Hybrid Deep Learning Model for Stock Price Prediction

Abstract

In this paper, we propose a novel stock price prediction model based on deep learning. With the success of deep learning algorithms in the field of Artificial Neural Network (ANN), we choose to solve the regression based problems (stock price prediction in our case). Stock price prediction is a challenging problem due to its random movement. This hybrid model is a combination of two well-known networks, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). We choose the S&P 500 historical time series data and use significant evaluation metrics such as mean squared error, mean absolute percentage error etc., that conventional approaches have used. In experiment section, we have described the effectiveness of each of the component of our model along with its performance gain over the state-of-the-art approach. Our prediction model provides less error by considering this random nature (change) for a large scale of data.