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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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/156581 |
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| 總結: | 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. |
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