Adaptive hidden Markov model with anomaly states for price manipulation detection

Cao, Y, Li, Y, Coleman, S, Belatreche, A 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.

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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:	Article
Themes:	Media, Digital Technology and the Creative Economy Subjects outside of the University Themes
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	IEEE Transactions on Neural Networks and Learning Systems
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Refereed:	Yes
ISSN:	2162-237X
Related URLs:	Publication
Funders:	Non funded research
Depositing User:	Yuhua Li
Date Deposited:	10 Nov 2014 18:08
Last Modified:	15 Feb 2022 15:47
URI:	https://usir.salford.ac.uk/id/eprint/33036

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