An Early Warning Signal (EWS) Model for Predicting Financial Crisis in Emerging African Economies

Abstract

The devastating effects of the global financial crisis (GFC) have led to a renewed, global interest in the development of an early warning signal (EWS) model. The purpose of the EWS model is to alert policymakers and other stakeholders to the possibility of the occurrence of a crisis. This study estimates a EWS model for predicting the financial crisis in four emerging African economies using a multinomial logit model and a data set covering the period of January 1980 to December 2017. The result of the study suggests that emerging African economies are more likely to face financial crisis as debts continue to rise without a corresponding capacity to withstand capital flow reversal as well as excessive foreign exchange risk due to currency exposure. The result further indicates that rising debt exposure raises the likelihood of the economies remaining in a state of crisis. This result confirms the significance of a financial stability framework that addresses the issues confronting Africa’s emerging economies such as rising debt profile, liquidity and currency risk exposure.

Enterprise Financial Risk Early Warning Based on Deep Neural Network

“…The middle layer and layer of the network are connected with each other, and the neurons communicate with each other through the same network weight. The forward three-layer NN can achieve a good simulation effect on the relationship between input and output, so it is widely used in the research of financial early warning [18]. The Algorithm of BP NN is based on the back-propagation Algorithm.…”