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Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network
Joint Authors
Lai, Kin Keung
Du, Jiangze
Wang, Jying-Nan
Jiang, Chonghui
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-30
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
The Chinese currency, RMB, is developing as an international currency.
Therefore, the effective strategy for trading RMB exchange rates would be attractive to international investors and policymakers.
In this paper, we have constructed hybrid EMD-MLP models to forecast RMB exchange rates and developed a trading strategy based on these models.
Empirical results show that the proposed hybrid EMD-MLP∗ model always performs best based on both NMSE and Dstat criteria when the forecasting period is greater than five days.
Moreover, we compare the models’ performance using different horizons and find that accuracy will increase with the growth of the forecasting horizons; however, the NMSE will become larger.
Lastly, we adopt the best performing model to develop trading strategies with longer forecasting horizons when considering the number of profitable trading activities.
If we consider a 0.3% transaction cost, the developed strategy will bring an annual return exceeding 10%, as well as enough trading opportunities.
American Psychological Association (APA)
Wang, Jying-Nan& Du, Jiangze& Jiang, Chonghui& Lai, Kin Keung. 2019. Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network. Complexity،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1132702
Modern Language Association (MLA)
Wang, Jying-Nan…[et al.]. Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network. Complexity No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1132702
American Medical Association (AMA)
Wang, Jying-Nan& Du, Jiangze& Jiang, Chonghui& Lai, Kin Keung. Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network. Complexity. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1132702
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132702