Using Generative Adversarial Networks for Detecting Stock Price Manipulation: The Stock Exchange of Thailand Case Study

Conference proceedings article

Authors/Editors

POJ TANGAMCHIT

Strategic Research Themes

Machine Learning (Big Data Analytics)

Publication Details

Author list: Leangarun T., Tangamchit P., Thajchayapong S.

Publisher: Hindawi

Publication year: 2020

Start page: 2162

End page: 2169

Number of pages: 8

ISBN: 9781728125473

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099701316&doi=10.1109%2fSSCI47803.2020.9308284&partnerID=40&md5=428b592c2b073de7dc1fdb40e3101526

Languages: English-Great Britain (EN-GB)

View on publisher site

Abstract

We implemented an automated system that uses unsupervised learning to detect stock price manipulation events. Generative adversarial networks (GANs) were trained with regular market transactions in a limit order book format. GANs can recognize normal trading behaviors of good governance stocks with the various price ranges, trading volume, and market capitalization. Stocks that were traded differently were assumed to be suspicious, thus required further manual investigation. We tested the system with 6 real manipulation cases that had been prosecuted from the stock exchange of Thailand. The proposed system can identify 5 out of 6 cases correctly with a very low false-positive rate. © 2020 IEEE.

Keywords

stock market, stock price manipulation detection