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Unsupervised Manipulation Detection Scheme for Insider Trading

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 717))

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Abstract

Stock price manipulation in capital markets is the use of illegitimate means to influence the price of traded stocks to attempt to reap illicit profit. Most of the existing attempts to detect such manipulations have either relied upon annotated trading data, using supervised methods or have been restricted to detecting a specific manipulation scheme. Several research in the past investigated the issue of insider trade detection mainly focusing on annotated data and few components involved in insider trades. This paper proposes a fully unsupervised model based on learning the relationships among stock prices in higher dimensions using non-linear transformation, i.e., Kernel-based Principal Component Analysis (KPCA). The proposed model is trained on input features appended with reference price data extracted from the trades executed at the Primary Market: the market of listing. This is intended to efficiently capture the cause/effect of price movements about which insider trading was potentially committed. A proposed kernel density estimate-based clustering method is further implemented to cluster normal and potentially manipulative trades based on the representation of principal components. The novelty of the proposed approach can be explained by automated selection of model parameters while avoiding labelling information. This approach is validated on stock trade data from Aquis Exchange PLC (AQX) and the Primary Market. The results show significant improvements in the detection performance over existing price manipulation detection techniques.

B. Rizvi—This research work is sponsored by Aquis Exchange PLC, London, and the UK Research and Innovation (UKRI).

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Author information

Authors and Affiliations

  1. Data Science Research Centre, University of Derby, Derby, UK

    Baqar Rizvi & Mohsen Farid

  2. Chief Regulatory Officer, Aquis Exchange PLC, London, UK

    Baqar Rizvi & David Attew

  3. School of Computing and Engineering, University of Derby, Derby, UK

    Mohsen Farid

Authors
  1. Baqar Rizvi
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  2. David Attew
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  3. Mohsen Farid
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Corresponding author

Correspondence to Baqar Rizvi .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Rizvi, B., Attew, D., Farid, M. (2023). Unsupervised Manipulation Detection Scheme for Insider Trading. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_24

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