埃及股票市场长期和短期预测的机器学习模型比较分析：以EGX30为重点
A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30
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中文总结 AI 辅助
该研究聚焦埃及EGX30股票市场，比较K近邻、随机森林等多种机器学习模型进行长短期预测，通过历史数据及相关指标评估，得出GRU在多周期预测中表现优，XGBoost在一日预测中佳，还凸显集成技术及KNN在长期预测中的作用。
中文摘要 AI 辅助
本研究专注于预测埃及市场的股票价格，重点是中东有影响力的金融中心EGX30。多数研究关注全球股票，而了解埃及等发展中国家股票趋势的需求日益增长。研究比较了用于预测EGX30趋势的不同机器学习模型，涵盖短期和长期预测。使用包括均方根误差、平均绝对百分比误差和决定系数等指标的历史EGX30数据，对K近邻、随机森林、极端梯度提升、长短期记忆网络和门控循环单元网络等模型进行评估。目标是确定考虑埃及独特市场动态下最有效的EGX30预测模型。结果表明，门控循环单元（GRU）在一周、一个月和两个月预测中优于其他模型，极端梯度提升（XGBoost）模型在一日预测中表现更佳。研究还显示了集成技术的重要性，尤其是在长期预测中。此外，K近邻（KNN）在长期预测中表现出令人惊讶的良好性能。
英文摘要
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.
