A Dendritic Cell Immune System Inspired Approach Stock Market Manipulation Detection

Market manipulation is the act of artificially influencing the price of a security to make profit through illegitimate schemes. It is evident from the literature that only a handful of methods had been proposed for stock market manipulation detection. Most of those methods either used supervised training or focused only on specific manipulation schemes. This paper introduces a semi-supervised learning method based on a hybridization of an altered dendritic cell immune system inspired approach and Kernel Density Estimation based clustering technique. Dendritic Cell Algorithm (DCA) mimics the human immune system in data processing using the danger theory model for anomaly detection. An important advantage of the proposed approach is that the DCA is adapted for scaling down the dimension of the input data set to a set of only three outputs that are then clustered using KDE clustering. This avoids the need for assigning different threshold parameters as in a conventional DCA, hence automating the detection process. Another important advantage is that supervised training is not required for signal categorization during the preprocessing phase of DCA. The proposed approach is validated on Level 1 stock price tick data obtained from the LOBSTER project which contains highly volatile and high frequency trading (HFT) time series. The considered manipulation schemes are Pump and Dump and Gouging or Spoof trading. The proposed approach is benchmarked against existing stock market manipulation detection approaches as well as existing anomaly detection techniques based on KNN, OCSVM, PCA and k-means. The obtained results show substantial improvements in terms of the area under the ROC curve (AUC) and the false alarm rate.