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Coarse and fine identification of collusive clique in financial market

Zhai, Jia, Cao, Yi, Yao, Yuan, Ding, Xuemei and Li, Yuhua 2017. Coarse and fine identification of collusive clique in financial market. Expert Systems with Applications 69 , pp. 225-238. 10.1016/j.eswa.2016.10.051

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Abstract

Collusive transactions refer to the activity whereby traders use carefully-designed trade to illegally manipulate the market. They do this by increasing specific trading volumes, thus creating a false impression that a market is more active than it actually is. The traders involved in the collusive transactions are termed as collusive clique. The collusive clique and its activities can cause substantial damage to the market's integrity and attract much attention of the regulators around the world in recent years. Much of the current research focused on the detection based on a number of assumptions of how a normal market behaves. There is, clearly, a lack of effective decision-support tools with which to identify potential collusive clique in a real-life setting. The study in this paper examined the structures of the traders in all transactions, and proposed two approaches to detect potential collusive clique with their activities. The first approach targeted on the overall collusive trend of the traders. This is particularly useful when regulators seek a general overview of how traders gather together for their transactions. The second approach accurately detected the parcel-passing style collusive transactions on the market through analysing the relations of the traders and transacted volumes. The proposed two approaches, on one hand, provided a complete cover for collusive transaction identifications, which can fulfil the different types of requirements of the regulation, i.e. MiFID II, on the other hand, showed a novel application of well-known computational algorithms on solving real and complex financial problem. The proposed two approaches are evaluated using real financial data drawn from the NYSE and CME group. Experimental results suggested that those approaches successfully identified all primary collusive clique scenarios in all selected datasets and thus showed the effectiveness and stableness of the novel application.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0957-4174
Date of Acceptance: 16 October 2016
Last Modified: 17 Feb 2020 11:45
URI: http://orca.cf.ac.uk/id/eprint/129127

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