Detecting wash trade in financial market using digraphs and dynamic programming

Cao, Y, Li, Y, Coleman, S, Belatreche, A and McGinnity, TM ORCID: https://orcid.org/0000-0002-9897-4748, 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. ISSN 2162-237X

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Abstract

Wash trade refers to the illegal activities of traders who utilise 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, wash trade can be extremely damaging to the proper functioning and integrity of capital markets. Existing work focuses on collusive clique detections based on certain assumptions of trading behaviours. Effective approaches for analysing and detecting wash trade in a real-life market have yet to be developed. This paper analyses and conceptualises 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 datasets from NASDAQ and the London Stock Exchange. Experimental results show that the proposed approach can effectively detect all primary wash trade scenarios across the selected datasets.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Neural Networks and Learning Systems
Creators:	Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T.M.
Publisher:	IEEE
Date:	November 2016
Volume:	27
Number:	11
ISSN:	2162-237X
Identifiers:	Number Type 10.1109/TNNLS.2015.2480959 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	19 Jul 2016 14:30
Last Modified:	16 Oct 2017 15:01
URI:	https://irep.ntu.ac.uk/id/eprint/28154

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