Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77779
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAung, Htet Myet
dc.date.accessioned2019-06-06T05:56:58Z
dc.date.available2019-06-06T05:56:58Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/77779
dc.description.abstractForeign exchange Market is one of the most important financial movement in the world and for the past few years, researchers have been trying to find a way to have an edge in forecasting exchange prices. With recent development in computer technology, artificial intelligence with algorithm has been a very hot topic in financial market. This kind of machine learning neural network could become an important figure in the future of financial market. There are various types of neural networks such as feedforward neural networks, recurrent neural networks. This project aims to cover some of the methods used in neural networks to forecast the exchange rate. Feed forward neural network with external variables for currency such as gold, crude oil price has been used to predict US dollar against Japanese Yen, Euro against USD and Great Britain Pound. The result from this neural network will be compared against a published research paper and Non-linear Autoregressive model with Exogenous Inputs. The neural network models are implemented by using MATLAB software and performance of each model will be determined by Means Square Error and Correlation Coefficient values in MATLAB.en_US
dc.format.extent49 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleForeign exchange prediction and trading using neural networksen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipoen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
Project No-P3056-172.pdf
  Restricted Access
858.87 kBAdobe PDFView/Open

Page view(s)

209
Updated on Aug 7, 2022

Download(s) 50

23
Updated on Aug 7, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.