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Research on Stock Forecasting Based on GPU and Complex-Valued Neural Network

  • Lina Jia
  • Bin Yang
  • Wei Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

Accurately and rapidly forecasting stock index is a difficult and hot research in the economic field. In this paper, complex-valued neural network (CVNN) model is proposed to predict stock price. In order to improve the time of training CVNN model, a parallel particle swarm optimization (PSO) based on graphics processing unit (GPU) is proposed to optimize the complex-valued parameters of CVNN model. Shanghai stock exchange composite index is selected to demonstrate the performance of CVNN model with parallel PSO algorithm. The experiment results reveal that our proposed method could improve stock index accurately and reduce training time sharply.

Keywords

Stock forecasting Neural network Complex-valued Graphics processing unit 

Notes

Acknowledgments

This work was supported by the Natural Science Foundation of China (No. 61702445), the PhD research startup foundation of Zaozhuang University (No.2014BS13), Zaozhuang University Foundation (No. 2015YY02), and Shandong Provincial Natural Science Foundation, China (No. ZR2015PF007).

References

  1. 1.
    Fama, E.F.: The behavior of stock-market prices. J. Bus. 38(1), 34–105 (1965)CrossRefGoogle Scholar
  2. 2.
    Chen, N.F., Roll, R., Ross, S.A.: Economic forces and the stock market. J. Bus. 59(3), 383–403 (1986)CrossRefGoogle Scholar
  3. 3.
    Pang, X., Zhou, Y., Wang, P., Lin, W., Chang, V.: An innovative neural network approach for stock market prediction. J. Supercomput. 1, 1–21 (2018)Google Scholar
  4. 4.
    Guan, H., Dai, Z., Zhao, A., He, J.: A novel stock forecasting model based on High-order-fuzzy-fluctuation trends and back propagation neural network. PLoS ONE 13(2), e0192366 (2018)CrossRefGoogle Scholar
  5. 5.
    Cao, J., Cui, H., Shi, H., Jiao, L.: Big data: a parallel particle swarm optimization-back-propagation neural network algorithm based on MapReduce. PLoS ONE 11(6), e0157551 (2016)CrossRefGoogle Scholar
  6. 6.
    Mei, S., He, M., Shen, Z.: Optimizing hopfield neural network for spectral mixture unmixing on GPU platform. IEEE Geosci. Remote Sens. Lett. 11(4), 818–822 (2013)Google Scholar
  7. 7.
    Beyeler, M., Oros, N., Dutt, N., Krichmar, J.L.: A GPU-accelerated cortical neural network model for visually guided robot navigation. Neural Netw. 72, 75–87 (2015)CrossRefGoogle Scholar
  8. 8.
    Shiraki, A., Masuda, N., Tanaka, T., Sugie, T., Ito, T.: Computer generated holography using a graphics processing unit. Opt. Express 14(2), 603–608 (2006)CrossRefGoogle Scholar
  9. 9.
    Brodtkorb, A.R., Hagen, T.R., Sætra, M.L.: Graphics processing unit (GPU) programming strategies and trends in GPU computing. J. Parall. Distrib. Comput. 73(1), 4–13 (2013)CrossRefGoogle Scholar
  10. 10.
    Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston (2002)Google Scholar
  11. 11.
    Chen, Y.H., Abraham, A.: Hybrid learning methods forstock index modeling. In: Kamruzzaman, J., Begg, R.K., Sarker, R.A. (eds.) Artificial Neural Networks in Finance, Health and Manufacturing: Potential and Challenges, IdeaGroup Inc. Publishers, USA (2006)Google Scholar
  12. 12.
    Chen, Y., Yang, B., Meng, Q., Zhao, Y., Abraham, A.: Time-series forecasting using a system of ordinary differential equations. Inf. Sci. 181(1), 106–114 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringZaozhuang UniversityZaozhuangChina

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