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Country risk assessment

Country risk assessment. A practical application using Self-Organizing Maps (SOM) Trieste, 7th February , 2005, ing. Mattia Ciprian. Summary. The purpose of this analysis is to produce a visual representation of countries based on credit risk analysis.

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Country risk assessment

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  1. Country risk assessment A practical application using Self-Organizing Maps (SOM) Trieste, 7th February, 2005, ing. Mattia Ciprian

  2. Summary The purpose of this analysis is to produce a visual representation of countries based oncredit risk analysis. This application is based on a data set containing financial, economic, military, social, communi-cation and transportation data from 138 countries around the world. The main purpose is to discover groupings of markets and countries with similar risk patterns AND detect new correlations between the variables. ing. Mattia Ciprian

  3. Data Source • The analysis is based on country obtained from: “CIA – The WORLD FACTBOOK 2004.” http://www.cia.gov/cia/publications/factbook/index.html • The source of data is reputable and we can safely say that the data is of high quality (assuming no errors are made in copying or printing of the data). ing. Mattia Ciprian

  4. Airports Birth rate Death rate Economic aid donor Electricity consumption Electricity production Exports GDP purchasing power parity Heliports Imports Industrial production growth rate Inflation rate Internet users Investment (gross fixed) (% of GDP) Irrigated land Mobile cellulars Net migration rate Oil consumption Oil production Population growth rate Total fertility rate Indicators ing. Mattia Ciprian

  5. Notes and Definitions of Indicators People Economics Transportations Communications ing. Mattia Ciprian

  6. CorrelationMatrix ing. Mattia Ciprian

  7. Correlated Variables These variables are strictly correlated (corr. > 0,90): ing. Mattia Ciprian

  8. SOM • The self-organizing map (SOM) is a new, effective software tool for the visualization of high-dimensional data. It implements an orderly mapping of a high-dimensional distribution onto a regular low-dimensional grid. Thereby it is able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. As it compresses information while preserving the most important topological and metric relationships of the primary data items on the display, it may also be thought to produce some kind of abstractions. These two aspects, visualization and abstraction, can be utilized in a number of ways in complex tasks such as process analysis, machine perception, control, and communication. (Kohonen, 1998) ing. Mattia Ciprian

  9. Data Visualization Birth rate Population growth rate Total fertility rate ing. Mattia Ciprian

  10. Comparing SOM to Statistical Approach • Comparing the maps of “birthrate” Vs “total fertility rate”, also without knowing the high correlation coefficient (0,98), we could deduce the indicators are strictly interrelated. ing. Mattia Ciprian

  11. Comparing SOM to Statistical Approach • As we already knew these two variables, “exports” and “imports” are correlated very well. • The correlation coefficient is 0,94 ing. Mattia Ciprian

  12. Why using SOM? • The SOM algorithm was developed in the first place for the visualization of nonlinear relations of multidimensional data. This feature puts Self – Organizing Maps over statistical methods. Correlation coefficient is 0,20 Net migration rate Population growth rate ing. Mattia Ciprian

  13. Local Correlations • Where the inflation rate is high the migration rate is quietly negative; vice versa the migration rate is high where the inflation rate is very low. • Correlation coefficient is -0,115 Net migration rate Inflation rate ing. Mattia Ciprian

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