Detecting wash trade in financial market using digraphs and dynamic programming

Cao, Yi, Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, Coleman, Sonya, Belatreche, Ammar and McGinnity, Thomas Martin 2016. Detecting wash trade in financial market using digraphs and dynamic programming. IEEE Transactions on Neural Networks and Learning Systems 27 (11) , pp. 2351-2363. 10.1109/TNNLS.2015.2480959

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Abstract

A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections based on certain assumptions of trading behaviors. Effective approaches for analyzing and detecting wash trade in a real-life market have yet to be developed. This paper analyzes and conceptualizes the basic structures of the trading collusion in a wash trade by using a directed graph of traders. A novel method is then proposed to detect the potential wash trade activities involved in a financial instrument by first recognizing the suspiciously matched orders and then further identifying the collusions among the traders who submit such orders. Both steps are formulated as a simplified form of the knapsack problem, which can be solved by dynamic programming approaches. The proposed approach is evaluated on seven stock data sets from the NASDAQ and the London Stock Exchange. The experimental results show that the proposed approach can effectively detect all primary wash trade scenarios across the selected data sets.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Data Innovation Research Institute (DIURI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information:	This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Publisher:	IEEE
ISSN:	2162-237X
Date of First Compliant Deposit:	7 February 2018
Date of Acceptance:	17 September 2015
Last Modified:	04 May 2023 22:54
URI:	https://orca.cardiff.ac.uk/id/eprint/108944

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