Deep Learning for Stock Market Prediction

Abstract

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 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.

Deep Learning for Stock Market Prediction

“…There are some advantages of using this method, such as it being easy to understand and interpret or able to work out problems with multi-outputs; on the contrary, creating over-complex trees that result in overfitting is a fairly common disadvantage. A schematic illustration of the Decision tree is shown in Figure 2 , adapted from [ 43 ].…”

“…During training data in a random forest, each tree learns from a random sample of the data points. A schematic illustration of the random forest, adapted from [ 43 ], is indicated in Figure 3 .…”

“…ANNs are single or multi-layer neural nets that are fully connected. Figure 4 shows a sample of ANN with an input and output layer and also two hidden layers, adapted from [ 43 ]. In a layer, each node is connected to every other node in the next layer.…”

“…According to general architecture, the output of the node (at time t − 1) will be passed to the input (at time t) and add the data of itself (at time t) to make the output (at time t); recurrently exploiting the neuron node to flow multiple node elements to create RNN. Figure 6 , adapted from [ 43 ] shows a simple architecture of RNN. Furthermore, Equations (2) and (3) indicate the recursive formulas of RNN [ 45 ].…”

Machine Learning for Modeling the Singular Multi-Pantograph Equations

“…In Reference [ 31 ], by the method of Lagaris et al, a neural approach is developed for solving MDEs and its efficiency is proved. Now days, the computation techniques in software engineering are used in various branch of scientific problem such as multimedia systems [ 32 , 33 ], security systems [ 34 ], forecasting problems [ 35 ], stock market prediction [ 36 ], control systems [ 37 ], internet of things [ 38 ], and so on. However these effective techniques quite rarely are applied on MDEs.…”