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

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Leangarun T., Tangamchit P., Thajchayapong S.

ผู้เผยแพร่: Hindawi

ปีที่เผยแพร่ (ค.ศ.): 2020

หน้าแรก: 2162

หน้าสุดท้าย: 2169

จำนวนหน้า: 8

ISBN: 9781728125473

นอก: 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

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

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.

คำสำคัญ

stock market, stock price manipulation detection