Title

A Temporal Recurrent Neural Network Approach To Detecting Market Anomaly Attacks Submission Type: Short Paper

Keywords

Anomaly Detection; Cognitive Hacking; Recurrent Neural Network; Sequence Prediction; Social Media; Twitter

Abstract

In 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.

Publication Date

12-24-2018

Publication Title

2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018

Number of Pages

160-162

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ISI.2018.8587397

Socpus ID

85061067169 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85061067169

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