Predicting the probability of financial crisis in emerging countries using an early warning system

Artificial neural network

Paria Assadolahi Nik, Mansour Jusoh, Abu Hassan Shaari Md Nor, Tamat Sarmidi

Research output: Contribution to journalArticle

Abstract

The first upsurge of rising economies in the world took place in early 2000s in Brazil, Russia, India, and China, which are known as BRIC countries. Since then, these countries have demonstrated great investment opportunities for financial services as well as the other real sectors’ services. Last decades have witnessed frequent financial disruptions in different kinds which resulted in ruining the economy with unwanted social consequences in BRIC as well as the other countries. The destructive consequences of crisis explain the major reasons for estimating the predicted probability of the crisis. This paper studies the factors associated with the emergence of financial crisis in BRIC countries during 1992-2011 using an Artificial Neural Network. In this context, we built, trained, and tested an Early Warning System (EWS) in order to find out the importance of different inputs in explaining the crisis. So-called model has proven itself by predicting the crisis and non-crisis dates very well. Comparing the importance and significance of all variables in the model, it was discovered that the domestic credit to private sector (% of GDP, domestic credit growth), Inflation, freedom, interest rate and economic growth were the most significant variables in this model, while the deposit insurance rate was found to be the least significant variable in explaining the crisis.

Original languageEnglish
Pages (from-to)25-40
Number of pages16
JournalJournal of Economic Cooperation and Development
Volume37
Issue number1
Publication statusPublished - 2016

Fingerprint

early warning system
neural network
financial crisis
credit
economy
financial service
tertiary sector
interest rate
social effects
inflation
insurance
private sector
economic growth
Russia
Brazil
Artificial neural network
Emerging countries
Financial crisis
Early warning system
India

ASJC Scopus subject areas

  • Business and International Management
  • Economics and Econometrics
  • Political Science and International Relations

Cite this

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title = "Predicting the probability of financial crisis in emerging countries using an early warning system: Artificial neural network",
abstract = "The first upsurge of rising economies in the world took place in early 2000s in Brazil, Russia, India, and China, which are known as BRIC countries. Since then, these countries have demonstrated great investment opportunities for financial services as well as the other real sectors’ services. Last decades have witnessed frequent financial disruptions in different kinds which resulted in ruining the economy with unwanted social consequences in BRIC as well as the other countries. The destructive consequences of crisis explain the major reasons for estimating the predicted probability of the crisis. This paper studies the factors associated with the emergence of financial crisis in BRIC countries during 1992-2011 using an Artificial Neural Network. In this context, we built, trained, and tested an Early Warning System (EWS) in order to find out the importance of different inputs in explaining the crisis. So-called model has proven itself by predicting the crisis and non-crisis dates very well. Comparing the importance and significance of all variables in the model, it was discovered that the domestic credit to private sector ({\%} of GDP, domestic credit growth), Inflation, freedom, interest rate and economic growth were the most significant variables in this model, while the deposit insurance rate was found to be the least significant variable in explaining the crisis.",
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