Exchange rate prediction using computational intelligence

Foreign exchange (FOREX) market has become a trend in the past few years which has been heavily covered by a number of researchers in order to come out with the reliable price forecasting. The thesis covered some methods from the field of computational intelligence to predict the exchange rate, comp...

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Main Author: Kurniawan, Nikolas
Other Authors: Ponnuthurai Nagaratnam Suganthan
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/71192
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-711922023-07-07T16:04:23Z Exchange rate prediction using computational intelligence Kurniawan, Nikolas Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Foreign exchange (FOREX) market has become a trend in the past few years which has been heavily covered by a number of researchers in order to come out with the reliable price forecasting. The thesis covered some methods from the field of computational intelligence to predict the exchange rate, comprises of a number of linear methods and machine learning techniques. Besides, a couple of data adjustment methods and features selection are applied to facilitate the system to produce an accurate prediction, which is found very critical to the system performance. It is found that the data set that has been decomposed based on the frequency performs relatively better than the untreated data. The best performance in terms of RMSE is the RVFL model with 1000 neurons and 6 input features when it is used with the decomposed price data. When the best algorithms are applied to all of the currency pairs used in the project, most of them have an RMSE value of less than 0.1 and direction accuracy around 80% given that all the previous values are known. The four best algorithms are used for the hybrid algorithm development which truncates the direction error even further than any single method with the accuracy of almost 90%. Bachelor of Engineering 2017-05-15T07:13:21Z 2017-05-15T07:13:21Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/71192 en Nanyang Technological University 83 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Kurniawan, Nikolas
Exchange rate prediction using computational intelligence
description Foreign exchange (FOREX) market has become a trend in the past few years which has been heavily covered by a number of researchers in order to come out with the reliable price forecasting. The thesis covered some methods from the field of computational intelligence to predict the exchange rate, comprises of a number of linear methods and machine learning techniques. Besides, a couple of data adjustment methods and features selection are applied to facilitate the system to produce an accurate prediction, which is found very critical to the system performance. It is found that the data set that has been decomposed based on the frequency performs relatively better than the untreated data. The best performance in terms of RMSE is the RVFL model with 1000 neurons and 6 input features when it is used with the decomposed price data. When the best algorithms are applied to all of the currency pairs used in the project, most of them have an RMSE value of less than 0.1 and direction accuracy around 80% given that all the previous values are known. The four best algorithms are used for the hybrid algorithm development which truncates the direction error even further than any single method with the accuracy of almost 90%.
author2 Ponnuthurai Nagaratnam Suganthan
author_facet Ponnuthurai Nagaratnam Suganthan
Kurniawan, Nikolas
format Final Year Project
author Kurniawan, Nikolas
author_sort Kurniawan, Nikolas
title Exchange rate prediction using computational intelligence
title_short Exchange rate prediction using computational intelligence
title_full Exchange rate prediction using computational intelligence
title_fullStr Exchange rate prediction using computational intelligence
title_full_unstemmed Exchange rate prediction using computational intelligence
title_sort exchange rate prediction using computational intelligence
publishDate 2017
url http://hdl.handle.net/10356/71192
_version_ 1772828582721093632