Does firm carbon risk matter to banks? Evidence from the US syndicated loan market.

Using two measures of firm carbon-risk exposure to capture convergent materiality, we examine whether the syndicated loan market considers borrowers' carbon-risk exposure in loan pricing. We find that carbon intensity does not constitute material information for banks, whereas environmental scores have a statistically significant but incidental economic effect on loan spreads. We also find inconclusive evidence that the market differentiates between firms in high and low environmentally sensitive industries.

Artificial intelligence and change management in small and medium-sized enterprises: an analysis of dynamics within adaptation initiatives.

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.

COVID-19 crisis and risk spillovers to developing economies: Evidence from Africa.

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.

Robust optimization approaches for portfolio selection: a comparative analysis.

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.

Additive support vector machines for pattern classification.

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.