Detecting Price Manipulation in the Financial Market

Yi Cao, Yuhua Li, SA Coleman, Ammar Belatreche, TM McGinnity

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)


Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudo-stationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.
Original languageEnglish
Title of host publicationUnknown Host Publication
Number of pages8
Publication statusPublished (in print/issue) - 27 Mar 2014
EventIEEE Computational Intelligence for Financial Engineering and Economics 2014 - Canary Wharf, London
Duration: 27 Mar 2014 → …


ConferenceIEEE Computational Intelligence for Financial Engineering and Economics 2014
Period27/03/14 → …


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