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Neural Computing and Applications

, Volume 24, Issue 3–4, pp 663–673 | Cite as

On biologically inspired predictions of the global financial crisis

  • Peter Sarlin
Original Article

Abstract

Early-warning models provide means for ex ante identification of elevated risks that may lead to a financial crisis. This paper taps into the early-warning literature by introducing biologically inspired models for predicting systemic financial crises. We create three models: a conventional statistical model, a back-propagation neural network (NN) and a neuro-genetic (NG) model that uses a genetic algorithm for choosing the optimal NN configuration. The models are calibrated and evaluated in terms of usefulness for policymakers that incorporates preferences between type I and type II errors. Generally, model evaluations show that biologically inspired models outperform the statistical model. NG models are, however, shown not only to provide largest usefulness for policymakers as an early-warning model, but also in form of decreased expertise and labor needed for, and uncertainty caused by, manual calibration of an NN. For better generalization of data-driven models, we also advocate adopting to the early-warning literature a training scheme that includes validation data.

Keywords

Systemic financial crises Early-warning model Neural networks Genetic algorithms Neuro-genetic model 

References

  1. 1.
    Reinhart CM, Rogoff KS (2008) Is the 2007 US sub-prime financial crisis so different? An international historical comparison. Am Econ Rev 98(2):339–344CrossRefGoogle Scholar
  2. 2.
    Reinhart CM, Rogoff KS (2009) The aftermath of financial crises. Am Econ Rev 99(2):466–472CrossRefGoogle Scholar
  3. 3.
    Edison HJ (2003) Do indicators of financial crises work? An evaluation of an early warning system. Int J Financ Econ 8(1):11–53CrossRefGoogle Scholar
  4. 4.
    Ramser J, Foster L (1931) A demonstration of ratio analysis. Bureau of Business Research. University of Illinois, Urbana, IL (Bulletin 40)Google Scholar
  5. 5.
    Frank C, Cline W (1971) Measurement of debt servicing capacity: an application of discriminant analysis. J Int Econ 1:327–344CrossRefGoogle Scholar
  6. 6.
    Kaminsky G, Lizondo S, Reinhart C (1998) Leading indicators of currency crises. IMF Staff Pap 45:1–48CrossRefGoogle Scholar
  7. 7.
    Berg A, Pattillo C (1999) Predicting currency crises—the indicators approach and an alternative. J Int Money Financ 18:561–586CrossRefGoogle Scholar
  8. 8.
    Demirgüc-Kunt A, Detragiache E (2000) Monitoring banking sector fragility: a multivariate logit approach. World Bank Econ Rev 14:287–307CrossRefGoogle Scholar
  9. 9.
    Lo Duca M, Peltonen T (2012) Assessing systemic risks and predicting systemic events. J Bank Financ (forthcoming)Google Scholar
  10. 10.
    Nag A, Mitra A (1999) Neural networks and early warning indicators of currency crisis. Reserve Bank India Occas Pap 20(2):183–222MathSciNetGoogle Scholar
  11. 11.
    Franck R, Schmied A (2003) Predicting currency crisis contagion from East Asia to Russia and Brazil: an artificial neural network approach. AMCB Working Paper No 2Google Scholar
  12. 12.
    Peltonen T (2006) Are emerging market currency crises predictable? A test. ECB Working Paper. No. 571Google Scholar
  13. 13.
    Sarlin P, Marghescu D (2011) Visual predictions of currency crises using self-organizing maps. Intell Syst Account Financ Manage 18(1):15–38CrossRefGoogle Scholar
  14. 14.
    Sarlin P, Marghescu D (2011) Neuro-genetic predictions of currency crises. Intell Syst Account Financ Manage 18(4):145–160CrossRefGoogle Scholar
  15. 15.
    Fioramanti M (2008) Predicting sovereign debt crises using artificial neural networks: a comparative approach. J Financ Stab 4(2):149–164CrossRefGoogle Scholar
  16. 16.
    Sarlin P, Peltonen TA (2011) Mapping the state of financial stability. ECB Working Paper No. 1382Google Scholar
  17. 17.
    Alessi L, Detken C (2011) Quasi real time early warning indicators for costly asset price boom/bust cycles: a role for global liquidity. Eur J Polit Econ 27(3):520–533CrossRefGoogle Scholar
  18. 18.
    Hollo D, Kremer M, Lo Duca M (2012) CISS—a composite indicator of systemic stress in the financial system. ECB Working Paper No. 1426Google Scholar
  19. 19.
    Ripley B (1994) Neural networks and related methods for classification. J R Stat Soc 56:409–456zbMATHMathSciNetGoogle Scholar
  20. 20.
    Hagan MT, Demuth HB, Beale M (1996) Neural network design. PWS Publishing, Boston, MAGoogle Scholar
  21. 21.
    Demyanyk YS, Hasan I (2010) Financial crises and bank failures: a review of prediction methods. Omega 38(5):315–324CrossRefGoogle Scholar
  22. 22.
    Lin CS, Khan HA, Chang RY, Wang YC (2008) A new approach to modeling early warning systems for currency crises: can a machine-learning fuzzy expert system predict the currency crises effectively? J Int Money Financ 27(7):1098–1121CrossRefGoogle Scholar
  23. 23.
    Karahoca D, Karahoca A, Yavuz Ö (2012) An early warning system approach for the identification of currency crises with data mining techniques. Neural Comput Appl (forthcoming). doi: 10.1007/s00521-012-1206-9
  24. 24.
    Manasse P, Roubini N, Schimmelpfennig A (2003) Predicting sovereign debt crises. IMF Working Paper, WP/03/221Google Scholar
  25. 25.
    Marghescu D, Sarlin P, Liu S (2010) Early-warning analysis for currency crises in emerging markets: a revisit with fuzzy clustering. Intell Syst Account Financ Manage 17(3–4):143–165CrossRefGoogle Scholar
  26. 26.
    Arciniegas Rueda IE, Arciniegas F (2009) SOM-based data analysis of speculative attacks’ real effects. Intell Data Anal 13(2):261–300Google Scholar
  27. 27.
    Resta M (2009) Early warning systems: an approach via self organizing maps with applications to emergent markets. In: Apolloni B, Bassis S, Marinaro M (eds) Proceedings of the 18th Italian workshop on neural networks. IOS Press, Amsterdam, pp 176–184Google Scholar
  28. 28.
    Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  29. 29.
    Bishop C (2007) Pattern recognition and machine learning. Springer, HeidelbergGoogle Scholar
  30. 30.
    Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArbourGoogle Scholar
  31. 31.
    Borio C, Lowe P (2002) Asset prices, financial and monetary stability: exploring the nexus. BIS Working Papers, No. 114Google Scholar
  32. 32.
    Borio C, Lowe P (2004) Securing sustainable price stability: should credit come back from the wilderness?. BIS Working Papers, No. 157Google Scholar
  33. 33.
    Sarlin P (2012) On policymakers’ loss functions and the evaluation of early warning systems. TUCS Technical Report No. 1054Google Scholar
  34. 34.
    Sarle W (1994) Neural networks and statistical models. In: Proceedings of the nineteenth annual SAS users group international conference. SAS Institute, Cary, NC, pp 1538–1550Google Scholar
  35. 35.
    Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366CrossRefGoogle Scholar
  36. 36.
    Rao BR, Fung G, Rosales R (2008) On the dangers of cross-validation. An experimental evaluation. In: Proceedings of the SIAM international conference on data mining (SDM), April 24–26, 2008, Atlanta, Georgia, USAGoogle Scholar
  37. 37.
    Laeven L, Valencia F (2011) The real effects of financial sector interventions during crises. IMF Working Paper, WP/11/45Google Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.Department of Information Technologies, Turku Centre for Computer ScienceÅbo Akademi UniversityTurkuFinland

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