| Peer-Reviewed

Study on Exchange Rate Forecasting Using Recurrent Neural Networks

Received: 16 November 2017     Published: 20 November 2017
Views:       Downloads:
Abstract

It focuses on the problems of forecasting exchange rate that is a nonlinear time series. A dynamics systems approach and the recurrent neural networks (RNN) were employed to modeling this nonlinear time series. The delay time was calculated using mutual information method and embedding dimension was confirmed by false nearest neighbors. The dataset was reconstructed form source time series for trained and verified the neural networks model. The quadratic optimization criterion was considered which the neural networks weights update algorithm were derived using gradient descent method for hidden layer; recurrent layer and output layer. The calculation flow chart was designed for neural networks learning and emulation. The reliability and stability of neural networks was confirmed by testing dataset. The results of simulation showed that the recurrent neural networks were preferably performance for prediction the change of exchange rate.

Published in International Journal of Economics, Finance and Management Sciences (Volume 5, Issue 6)
DOI 10.11648/j.ijefm.20170506.14
Page(s) 300-303
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Neural Networks, Forecasting Exchange Rate, Nonlinear Time Series

References
[1] Xing Chengdong, Zhang Quan. Research on the Impact of RMB Exchange Rate Fluctuation on Chinese Stock Market-From the Perspective of Index. International Journal of Economics, Finance and Management Sciences. 2015 3(5): 641-645.
[2] Md. Zahangir Alam, Muhammad Abdur Rahim. Nexus between stock exchange index and exchange rates. International Journal of Economics, Finance and Management Sciences. 2013; 1(6): 330-334.
[3] Diebold, F. X., Nason, J. A.. Nonparametric exchange rate prediction. Journal of International Economics 28, 315–332.1990.
[4] Y. Kajitani, K. W. Hipel, and A. I. McLeod, Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data, Journal of Forecasting, vol. 24, no. 2, pp. 105–117, 2005.
[5] T. Takahama, S. Sakai, A. Hara, and N. Iwane, Predicting stock price using neural networks optimized by differential evolutionwith degeneration, International Journal of Innovative Computing, Information and Control, vol. 5, no. 12, pp. 5021–5031, 2009.
[6] D. Xiao and J. Wang, Modeling stock price dynamics by continuum percolation system and relevant complex systems analysis, Physica A: Statistical Mechanics and its Applications, vol. 391, no. 20, pp. 4827–4838, 2012.
[7] M. Zounemat-Kermani, Principal component analysis (PCA)for estimating chlorophyll concentration using forward andgeneralized regression neural networks, Applied Artificial Intelligence, vol. 28, no. 1, pp. 16–29, 2014.
[8] R. Ebrahimpour, H. Nikoo, S. Masoudnia, M. R. Yousefi, and M. S. Ghaemi, Mixture of mlp-experts for trend forecasting of time series: a case study of the tehran stock exchange, International Journal of Forecasting, vol. 27, no. 3, pp. 804–816, 2011.
[9] D. O. Faruk, A hybrid neural network and ARIMA model for water quality time series prediction, Engineering Applications of Artificial Intelligence, vol. 23, no. 4, pp. 586–594, 2010.
[10] M. Tripathy, Power transformer differential protection using neural network principal component analysis and radial basis function neural network, Simulation Modelling Practice and Theory, vol. 18, no. 5, pp. 600–611, 2010.
[11] P.-C. Chang, D.-D. Wang, and C.-L. Zhou, A novel model by evolving partially connected neural network for stock price trend forecasting, Expert Systems with Applications, vol. 39, no. 1, pp. 611–620, 2012.
[12] T. H. Roh, Forecasting the volatility of stock price index, Expert Systems with Applications, vol. 33, no. 4, pp. 916–922, 2007.
[13] G. E. Batista, E. J. Keogh, O. M. Tataw, and V. M. de Souza, CID: An efficient complexity-invariant distance for time series, Data Mining and Knowledge Discovery, vol. 28, no. 3, pp. 634–669, 2014.
[14] M. Zounemat-Kermani, Principal component analysis (PCA)for estimating chlorophyll concentration using forward and generalized regression neural networks, Applied Artificial Intelligence, vol. 28, no. 1, pp. 16–29, 2014.
[15] Z. Q. Guo, H. Q. Wang, and Q. Liu, Financial time series forecasting using LPP and SVM optimized by PSO, Soft Computing, vol. 17, no. 5, pp. 805–818, 2013.
[16] H. L. Niu and J. Wang, Financial time series prediction by a random data-time effective RBF neural network, Soft Computing, vol. 18, no. 3, pp. 497–508, 2014.
[17] Kennel M B, Brown R, Abarbanel H D I. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A, 1992, 45: 3403-3411.
[18] Lin T, Horne B G, Tino P, Giles C L. A delay damage model selection algorithm for NARX neural networks. IEEE Transactions on Signal Processing, 1997 45(11), 2719-2730.
Cite This Article
  • APA Style

    Yuxi Ye. (2017). Study on Exchange Rate Forecasting Using Recurrent Neural Networks. International Journal of Economics, Finance and Management Sciences, 5(6), 300-303. https://doi.org/10.11648/j.ijefm.20170506.14

    Copy | Download

    ACS Style

    Yuxi Ye. Study on Exchange Rate Forecasting Using Recurrent Neural Networks. Int. J. Econ. Finance Manag. Sci. 2017, 5(6), 300-303. doi: 10.11648/j.ijefm.20170506.14

    Copy | Download

    AMA Style

    Yuxi Ye. Study on Exchange Rate Forecasting Using Recurrent Neural Networks. Int J Econ Finance Manag Sci. 2017;5(6):300-303. doi: 10.11648/j.ijefm.20170506.14

    Copy | Download

  • @article{10.11648/j.ijefm.20170506.14,
      author = {Yuxi Ye},
      title = {Study on Exchange Rate Forecasting Using Recurrent Neural Networks},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {5},
      number = {6},
      pages = {300-303},
      doi = {10.11648/j.ijefm.20170506.14},
      url = {https://doi.org/10.11648/j.ijefm.20170506.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20170506.14},
      abstract = {It focuses on the problems of forecasting exchange rate that is a nonlinear time series. A dynamics systems approach and the recurrent neural networks (RNN) were employed to modeling this nonlinear time series. The delay time was calculated using mutual information method and embedding dimension was confirmed by false nearest neighbors. The dataset was reconstructed form source time series for trained and verified the neural networks model. The quadratic optimization criterion was considered which the neural networks weights update algorithm were derived using gradient descent method for hidden layer; recurrent layer and output layer. The calculation flow chart was designed for neural networks learning and emulation. The reliability and stability of neural networks was confirmed by testing dataset. The results of simulation showed that the recurrent neural networks were preferably performance for prediction the change of exchange rate.},
     year = {2017}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Study on Exchange Rate Forecasting Using Recurrent Neural Networks
    AU  - Yuxi Ye
    Y1  - 2017/11/20
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ijefm.20170506.14
    DO  - 10.11648/j.ijefm.20170506.14
    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
    SP  - 300
    EP  - 303
    PB  - Science Publishing Group
    SN  - 2326-9561
    UR  - https://doi.org/10.11648/j.ijefm.20170506.14
    AB  - It focuses on the problems of forecasting exchange rate that is a nonlinear time series. A dynamics systems approach and the recurrent neural networks (RNN) were employed to modeling this nonlinear time series. The delay time was calculated using mutual information method and embedding dimension was confirmed by false nearest neighbors. The dataset was reconstructed form source time series for trained and verified the neural networks model. The quadratic optimization criterion was considered which the neural networks weights update algorithm were derived using gradient descent method for hidden layer; recurrent layer and output layer. The calculation flow chart was designed for neural networks learning and emulation. The reliability and stability of neural networks was confirmed by testing dataset. The results of simulation showed that the recurrent neural networks were preferably performance for prediction the change of exchange rate.
    VL  - 5
    IS  - 6
    ER  - 

    Copy | Download

Author Information
  • School of Finance, Harbin University of Commerce, Harbin, China

  • Sections