The Market Reaction to Trump\u27s Trade War

This event study looks at the market reaction to the global trade tensions that began in the first half of 2018. The events regarding new developments around the use of tariffs are organized in chronological order, and the stocks of certain impacted companies are looked at to see if they were positively or negatively affected by the news. To summarize the market reaction to tariffs, I use a zero cost portfolio consisting of long positions in those expected to be positively impacted and short positions in those expected to be negatively impacted. If this portfolio sees a larger return on the day of a given event, it is considered that the market reacted more severely to the news. For a further breakdown, the events are grouped together by the countries involved with the event and by the type of event. I look at tariffs imposed by the United States, the European Union, Canada, Mexico, and China. The event types include announcements of plans for new tariffs, announcements of exemptions from tariffs, and the formal implementation of tariffs. I find that the most significant market reaction took place in the early months of the trade war, which is evident in that there appears to be the widest spread in returns between those positively and those negatively impacted during this time. As the trade war dragged on in 2018, tariffs were imposed on a broader range of products, and the market reaction became less severe. This information could be useful to traders and asset managers going forward as it appears much of the impact of these tariffs is already reflected in stock prices

Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi

SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies

Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap