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Conference Paper: A Temporal Recurrent Neural Network Approach to Detecting Market Anomaly Attacks

TitleA Temporal Recurrent Neural Network Approach to Detecting Market Anomaly Attacks
Authors
Issue Date2018
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001810
Citation
Proceedings of 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), Miami, FL, USA, 9-11 November 2018, p. 160-162 How to Cite?
AbstractIn recent years, the spreading of malicious social media messages about financial stocks has threatened the security of financial market. Market Anomaly Attacks is an illegal practice in the stock or commodities markets that induces investors to make purchase or sale decisions based on false information. Identifying these threats from noisy social media datasets remains challenging because of the long time sequence in these social media postings, ambiguous textual context and the difficulties for traditional deep learning approaches to handle both temporal and text dependent data such as financial social media messages. This research developed a temporal recurrent neural network (TRNN) approach to capturing both time and text sequence dependencies for intelligent detection of market anomalies. We tested the approach by using financial social media of U.S. technology companies and their stock returns. Compared with traditional neural network approaches, TRNN was found to more efficiently and effectively classify abnormal returns.
Persistent Identifierhttp://hdl.handle.net/10722/278670
ISBN

 

DC FieldValueLanguage
dc.contributor.authorHuang, Y-
dc.contributor.authorChung, WY-
dc.contributor.authorTang, X-
dc.date.accessioned2019-10-21T02:11:51Z-
dc.date.available2019-10-21T02:11:51Z-
dc.date.issued2018-
dc.identifier.citationProceedings of 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), Miami, FL, USA, 9-11 November 2018, p. 160-162-
dc.identifier.isbn978-1-5386-7849-7-
dc.identifier.urihttp://hdl.handle.net/10722/278670-
dc.description.abstractIn recent years, the spreading of malicious social media messages about financial stocks has threatened the security of financial market. Market Anomaly Attacks is an illegal practice in the stock or commodities markets that induces investors to make purchase or sale decisions based on false information. Identifying these threats from noisy social media datasets remains challenging because of the long time sequence in these social media postings, ambiguous textual context and the difficulties for traditional deep learning approaches to handle both temporal and text dependent data such as financial social media messages. This research developed a temporal recurrent neural network (TRNN) approach to capturing both time and text sequence dependencies for intelligent detection of market anomalies. We tested the approach by using financial social media of U.S. technology companies and their stock returns. Compared with traditional neural network approaches, TRNN was found to more efficiently and effectively classify abnormal returns.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001810-
dc.relation.ispartofIEEE International Conference on Intelligence and Security Informatics (ISI)-
dc.rightsIEEE International Conference on Intelligence and Security Informatics (ISI). Copyright © IEEE.-
dc.rights©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleA Temporal Recurrent Neural Network Approach to Detecting Market Anomaly Attacks-
dc.typeConference_Paper-
dc.identifier.emailChung, WY: wchun@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ISI.2018.8587397-
dc.identifier.scopuseid_2-s2.0-85061067169-
dc.identifier.hkuros307656-
dc.identifier.spage160-
dc.identifier.epage162-
dc.publisher.placeUnited States-

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