Detecting Stock Market Manipulation using Supervised Learning Algorithms
Market manipulation remains the biggest concern of investors in today’s securities market, despite fast and strict responses from regulators and exchanges to market participants that pursue such practices. The existing methods in the industry for detecting fraudulent activities in securities market rely heavily on a set of rules based on expert knowledge. The securities market has deviated from its traditional form due to new technologies and changing investment strategies in the past few years. The current securities market demands scalable machine learning
algorithms supporting identification of market manipulation
activities. In this paper we use supervised learning algorithms
to identify suspicious transactions in relation to market manipulation in stock market. We use a case study of manipulated stocks during 2003. We adopt CART, conditional
inference trees, C5.0, Random Forest, Naïve Bayes, Neural
Networks, SVM and kNN for classification of manipulated samples. Empirical results show that Naïve Bayes outperform
other learning methods achieving F2 measure of 53% (sensitivity and specificity are 89% and 83% respectively).
Citation
K. Golmohammadi,
O. Zaiane,
D. Diaz.
"Detecting Stock Market Manipulation using Supervised Learning Algorithms ". International Conference on Data Science and Advanced Analytics, Shanghai, China, October 2014.
| Keywords: |
supervised learning, classification, data mining, fraud detection, market manipulation, stock market manipulation |
| Category: |
In Conference |
| Web Links: |
Webdocs |
BibTeX
@incollection{Golmohammadi+al:14,
author = {Koosha Golmohammadi and Osmar R. Zaiane and David Diaz},
title = {Detecting Stock Market Manipulation using Supervised Learning
Algorithms },
booktitle = {International Conference on Data Science and Advanced Analytics},
year = 2014,
}Last Updated: September 10, 2020
Submitted by Sabina P 
