A Dendritic Cell Immune System Inspired Approach Stock Market Manipulation Detection

Baqar Rizvi, Ammar Belatreche, Ahmed Bouridane

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation (CEC 2019): 10-13 June 2019, Wellington, New Zealand
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages3325-3333
ISBN (Electronic)9781728121536
ISBN (Print)9781728121543
DOIs
Publication statusPublished - Jun 2019
EventIEEE Congress on Evolutionary Computation - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019
http://cec2019.org/index.html

Conference

ConferenceIEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2019
CountryNew Zealand
CityWellington
Period10/06/1913/06/19
Internet address

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