Forecasting of stock market returns is a challenging research activity that is now expanding with the availability of new data sources, markets, financial instruments, and algorithms. At its core, the predictability of prices still raises important questions. Here we discuss the economic significance of the prediction accuracy. To develop this question, we collect the daily series prices of almost half of the publicly traded companies around the world over a period of ten years and formulate some trading strategies based on their prediction. Proper visualization of these data together with the use of the No Free Lunch theoretical framework give some unexpected results that show how the a priori less accurate algorithms and inefficient strategies can offer better results than the a priori best alternatives in some particular subsets of data that have a clear interpretation in terms of economic sectors and regions.

El escenario actual de crisis y cambios ha llevado a pensar en el emprendimiento como una nueva v The current scenario of crisis and change has prompted the idea of entrepreneurship as a way to develop new media business models that can be promoted by university training. In this study, we aim to assess the effects of such training. A qualitative study was conducted using in-depth interviews of Spanish journalism and communication entrepreneurs who have undergone university training in business creation and management. Our results show the positive effects of this training on entrepreneurship both in general and on specific aspects of entrepreneurial projects such as organization, business plan/model, marketing, innovation, social aspects and quality of life. Different patterns between the effects of university training on new initiatives and advanced projects were also observed. In this respect, the training supported the creation of new businesses and the development of existing ones. Finally, the suggestions for improving training and the limitations to entrepreneurship have revealed the importance of providing this type of education with a more practical, up-to-date approach that is interconnected with the business and university world. Therefore, examples of this work can be of vital importance in opening up new opportunities for sector development to enable future generations of journalists to fulfill their important social function.

An important concern that 5G networks face is supporting a wide range of services and use cases with heterogeneous requirements. Radio access network (RAN) slices, understood as isolated virtual networks that share a common infrastructure, are a possible answer to this very demanding scenario and enable virtual operators to provide differentiated services over independent logical entities. This article addresses the feasibility of forming 5G slices, answering the question of whether the available capacity (resources) is sufficient to satisfy slice requirements. As spectral efficiency is one of the key metrics in 5G networks, we introduce the minislot-based slicing allocation (MISA) model, a novel 5G slice resource allocation approach that combines the utilization of both complete slots (or physical resource blocks) and mini-slots with the adequate physical layer design and service requirement constraints. We advocate for a probabilistic characterization that allows to estimate feasibility and characterize the behavior of the constraints, while an exhaustive search is very computationally demanding and the methods to check feasibility provide no information on the constraints. In such a characterization, the concept of phase transition allows for the identification of a clear frontier between the feasible and infeasible regions. Our method relies on an adaptation of the Wang-Landau algorithm to determine the existence of, at least, one solution to the problem. The conducted simulations show a significant improvement in spectral efficiency and feasibility of the MISA approach compared to the slot-based formulation, the identification of the phase transition, and valuable results to characterize the satisfiability of the constraints.

Group decision making is a topic of growing interest in today's complex societies. One of the key technologies in this area is the prediction market, where a group of experts plays a fake stock market with assets that represent the outcomes of an uncertain event. The particular problem we address in this paper is the expert selection in these markets to improve their reliability. To aggregate decisions from a particular group of experts, instead of using prices as is typically done, we define a market deconstruction considering player portfolios. This decision technology makes the behaviors of experts toward their decisions available through their portfolios evolution. Our main contribution is the identification of two Persistent Homological Invariants able to classify experts in groups based on the histories of their portfolios. Interestingly, this translates into the definition of essentially two dominant groups. A simulation of the Prediction Market with artificial agents allow us to interpret these two classes as rational and irrational players, following the Microeconomic jargon. Four experiments with experts in the insurance sector help us to illustrate the relationship between these two player types with the prediction reliability of the market.

Una propuesta para impulsar el espíritu emprendedor y la capacitación en el futuro de la profesión periodística