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Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming

Cao, Yi and Li, Yuhua and Coleman, Sonya and Belatreche, Ammar and McGinnity, Thomas Martin (2015) 'Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming.' IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X

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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
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of > Centre for Computational Finance and Economic Agents
Depositing User: Jim Jamieson
Date Deposited: 05 Aug 2016 11:57
Last Modified: 05 Aug 2016 11:58
URI: http://repository.essex.ac.uk/id/eprint/17367

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