Development of exchange rate estimation method by using Artificial Neural Networks

M. Niamul Bary, M. Habib Ullah, Mohammad Tariqul Islam, M. R. Ahsan

Research output: Contribution to journalArticle

Abstract

This study is presented the feasibility of cross-referencing of exchange rates to estimate exchange rates on a short-term basis. The cross-referencing technique suggested herein was used to predict EURO currency based on the exchange rate relations modeled by using Artificial Neural Networks. Foreign exchange rates namely UK Pound (GBP), Switzerland Francs (CHF), Canadian Dollar (CAD) and Singaporean Dollar (SGD) have been selected to estimate the EURO currencies based on the data collected from the past 10 years from 1999 to 2008. The main objective this paper is to estimate EURO currency trend based on the cross-referenced relations with the other four currencies by using Artificial Neural Networks. Promising result is shown that the Artificial Neural Networks has been found to be appropriate for modeling and simulation in the data assessments. The paper gives detailed results regarding the use of Artificial Neural Networks for modeling EURO trends in terms of other currencies.

Original languageEnglish
Pages (from-to)3860-3864
Number of pages5
JournalJournal of Applied Sciences
Volume11
Issue number24
DOIs
Publication statusPublished - 2011

Fingerprint

Artificial neural network
Exchange rates
Currency
Switzerland
Foreign exchange rates
Modeling and simulation
Modeling

Keywords

  • Artificial neural networks
  • CAD
  • CHF
  • EURO
  • Exchange rate estimation

ASJC Scopus subject areas

  • General

Cite this

Development of exchange rate estimation method by using Artificial Neural Networks. / Niamul Bary, M.; Habib Ullah, M.; Islam, Mohammad Tariqul; Ahsan, M. R.

In: Journal of Applied Sciences, Vol. 11, No. 24, 2011, p. 3860-3864.

Research output: Contribution to journalArticle

Niamul Bary, M. ; Habib Ullah, M. ; Islam, Mohammad Tariqul ; Ahsan, M. R. / Development of exchange rate estimation method by using Artificial Neural Networks. In: Journal of Applied Sciences. 2011 ; Vol. 11, No. 24. pp. 3860-3864.
@article{127162ce82b547bebe5637488747e2fb,
title = "Development of exchange rate estimation method by using Artificial Neural Networks",
abstract = "This study is presented the feasibility of cross-referencing of exchange rates to estimate exchange rates on a short-term basis. The cross-referencing technique suggested herein was used to predict EURO currency based on the exchange rate relations modeled by using Artificial Neural Networks. Foreign exchange rates namely UK Pound (GBP), Switzerland Francs (CHF), Canadian Dollar (CAD) and Singaporean Dollar (SGD) have been selected to estimate the EURO currencies based on the data collected from the past 10 years from 1999 to 2008. The main objective this paper is to estimate EURO currency trend based on the cross-referenced relations with the other four currencies by using Artificial Neural Networks. Promising result is shown that the Artificial Neural Networks has been found to be appropriate for modeling and simulation in the data assessments. The paper gives detailed results regarding the use of Artificial Neural Networks for modeling EURO trends in terms of other currencies.",
keywords = "Artificial neural networks, CAD, CHF, EURO, Exchange rate estimation",
author = "{Niamul Bary}, M. and {Habib Ullah}, M. and Islam, {Mohammad Tariqul} and Ahsan, {M. R.}",
year = "2011",
doi = "10.3923/jas.2011.3860.3864",
language = "English",
volume = "11",
pages = "3860--3864",
journal = "Journal of Applied Sciences",
issn = "1812-5654",
publisher = "Asian Network for Scientific Information",
number = "24",

}

TY - JOUR

T1 - Development of exchange rate estimation method by using Artificial Neural Networks

AU - Niamul Bary, M.

AU - Habib Ullah, M.

AU - Islam, Mohammad Tariqul

AU - Ahsan, M. R.

PY - 2011

Y1 - 2011

N2 - This study is presented the feasibility of cross-referencing of exchange rates to estimate exchange rates on a short-term basis. The cross-referencing technique suggested herein was used to predict EURO currency based on the exchange rate relations modeled by using Artificial Neural Networks. Foreign exchange rates namely UK Pound (GBP), Switzerland Francs (CHF), Canadian Dollar (CAD) and Singaporean Dollar (SGD) have been selected to estimate the EURO currencies based on the data collected from the past 10 years from 1999 to 2008. The main objective this paper is to estimate EURO currency trend based on the cross-referenced relations with the other four currencies by using Artificial Neural Networks. Promising result is shown that the Artificial Neural Networks has been found to be appropriate for modeling and simulation in the data assessments. The paper gives detailed results regarding the use of Artificial Neural Networks for modeling EURO trends in terms of other currencies.

AB - This study is presented the feasibility of cross-referencing of exchange rates to estimate exchange rates on a short-term basis. The cross-referencing technique suggested herein was used to predict EURO currency based on the exchange rate relations modeled by using Artificial Neural Networks. Foreign exchange rates namely UK Pound (GBP), Switzerland Francs (CHF), Canadian Dollar (CAD) and Singaporean Dollar (SGD) have been selected to estimate the EURO currencies based on the data collected from the past 10 years from 1999 to 2008. The main objective this paper is to estimate EURO currency trend based on the cross-referenced relations with the other four currencies by using Artificial Neural Networks. Promising result is shown that the Artificial Neural Networks has been found to be appropriate for modeling and simulation in the data assessments. The paper gives detailed results regarding the use of Artificial Neural Networks for modeling EURO trends in terms of other currencies.

KW - Artificial neural networks

KW - CAD

KW - CHF

KW - EURO

KW - Exchange rate estimation

UR - http://www.scopus.com/inward/record.url?scp=84855214973&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84855214973&partnerID=8YFLogxK

U2 - 10.3923/jas.2011.3860.3864

DO - 10.3923/jas.2011.3860.3864

M3 - Article

VL - 11

SP - 3860

EP - 3864

JO - Journal of Applied Sciences

JF - Journal of Applied Sciences

SN - 1812-5654

IS - 24

ER -