ETF predication with machine learning algorithms

There are three stages to this report. The first stage of this paper explains how to use random forest and SVM to tackle the price trend prediction problem. After comparing the data, SVM comes out on top, with an accuracy of up to 80%. The second stage of this study involves creating a recurrent neu...

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Bibliographic Details
Main Author:	Jin, Ziyan
Other Authors:	Wong Jia Yiing, Patricia
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2022
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/158294
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-158294
record_format	dspace
spelling	sg-ntu-dr.10356-1582942023-07-07T18:56:02Z ETF predication with machine learning algorithms Jin, Ziyan Wong Jia Yiing, Patricia School of Electrical and Electronic Engineering EJYWong@ntu.edu.sg Engineering::Electrical and electronic engineering There are three stages to this report. The first stage of this paper explains how to use random forest and SVM to tackle the price trend prediction problem. After comparing the data, SVM comes out on top, with an accuracy of up to 80%. The second stage of this study involves creating a recurrent neural network to forecast a specific value of ETF price and producing a result with MSE 0.00078. The final step of this article demonstrates how to choose an appropriate index ETF for a certain index. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-31T07:50:08Z 2022-05-31T07:50:08Z 2022 Final Year Project (FYP) Jin, Z. (2022). ETF predication with machine learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158294 https://hdl.handle.net/10356/158294 en application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Jin, Ziyan ETF predication with machine learning algorithms
description	There are three stages to this report. The first stage of this paper explains how to use random forest and SVM to tackle the price trend prediction problem. After comparing the data, SVM comes out on top, with an accuracy of up to 80%. The second stage of this study involves creating a recurrent neural network to forecast a specific value of ETF price and producing a result with MSE 0.00078. The final step of this article demonstrates how to choose an appropriate index ETF for a certain index.
author2	Wong Jia Yiing, Patricia
author_facet	Wong Jia Yiing, Patricia Jin, Ziyan
format	Final Year Project
author	Jin, Ziyan
author_sort	Jin, Ziyan
title	ETF predication with machine learning algorithms
title_short	ETF predication with machine learning algorithms
title_full	ETF predication with machine learning algorithms
title_fullStr	ETF predication with machine learning algorithms
title_full_unstemmed	ETF predication with machine learning algorithms
title_sort	etf predication with machine learning algorithms
publisher	Nanyang Technological University
publishDate	2022
url	https://hdl.handle.net/10356/158294
_version_	1772826743854333952

ETF predication with machine learning algorithms

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