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

Yi Cao, Yuhua Li, Sonya Coleman, Ammar Belatreche, Martin McGinnity

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Price manipulation refers to the act of using illegaltrading behaviour to manually change an equity price withthe aim of making profits. With increasing volumes of trading,price manipulation can be extremely damaging to the properfunctioning and integrity of capital markets. Effective approachesfor analysing and real-time detection of price manipulation areyet to be developed. This paper proposes a novel approach,called Hidden Markov Model with Abnormal States (HMMAS),which models and detects price manipulation activities. Togetherwith the wavelet decomposition for features extraction andGaussian Mixture Model for Probability Density Function (PDF)construction, the HMMAS model detects price manipulationand identifies the type of the detected manipulation. Evaluationexperiments of the model were conducted on six stock tick datafrom NASDAQ and London Stock Exchange (LSE). The resultsshowed that the proposed HMMAS model can effectively detectprice manipulation patterns
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages3014-3019
Number of pages6
DOIs
Publication statusPublished - Oct 2013
EventIEEE International Conference on Systems, Man, and Cybernetics - Manchester
Duration: 1 Oct 2013 → …

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics
Period1/10/13 → …

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