Many-to-Many Pair Trading

Yingying WANG; Xiaodong LI; Pangjing WU; Haoran XIE

doi:10.1007/978-3-031-25158-0_31

Many-to-Many Pair Trading

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

^*Corresponding author for this work

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

Abstract

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 language	English
Title of host publication	Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings
Editors	Bohan LI, Chuanqi TAO, Lin YUE, Xuming HAN, Diego CALVANESE, Toshiyuki AMAGASA
Publisher	Springer, Cham
Chapter	31
Pages	399-407
Number of pages	9
ISBN (Electronic)	9783031251580
ISBN (Print)	9783031251573
DOIs	https://doi.org/10.1007/978-3-031-25158-0_31
Publication status	Published - 10 Feb 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13421 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-25158-0_31

https://link.springer.com/10.1007/978-3-031-25158-0_31

Cite this

WANG, Y., LI, X., WU, P., & XIE, H. (2023). Many-to-Many Pair Trading. In B. LI, C. TAO, L. YUE, X. HAN, D. CALVANESE, & T. AMAGASA (Eds.), Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings (pp. 399-407). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13421 LNCS). Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_31

WANG, Yingying ; LI, Xiaodong ; WU, Pangjing et al. / Many-to-Many Pair Trading. Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings. editor / Bohan LI ; Chuanqi TAO ; Lin YUE ; Xuming HAN ; Diego CALVANESE ; Toshiyuki AMAGASA. Springer, Cham, 2023. pp. 399-407 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Many-to-Many Pair Trading",

abstract = "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.",

keywords = "Many-to-many pair trading, Association rule mining, Unsupervised learning, Bipartite graph, Cointegration, Distance",

author = "Yingying WANG and Xiaodong LI and Pangjing WU and Haoran XIE",

note = "Funding Information: 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. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

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doi = "10.1007/978-3-031-25158-0_31",

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WANG, Y, LI, X, WU, P & XIE, H 2023, Many-to-Many Pair Trading. in B LI, C TAO, L YUE, X HAN, D CALVANESE & T AMAGASA (eds), Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13421 LNCS, Springer, Cham, pp. 399-407. https://doi.org/10.1007/978-3-031-25158-0_31

Many-to-Many Pair Trading. / WANG, Yingying; LI, Xiaodong; WU, Pangjing et al.
Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings. ed. / Bohan LI; Chuanqi TAO; Lin YUE; Xuming HAN; Diego CALVANESE; Toshiyuki AMAGASA. Springer, Cham, 2023. p. 399-407 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13421 LNCS).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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AU - WANG, Yingying

AU - LI, Xiaodong

AU - WU, Pangjing

AU - XIE, Haoran

N1 - Funding Information: 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. Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023/2/10

Y1 - 2023/2/10

N2 - 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.

AB - 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.

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KW - Unsupervised learning

KW - Bipartite graph

KW - Cointegration

KW - Distance

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DO - 10.1007/978-3-031-25158-0_31

M3 - Conference paper (refereed)

SN - 9783031251573

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings

A2 - LI, Bohan

A2 - TAO, Chuanqi

A2 - YUE, Lin

A2 - HAN, Xuming

A2 - CALVANESE, Diego

A2 - AMAGASA, Toshiyuki

PB - Springer, Cham

ER -

WANG Y, LI X, WU P, XIE H. Many-to-Many Pair Trading. In LI B, TAO C, YUE L, HAN X, CALVANESE D, AMAGASA T, editors, Web and Big Data - 6th International Joint Conference, APWeb-WAIM 2022, Proceedings. Springer, Cham. 2023. p. 399-407. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-25158-0_31

Many-to-Many Pair Trading

Abstract

Publication series

Bibliographical note

Keywords

UN SDGs

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