Supplementary Material for "A Machine Learning Attack on Illegal Trading"

7 Pages Posted: 21 Jan 2021 Last revised: 24 Jan 2021

See all articles by Robert James

Robert James

The University of Sydney

Henry Leung

University of Sydney Business School; Financial Research Network (FIRN)

Artem Prokhorov

University of Sydney Business School; Saint Petersburg State University; University of Montreal - Centre interuniversitaire de recherche en économie quantitative (CIREQ)

Date Written: November 12, 2020

Abstract

This supplement contains information regarding the implementation of the Gaussian Mixture Model (GMM), One-Class Support Vector Machine (OCSVM) and Isolation Forrest (iForest) algorithms which are used as anomaly detection benchmarks in the paper “A Machine Learning Attack on Illegal Trading”.

JEL Classification: C1,G1,G2

Suggested Citation

James, Robert and Leung, Henry and Prokhorov, Artem, Supplementary Material for "A Machine Learning Attack on Illegal Trading" (November 12, 2020). Available at SSRN: https://ssrn.com/abstract=3727753 or http://dx.doi.org/10.2139/ssrn.3727753

Robert James (Contact Author)

The University of Sydney ( email )

University of Sydney
Sydney, NSW 2006
Australia

Henry Leung

University of Sydney Business School ( email )

402, H69
The University of Sydney
Sydney, NSW 2006
Australia

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Artem Prokhorov

University of Sydney Business School ( email )

Cnr. of Codrington and Rose Streets
Sydney, NSW 2006
Australia

Saint Petersburg State University ( email )

7-9, Universitetskaya nab.
Saint Petersburg, 199034
Russia

University of Montreal - Centre interuniversitaire de recherche en économie quantitative (CIREQ) ( email )

3150, rue Jean-Brillant
Montreal, QC H3T 1N8
Canada

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