Many-to-Many Pair Trading

Yingying WANG, Xiaodong LI*, Pangjing WU, Haoran XIE

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review


Pair trading is a market neutral strategy commonly used in hedge funds. There are two main phases of the pair trading: pair formation phase and trading phase. Finding profitable stock pairs during the pair formation phase is an important issue. Previous one-to-one pair trading mostly limits the pair formation phase to a small number of stocks. In this paper, we make an innovative improvement by extending one-to-one pair trading to many-to-many pair trading. We propose a framework that involves association rule algorithms and the OPTICS clustering algorithm, and uses the bipartite graph partition algorithm to form many-to-many pairs. Experimental results show that the framework proposed in this paper is effective in selecting many-to-many pairs for pair trading and more trading opportunities are obtained compared with traditional pair trading.
Original languageEnglish
Title of host publicationWeb and Big Data. APWeb-WAIM 2022
PublisherSpringer, Cham
ISBN (Electronic)9783031251580
ISBN (Print)9783031251573
Publication statusPublished - 10 Feb 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61602149, and in part by the Fundamental Research Funds for the Central Universities, China under Grant No. B210202078.


  • Many-to-many pair trading
  • Association rule mining
  • Unsupervised learning
  • Bipartite graph
  • Cointegration
  • Distance


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