Biblio

Social media are increasingly reflecting and influencing the behavior of human and financial market. Cognitive hacking leverages the influence of social media to spread deceptive information with an intent to gain abnormal profits illegally or to cause losses. Measuring the information content in financial social media can be useful for identifying these attacks. In this paper, we developed an approach to identifying social media features that correlate with abnormal returns of the stocks of companies vulnerable to be targets of cognitive hacking. To test the approach, we collected price data and 865,289 social media messages on four technology companies from July 2017 to June 2018, and extracted features that contributed to abnormal stock movements. Preliminary results show that terms that are simple, motivate actions, incite emotion, and uses exaggeration are ranked high in the features of messages associated with abnormal price movements. We also provide selected messages to illustrate the use of these features in potential cognitive hacking attacks.

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.