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Publication Type : Conference Paper
Thematic Areas : Scientific reports
Publisher : Procedia Computer Science,.
Source : Procedia Computer Science, Volume 132, p.1351 - 1362 (2018)
Keywords : Artificial Neural Network, Deep learning, Mean absolute percentage error, National Stock Exchange, New York Stock Exchange
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering, Computer Science, Electronics and Communication
Year : 2018
Abstract : The neural network, one of the intelligent data mining technique that has been used by researchers in various areas for the past 10 years. Prediction and analysis of stock market data have got an important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). In this paper, we are using four types of deep learning architectures i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available. Here we are using day-wise closing price of two different stock markets, National Stock Exchange (NSE) of India and New York Stock Exchange (NYSE). The network was trained with the stock price of a single company from NSE and predicted for five different companies from both NSE and NYSE. It has been observed that CNN is outperforming the other models. The network was able to predict for NYSE even though it was trained with NSE data. This was possible because both the stock markets share some common inner dynamics. The results obtained were com- pared with ARIMA model and it has been observed that the neural networks are outperforming the existing linear model (ARIMA).
Cite this Research Publication : H. M, Dr. E. A. Gopalakrishnan, Vijay Krishna Menon, and Dr. Soman K. P., “NSE Stock Market Prediction Using Deep-Learning Models”, in Procedia Computer Science, 2018, vol. 132, pp. 1351 - 1362.