Immune inspired Dendritic Cell Algorithm for Stock Price Manipulation detection

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%.