Towards Extracting Causal Graph Structures from Trade Data and Smart Financial Portfolio Risk Management

Risk managers of asset management companies monitor portfolio risk metrics such as the Value at Risk in order to analyze and to communicate the risks timely to portfolio managers, and to ensure regulatory compliance. They must investigate the possible causes if a portfolio risk significantly increases or breaches a regulatory limit. However, monitoring can quickly become overwhelming, time and labor-intensive as each risk manager has to deal with over a hundred portfolios, numerous daily market data, and hundreds of risk factors of the supervised portfolios and of their securities. Particularly, understanding the interrelations between incidents in different portfolios beyond high level indicators is important. However, analyzing these interrelations manually is one of the most difficult tasks. In this paper, we describe and demonstrate how automatically generating causal graphs can address the capacity problem of practitioners in risk management, who are facing more and more capital markets based risk data daily on the portfolio level alone. Based on a proof of concept implementation, we compare a pairwise causal-inference-based approach with a clustering-based construction approach. We discuss the advantages and disadvantages of both approaches, both computationally and based on the resulting structure. Based on our initial findings, we outline further challenges and research topics

Predicting B2B Customer Churn for Software Maintenance Contracts

Customer churn prediction is a well-known application of machine learning and data mining in Customer Relationship Management, which allows a company to predict the probability of its customer churning. In this study, we extended the application of customer churn prediction to the context of software maintenance contract. In addition, we examined the predictive power of economic factors. Random forest, gradient boosting machine, stacking of random forest and gradient boosting machine, XGBoost, and long short-term memory networks were applied. While an ensemble model and XGBoost performed best, macroeconomic variables did not yield statistically significant improvement in any prediction

Ubiquitäre Systeme (Seminar) und Mobile Computing (Proseminar) SS 2019 : Mobile und Verteilte Systeme Ubiquitous Computing. Teil XIX

Die Seminarreihe Mobile Computing und Ubiquitäre Systeme existiert seit dem Wintersemester 2013/2014. Seit diesem Semester findet das Proseminar Mobile Computing am Lehrstuhl fur Pervasive Computing System statt. Die Arbeiten des Proseminars werden seit dem mit den Arbeiten des zweiten Seminars des Lehrstuhls, dem Seminar Ubiquitäre Systeme, zusammengefasst und gemeinsam veröffentlicht. Die Seminarreihe Ubiquitäre Systeme hat eine lange Tradition in der Forschungsgruppe TECO. Im Wintersemester 2010/2011 wurde die Gruppe Teil des Lehrstuhls für Pervasive Computing Systems. Seit dem findet das Seminar Ubiquitäre Systeme in jedem Semester statt. Ebenso wird das Proseminar Mobile Computing seit dem Wintersemester 2013/2014 in jedem Semester durchgeführt. Seit dem Wintersemester 2003/2004 werden die Seminararbeiten als KIT-Berichte veröffentlicht. Ziel der gemeinsamen Seminarreihe ist die Aufarbeitung und Diskussion aktueller Forschungsfragen in den Bereichen Mobile und Ubiquitous Computing. Dieser Seminarband fasst die Arbeiten der Seminare des Sommersemesters 2019 zusammen. Wir danken den Studierenden für ihren besonderen Einsatz, sowohl während des Seminars als auch bei der Fertigstellung dieses Bandes

Adoption of energy-efficiency measures in SMEs - An empirical analysis based on energy audit data

International audienceThis paper empirically investigates the factors driving the adoption of energy-efficiency measures by small and medium-sized enterprises (SMEs). Our analyses are based on cross-sectional data from SMEs which participated in a German energy audit program between 2008 and 2010. In general, our findings appear robust to alternative model specifications and are consistent with the theoretical and still scarce empirical literature on barriers to energy efficiency in SMEs. More specifically, high investment costs, which are captured by subjective and objective proxies, appear to impede the adoption of energy-efficient measures, even if these measures are deemed profitable. Similarly, we find that lack of capital slows the adoption of energy-efficient measures, primarily for larger investments. Hence, investment subsidies or soft loans (for larger invest-ments) may help accelerating the diffusion of energy-efficiency measures in SMEs. Other barriers were not found to be statistically significant. Finally, our findings provide evidence that the quality of energy audits affects the adoption of energy-efficiency measures. Hence, effective regulation should involve quality standards for energy au-dits, templates for audit reports or mandatory monitoring of energy audits