The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates
Abstract
This chapter applies the Backpropagation Neural Network (BPNN) trained with different numerical algorithms and technical analysis indicators as inputs to forecast daily US/Canada, US/Euro, US/Japan, US/Korea, US/Swiss, and US/UK exchange rate future price. The training algorithms are the Fletcher-Reeves, Polak-Ribiére, Powell-Beale, quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), and the Levenberg-Marquardt (LM). The standard Auto Regressive Moving Average (ARMA) process is adopted as a reference model for comparison. The performance of each BPNN and ARMA process is measured by computing the Mean Absolute Error (MAE), Mean Absolute Deviation (MAD), and Mean of Squared Errors (MSE). The simulation results reveal that the LM algorithm is the best performer and show strong evidence of the superiority of the BPNN over ARMA process. In sum, because of the simplicity and effectiveness of the approach, it could be implemented for real business application problems to predict US currency exchange rate future price.
Related Content
|
Mohamed Arezki Mellal.
© 2022.
9 pages.
|
|
Tahir Cetin Akinci, Ramazan Caglar, Gokhan Erdemir, Aydin Tarik Zengin, Serhat Seker.
© 2022.
11 pages.
|
|
Sunanda Hazra, Provas Kumar Roy.
© 2022.
16 pages.
|
|
Ragab A. El-Sehiemy, Almoataz Y. Abdelaziz.
© 2022.
23 pages.
|
|
Khaled Dassa, Abdelmadjid Recioui.
© 2022.
35 pages.
|
|
Anupama Kumari, Mukund Madhaw, C. B. Majumder, Amit Arora.
© 2022.
21 pages.
|
|
Mandrita Mondal.
© 2022.
20 pages.
|
|
|