Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is Value-at-Risk (VaR). VaR estimates based on linear and parametric models can lead to biased results or even underestimation...

Many Southeast European countries are currently undergoing a process of liberalization of electric power markets. The paper analyses day-ahead price dynamics on some of these new markets and in Germany as a benchmark of a completely decentralized Western European market. To that end, several...

Marketing messages are most effective if they reach the right customers. Deciding which customers to contact is thus an important task in campaign planning. The paper focuses on empirical targeting models. We argue that common practices to develop such models do not account sufficiently for...

Data from social media has created opportunities to understand how and why people move through their urban environment and how this relates to criminal activity. To aid resource allocation decisions in the scope of predictive policing, the paper proposes an approach to predict weekly crime...

Open-ended responses are widely used in market research studies. Processing of such responses requires labor-intensive human coding. This paper focuses on unsupervised topic models and tests their ability to automate the analysis of open-ended responses. Since state-of-the-art topic models...

Uplift modeling combines machine learning and experimental strategies to estimate the differential effect of a treatment on individuals' behavior. The paper considers uplift models in the scope of marketing campaign targeting. Literature on uplift modeling strategies is fragmented across...

Second-hand car markets contribute to billions of Euro turnover each year but hardly generate profit for used car dealers. The paper examines the potential of sophisticated data-driven pricing systems to enhance supplier-side decision making and escape the zero-profit-trap. Profit maximization...

Software-as-a-service applications are experiencing immense growth as their comparatively low cost makes them an important alternative to traditional software. Following the initial adoption phase, vendors are now concerned with the continued usage of their software. To analyze the influence of...

Deep learning has substantially advanced the state-of-the-art in computer vision, natural language processing and other elds. The paper examines the potential of contemporary recurrent deep learning architectures for nancial time series forecasting. Considering the foreign exchange market as...

Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under...