Classification Tree Analysis as Applied to Mena Regions

IICR and ICRG country risk scores are examined in a decision tree framework to analyze groupings on countries, including Middle East and North Africa. INTRODUCTION The purpose of this paper is to examine if Middle East and North Africa (MENA) countries form a distinct financial risk group, based on financial predictor variables and country risk scores. Country risk scores are calculated internally by banks and other financial institutions to assess various risks facing international capital flows to countries around the world. What exactly determine each of the scores depend on the perspectives of the institutions which do the analysis and are kept confidential. The resulting risk scores are published or are distributed to clients of the firm. The scores are calculated as a weighted basket of both financial and non-financial risk. A large category of non-financial risk is political risk, which includes a government takeover of the investment as well as other factors. Other factors may include consumers’ preference, if strong, for domestic goods, legal and political infrastructure for foreign capital, the probability of war or political instability and obstruction of financial transfers. Financial risk variables are also very important in composing the risk indexes. The health of the balance of payments accounts, the possibility of recessions, the health of the banking system, the growth potential of GNP, regulatory and financial environment are some of the variables considered in calculating financial risk. Uses of country risk scores are more an art than a science, especially since the scores are in a continual basis (from zero to 100 in theory). Bhalla (1983) promoted the use of financial versus socio-political risk matrices which are still used by some fund managers. The clustering of lower risk countries (both in political and economic) terms has been one of the primary reasons where majority of financial flows in the world are concentrated in a few developed countries (the EU countries) and in few developing ones (Mexico, India and China). A previous study has examined the relationship between international poverty and country risk scores (Cinar, 2000) and this study concentrates on whether the MENA region exhibits certain special country risks when compared with other regions in the world. Many services measure country risk, including Bank of America World Information Services, Business Environment Risk Intelligence (BERI) S.A., Control Risks Information Services (CRIS), Economist Intelligence Unit (EIU), Euromoney, Institutional Investor, Standard & Poor's Rating Group (S&P), Political Risk Services: International Country Risk Guide (ICRG), Political Risk Services: Coplin-O'Leary Rating System, and Moody's Investors Service. Each of the above institutions provide qualitative and quantitative information as well as a single index or rating. The two most prominent among the above are the Institutional Investor (IICR) and International Country Risk Guide (ICRG). Institutional Investor credit ratings are based on a survey of leading international bankers, who are asked to rate each country on a scale from 0 to 100 (100-best credit rating) which are then averaged (with larger weights assigned to institutions with large world-wide exposures). Each institution uses their own internal formula to rank the importance of different factors affecting the countries. Erb et. al. (1996) provide a cross-country comparison of S&P and Moody's ratings with the IICR and ICRG ratings as of October 1995. They find that S&P and Moody's ratings have a close correspondence with the IICR credit-risk measure (rank order correlation of 95 percent) and with the ICRG financial rating (rank order correlation of 90 percent) and the correlations are weaker for the other measures. They conclude by noting the ICRG composite index contains considerable information in terms of forecasting risk-adjusted returns on the portfolios they construct. Erb, et. al (1995) also find that country credit ratings have substantial predictive power in differentiating between high and low expected portfolio returns and are very effective tools. Following their studies, this study examines IICR and ICRG credit ratings with respect to MENA countries. Data in the learning sample are available on these two scores (as well as a set of financial and other variables) for 63 countries. STUDY METHOD: CLASSIFICATION TREES, A NON-PARAMETRIC APPROACH Country risk scores do not satisfy the stringent theoretical and distributional assumptions (such as white noise errors, normality assumptions) of more traditional methods. Instead, a non-parametric method from artificial intelligence is used in this study: classification trees are used to predict high versus low risk countries, or MENA, non-MENA countries by using country-based predictor variables. Classification tree analysis is one of the main techniques used in data mining. The analysis is similar to discriminant analysis, cluster analysis, and is nonparametric statistics with nonlinear estimation. The decision process used by classification trees provide an efficient method for sorting countries with high versus low risk, given the black box formulations which are used in calculating the country risk scores. Currently, classification tree analysis is widely used in applied fields such as diagnosis in medicine, data structures in computer sciences and decision theory in psychology. Classification trees can be quite complex and graphical procedures are used to visually interpret trees. They are hierarchical in nature (Breiman, et.al., 1984) and are more flexible that traditional (such as discriminant or regression) analysis. They examine the effects of the predictor variables one at a time, rather than just all at once, where the predictor variables could be a combination of continuous and categorical variables when univariate splits are used. This allows one the freedom from stringent assumptions such as non-collinearity and homoskedasticity on the predictor variables. This study uses the algorithms used in CART, as described by Breiman et al. (1984). CART (Classification And Regression Trees) is a classification tree program that uses an exhaustive grid search of all possible univariate splits to find the splits for a classification tree. CART searches can be lengthy when there are a large number of predictor variables with many levels, and it is biased toward choosing predictor variables with more levels for splits, but because it employs an exhaustive search, it is guaranteed to find the splits producing the best classification. The trees are constructed such that there are terminal nodes which are points on the tree which show terminal decisions. The trees start with the top decision node which is called the root node. The root node is split, forming two new nodes. The numbers below the root node show the value of the predictor variable in which there is a split. When univariate splits are performed, the predictor variables can be ranked for their potential importance in determining the classification of the dependent variable. The purpose of classification tree analysis is to obtain the most accurate classification/prediction where the most accurate prediction is defined as the prediction with the minimum costs. Therefore, the best prediction has the lowest misclassification rate. The relationships between prior probabilities, misclassification costs, and case weights, which can become quite complex, can be found in Breiman et al, (1984) and Ripley (1996). A very important part of classification tree analysis is to select the node splits on the predictor variables which are used to predict the categorical dependent variable. Due to the hierarchical nature of classification trees, these splits are selected one at time, starting with the split at the root node. CART uses an exhaustive search for univariate splits. With this method, all possible splits for each predictor variable at each node are examined to find the split producing the largest improvement in goodness of fit which is measured by the Gini measure of node impurity. This is a measure which reaches a value of zero when only one class is present at a node. The tree is stopped and pruned by rules given in Breiman et al. (1984) for selecting the "right-sized" tree. Resubstitution costs (e.g., the misclassification rate in the learning sample) rather consistently decrease as tree size increases. The Bayesian decision rule, splitting rules and the cost function are not reported here and can be obtained from the author. The ability to predict out of sample is the true ‘test’ for any statistical process. Classification trees are no exception. The trees grown in this study were also applied to out-of-sample data for 1996-1998. For parsimony, those results are summarized in the text. The learning sample results are provided in detail below. DATA AND CLASSIFICATION TREES ON INTERNATIONAL COUNTRY RISK AND MENA COUNTRIES Learning sample data come from World Development Report 2000/2001. Using the 2000 data, countries were partitioned into categories of ‘risky’ versus ‘non-risky’ based on the values of the scores. ICGR and IICR scores were considered to denote ‘risky’ if they were 70 or below. The MENA countries for which the scores are available are listed in Table 1. TABLE 1: COUNTRY RISK SCORES OF MENA COUNTRIES