Stock prediction using deep learning

• Published in: Multimedia tools and applications Vol. 76; no. 18; pp. 18569 - 18584
• Main Authors: Singh, Ritika, Srivastava, Shashi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2017; Springer Nature B.V
• Subjects: Accuracy; Artificial neural networks; Basis functions; Computer Communication Networks; Computer Science; Correlation coefficients; Data Structures and Information Theory; Deep learning; Digital imaging; Digital signal processing; Economic forecasting; Image classification; Learning theory; Machine learning; Markets; Multimedia; Multimedia Information Systems; Natural language processing; Neural networks; Object recognition; Predictions; Principal components analysis; Radial basis function; Recurrent neural networks; Securities markets; Special Purpose and Application-Based Systems; Speech recognition; Time series; Two dimensional analysis; Multimedia; Neural Network; 2D; Stock Prediction; Radial Basis Function Neural Network; Regularization; Deep Learning; PCA
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-016-4159-7

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. Methods applied in digital signal processing can be applied to stock data as both are time series. Similarly, learning outcome of this paper can be applied to speech time series data. Deep learning for stock prediction has been introduced in this paper and its performance is evaluated on Google stock price multimedia data (chart) from NASDAQ. The objective of this paper is to demonstrate that deep learning can improve stock market forecasting accuracy. For this, (2D) 2 PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D) 2 PCA + Radial Basis Function Neural Network (RBFNN). It is found that the proposed method is performing better than the existing method RBFNN with an improved accuracy of 4.8% for Hit Rate with a window size of 20. Also the results of the proposed model are compared with the Recurrent Neural Network (RNN) and it is found that the accuracy for Hit Rate is improved by 15.6%. The correlation coefficient between the actual and predicted return for DNN is 17.1% more than RBFNN and it is 43.4% better than RNN.