This study aims at analysing the relationship between board gender diversity (BGD) and financial performance for firms with headquarters based in Latin America (LatAm). Using panel data from 243 listed firms during the period 2012–2018, we find that BGD in LatAm does not lead to any change in firm performance, mainly because the underrepresentation of women on boards in the region compared to their male counterparts. As a result, the ability to improve the firms’ reputation and the relationships with stakeholders as well as the capability to better monitor performance and bring new ideas by female board members are then unavailable to enhance the firms’ performance. The paper also analyses the moderating effect of board independence on the relationship between BGD and firm performance. These study’s findings may be used for providing a roadmap that researchers and investorss can use to improve their understanding of BGD.

The purpose of this study is to analyse the extant literature on sustainable financial products (SFP) with a comprehensive understanding of the status quo and research trends as well as characterise the existing SFP in the Latin America banking industry. In this way, research papers derived from Scopus as well as institutional reports such as main documents, sustainability reports, and product portfolios publicly available on webpages from public, private, and development banks are used to create a database of SFP where their main characteristics are included and classified. Based on the research trends identified, the results show the development of financial products focused on environmental, social, and government (ESG) matters, mainly from the credit side, of more sustainable financial markets and products under fintech ecosystems. The results show that because of regulatory and government support through mechanisms such as green protocols and social and environmental responsibility policies, private financial institutions of Brazil, Colombia, and Argentina have led the development of both social and green financial products. These study’s findings may be used for several policymakers to broaden the opportunities available in sustainable financing and thus, provide a roadmap that researchers and practicing professionals can use to improve their understanding of SFP. Finally, the study presents the potential for further research in the field, both with a qualitative and a quantitative approach.

Science seeks strategies to mitigate global warming and reduce the negative impacts of the long-term use of fossil fuels for power generation. In this sense, implementing and promoting renewable energy in different ways becomes one of the most effective solutions. The inaccuracy in the prediction of power generation from photovoltaic (PV) systems is a significant concern for the planning and operational stages of interconnected electric networks and the promotion of large-scale PV installations. This study proposes the use of Machine Learning techniques to model the photovoltaic power production for a system in Medellín, Colombia. Four forecasting models were generated from techniques compatible with Machine Learning and Artificial Intelligence methods: K-Nearest Neighbors (KNN), Linear Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results obtained indicate that the four methods produced adequate estimations of photovoltaic energy generation. However, the best estimate according to RMSE and MAE is the ANN forecasting model. The proposed Machine Learning-based models were demonstrated to be practical and effective solutions to forecast PV power generation in Medellin.