Abstract
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)2PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D)2PCA + 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.
Similar content being viewed by others
References
Atsalakis GS, Valavanis KP (2009) Surveying stock market forecasting techniques--Part II: soft computing methods. Expert Syst Appl 36:5932–5941
Elman JL (1990) Finding structure in time. Cogn Sci 14:179–211
Erhan D, Bengio Y, Courville A, Manzagol PA, Vincent P, Bengio S (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11(Feb):625–660
Guo Z, Wang H, Yang J, Miller DJ (2015) A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network. PLoS One 10, e0122385
Gupta P (2015) Deep Learning - Regularisation. https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiGiNiWyuDJAhXDkI4KHZQbBEcQFggbMAA&url=https%3A%2F%2Fcs.nyu.edu%2Fmishra%2FCOURSES%2F15.Summer%2FL4DNN.pdf&usg=AFQjCNEXq89m3B5prfmdk4s2eThK9YKA&sig2=z-wO9QRxYdD2ZS204B95ig&bvm=bv.110151844,d.c2E. Accessed 5 July 2016
Guresen E, Kayakutlu G, Daim TU (2011) Using artificial neural network models in stock market index prediction. Expert Syst Appl 38:10389–10397
Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(80):504–507
Huang CJ, Yang DX, Chuang YT (2008) Application of wrapper approach and composite classifier to the stock trend prediction. Expert Syst Appl 34:2870–2878. doi:10.1016/j.eswa.2007.05.035
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Adv. Neural Inf. Process. Syst. 1097–1105
Kuremoto T, Kimura S, Kobayashi K, Obayashi M (2014) Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137:47–56
Kuremoto T, Obayashi M, Kobayashi K, et al (2014) Forecast chaotic time series data by DBNs. In: Image Signal Process. (CISP), 2014 7th. Int Congr 1130–1135
Kwon YK, Moon BR (2007) A hybrid neurogenetic approach for stock forecasting. IEEE Trans Neural Netw 18:851–864. doi:10.1109/TNN.2007.891629
Larochelle H, Bengio Y, Louradour J, Lamblin P (2009) Exploring strategies for training deep neural networks. J Mach Learn Res 10:1–40
Le Roux N, Bengio Y (2010) Deep belief networks are compact universal approximators. Neural Comput 22:2192–2207
Lendasse A, de Bodt E, Wertz V, Verleysen M (2000) Non-linear financial time series forecasting-Application to the Bel 20 stock market index. Eur J Econ Soc Syst 14:81–91
Malkiel BG (2007) A random walk down Wall Street: the time-tested strategy for successful investing. WW Norton & Company
Mohamed A, Sainath TN, Dahl G, et al (2011) Deep belief networks using discriminative features for phone recognition. In: 2011 I.E. Int. Conf. Acoust. speech signal Process. 5060–5063
Murphy JJ (1999) Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin
Pan H, Tilakaratne C, Yearwood J (2003) Predicting the Australian stock market index using neural networks exploiting dynamical swings and intermarket influences. In: Australas. Jt. Conf. Artif. Intell. 327–338
Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Syst Appl 42:259–268
Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock market index using fusion of machine learning techniques. Expert Syst Appl 42:2162–2172
Shen W, Guo X, Wu C, Wu D (2011) Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge-Based Syst 24:378–385. doi:10.1016/j.knosys.2010.11.001
Situngkir H, Surya Y (2004) Neural network revisited: perception on modified Poincare map of financial time-series data. Phys A Stat Mech Appl 344:100–103
Sun F, Toh K-A, Romay MG, Mao K (2014) Extreme Learning Machines 2013: Algorithms and Applications. Springer
Sutskever I, Hinton GE (2008) Deep, narrow sigmoid belief networks are universal approximators. Neural Comput 20:2629–2636
Takeuchi L, Lee Y-YA (2013) Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks http://cs229.stanford.edu/proj2013/TakeuchiLeeApplyingDeepLearningToEnhanceMomentumTradingStrategiesInStocks.pdf
Teixeira LA, De Oliveira ALI (2010) A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert Syst Appl 37:6885–6890
White H (1988) Economic prediction using neural networks: the case of IBM daily stock returns. In: IEEE Int Conf. Neural Networks. 451–458
Yu D, Deng L, Wang S (2009) Learning in the deep-structured conditional random fields. In: Proc. NIPS Work. 1–8
Yu, K., Xu, W. and Gong, Y. (2009) Deep learning with kernel regularization for visual recognition. In Advances in Neural Information Processing Systems. 1889–1896
Zeiler MD (2012) ADADELTA: an adaptive learning rate method. arXiv Prepr. arXiv1212.5701
Zhang D, Zhou Z (2005) (2D)2PCA : 2-Directional 2-Dimensional PCA for Efficient Face Representation and Recognition. Neurocomputing 69:224–231. doi:10.1016/j.neucom.2005.06.004
Zuo Z, Wang G (2014) Learning discriminative hierarchical features for object recognition. IEEE Sig Proc Lett 21:1159–1163
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Singh, R., Srivastava, S. Stock prediction using deep learning. Multimed Tools Appl 76, 18569–18584 (2017). https://doi.org/10.1007/s11042-016-4159-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-4159-7