RESUMO
This study provides new evidence on how risk spillovers occur from the United States to developing economies in Africa during the COVID-19 pandemic. The results show that downside risk exposures of African markets, financial firms and banks particularly increased during Phase I (30 January to 30 April 2020). The nature and magnitude of downside risk exposures of African financial markets were similar to those of the United States. Our results also reveal that the United States is a net transmitter of risk spillovers while Nigeria, South Africa, Egypt and Morocco are net recipients. Our conclusions offer guidance to risk managers, policymakers and investors.
RESUMO
Given the increasingly significant role of small and medium-sized enterprises (SMEs) in the global economy and the ever more competitive markets in which these companies operate, SMEs' ability to adopt artificial intelligence (AI) technologies is of utmost importance. Due to constantly evolving social, environmental, and technological scenarios, the managers of these firms must increasingly focus on incorporating new tools such as AI into SME operations in order to enjoy their benefits. However, the subjectivity and complexity of this adaptation process makes integrated analyses of key factors challenging. The present study sought to develop a multi-criteria decision-support system that applies cognitive mapping and the decision-making trial and evaluation laboratory technique in a neutrosophic context. The main objective is to overcome the limitations of previous studies and models by structuring the decision problem and identifying and understanding which factors should be central to adaptation initiative analyses. A panel of experts in AI were recruited to facilitate the construction of an analysis system that takes into account indeterminacy in decision-making processes. The results were validated by both the panel members and project managers at COTEC Portugal-a leading think-and-action network that seeks to advance technology diffusion and business innovation cooperation. The proposed system's practical implications and benefits are also analyzed.
RESUMO
Robust optimization (RO) models have attracted a lot of interest in the area of portfolio selection. RO extends the framework of traditional portfolio optimization models, incorporating uncertainty through a formal and analytical approach into the modeling process. Although several RO models have been proposed in the literature, comprehensive empirical assessments of their performance are rather lacking. The objective of this study is to fill in this gap in the literature. To this end, we consider different types of RO models based on popular risk measures and conduct an extensive comparative analysis of their performance using data from the US market during the period 2005-2020. For the analysis, two different robust versions of the mean-variance model are considered, together with robust models for conditional value-at-risk and the Omega ratio. The robust versions are compared against the nominal ones through various portfolio performance metrics, focusing on out-of-sample results.
RESUMO
Support vector machines (SVMs) are one of the most popular methodologies for the design of pattern classification systems with sound theoretical foundations and high generalizing performance. The SVM framework focuses on linear and nonlinear models that maximize the separating margin between objects belonging in different classes. This paper extends the SVM modeling context toward the development of additive models that combine the simplicity and transparency/interpretability of linear classifiers with the generalizing performance of nonlinear models. Experimental results are also presented on the performance of the new methodology over existing SVM techniques.
