Chance Discovery with Self-Organizing Maps: Discovering Imbalances in Financial Networks

  • Peter Sarlin
Part of the Studies in Computational Intelligence book series (SCI, volume 423)


In this chapter, we introduce the Self-Organizing Map (SOM) from the viewpoint of Chance Discovery. The SOM paradigm supports several principal parts of Chance Discovery: visualization of temporal multivariate data, discovering rare clusters bridging frequent ones, detecting the degree of event rarity or outliers, and dealing with continuously evolving structures of real world data. Here, we further enhance the standard SOM paradigm by combining it with network analysis. Thus, we enable a simultaneous view of the data topology of the SOM and a network topology of relationships between objects on the SOM. The usefulness of the Self-Organizing Network Map (SONM) for Chance Discovery is demonstrated on a dataset of macro-financial measures. While the standard SOM visualizes country-specific vulnerabilities by positions on the map, the SONM also includes bilateral financial exposures to show the size of linkages between economies and chances of shock propagation from one country to the rest of the world.


Chance Discovery Self-Organizing Map (SOM) network analysis exploratory data analysis financial stability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ohsawa, Y.: Chance Discovery for Making Decisions in Complex Real World. New Generation Computing 20(2), 143–163 (2002)zbMATHCrossRefGoogle Scholar
  2. 2.
    Tsang, E.P.K., Markose, S., Er, H.: Chance discovery in stock index option and future arbitrage. New Mathematics and Natural Computation 1(3), 435–447 (2005)zbMATHCrossRefGoogle Scholar
  3. 3.
    Ohsawa, Y.: Modelling the process of chance discovery. In: Ohsawa, Y., McBurney, P. (eds.) Chance Discovery, pp. 2–15. Springer, Heidelberg (2003)Google Scholar
  4. 4.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, California (1993)Google Scholar
  5. 5.
    Greenacre, M.J.: Correspondence Analysis in Practice. Chapman & Hall, London (2007)zbMATHCrossRefGoogle Scholar
  6. 6.
    Ohsawa, Y., Benson, N.E., Yachida, M.: KeyGraph: Automatic Indexing by Cooccurrence Graph based on Building Construction Metaphor. In: Proc. Advanced Digital Library Conference, pp. 12–18. IEEE Press, Los Alamitos (1998)Google Scholar
  7. 7.
    Abe, A., Hagita, N., Furutani, M., Furutani, Y., Matsuoka, R.: An interface for medical diagnosis support. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS (LNAI), vol. 4693, pp. 909–916. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 66, 59–69 (1982)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (2001)zbMATHCrossRefGoogle Scholar
  10. 10.
    Ultsch, A., Siemon, H.P.: Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of the International Conference on Neural Networks, pp. 305–308. Kluwer, Dordrecht (1990)Google Scholar
  11. 11.
    Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)CrossRefGoogle Scholar
  12. 12.
    Ward, J.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244 (1963)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Matsuo, Y.: Prediction, Forecasting and Chance Discovery. In: Ohsawa, Y., McBurney, P. (eds.) Chance Discovery, pp. 30–43. Springer, Heidelberg (2003)Google Scholar
  14. 14.
    Boulet, R., Jouve, B., Rossi, F., Villa, N.: Batch kernel SOM and related Laplacian methods for social network analysis. Neurocomputing 71(7-9), 1257–1273 (2008)CrossRefGoogle Scholar
  15. 15.
    Goda, S., Ohsawa, Y.: Chance Discovery in Credit Risk Management - Time Order Method and Directed KeyGraph for Estimation of Chain Reaction Bankruptcy Structure. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 247–254. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Sarlin, P., Peltonen, T.A.: Mapping the State of Financial Stability. ECB Working Paper, No. 1382 (September 2011)Google Scholar
  17. 17.
    Lo Duca, M., Peltonen, T.A.: Macro-Financial Vulnerabilities and Future Financial Stress — Assessing Systemic Risks and Predicting Systemic Events. ECB Working Paper, No. 1311 (2011)Google Scholar
  18. 18.
    Borio, C., Lowe, P.: Asset Prices, Financial and Monetary Stability: Exploring the Nexusd. BIS Working Papers, No. 114 (2002)Google Scholar
  19. 19.
    Borio, C., Lowe, P.: Securing Sustainable Price Stability: Should Credit Come Back from the Wilderness? BIS Working Papers, No. 157 (2004)Google Scholar
  20. 20.
    Sammon Jr., J.W.: A Non-Linear Mapping for Data Structure Analysis. IEEE Transactions on Computers 18(5), 401–409 (1969)CrossRefGoogle Scholar
  21. 21.
    Sarlin, P., Eklund, T.: Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series. In: Laaksonen, J., Honkela, T. (eds.) WSOM 2011. LNCS, vol. 6731, pp. 40–50. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  22. 22.
    Sarlin, P., Eklund, T.: Financial Performance Analysis of European Banks using a Fuzzified Self-Organizing Map. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 186–195. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Sarlin, P., Yao, Z., Eklund, T.: Probabilistic Modeling of State Transitions on the Self-Organizing Map: Some Temporal Financial Applications. In: Proc. of the 45th Hawaii International Conference on System Sciences, HICSS 2012 (forthcoming, 2012)Google Scholar
  24. 24.
    Chappell, G., Taylor, J.: The temporal Kohonen map. Neural Networks 6, 441–445 (1993)CrossRefGoogle Scholar
  25. 25.
    Strickert, M., Hammer, B.: Merge SOM for temporal data. Neurocomputing 64, 39–72 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Turku Centre for Computer Science – TUCS, Department of Information TechnologiesÅbo Akademi UniversityTurkuFinland

Personalised recommendations