Deep learning for stock market forecasting using numerical and textual information (Long-Short Term Memory approach)
Subject Areas : Stock Exchange
seyyedeh mozhgan
beheshti masalegou
1
(Department of information technology management ,Central Tehran Branch , Islamic Azad University, Tehran , Iran)
Mohammad ali
Afshar kazemi
2
(Department of Industrial Management, Central Tehran Branch. Islamic Azad University, Tehran, Iran)
jalal
Haghighat monfared
3
(Department of Industrial Management, , Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Ali
Rezaeian
4
(Department of Governmental Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran)
Keywords: technical indicators, deep learning, natural language processing, Stock market prediction, Long Short Term Memory,
Abstract :
Stock prices are influenced by many factors, making forecasting challenging. This prediction is often ineffective if it only considers numerical data or textual information. This research aims to provide a method of forecasting the future price of stocks based on the structure of a deep neural network using price data, a set of technical indicators, and news headlines as input to the model. For this purpose, Dow Jones stock data and Reddit channel news data have been used. Technical features are extracted from the stock data, and the news data are converted into a feature vector by the Bag of Words method and fed into the Long-Short term memory network for prediction. Accuracy is used as a performance evaluation measure and experiments on two data sets. The only numerical and only text has been used to evaluate the simultaneous use of two information sources. Also, three networks, SVM, MLP, and RNN, have been used to evaluate the model. The results show that the LSTM model achieved the highest prediction accuracy of 69.19% using news and financial data. News data is 65.62% accurate, and numerical data is 51.89%. Also, the LSTM model performs better than SVM, MLP, and RNN neural networks.
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