Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

Baqar Rizvi, Ammar Belatreche, Ahmed Bouridane, Ian Watson

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
82 Downloads (Pure)


Stock price manipulation uses illegitimate means to artificially influence market prices of several stocks. It causes massive losses and undermines investors’ confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. The results show a comprehensive assessment of the model on multiple datasets and a significant performance enhancement in terms of the F-measure values with a significant reduction in false alarm rate (FAR) has been achieved.
Original languageEnglish
Article number9146609
Pages (from-to)135989-136003
Number of pages15
JournalIEEE Access
Early online date23 Jul 2020
Publication statusPublished - 5 Aug 2020


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