Self-organizing maps as a tool to compare financial macroeconomic imbalancesThe European, Spanish and German case

  1. López Iturriaga, Félix Javier
  2. Pastor Sanz, Iván
Journal:
The Spanish Review of Financial Economics

ISSN: 2173-1268

Year of publication: 2013

Volume: 11

Issue: 2

Pages: 69-84

Type: Article

DOI: 10.1016/J.SRFE.2013.07.001 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: The Spanish Review of Financial Economics

Abstract

The economic recession in the European countries during the current financial crisis and the widespread worsening of the financial situation have resulted in wide macroeconomic differences across countries. In this paper we use the method of self-organizing maps (SOM) to compare the macroeconomic financial imbalances among European countries. We detect different profiles of countries and identify the public expenditure and the saving rate as the most critical variables that impacts on the national financial situation. In addition, since several countries of the European Union have regions with some degree of economic and financial competences, we study the influence of the regions on the whole country. Thus, we classify and compare the Spanish and German regions and we prove the impact of the regional situation on the whole country situation.

Bibliographic References

  • Alfaro Cortés E., Gámez Martínez M., García Rubio N. A socioeconomic typology of the European regions using self-organizing maps. Fifty-Third International Atlantic Economic Conference 2002.
  • Alfaro Cortés E., Gámez Martínez M., García Rubio N. Una clasificación socioeconómica de las regiones europeas mediante Mapas de Kohonen. University of Castile La Mancha Working Paper n. 2-1 2003.
  • Arciniegas Rueda I.E., Arciniegas F. SOM-based data analysis of speculative attacks' real effects. Intelligent Data Analysis 2009, 13(2):261-300.
  • Armstrong H., De Kervenoael R.J., Li X., Read R. A comparison of the economic performance of different micro-states, and between micro-states and larger countries. World Development 1998, 26(4):639-656.
  • Baesens B., Gestel T.V., Stepanova M., Van den Poel D., Vanthienen J. Neural network survival analysis for personal loan data. Journal of the Operational Research Society 2005, 56(9):1089-1098.
  • Becerra-Fernandez I., Zanakis S.H., Walczak S. Knowledge discovery techniques for predicting country investment risk. Computers and Industrial Engineering 2002, 43(4):787-800.
  • Bennell J.A., Crabbe D., Thomas S., Gwilym O.A. Modelling sovereign credit ratings: neural networks versus ordered probit. Expert Systems with Applications 2006, 30(3):415-425.
  • Brockett P.L., Cooper W.W., Golden L.L., Pitaktong U. A neural network method for obtaining and early warning of insurer insolvency. Journal of Risk and Insurance 1994, 61(3):402-424.
  • Davies D.L., Bouldin D.W. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI 1979, 1(2):224-227.
  • Dreisbach D. A note on Chui, Gai and Haldane's "Sovereign liquidity crisis: analytics and implications for public policy". Journal of Banking & Finance 2007, 32(4):624-629.
  • Dutta S., Shekhar S. Bond rating: a non-conservative application of neural networks. Proceedings of IEEE International Conference on Neural Networks, vol. 2 1988, 443-450.
  • Falavigna G. Financial ratings with scarce information: a neural network approach. Expert Systems with Applications 2012, 39(2):1784-1792.
  • Fernández Llera R. Evaluación de los ratings de la deuda autonómica a través del análisis discriminante. Investigaciones Regionales 2006, 8:105-122.
  • Fioramanti M. Predicting sovereign debt crises using artificial neural networks: a comparative approach. Journal of Financial Stability 2008, 4(2):149-164.
  • Fraley C., Raftery A.E. How many clusters? Which clustering method? Answers via model-based cluster analysis. Computer Journal 1998, 41:578-588.
  • Franck R., Schmied A. Predicting Currency Crisis Contagion from East Asia to Russia and Brazil: An Artificial Neural Network Approach. Bar-Ilan University AMCB Working Papers n. 2 2003.
  • Garson G.D. A comparison of neural network and expert systems algorithms with common multivariate procedures for analysis of social science data. Social Science Computer Review 1991, 9:399-434.
  • Gonzalez S. Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models. Department of Finance Canada Working Papers n. 2000-07 2000.
  • Guresen E., Kayakutlu G., Daim T.U. Using artificial neural network models in stock market index prediction. Expert Systems with Applications 2011, 38(8):10389-10397.
  • Hair J.F., Anderson R.E., Tatham R.L., Black W.C. Multivariate Analysis 1999, Prentice Hall, Madrid.
  • Herrero Á., Corchado E., Jiménez A. Unsupervised neural models for country and political risk analysis. Expert Systems with Applications 2011, 38(11):13641-13661.
  • Höglund H. Detecting earnings management with neural networks. Expert Systems with Applications 2012, 39(10):9564-9570.
  • Huang W., Nakamori Y., Wang S.Y. Forecasting stock market movement direction with support vector machine. Computers and Operations Research 2005, 32:2513-2522.
  • Hunter A., Kennedy L., Henry J., Ferguson I. Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Computer Methods and Programs in Biomedicine 2000, 62:11-19.
  • Ingaramo D.A., Leguizamón G., Errecalde M. Adaptive clustering with artificial ants. Journal of Computer Science and Technology 2005, 5(4):264-271.
  • Jaráiz Cabanillas F., Pérez Pintor J., Gutiérrez J.A. Dinâmica socioeconÔmica e acessibilidade: repercussão sobre as mudanças dos usos do solo na Raia Central Ibérica Revista Portuguesa de Estudos Regionais 30 2012.
  • Kaski S., Kohonen T. Winner-take-all networks for physiological models of competitive learning. Neural Networks 1994, 7:973-984.
  • Khashman A. Neural networks for credit risk evaluation: investigation of different neural models and learning schemes. Expert Systems with Applications 2010, 37(9):6233-6239.
  • Kiang M.Y. Extending the Kohonen self-organizing map networks for clustering analysis. Computational Statistics & Data Analysis 2001, 38(2):161-180.
  • Kiang M.Y., Hu M.Y., Fisher D.M. The effect of sample size on the extended self-organizing map network for market segmentation. Decision Support Systems 2006, 42(1):36-47.
  • Kim J.W., Weistroffer H.R., Redmond R.T. Expert systems for bond rating: a comparative analysis of statistical, rule-based and neural network systems. Expert Systems 1993, 10:167-188.
  • Kim M.-J., Kang D.-K. Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications 2010, 37(4):3373-3379.
  • Kohonen T. Physiological interpretation of the self-organizating map algorithm. Neural Networks 1993, 6:895-905.
  • Kronthaler F. A Study of the Competitiveness of Regions Based on a Cluster Analysis: The Example of East Germany 2003, European Congress of the Regional Science Association, Jyvaeskyla.
  • Lee S., Choi W.S. A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Expert Systems with Applications 2013, 40(8):2941-2946.
  • Maher J.J., Sen T.K. Predicting bond ratings using neural networks: a comparison with logistic regression. Intelligent Systems in Accounting, Finance & Management 1997, 6(1):59-72.
  • Manasse P., Roubini N. Rules of thumb for sovereign debt crises. Journal of International Economics 2009, 78(2):192-205.
  • Martínez García E., Colldeforns M. Una aproximación cuantitativa de las cargas del Estado y de las CC.AA. Distribución de competencias y gasto efectuado. Revista de Estudios Regionales 2003, 2:143-151.
  • Milligan G.W., Cooper M.C. An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika 1980, 45(3):159-179.
  • Min J.H., Lee Y.-C. A practical approach to credit scoring. Expert Systems with Applications 2008, 35(4):1762-1770.
  • Mingoti S.A., Lima J.O. Comparing SOM neural network with fuzzy cmeans, K-means and traditional hierarchical clustering algorithms. European Journal of Operational Research 2006, 174:1742-1759.
  • Mokhatab Rafiei F., Manzari S.M., Bostanian S. Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence. Expert Systems with Applications 2011, 38(8):10210-10217.
  • Moreno D., Olmeda I. Is the predictability of emerging and developed stock markets really exploitable?. European Journal of Operational Research 2007, 182(1):436-454.
  • Moreno D., Marco P., Olmeda I. Self-organizing maps could improve the classification of Spanish mutual funds. European Journal of Operational Research 2006, 147:1039-1054.
  • Nag A.K., Mitra A. Neural networks and early warning indicators of currency crisis. Reserve Bank of India Occasional Papers 1999, 20(2):183-222.
  • Peralta M.J., Rúa A., Fernández L., Borrás F. Tipología socioeconómica de las regiones europeas. Comparativa estadística 2000, Consejería de Hacienda de la Comunidad de Madrid, Madrid.
  • Pyle D. Data Preparation for Data Mining 1999, Morgan Kaufman Publishers, San Francisco.
  • Rambhia A.H., Glenny R., Hwang J. Critical input data channels selection for progressive work exercise test by neural network sensitivity analysis. IEEE International Conference on Acoustics, Speech, and Signal Processing 1994, 1097-1100.
  • Ravi Kumar P., Ravi V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review. European Journal of Operational Research 2007, 180(1):1-28.
  • Ríos A., López M.C., Pérez P.P. El gasto regional por funciones: análisis y clasificación de las CC.AA. Revista de Estudios Regionales 2007, 78:387-400.
  • Sanz I., Velázquez F. The Evolution and Convergence of the Government Expenditure Composition in the OECD Countries: An Analysis of the Functional Distribution. Universidad Complutense de Madrid. Facultad de CC. Económicas Working Paper n. 9/2001 2001.
  • Shin K.-S., Lee Y.-J. A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications 2002, 23(3):321-328.
  • Surkan A.J., Singleton J.C. Neural networks for bond rating improved by multiple hidden layers. Proceedings of IEEE International Conference on Neural Networks, vol. 2 1990, 163-168.
  • Trevino L., Thomas S. Local versus foreign currency ratings: what determines sovereign transfer risk?. Journal of Fixed Income 2001, 11(1):65-76.
  • Vellido A., Lisboa P.J.G., Vaughan J. Neural networks in business: a survey of applications (1992-1998). Expert Systems with Applications 1999, 17(1):51-70.
  • Wong B.K., Selvi Y. Neural network applications in finance: a review and analysis of literature (1990-1996). Information & Management 1998, 34(3):129-139.
  • Yim J., Mitchell H. Comparison of country risk models: hybrid neural networks, logit models, discriminant analysis and cluster techniques. Expert Systems with Applications 2005, 28(1):137-148.
  • Zurada J.M., Malinowski A., Cloete I. Sensitivity analysis for minimization of input data dimension for feedforward neural network. Proceedings of ISCAS'1994 1994, 447-450.
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