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Title: Foreign exchange prediction and trading using neural networks
Authors: Aung, Htet Myet
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: Foreign 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.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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