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Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Cao, Y, Li, Yuhua, Coleman, SA, Belatreche, Ammar and McGinnity, TM (2015) Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection. IEEE Transactions on Neural Networks and Learning Systems, 26 (2). pp. 318-330. [Journal article]

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DOI: 10.1109/TNNLS.2014.2315042

Abstract

Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:30923
Deposited By: Dr Sonya Coleman
Deposited On:23 Jan 2015 12:48
Last Modified:23 Jan 2015 12:48

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