Stock Price Manipulation Detection using Generative Adversarial Networks

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Publication Details

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

Publisher: Hindawi

Publication year: 2019

Start page: 2104

End page: 2111

Number of pages: 8

ISBN: 9781538692769

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062797533&doi=10.1109%2fSSCI.2018.8628777&partnerID=40&md5=42270ed5a5acf0ec85c6be04e06697cc

Languages: English-Great Britain (EN-GB)

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Abstract

We implemented Generative Adversarial Networks (GANs) for detecting abnormal trading behaviors caused by stock price manipulations. Long short-term memory (LSTM) was used as a base structure of our GANs, which learned normal market behaviors in an unsupervised way. After the training, the discriminator network of GANs was used as a detector to discriminate between normal and manipulative trading. Our work is different from the previous work in that we did not use manipulation cases to train the neural networks. Instead, we used normal data to train them, and simulated manipulation cases were only used for testing purposes. The detection system was tested with the trading data from the Stock Exchange of Thailand (SET). It can achieve 68.1% accuracy in detecting pump-and-dump manipulations in unseen market data. ฉ 2018 IEEE.

Keywords

Generative adversarial networks