Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

The Power of Alumni Networks - Success of Startup Companies Correlates With Online Social Network Structure of Its Founders

In this paper we analyze the success of startups in Germany by looking at the social network structure of their founders on the German-language business-networking site XING. We address two related research questions. First we examine university-wide networks, constructing alumni networks of 12 German universities, with the goal of identifying the most successful founder networks among the 12 universities. Second, we also look at individual actor network structure, to find the social network attributes of the most successful founders. We automatically collected the publicly accessible portion of XING, filtering people by attributes indicative of their university, and roles as founders, entrepreneurs, and CEOs. We identified 51,976 alumni, out of which 14,854 have entrepreneurship attributes. We also manually evaluated the financial success of a subsample of 80 entrepreneurs for each university. We found that universities, which are more central in the German university network, provide a better environment for students to found more and more successful startups. University networks whose alumni have a stronger “old-boys-network”, i.e. a larger share of their links with other alumni of their alma mater, are more successful as founders of startups. On the individual level the same holds true: the more links founders have with alumni of their university, the more successful their startup is. Finally, the absolute amount of networking matters, i.e. the more links entrepreneurs have, and the higher their betweenness in the online network of university alumni, the more successful they are

Breakdown: Predictive Values of Tweets, Forums and News in EUR/USD Trading

Predictive indicators for financial markets based on online buzz has been a frequent topic during the last years. Recent studies use a range of alternative sources for building these sentiment indices, with each purporting to have predictive value. Therefore a question mark remains regarding the comparability of findings across different types of sources, e.g. do indicators based on Tweets perform equally well or better than those built on news? This study addresses how competing sentiment indicators affect EUR/USD trading. To identify the indicator having the best predictive value we estimate expected returns for individual sources and forecast models via backtesting. Our findings support the notion that the predictive value depends on the source of the sentiment-indicator, on timing aspects, with more recent sentiments having greater predictive strength, and on the type of rule (e.g. buy / sell) harnessed

Electron Identification up to 100 GeV by Means of Transition Radiation

We report on measurements with different transition radiation detector configurations, which were performed with the aim of pion-electron-discrimination in the momentum range between 1 GeV/c and 100 GeV/c. The test set-up consisted of four polypropylene fibre radiators with proportional wire chambers as photon detectors. We tested about 50 combinations varying the diameter of the fibre, the density of the radiators and the thickness of the chambers. Results of measurements performed at a DESY testbeam at energies between 0.6 and 6.6 GeV and of extrapolations to particle momenta up to 100 GeV/c are discussed. At 95% electron efficiency a pion contamination of a few per cent can be achieved over the full energy range

Electron Identification up to 100 GeV by Means of Transition Radiation

We report on measurements with different transition radiation detector configurations, which were performed with the aim of pion-electron-discrimination in the momentum range between 1 GeV/c and 100 GeV/c. The test set-up consisted of four polypropylene fibre radiators with proportional wire chambers as photon detectors. We tested about 50 combinations varying the diameter of the fibre, the density of the radiators and the thickness of the chambers. Results of measurements performed at a DESY testbeam at energies between 0.6 and 6.6 GeV and of extrapolations to particle momenta up to 100 GeV/c are discussed. At 95% electron efficiency a pion contamination of a few per cent can be achieved over the full energy range

Web Science 2.0: Identifying Trends through Semantic Social Network Analysis

We introduce a novel set of social network analysis based algorithms for mining the Web, blogs, and online forums to identify trends and find the people launching these new trends. These algorithms have been implemented in Condor, a software system for predictive search and analysis of the Web and especially social networks. Algorithms include the temporal computation of network centrality measures, the visualization of social networks as Cybermaps, a semantic process of mining and analyzing large amounts of text based on social network analysis, and sentiment analysis and information filtering methods. The temporal calculation of betweenness of concepts permits to extract and predict long-term trends on the popularity of relevant concepts such as brands, movies, and politicians. We illustrate our approach by qualitatively comparing Web buzz and our Web betweenness for the 2008 US presidential elections, as well as correlating the Web buzz index with share prices