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Detecting stock market manipulation using supervised learning algorithms

Market manipulation remains the biggest concern of investors in today's securities market, despite fast and strict responses from regulators and exchanges to market participants that pursue such practices. The existing methods in the industry for detecting fraudulent activities in securities market rely heavily on a set of rules based on expert knowledge. The securities market has deviated from its traditional form due to new technologies and changing investment strategies in the past few years. The current securities market demands scalable machine learning algorithms supporting identification of market manipulation activities. In this paper we use supervised learning algorithms to identify suspicious transactions in relation to market manipulation in stock market. We use a case study of manipulated stocks during 2003. We adopt CART, conditional inference trees, C5.0, Random Forest, Naïve Bayes, Neural Networks, SVM and kNN for classification of manipulated samples. Empirical results show that Naïve Bayes outperform other learning methods achieving F2 measure of 53% (sensitivity and specificity are 89% and 83% respectively).