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

机器学习模型用于埃及股市预测的比较

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究分析了各种机器学习模型预测埃及证券交易所EGX30指数的有效性。该研究比较了K近邻、随机森林、极端梯度提升和循环神经网络（LSTM、GRU）等模型。结果表明，门控循环单元（GRU）模型在一周、一个月和两个月的预测中表现最佳，而极端梯度提升（XGBoost）在一日预测中表现出色。研究还强调了集成技术在长期预测中的优势，并指出K近邻在长期预测中表现出乎意料地强劲。 AI

影响 为发展中经济体的金融市场预测提供了有效的机器学习模型见解。

排序理由 学术论文分析用于金融预测的机器学习模型。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

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机器学习模型用于埃及股市预测的比较

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学术论文分析用于金融预测的机器学习模型。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Muhammed Walid, Ahmed El-Naeimy, Hosam Moubarak, Walid Gomaa ·

    埃及股市长短期预测机器学习模型比较分析：聚焦EGX30

    arXiv:2607.14391v1 Announce Type: new 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 st…

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