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Stock market manipulation is detrimental to traders and
corporations, causes unnecessary price fluctuations, and only
benefits financial criminals. The research presented here
determines an appropriate model to help identify stocks witnessing
activities that are indicative of potential manipulation through
three separate but related studies. In Developing an Effective
Model for Detecting Trade-Based Market Manipulation, classifiers
based on three different techniques namely discriminant analysis, a
composite classifier based on Artificial Neural Network and Genetic
Algorithm and support Vector Machines is proposed. The proposed
models help investigators, with varying degree of accuracy, to
arrive at a shortlist of securities which could be subject to
further detailed investigation to detect the type and nature of the
manipulation, if any. Following a fluid outline, Developing an
Effective Model for Detecting Trade-Based Market Manipulation,
introduces the topic, explores the aims and scopes of the research,
before delving into the data and modelling to explore their
application to the stock market to detect price manipulation.
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