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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 (2016) Coarse and fine identification of collusive clique in financial market Expert Systems with Applications, 69. pp. 225-238.

Coarse and fine identification of collusive clique in financial market.pdf - Accepted version Manuscript

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Collusive transactions refer to the activity whereby traders use carefully-designed trade to illegally ma- nipulate 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 poten- tial 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 activi- ties. 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 sec- ond 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
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
Zhai, Jia
Yao, Yuan
Ding, Xuemei
Li, Yuhua
Date : 24 October 2016
DOI : 10.1016/j.eswa.2016.10.051
Copyright Disclaimer : © 2016 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Uncontrolled Keywords : Collusive clique; Clustering; Knapsack problem; Dynamic programming
Depositing User : Clive Harris
Date Deposited : 11 Sep 2017 14:11
Last Modified : 16 Oct 2019 19:29

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