Machine learning in pair trading

Machine learning in pair trading

This research seeks to develop and compare traditional threshold methods and machine learning models applied in pair trading of stocks. Unsupervised machine learning firstly segments stocks into different clusters based on stocks’ return and volatility profile. Stock pairs would be filtered ou...

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Bibliographic Details
Main Author: Wang, Guanlan
Other Authors: Bo An
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Engineering::Computer science and engineering
Online Access:https://hdl.handle.net/10356/156581
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-156581
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spelling sg-ntu-dr.10356-1565812022-04-20T08:17:24Z Machine learning in pair trading Wang, Guanlan Bo An School of Computer Science and Engineering boan@ntu.edu.sg Engineering::Computer science and engineering This research seeks to develop and compare traditional threshold methods and machine learning models applied in pair trading of stocks. Unsupervised machine learning firstly segments stocks into different clusters based on stocks’ return and volatility profile. Stock pairs would be filtered out according to several criteria. Traditional threshold method, XGBoost, and LSTM models are applied to the pair trading mechanism with performance compared. Bachelor of Engineering (Computer Science) 2022-04-20T08:17:24Z 2022-04-20T08:17:24Z 2022 Final Year Project (FYP) Wang, G. (2022). Machine learning in pair trading. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156581 https://hdl.handle.net/10356/156581 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::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Wang, Guanlan
Machine learning in pair trading
description This research seeks to develop and compare traditional threshold methods and machine learning models applied in pair trading of stocks. Unsupervised machine learning firstly segments stocks into different clusters based on stocks’ return and volatility profile. Stock pairs would be filtered out according to several criteria. Traditional threshold method, XGBoost, and LSTM models are applied to the pair trading mechanism with performance compared.
author2 Bo An
author_facet Bo An
Wang, Guanlan
format Final Year Project
author Wang, Guanlan
author_sort Wang, Guanlan
title Machine learning in pair trading
title_short Machine learning in pair trading
title_full Machine learning in pair trading
title_fullStr Machine learning in pair trading
title_full_unstemmed Machine learning in pair trading
title_sort machine learning in pair trading
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/156581
_version_ 1731235769960366080

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