Abstract
Stock price manipulation is a term given to illicit or unlawful activities that tries to artificially impact a security’s price. This alters the prime objective of transaction of stocks legally. This research presents a detection model for Stock price manipulation schemes like Pump & Dump and Spoof Trading. The proposed research is validated on tick data, containing time series with high volatility and high frequency trading that makes the detection process more difficult. A few number of past researches for price manipulation detection have been conducted based on unsupervised learning. Additionally, the existing researches also targeted specific manipulation schemes and a general detection method suitable enough to capture other anomalies is missing. This research proposes an unsupervised learning technique where Dendritic Cell Algorithm is combined with non-parametric density estimation based clustering method for detecting price manipulation. The outcome of the proposed approach are evaluated using the area under Receiver Operating Characteristics (ROC) curve and a maximum value of 0.98 is achieved. The results in this work compared with existing benchmark approaches in unsupervised learning reports a significant improvement of 18%.
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Rizvi, B., Belatreche, A., Bouridane, A. (2020). Immune Inspired Dendritic Cell Algorithm for Stock Price Manipulation Detection. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_27
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