The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. 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 were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were 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 networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

中文翻译：

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股市预测的深度学习


由于其固有的动态性、非线性和复杂性，股票群体价值的预测一直对股东有吸引力且具有挑战性。本文主要关注股市群体的未来预测。选择德黑兰证券交易所的多元化金融、石油、非金属矿产和基本金属四个类别进行实验评估。根据 10 年的历史记录收集各组的数据。价值预测是提前 1、2、5、10、15、20 和 30 天创建的。利用各种机器学习算法来预测股票市场组的未来价值。我们采用了决策树、装袋、随机森林、自适应增强 (Adaboost)、梯度增强和极限梯度增强 (XGBoost)、人工神经网络 (ANN)、循环神经网络 (RNN) 和长短期记忆 (LSTM) 。选择十个技术指标作为每个预测模型的输入。最后，基于四个指标给出了每种技术的预测结果。在本文使用的所有算法中，LSTM 显示出更准确的结果，模型拟合能力最高。此外，对于基于树的模型，Adaboost、Gradient Boosting 和 XGBoost 之间经常存在激烈的竞争。