Proceedings of the 2017 International Conference on Education Science and Economic Management (ICESEM 2017)

Prediction of Short Term Exchange Rate Using BP Neural Network

Authors
Jun Liu
Corresponding Author
Jun Liu
Available Online October 2017.
DOI
10.2991/icesem-17.2017.96How to use a DOI?
Keywords
BP neural network; Exchange rate prediction; Time series; Matlab simulation
Abstract

Exchange rate prediction accuracy is often about the survival of an enterprise or even national economic environment security. The effect of predicting the short-term exchange rate with general statistical method is not ideal. This paper proposed a multi-layer BP neural network model to predict short-term exchange rate. We conducted simulation experiments in Matlab 2010b, using data from China Merchants Bank foreign exchange market analysis software V2.8. The results show a better goodness of fit. It indicates that BP neural network can be of practical use in predicting short term exchange rate with high rate of accuracy.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 International Conference on Education Science and Economic Management (ICESEM 2017)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
October 2017
ISBN
978-94-6252-402-6
ISSN
2352-5398
DOI
10.2991/icesem-17.2017.96How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jun Liu
PY  - 2017/10
DA  - 2017/10
TI  - Prediction of Short Term Exchange Rate Using BP Neural Network
BT  - Proceedings of the 2017 International Conference on Education Science and Economic Management (ICESEM 2017)
PB  - Atlantis Press
SP  - 432
EP  - 435
SN  - 2352-5398
UR  - https://doi.org/10.2991/icesem-17.2017.96
DO  - 10.2991/icesem-17.2017.96
ID  - Liu2017/10
ER  -