INDUSTRIALIZATION OF DERIVATIVE DESIGN: INTEGRATED RISK MANAGEMENT WITH THE FINANCIAL INFORMATION SYSTEM WARRANT-PRO-2

Risk management is essential in a modern financial services industry. Derivative instruments like options have a particular status. Appropriate derivatives allow financial service providers to redistribute risks towards others. The process of creating customer tailored derivatives is not wellinvestigated today. With the financial information system (FIS) WARRANT-PRO-2 derivative prices are computed for given payments. The deviation, for example, from a predefinable Delta of an option can be minimized. Automatic creation of optimally synthesized options is very promising for buyer and seller. An example is presented to show the easy process of creating a customer tailored option

Load Management in Power Grids - Towards a Decision Support System for Portfolio Operators

Decentralized renewable energy sources become more and more common. This leads to stability problems in power grids. Conventional energy sources are easy to control. In contrast, wind and solar power are much more difficult to forecast. Forecasts are only possible short term and are more imprecise. Producers and consumers of energy can try to help reducing stability problems. Contributions towards a decision support system are proposed and recommend how to alter the behavior of producers and consumers. On the producer side centrally controlled heat and power plants are able to shift load in a virtual power plant. The plant operator offers a load curve based on forecasts. The centrally controlled heat and power plants help to mitigate the effect of revised forecasts. An incentive based control on the consumer side is also proposed. Smart appliances react to pricing information. They alter their execution window towards the cheapest time slot, if possible. The exact behavior of appliances in the expected field experiment is still partially unknown. It is necessary to simulate the behavior of these appliances and to train an artificial neural network. The artificial neural network allows computing the pricing signal leading to a desired load shift

Decision Support for the Automotive Industry: Forecasting Residual Values using Artificial Neural Networks

The leasing business is one of the most important distribution channels for the automotive industry. This implies that forecasting accurate residual values for the vehicles is a major factor for determining monthly leasing rates: Either a systematic overestimation or underestimation of future residual values can incur large potential losses in resale value or, respectively, competitive disadvantages. In this paper, an operative DSS with the purpose of facilitating residual value related management decisions is introduced, with a focus on its forecasting capabilities. Practical implications are discussed, a multi-variate linear model and an artificial neural network approach are benchmarked and further, the effects of price trends and seasonal influences are investigated. The analysis is based on more than 150,000 data sets from a major German car manufacturer. We show that artificial neural network ensembles with only a few input variables are capable of achieving a significant improvement in forecasting accuracy

Decision Support for the Automotive Industry - Forecasting Residual Values Using Artificial Neural Networks

In the automotive industry, it is very common for new vehicles to be leased rather than sold. This implies forecasting an accurate residual value for the vehicles, which is a major factor for determining monthly leasing rates. Either a systematic overestimation or underestima- tion of future residual values can incur large potential losses in resale value or, respectively, competitive disad- vantages. For the purpose of facilitating residual value related management decisions, an operative decision sup- port system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural net- works for this application is demonstrated in a case study based on more than 250,000 data sets of leasing contracts from a major German car manufacturer, completed between 2011 and 2017. The importance of determining price factors and the effect of different time horizons on forecasting accuracy are investigated and practical impli- cations are discussed. In addition, the authors neither found a significant explanatory nor predictive power of external economic factors, which underlines the importance of collecting and taking advantage of vehicle-specific data or, in more general terms, the exclusive data of corporations, which is often only available internally

„Green by IT“ – Nachhaltiger Gütertransport durch Entscheidungsunterstützungssysteme

Der Beitrag stellt ein Software Artefakt vor, das Disponenten beim Routing von Aufträgen in einem Transportnetzwerk unterstützt. Grundlage ist ein problembezogenes Modell zur Beschreibung der Transportprozesse in intermodalen Netzstrukturen. Ein hybrider Routingalgorithmus zur Lösung des operativen Transportproblems setzt darauf auf. Dieser führt das Routing der Transportaufträge in einem Netzwerk aus Knoten und Kanten aus. Unser Ansatz optimiert den Mobilitätsmix und trägt zu effizienterem und nachhaltigerem Gütertransport bei

COARSE-GRAINED PARALLELIZATION OF THE ADVANCED NEUROSIMULATOR FAUN 1.0 WITH PVM AND THE ENHANCED CORNERED RAT GAME REVISITED

Today artificial neural networks are very useful to solve complex dynamic games of various types, i.e., to approximate optimal strategies with sufficient accuracy. Exemplarily four synthesis approaches for the solution of zero-sum, noncooperative dynamic games are outlined and discussed. Either value function, adjoint vector components or optimal strategies can be synthesized as functions of the state variables. In principle all approaches enable the solution of dynamic games. Nevertheless every approach has advantages and disadvantages which are discussed. The neural network training usually is very difficult and computationally very expensive. The coarse-grained parallelization FAUN 1.0-HPC-PVM of the advanced neurosimulator FAUN uses PVM subroutines and runs on heterogeneous and decentralized networks interconnecting general-purpose workstations, PCs and also high-performance computers. Computing times of days, weeks or months can be cut down to hours. An enhanced cornered rat game — formulated and analyzed in 1993 — serves as an example. Optimal strategies for cat and rat are synthesized. For this purpose open-loop representations of optimal strategies on an equidistant grid in the state space are used. An important end game modification is presented.Dynamic games, artificial neural networks, parallel computation, synthesis of optimal strategies, cornered rat game, 49N70, 49N75, 49N90, 65Y05, 68T05, 68T20, 68W10, 68W25, 91A05, 91A10, 91A23, 91A25, 92B20

Decision Analytics with Heatmap Visualization for Multi-step Ensemble Data - An Application of UncertaintyModeling to Historical Consistent Neural Network and Other Forecasts

Today’s forecasting techniques, which are integrated into several information systems, often use ensembles that represent different scenarios. Aggregating these forecasts is a challenging task: when using the mean or median (common practice), important information is lost, especially if the underlying distribution at every step is multimodal. To avoid this, the authors present a heatmap visualization approach. It is easy to visually distinguish regions of high activity (high probability of realization) from regions of low activity. This form of visualization allows to identify splitting paths in the forecast ensemble and adds a “third alternative” to the decision space. Most forecast systems only offer “up” or “down”: the presented heatmap visualization additionally introduces “don’t know”. Looking at the heatmap, regions can be identified in which the underlying forecast model cannot predict the outcome. The authors present a software prototype with interactive visualization to support decision makers and discuss the information gained by its use. The prototype has already been presented to and discussed with researchers and practitioners

Effects of ethanol on cytokine production after surgery in a murine model of gram-negative pneumonia

Background: Both alcohol abuse and surgery have been shown to impair immune function. The frequency of postoperative infectious complications is 2- to 5-fold increased in long-term alcoholic patients, leading to prolonged hospital stay. Following surgery, an increase in interleukin (IL)-6 has been shown to be associated with increased tissue injury and interleukin 1-(IL-10) is known to represent an anti-inflammatory signal. The purpose of this study was to test the hypothesis that several days of excess alcohol consumption results in more pronounced immunosuppression. We assume that alcoholic animals show increased levels of IL-10 in response to infection and increased IL-6 due to a more pronounced lung pathology. Methods: Thirty-two female Balb/c mice were pretreated with ethanol (EtOH) at a dose of (3.8 mg/g body weight) or saline (NaCl) for 8 days. At day 8 of the experiment all mice underwent a median laparotomy. Two days postsurgery mice were either applicated 104 CFU Klebsiella pneumoniae or received sham-infection with saline. A total number of 4 groups (EtOH/K. pneumoniae; NaCl/K. pneumoniae; EtOH/Sham-infection, NaCl/Sham-infection) was investigated and a clinical score evaluated. Twenty-four hours later mice were killed; lung, spleen, and liver were excised for protein isolation and histological assessment. IL-6 and IL-10 levels were detected by ELISA. Results: Alcohol-exposed mice exhibited a worsened clinical appearance. The histological assessment demonstrated a distinct deterioration of the pulmonary structure in alcohol-treated animals. In the lung, IL-6 and IL-10 was significantly increased in alcohol-exposed infected mice compared to saline-treated infected mice. The clinical score correlated significantly with IL-6 (r = 0.71; p < 0.01) and IL-10 levels (r = 0.64; p < 0.01) in the lung. Conclusions: Ethanol treatment in this surgical model led to a more severe pulmonary infection with K. pneumoniae which was associated with more tissue destruction and increased levels of IL-6 and IL-10 and a worsened clinical score