Stock Market Price Forecasting by Using Deep Learning

  • Authors

    • Madhusudan Reddy
    • Arun Gade
    • Sreekarreddy .
    • P Prabhu
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16442
  • Stock market forecasts are an attempt to determine the future value of corporate capital or other financial products consumed in the stock market. If the future stock price forecast succeeds, you can gain great profit. The efficient market presents all th

  • Stock market forecasts are an attempt to determine the future value of corporate capital or other financial products consumed in the stock market. If the future stock price forecast succeeds, you can gain great profit. The efficient market presents all the current stock price information, which shows that price fluctuations are not the basis for unnecessary new information. Others disagree that people who have these ideas have many methods and techniques to help them get future information. [1]

     

     

  • References

    1. [1] YudongZhang,LenanWuStock market prediction of S&P 500 via combination of improved BCO approach and BP neural network; School of Information Science and Engineering, Southeast University, Room 204, Jianxiong Yuan, Nanjing 210096, China

      [2] Ritanjali Majhi, & Panda, G., (2007). Stock market prediction of S&P 500 and DJIA using bacterial foraging optimization technique. In 2007 IEEE congress on evolutionary computation (CEC 2007)

      [3] Tan, T. Z., Quek, C., & Ng, G. S. (2005). Brain inspired genetic complimentary learning for stock market prediction. In IEEE congress on evolutionary computation, 2–5th September (Vol. 3, pp. 2653–2660).) (pp. 2569–2579).

      [4] L.Q. Han Tutorial of artificial neural network Beijing Posts & Telecommunication Press (2006) (in Chinese)

      [5] K. Miao, F. Chen, Z.G. ZhaoStock price forecast based on bacterial colony RBF neural network Journal of QingDao University, 20 (2) (2007), pp. 50-54 (in Chinese)

      [6] K.J. Oh, K.-J. KimAnalyzing stock market tick data using piecewise non linear model Expert System with Applications, 22 (3) (2002), pp. 249-255

      [7] M.T. Philip, K. Poul, S.O. Choy, K. Reggie, S.c. Ng, 1. Mark, T.Jonathan, K. Kai, and W. Tak-Lam, â€Design and Implementation of NN5 for Hong Stock Price Forecastingâ€, Journal of Engineering Application of Artificial Intelligence, vol. 20,2007, pp.453-461

      [8] M.T. Philip, K. Poul, S.O. Choy, K. Reggie, S.c. Ng, 1. Mark, T.Jonathan, K. Kai, and W. Tak-Lam, â€Design and Implementation of NN5 for Hong Stock Price Forecastingâ€, Journal of Engineering Application of Artificial Intelligence, vol. 20,2007, pp.453-461

      [9] T. Kimoto, K. Asawaka, M. Y oda, and M. Takeoka, â€Stock Market Prediction System with Modular Neural Network â€, Processing of International Joint Conference on Neural Networks, 1990, pp. 1-6.

      [10] H. Mizono, M. Kosaka, H. Yajma, and N. Komoda, â€Application of Neural Network to Technical Anaysis of Stock Market Prediction â€, Studies in Informatics and Control, 1998, Vol.7, No.3, pp.1l1-120

      [11] S. R. Sexton, R. E. Dorsey and 1. D. Johnson, â€Toward Global Optimization of Neural Network A Comparison of the Genetic Algorithm and Backpropagationâ€, 1998, Decision Support Systems voL22, pp.I17- 185.

      [12] E. Peters order in capital markets: A new view of cycles, prices, market volatility John Wiley & Sons Inc (1996)

      [13] Lawrence, S., Tsoi, A. C., & Giles, C. L. (1996). Noisy time series prediction using symbolic representation and recurrent neural network grammatical inference. Technical report UMIACS-TR-96-27 and CS-TR3625. Institute for Advanced Computer Studies, University of Maryland.

      [14] Phua, P. K. H. Ming, W. Lin, â€Neural Network With Genetic Algorithms For Stocks Prediction†, Fifth Conferences of the Association of AsianPacific Operations Research Societies, 2000, 51h 7th July, Singapore

      [15] D. E. Rumelhart, G. E. Hinton, R. 1. Wiliams, â€Learning Internal Representation by Error Propagation Parallel Distributed Processing Explorations in the Microstructures of Cognition â€, McClelland J. L. (eds.), 1:318-362, MIT Press, Cambridge, 1986.

      [16] T. Blumensath and M. E. Davies “Iterative hard thresholding for compressed sensing,†Applied and Computational Harmonic Analysis, vol. 27, no. 3, pp. 265–274, 2009.

      [17] A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,†SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183–202, 2009.

      [18] E. J. Cand‘es “The restricted isometry property and its implications for compressed sensing,†ComptesRendusMathematique, vol. 346, no. 9, pp. 589–592, 2008.

      [19] . L. Malloy and R. D. Nowak “Near-optimal adaptive compressed sensing,†Information Theory, IEEE Transactions on, vol. 60, no. 7, pp. 4001–4012, 2014.

      [20] E. Cand‘es, J. Romberg, and T. Tao “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,â€IEEETrans.Inform.Theory,vol.52,no.2,pp.489–509,February 2006.

      [21] C.Dong,C.C.Loy,K.He,andX.Tang,“Learningadeepconvolutional network for image super-resolution,†in Proc. European Conf. Comp. Vision (ECCV). Springer, 2014, pp. 184–199.

      [22] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,†Cognitive Modeling, vol. 5, p. 3, 1988.

      [23] ] Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layerwise training of deep networks,†Proc. Adv. in Neural Processing Systems (NIPS), vol. 19, pp. 153–160, 2007.

      [24] G. E. Hinton “Training products of experts by minimizing contrastive divergence,†Neural Computation, vol. 14, no. 8, pp. 1771–1800, 2002

      [25] Ahmadi, H., (1990). Testability of the arbitrage pricing theory by neural networks. In Proceedings of the international joint conference on neural networks (IJCNN), June (pp. 385–393) Washington DC.

      [26] S. Neenwi, P. O. Asagba, L. G. Kabari â€Predicting the Nigerian Stock Market Using Artificial Neural Networkâ€, European Journal of Computer Science and Information, VoLI , No.1, pp.30-39, June 2013

      [27] Robyn Ball and Philippe Tissot â€Demonstration of Artificial Neural Network in Matlabâ€, Division of Nearhsore Research, Texas A&M University, 2006

      [28] D. E. Rumelhart, G. E. Hinton, R. 1. Wiliams â€Learning Internal Representation by Error Propagation Parallel Distributed Processing Explorations in the Microstructures of Cognition McClelland J. L. (eds.), 1:318-362, MIT Press, Cambridge, 1986. [

      [29] A.A. Basma, N. KallasModeling soil collapse by artificial neural networks Geotechnical and Geological Engineering, 22 (2004), pp. 427- 438

      [30] https://en.wikipedia.org/wiki/Stock_market_prediction

      [31] https://en.wikipedia.org/wiki/Artificial_neural_network

  • Downloads

  • How to Cite

    Reddy, M., Gade, A., ., S., & Prabhu, P. (2018). Stock Market Price Forecasting by Using Deep Learning. International Journal of Engineering & Technology, 7(3.12), 627-631. https://doi.org/10.14419/ijet.v7i3.12.16442