Use of Markov chain models for forecasting financial crises in Morocco

Abstract

This article presents an innovative method for forecasting financial crises in Morocco, covering the period from 1980 to 2023 through the use of Markov chain models. The analysis focuses on the quarterly evolution of Moroccan GDP, identifying phases of economic slowdown and recovery. The results demonstrate the resilience of the Moroccan economy to various economic disturbances while maintaining generally stable growth. To improve the accuracy of these models, it is proposed to integrate additional economic and political data. The article recommends the adoption of flexible economic policies that allow for a quick response to economic fluctuations and regular monitoring of economic indicators to anticipate and effectively manage future crises. Finally, the use of machine learning techniques is suggested to refine predictions and enhance financial risk management.