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Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.
This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
In the upcoming years, the internet of things (IoT)will enrich daily life. The combination of artificial intelligence(AI) and highly interoperable systems will bring context-sensitive multi-domain services to reality. This paper describesa concept for an AI-based smart living platform with open-HAB, a smart home middleware, and Web of Things (WoT) askey components of our approach. The platform concept con-siders different stakeholders, i.e. the housing industry, serviceproviders, and tenants. These activities are part of the Fore-Sight project, an AI-driven, context-sensitive smart living plat-form.
Theory building is not only underdeveloped in IT services management research, but in
general in IS. Given the paradigm shift that comes from the development away from a
networked economy towards a network economy, the lack of spending enough attention to
theorizing in IS becomes even more obvious. In the light of other "megatrends" in IS
research, such as the increasing professionalization and use of statistical methods and the
exploitation of extremely large sets of data (often harvested from social media sites), we
might lose interest in theorizing in the presence of the tremendous amount of available
empirical data. In this position paper, the author advocates that services science researchers
should focus on rigor and relevance in their research approaches.
In diesem Beitrag zur Frage nach dem Ausmaß von Einkommensarmut von Familien stehen zwei Aspekte im Mittelpunkt. – Zum einen ist im Vorfeld von Verteilungsanalysen die Art der Einkommensgewichtung in Mehrpersonenhaushalten zu klären. Nach Abwägung verschiedener Ansätze zur Ableitung einer Äquivalenzskala wurde eine Präferenz für ein institutionell orientiertes Gewichtungsschema, approximiert durch die alte OECD-Skala, begründet. – Zum anderen wurde der Einfluss der Frauenerwerbsbeteiligung auf die Einkommenssituation von Familien mit Kindern empirisch untersucht. Von prekären Einkommensverhältnissen und Einkommensarmut sind vor allem Familien mit geringfügig beschäftigter oder nichterwerbstätiger Partnerin sowie Alleinerziehende – Letztere wiederum bei fehlender Erwerbstätigkeit besonders stark – betroffen, wobei in den neuen Ländern die Situation wesentlich brisanter ist als in den alten Ländern. Bei politischen Maßnahmen sollten Erwerbswünsche der Frauen und Bedürfnisse der Familien berücksichtigt werden. Von daher sind Transfers im Rahmen des Familienleistungsausgleichs und die öffentliche Förderung von Kinderbetreuungseinrichtungen nicht als konkurrierende, sondern eher als komplementäre Konzepte zu diskutieren.
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.
Considering the circumstance that literature dealing with the economic performance of agri-food businesses in general, or particularly with the German agricultural sector, mainly deals with strictly agricultural-related theory in order to explain the economic success of agri-food businesses, the present paper aims to extend existing discourses to further areas of thought. Consequently, the characteristics: a) increased size of agribusiness, b) pull-strategies, c) the development of new markets and d) focus on the processing industry, that all correspond to the current picture of the German agricultural sector and are considered to be significantly responsible for recently managing to outpace the French agri-food sector, will be first explained in their success against the background of mainly non-agricultural-related literature. By doing so helpful and rather unnoted perspectives can be contributed to existing discourses. Second, the paper presents scatter plots which portray correlations between a) the added value of agriculture and the regular labor force, b) the added value of agriculture and the number of agricultural holdings and c) the added value of agriculture and the number of enterprises concerning milk consumption. Corresponding scatter plots which show different developments in Germany and France can be related to the findings of the first part of the paper and allow new perspectives in existing discourses as well.
The debate on monetary and fiscal policy is heavily influenced by estimates of the equilibrium real interest rate. In particular, this concerns estimates derived from a simple aggregate demand and Phillips curve model with time-varying components as proposed by Laubach and Williams (2003). For example, Summers (2014a) refers to these estimates as important evidence for a secular stagnation and the need for fiscal stimulus. Yellen (2015, 2017) has made use of such estimates in order to explain and justify why the Federal Reserve has held interest rates so low for so long. First, we re-estimate the United States equilibrium rate with the methodology of Laubach and Williams (2003). Then, we build on their approach and an alternative specification to provide new estimates for the United States, Germany, the euro area and Japan. Third, we subject these estimates to a battery of sensitivity tests. Due to the great uncertainty and sensitivity that accompany these equilibrium rate estimates, the observed decline in the estimates is not a reliable indicator of a need for expansionary monetary and fiscal policy. Yet, if these estimates are employed to determine the appropriate monetary policy stance, such estimates are better used together with the consistent estimate of the level of potential output.