infonet-economy

Die gegenwärtige Goodwillbilanzierung ist durch konzeptionelle Widersprüche und spekulative Elemente geprägt. Insbesondere aufgrund impairment-only approach. Dabei wird vernachlässigt, dass der bilanzierte Goodwill im Grunde eine Investition ist, die sich amortisieren muss. Erworbener Goodwill sollte deshalb bilanziell v.a. als Investition betrachtet und systematisch abgeschrieben werden. Spekulation über einen impliziten,aus dem Unternehmenswert abgeleiteten Goodwill ist Anlegern zu überlassen, nicht dem Unternehmen. Die Goodwillbilanzierung bedarf deshalb einer längst überfälligen Korrektur.

CSR-Informationen rücken bei Öffentlichkeit und Investoren zunehmend
in den Fokus. Sie geben Aufschluss über die ökologische und soziale, aber auch ökonomische Nachhaltigkeitsperformance und -strategie von Unternehmen. Kapitalmarktorientierte empirische Studien legen die Wertrelevanz von CSR-Informationen nahe. Sofern Investoren und Öffentlichkeit diese Informationen richtig verarbeiten können, bieten sie damit das Potenzial, den Shareholder-Value zu steigern und gleichzeitig auch die ökologische und soziale Einstellung von Unternehmen positiv zu beeinflussen.
Im folgenden Beitrag wird erörtert, inwieweit der Status quo des CSR-Reporting im Jahr 2014 zu Informationen führt, die Aufschluss über den Fundamentalwert von Unternehmen geben. Der Beitrag präsentiert hierzu ein Benchmarking der Bewertungsrelevanz von CSR-Informationen der europäischen Elektrizitätsindustrie, die gemäss empirischen Studien eine Vorreiterrolle beim CSR-Reporting innehat.

We study bounding approximations for a multistage stochastic program with expected value constraints. Two simpler approximate stochastic programs, which provide upper and lower bounds on the original problem, are obtained by replacing the original stochastic data process by finitely supported approximate processes. We model the original and approximate processes as dependent random vectors on a joint probability space. This probabilistic coupling allows us to transform the optimal solution of the upper bounding problem to a near-optimal decision rule for the original problem. Unlike the scenario tree based solutions of the bounding problems, the resulting decision rule is implementable in all decision stages, i. e., there is no need for dynamic reoptimization during the planning period. Our approach is illustrated with a mean-risk portfolio optimization model.

We analyze the transport through a narrow ballistic superconductor-normal-metal-superconductor Josephson contact with non-ideal transmission at the superconductor-normal-metal interfaces, e.g., due to insulating layers, effective mass steps, or band misfits (SIN interfaces). The electronic spectrum in the normal wire is determined through the combination of Andreev- and normal reflection at the SIN interfaces. Strong normal scattering at the SIN interfaces introduces electron- and hole-like resonances in the normal region which show up in the quasi-particle spectrum. These resonances have strong implications for the critical supercurrent I_c which we find to be determined by the lowest quasi-particle level: tuning the potential µ_{x0} to the points where electron- and hole-like resonances cross, we find sharp peaks in I_c, resulting in a transitor effect. We compare the performance of this Resonant Josephson-Transistor (RJT) with that of a Superconducting Single Electron Transistor (SSET).

This book investigates convex multistage stochastic programs whose objective and constraint functions exhibit a generalized nonconvex dependence on the random parameters. Although the classical Jensen and Edmundson-Madansky type bounds or their extensions are generally not available for such problems, tight bounds can systematically be constructed under mild regularity conditions. A distinct primal-dual symmetry property is revealed when the proposed bounding method is applied to linear stochastic programs. Exemplary applications are studied to assess the performance of the theoretical concepts in situations of practical relevance. It is shown how market power, lognormal stochastic processes, and risk-aversion can be properly handled in a stochastic programming framework. Numerical experiments show that the relative gap between the bounds can typically be reduced to a few percent at reasonable problem dimensions.

The trading activity in the German intraday electricity market has increased significantly over the last years. This is partially due to an increasing share of renewable energy, wind and photovoltaic, which requires power generators to balance out the forecasting errors in their production. We investigate the bidding behavior in the intraday market by looking at both last prices and continuous bidding, in the context of a fundamental model. A unique data set of 15-minute intraday prices and intraday-updated forecasts of wind and photovoltaic has been employed and price bids are modelled by prior information on fundamentals. We show that intraday prices adjust asymmetrically to both forecasting errors in renewables and to the volume of trades dependent on the threshold variable demand quote, which reflects the expected demand covered by the planned traditional capacity in the day-ahead market. The location of the threshold can be used by market participants to adjust their bids accordingly, given the latest updates in the wind and photovoltaic forecasting errors and the forecasts of the control area balances.

Day-ahead electricity prices are generally used as reference prices for decisions done in energy trading, e.g. purchase and sale strategies are typically based on the day-ahead spot prices. Therefore, well-performing forecast methods for day-ahead electricity prices are essential for energy traders and supply companies. In this paper, a methodology based on artifficial neuronal networks (ANN) is presented to forecast electricity prices. As the performance of an ANN forecast model depends on appropriate input parameter sets, the focus is set on the selection and preparation of fundamental data that has a noticeable impact on electricity prices. This is done with the help of different cluster algorithms, but also by comparing the results of the pre-selected model configurations in combination with different input parameter settings. After the determination of the optimal input parameters, affecting day-ahead electricity prices, and well-performing ANN configuration, the developed ANN model is applied for in-sample and out-of-sample analyses. The results show that the overall methodology leads to well-fitting electricity price forecasts, whereas forecast errors are lower than other forecast models for electricity prices known from the literature.