论文标题
股票市场预测的深度学习
Deep learning for Stock Market Prediction
论文作者
论文摘要
对股票群体价值的预测一直是吸引人的，对股东充满挑战。本文集中于股票市场群体的未来预测。从德黑兰证券交易所（Tehran Stock Exchange）中选择了四个属于多元化的财务，石油，非金属矿物质和基本金属进行实验评估。根据十年的历史记录收集这些组的数据。值预测是针对提前1、2、5、10、15、20和30天创建的。机器学习算法用于预测股票市场群体的未来价值。我们采用了决策树，行李，随机森林，自适应增强（ADABOOST），梯度提升和极端梯度提升（XGBoost）以及人工神经网络（ANN），经常性神经网络（RNN）和长期短期记忆（LSTM）。选择十个技术指标作为每个预测模型的输入。最后，基于三个指标的每种技术提出了预测的结果。在本文中使用的所有算法中，LSTM以最高的模型拟合能力显示出更准确的结果。同样，对于基于树的模型，Adaboost，梯度提升和XGBoost之间通常会有激烈的竞争。
Prediction of stock groups' values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all the algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.
