Sovereign debt monitor: A visual Self-organizing maps approach

In the 1980s and at the turn of last century, severe global waves of sovereign defaults occurred in less developed countries. To date, the forecasting and monitoring results of debt crises are still at a preliminary stage, while the issue is at present highly topical. This paper explores whether the application of the Self-organizing map (SOM), a neural network-based visualization tool, facilitates the monitoring of multidimensional financial data. First, this paper presents a SOM model for visual benchmarking and for visual analysis of the evolution of debt crisis indicators. Second, the method pairs the SOM with a geospatial dimension by mapping the ‘probability’ of a crisis on a geographic map. This paper demonstrates that the SOM is a feasible tool for monitoring indicators of sovereign defaults.

[1]  Mikael Collan,et al.  A Visualization and Clustering Approach to Analyzing the Early Warning Signals of Currency Crises , 2010 .

[2]  Alessio Ciarlone,et al.  Designing an early warning system for debt crises , 2005 .

[3]  G. Ghisellini.  **title** Asp Conference Series, Vol. **volume**, **publication Year** **editors** , 2000 .

[4]  Kenneth S. Rogoff,et al.  From Financial Crash to Debt Crisis , 2010 .

[5]  T. Peltonen,et al.  Are Emerging Market Currency Crises Predictable? - a Test , 2006, SSRN Electronic Journal.

[6]  Boris Igelnik.  Method for visualization of multivariate data in a lower dimension , 2001, IS&T/SPIE Electronic Imaging.

[7]  Guilherme De A. Barreto,et al.  Time Series Prediction with the Self-Organizing Map: A Review , 2007, Perspectives of Neural-Symbolic Integration.

[8]  Ismael E. Arciniegas Rueda,et al.  SOM-based data analysis of speculative attacks' real effects , 2009, Intell. Data Anal..

[9]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[10]  T. Kohonen,et al.  Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum , 2003 .

[11]  Peter Sarlin.  Visual monitoring of financial stability with a self-organizing neural network , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[12]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[13]  Aitor Erce,et al.  Recent Episodes of Sovereign Debt Restructurings: A Case-Study Approach , 2008 .

[14]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[15]  Dorina Marghescu.  Multi-dimensional Data Visualization Techniques for Exploring Financial Performance Data , 2007, AMCIS.

[16]  Marco Fioramanti,et al.  Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach , 2006 .

[17]  Jonathan C. Roberts,et al.  Visual Data Exploration and Analysis VIII , 2001 .

[18]  Juha Vesanto,et al.  On the Decomposition of the Self-Organizing Map Distortion Measure , 2003 .

[19]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[20]  Antonio Spilimbergo,et al.  Crises and Liquidity: Evidence and Interpretation , 2001, SSRN Electronic Journal.

[21]  T. Kohonen,et al.  Visual Explorations in Finance with Self-Organizing Maps , 1998 .

[22]  Paolo Manasse,et al.  Predicting Sovereign Debt Crises , 2003 .

[23]  Peter Sarlin,et al.  Visual Predictions of Currency Crises Using Self-Organizing Maps , 2010, ICDM Workshops.

[24]  Tariq Samad,et al.  Self–organization with partial data , 1992 .

[25]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[26]  Elena Kalotychou,et al.  Optimal design of early warning systems for sovereign debt crises , 2007 .

[27]  Hannu Vanharanta,et al.  Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking , 2003, Inf. Vis..

[28]  Teuvo Kohonen,et al.  Visual Explorations in Finance , 1998 .