Forecasting the Effects of the COVID-19 Crisis on Economic Growth and the Microfinance Sector in Latin America: An Approach with Fuzzy Neural Networks

Abstract

The objective of this research is to identify the impact of the COVID-19 contingency on economic activity and the microfinance sector in Argentina, Colombia, Ecuador, Mexico, and Peru. Through a fuzzy autoregressive neural network with a pentagonal membership function, and correlational analysis, which allows the identification of levels of impact of the contingency and inferences of the effects in the microfinance industry. The results showed that the agricultural sector will be the most affected by the current crisis, followed by the tertiary activities and the industry, these effects were observed in the five economies analyzed; besides, we found empirical evidence of the countercyclical condition of the Popular Savings and Credit Entities, for which we identified that in this economic sector we expect increases in profitability, a decrease of credit and liquidity risks, being the Mexican savings and credit sector the exception in these results. In conclusion, today more than ever, financial institutions play a relevant role in achieving the best possible reconstruction of the economic and social environment of Latin American families.