Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

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

Research output: Contribution to journalArticlepeer-review

70 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)318-330
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number2
Early online date22 Apr 2014
Publication statusPublished (in print/issue) - 28 Feb 2015


  • Anomaly detection
  • capital market microstructure
  • feature extraction
  • hidden Markov model (HMM)
  • market abuse
  • price manipulation


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