Immune inspired Dendritic Cell Algorithm for Stock Price Manipulation detection

Abbas, Baqar, Belatreche, Ammar and Bouridane, Ahmed (2020) Immune inspired Dendritic Cell Algorithm for Stock Price Manipulation detection. In: Intelligent Systems and Applications: IntelliSys 2019. Advances in Intelligent Systems and Computing, 1037 . Springer, Cham, pp. 352-361. ISBN 9783030295158, 9783030295165

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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%.

Item Type:	Book Section
Uncontrolled Keywords:	Market manipulation, Dendritic, Cell, Algorithm, Immune system, Anomaly detection, KDE clustering
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Organisation
Depositing User:	Becky Skoyles
Date Deposited:	29 May 2019 11:14
Last Modified:	16 Sep 2020 09:14
URI:	http://nrl.northumbria.ac.uk/id/eprint/39415

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