Foreign exchange prediction and trading using random forests

The aim of the study is to predict foreign exchange prediction and trading strategies using Random Forests. The application of machine learning technology in market forecasting has been widely established in the scientific community. Developing high-precision techniques for predicting financial time...

Full description

Saved in:
Bibliographic Details
Main Author: Tong, Xiaoshan
Other Authors: Wang Lipo
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/77600
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-77600
record_format dspace
spelling sg-ntu-dr.10356-776002023-07-07T15:54:12Z Foreign exchange prediction and trading using random forests Tong, Xiaoshan Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The aim of the study is to predict foreign exchange prediction and trading strategies using Random Forests. The application of machine learning technology in market forecasting has been widely established in the scientific community. Developing high-precision techniques for predicting financial time series is critical for economists, researchers, and analysts. Complex machine learning techniques, such as artificial neural networks, support vector machines (SVMs) and random forests, provide sufficient learning capabilities and are more likely to capture complex nonlinear models that dominate the financial market. This paper discussed the application of random forests in predicting the exchange rate of the Euro against the US dollar. Taking the Long-Short-Term Memory Neural Network (LSTM) as a reference, compare the role of the two in this application, in order to promote the future development of machine learning technology in the scientific community. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-03T04:47:23Z 2019-06-03T04:47:23Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77600 en Nanyang Technological University 46 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
Tong, Xiaoshan
Foreign exchange prediction and trading using random forests
description The aim of the study is to predict foreign exchange prediction and trading strategies using Random Forests. The application of machine learning technology in market forecasting has been widely established in the scientific community. Developing high-precision techniques for predicting financial time series is critical for economists, researchers, and analysts. Complex machine learning techniques, such as artificial neural networks, support vector machines (SVMs) and random forests, provide sufficient learning capabilities and are more likely to capture complex nonlinear models that dominate the financial market. This paper discussed the application of random forests in predicting the exchange rate of the Euro against the US dollar. Taking the Long-Short-Term Memory Neural Network (LSTM) as a reference, compare the role of the two in this application, in order to promote the future development of machine learning technology in the scientific community.
author2 Wang Lipo
author_facet Wang Lipo
Tong, Xiaoshan
format Final Year Project
author Tong, Xiaoshan
author_sort Tong, Xiaoshan
title Foreign exchange prediction and trading using random forests
title_short Foreign exchange prediction and trading using random forests
title_full Foreign exchange prediction and trading using random forests
title_fullStr Foreign exchange prediction and trading using random forests
title_full_unstemmed Foreign exchange prediction and trading using random forests
title_sort foreign exchange prediction and trading using random forests
publishDate 2019
url http://hdl.handle.net/10356/77600
_version_ 1772826153325690880