In this paper we propose a new two-level methodology for assessing countries'/companies' economic/financial performance. The methodology is based on two major techniques of grouping data: cluster analysis and predictive classification models. First we use cluster analysis in terms of self-organizing maps to find possible clusters in data in terms of economic/financial performance. We then interpret the maps and define outcome values (classes) for each data row. Lastly we build classifiers using two different predictive models (multinomial logistic regression and decision trees) and compare the accuracy of these models. Our findings claim that the results of the two classification techniques are similar in terms of accuracy rate and class predictions. Furthermore, we focus our efforts on understanding the decision process corresponding to the two predictive models. Moreover, we claim that our methodology, if correctly implemented, extends the applicability of the self-organizing map for clustering of financial data, and thereby, for financial analysis.
['Adrian Costea', 'Tomas Eklund']
A two-level approach to making class predictions