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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020
EditorsWookey Lee, Luonan Chen, Yang-Sae Moon, Julien Bourgeois, Mehdi Bennis, Yu-Feng Li, Young-Guk Ha, Hyuk-Yoon Kwon, Alfredo Cuzzocrea
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages209-216
Number of pages8
ISBN (Electronic)9781728160344
DOIs
StatePublished - Feb 2020
Event2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020 - Busan, Korea, Republic of
Duration: 19 Feb 202022 Feb 2020

Publication series

NameProceedings - 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
CountryKorea, Republic of
CityBusan
Period19/02/2022/02/20

Keywords

  • Deep-learning
  • Pairs-trading
  • Structural-break-detection

Fingerprint Dive into the research topics of 'Online structural break detection for pairs trading using wavelet transform and hybrid deep learning model'. Together they form a unique fingerprint.

Cite this