@inproceedings{240a501b313641ee98b2814b97a751db,
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 = "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.",
year = "2023",
month = feb,
day = "10",
doi = "10.1007/978-3-031-25158-0_31",
language = "English",
isbn = "9783031251573",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Cham",
pages = "399--407",
booktitle = "Web and Big Data. APWeb-WAIM 2022",
}