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Identification of Stock Market Manipulation with Deep Learning

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conference contribution
posted on 2022-01-01, 00:00 authored by Jillian Tallboys, Ye ZhuYe Zhu, Sutharshan RajasegararSutharshan Rajasegarar
Anomaly detection is a common and critical data mining task, it seeks to identify observations that differ significantly from others. Anomalies may indicate rare but significant events that require action. Market manipulation is an activity that undermines stock markets worldwide. This paper shares five large real-world, labelled data sets of anomalous stock market data where market manipulation is alleged to have occurred. Cutting edge deep learning techniques are then shown to successfully detect the anomalous periods. An LSTM based method with dynamic thresholding is particularly promising in this domain as it was able to identify contextual local anomalies in the data quickly, taking seconds to score two years of trading data for each stock, which can often be a challenge for deep learning approaches.

History

Volume

13087

Pagination

408-420

Location

Sydney, Australia

Open access

  • Yes

Start date

2022-02-02

End date

2022-02-04

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

978-3-030-95404-8

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

Li B, Yue L, Jiang J, Chen W, Li X, Long G, Fang F, Yu H

Title of proceedings

Advanced Data Mining and Applications

Event

International Conference on Advanced Data Mining and Applications (ADMA)

Publisher

Springer

Place of publication

Berlin, Germany

Series

Lecture Notes in Computer Science (LNAI)