Apply GRU for multi-week stock predictions.
• Utilize XGBoost for daily market forecasts.
• Consider KNN for long-term trend analysis.

Topics

• Stock Market Forecasting
• EGX30
• Machine Learning
• GRU
• XGBoost
• Ensemble Learning
• Financial Technology

Best for: Research Scientist, AI Engineer, Machine Learning Engineer, AI Scientist, Data Scientist, Investor

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.