Detecting wash trade in financial market using digraphs and dynamic programming

Yi Cao, Yuhua Li, Sonya Coleman, Ammar Belatreche, Thomas Martin Mcginnity

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)2351-2363
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number11
Early online date14 Oct 2015
Publication statusPublished - 1 Nov 2016


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