NSE Stock Market Prediction Using Deep-Learning Models

M Hiransha, E. A. Gopalakrishnan, Vijay Menon, K. P. Soman

2018-01-01otherProcedia Computer Science615 citationsdiamond

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Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

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).

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References

2003Time series forecasting using a hybrid ARIMA and neural network model

1998Forecasting with artificial neural networks:

2017Stock price prediction using LSTM, RNN and CNN-sliding window model

2011Using artificial neural network models in stock market index prediction

2016Deep Learning for Finance: Deep Portfolios

2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index

2015Deep learning for event-driven stock prediction

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