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This study advances a novel multilayer network model to explore the connection between different aspects of Technological Innovation in European Union (EU) countries. We follow a fuzzy clustering approach and consider three variables: Research and Development (R&D), High-Tech Exports (HTE), and Human Resources in Science and Technology (HRST). We consider Eurostat data from 2018 to 2023. The variables form the layers, the EU countries are the nodes of the layers, and the weighted intra-layer links are assumed to increase with respect to the similarity of the countries in terms of the related variable. Interlayer connections are modeled probabilistically using a fuzzy clustering approach: two countries in different layers are strongly connected if they belong more probably to the same cluster in the related layers. The analysis offers insights into the patterns of EU countries in terms of Technological Innovation (TI) processes. The proposed framework allows its applicability to a wide set of real-world contexts.

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor’s contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability.

Several studies deal with the development of advanced statistical methods for predicting football match results. These predictions are then used to construct profitable betting strategies. Even if the most popular bets are based on whether one expects that a team will win, lose, or draw in the next game, nowadays a variety of other outcomes are available for betting purposes. While some of these events are binary in nature (e.g. the red cards occurrence), others can be seen as binary outcomes. In this paper we propose a simple framework, based on score-driven models, able to obtain accurate forecasts for binary outcomes in soccer matches. To show the usefulness of the proposed statistical approach, two experiments to the English Premier League and to the Italian Serie A are provided for predicting red cards occurrence, Under/Over and Goal/No Goal events.

Market inefficiency is a latent concept, and it is difficult to be measured by means of a single indicator. In this paper, following both the adaptive market hypothesis (AMH) and the fractal market hypothesis (FMH), we develop a new time-varying measure of stock market inefficiency. The proposed measure, called composite efficiency index (CEI), is estimated as the synthesis of the most common efficiency measures such as the returns’ autocorrelation, liquidity, volatility, and a new measure based on the Hurst exponent, called the Hurst efficiency index (HEI). To empirically validate the indicator, we compare different European stock markets in terms of efficiency over time.

The most common purpose of seasonal adjustment is to provide an estimate of the current trend so that judgmental short-term forecasts can be made. Bell (Proceedings of the American Statistical Association, 1995) formally considered how model-based seasonal adjustment could be done in order to facilitate the forecasting, showing that, from a theoretical perspective, this objective could be best served by not revising the data for seasonality. However, this study was lacking an empirical investigation to determine if this approach would realize any advantages when applied in practice. Aim of this paper is to assess whether is convenient, from forecasting perspective, to adjust data for seasonality or directly use a model which accounts for seasonality. In particular, we serve this scope by both a simulation study and an application with real data related to Industrial Production Index. We show that pre-adjusting the time series for seasonality allows for forecasting improvements in terms of accuracy. In the end we evaluated also the best seasonally adjustment method for forecasting purposes. Empirical evidence shows that the forecasts among seasonal adjustment methods are statistically different and that, while for the Italian TRAMO-SEATS outperform X13-ARIMA-SEATS, the opposite happen for the U.S. Industrial Production. Results from the simulation suggest that the forecasts among the considered seasonal adjustment methods are not statistically different for very short and long time period. However, seasonal adjustment methods lead to statistically different forecasts for medium and for very long time period.

(1) Background: SARS-CoV-2 infection is notably mild in children, though comorbidities may increase the risk of hospitalization and may represent a risk for increased disease severity. There is an urgent need for targeted therapies with an acceptable efficacy and safety profile. To date, most of the medicines for COVID-19-specific treatment are prescribed off-label for children due to a lack of clinical trials and consequent evidence in this population. (2) Methods: This was a retrospective, observational study investigating the safety of treatments for the prevention of severe COVID-19 in fragile pediatric patients who received monoclonal antibodies and antivirals for mild-to-moderate symptoms between December 2021 and July 2022. (3) Results: Thirty-two patients were included. Monoclonal antibodies were prescribed to 62%, intravenous antivirals to 22%, and oral antivirals to 16% of children. Sotrovimab was the most frequently prescribed drug among monoclonal antibodies and overall (59%). The second most prescribed drug was remdesivir (22%). No severe adverse drug reaction was reported. There was no progression to severe disease and no death cases due to COVID-19 or drug administration. At drug-type stratification, resolution of symptoms and swab positivity time showed no difference between the two groups at 7 and 28 days. Off-label prescriptions were 84% overall, and in similar proportions between the two groups. (4) Conclusions: in this small sample, antivirals seemed safe and showed no differences in efficacy as compared to MAbs for the early treatment of COVID-19 in fragile children, thus representing a valuable choice, even when administered off-label.

In this paper, we investigate the usefulness of forecasting in a high-dimensional framework where the number of assets is larger than the temporal observations. The benefit of forecasting lies in the concept of , which means anticipating future market conditions. We find that when high-dimensional econometric approaches are used, forecasting either the mean or the covariance is better than predicting both and then approaches based on static estimates. Moreover, we find that timing portfolios also perform better than the naive strategy. Considering the portfolio returns over time, we find that a possible explanation for the better performance of volatility-timing portfolios is that they better manage risk during periods of high uncertainty.

This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007–2020. The study is developed through three methodological steps. First, we consider the networked scientific institutions by stating a link between two organizations when they are partners in the same funded project. In doing so, we build yearly complex networks. We compute four nodal centrality measures with relevant, informative content for each of them. Second, we implement a rank-size procedure on each network and each centrality measure by testing four meaningful classes of parametric curves to fit the ranked data. At the end of such a step, we derive the best fit curve and the calibrated parameters. Third, we perform a clustering procedure based on the best-fit curves of the ranked data for identifying regularities and deviations among years of research and scientific institutions. The joint employment of the three methodological approaches allows a clear view of the research activity in Europe in recent years.

In high-dimensional portfolio selection, traditional asset allocation techniques often yield suboptimal results out-of-sample, while equally weighted portfolios have shown better performances in such scenarios. To leverage the advantages of diversification while addressing the curse of dimensionality, we turn to clustering techniques. Specifically, we explore the application of k -means clustering for time series, which offers a clear financial interpretation as the prototype of each cluster represents an equally weighted portfolio of the assets within the cluster. In this paper, we conduct a comprehensive comparison of various time series clustering techniques in the context of portfolio performance. By evaluating the out-of-sample performance of portfolios constructed using different clustering approaches, we aim to identify the most effective method for investment purposes.