  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.
