A comparative analysis of the alternative risk measurement methods of countries

As a result of the rapid growth of the international debt of developing countries, country risk has become an important topic for the official institutions and private market operators involved in international financial operations. In the last two decades a great number of studies are presented in specifying the determinants of country risk analysis. Also managers are more concerned than ever about forecasting potential economic and political risks. Accordingly, the number of country risk ratings agencies has increased substantially. Although these agencies produce country risk ratings as measures of the ability and willingness of country to service their financial obligations, the relevance and accuracy of the various agency risk ratings arre open to questions. These agencies have also been criticized for their judgment, which has decided on political views. Furthermore, many people blame the way that credit agencies actually arrive at ratings for countries, for they make many countries appear stronger than they actually were or weaker than they actually were. For these reasons, this study examines the ability of relatively new technique, hybrid ANN?s, to predict country risk rating. Also, hybrid model is compared with conventional ANN and discriminant analysis in order to putforward the merits of hybrid ANN that are responsible for the higher level of performance. The comparison is based on ninety countries and twenty five variables, which are economic, financial and political. Results demonstrate that hybrid neural network may be an accurate tool for country risk analysis.