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A Hidden Markov Model with Abnormal States for Detecting Stock Price Manipulation

Cao, Yi, Li, Yuhua, Coleman, Sonya, Belatreche, Ammar and McGinnity, T.M. (2014) A Hidden Markov Model with Abnormal States for Detecting Stock Price Manipulation In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct 2013, Manchester, UK.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
Date : 27 January 2014
Identification Number : 10.1109/SMC.2013.514
Copyright Disclaimer : © 2013 IEEE
Uncontrolled Keywords : Hidden Markov Model; Anomaly Detection; Capital Market Price Manipulation
Depositing User : Clive Harris
Date Deposited : 11 Sep 2017 15:34
Last Modified : 11 Sep 2017 15:34

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