Machine learning techniques for short term stock movements classification for Moroccan stock exchange
Machine learning techniques for short term stock movements classification for Moroccan stock exchange
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DOI：
10.1109/sita.2016.7772259
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Badre Labiad;A. Berrado;L. Benabbou
中科院分区：
文献类型：
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作者：
Badre Labiad;A. Berrado;L. Benabbou
准确的股票价格预测对于投资者和交易者做出明智的交易决策至关重要。然而，由于其非线性和非平稳性，价格具有复杂的行为。在本文中，三种机器学习技术被实现来预测摩洛哥股市的非常短期（提前10分钟）的变化：随机森林（RF），梯度提升树（GBT）和支持向量机（SVM）。选择技术指标作为输入变量，进行特征选择和样本选择步骤，以提高预测精度和训练时间。一个为期八年的一天的价格（tick-by-tick数据）的摩洛哥电信（IAM）股票作为实验数据库，以评估所选模型的性能。实验结果表明，RF和GBT是上级优于SVM为我们的数据集。此外，RF和GBT的低计算复杂度和减少的训练时间适合于短期预测。
Accurate stock price forecasting is important for investors and traders to make informed trading decision. However, prices have a complex behavior due to their nonlinearity and nonstationarity. In this paper three Machine learning techniques are implemented to predict a very short term (10 minutes ahead) variations of the Moroccan stock market: Random Forest (RF), Gradient Boosted Trees (GBT) and Support Vector Machine (SVM). A selection of technical indicators was used as inputs variables and a feature selection and samples selection steps were performed to improve prediction accuracy and training time. An eight-year period of intraday prices (tick-by-tick data) of Maroc Telecom (IAM) stocks is employed as experimental database to evaluate the performances of the selected models. The experimental results have shown that RF and GBT are superior to SVM for our dataset. Further, the low computational complexity and reduced training time of RF and GBT are suitable for short term forecasting.