A hidden Markov model with abnormal states for detecting stock price manipulation

Cao, Y; Li, Y; Coleman, S; Belatreche, A; McGinnity, TM

Abstract

Price manipulation refers to the act of using illegal trading behaviour to manually change an equity price with the aim of making profits. With increasing volumes of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. Effective approaches for analysing and real-time detection of price manipulation are yet to be developed. This paper proposes a novel approach, called Hidden Markov Model with Abnormal States (HMMAS), which models and detects price manipulation activities. Together with the wavelet decomposition for features extraction and Gaussian Mixture Model for Probability Density Function (PDF) construction, the HMMAS model detects price manipulation and identifies the type of the detected manipulation. Evaluation experiments of the model were conducted on six stock tick data from NASDAQ and London Stock Exchange (LSE). The results showed that the proposed HMMAS model can effectively detect price manipulation patterns.

Citation

Cao, Y., Li, Y., Coleman, S., Belatreche, A., & McGinnity, T. (2013, October). A hidden Markov model with abnormal states for detecting stock price manipulation. Presented at Institute of Electrical and Electronics Engineers (IEEE) International Conference on Systems, Man, and Cybernetics, Manchester

• Presentation Conference Type: Other
• Conference Name: Institute of Electrical and Electronics Engineers (IEEE) International Conference on Systems, Man, and Cybernetics
• Conference Location: Manchester
• Start Date: Oct 13, 2013
• End Date: Oct 16, 2013
• Publication Date: Oct 1, 2013
• Deposit Date: Jul 27, 2015
• Publisher: Institute of Electrical and Electronics Engineers
• Publisher URL: http://dx.doi.org/10.1109/SMC.2013.514
• Related Public URLs: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6689802 /> http://www.smc2013.org/
• Additional Information: Event Type : Conference