Online structural break detection for pairs trading using wavelet transform and hybrid deep learning model

Shen Hang Huang, Wen-Yueh Shih, Jing You Lu, Hao Han Chang, Chao Hsien Chu, Jun Zhe Wang, Jiun-Long Huang, Tian-Shyr Dai

研究成果: Conference contribution同行評審

摘要

Pairs trading is a statistical arbitrage strategy which first monitors two stocks whose prices are cointegrated, and then makes arbitrage when the prices of these two stocks get non-conintegrated. The phenomenon that the cointegration relationship between two stocks does not exist any longer is called structural break, and detecting structural breaks is important for pairs trading. To detect structural breaks as soon as possible, we propose in this paper a hybrid deep learning model using both frequency-domain and time-domain features to detect structural breaks. To evaluate the performance of traditional methods and our model, we collect the historical tick data of the top 150 companies from Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) for experiments. Experimental results show that our proposed method is able to detect structural breaks more accurately than tradition methods.

原文English
主出版物標題Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
編輯Wookey Lee, Luonan Chen, Yang-Sae Moon, Julien Bourgeois, Mehdi Bennis, Yu-Feng Li, Young-Guk Ha, Hyuk-Yoon Kwon, Alfredo Cuzzocrea
發行者Institute of Electrical and Electronics Engineers Inc.
頁面209-216
頁數8
ISBN(電子)9781728160344
DOIs
出版狀態Published - 二月 2020
事件2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 - Busan, Korea, Republic of
持續時間: 19 二月 202022 二月 2020

出版系列

名字Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020

Conference

Conference2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
國家Korea, Republic of
城市Busan
期間19/02/2022/02/20

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