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Title: Machine learning in pair trading
Authors: Wang, Guanlan
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Wang, G. (2022). Machine learning in pair trading. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: 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.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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