Using Neural Networks to Tune the Fluctuation of Daily Financial Condition Indicator for Financial Crisis Forecasting

  • Kyong Joo Oh
  • Tae Yoon Kim
  • Chiho Kim
  • Suk Jun Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


Recently, Oh et al. [11, 12] developed a daily financial condition indicator (DFCI) which issues an early warning signal based on the daily monitoring of financial market volatility. The major strength of DFCI is that it is expected to serve as a quite useful early warning system (EWS) for the new type of crisis which starts as an instability of the financial markets and then develops into a major crisis (e.g., 1997 Asian crises). One of the problems with DFCI is that it may show a high degree of fluctuation because it handles daily variable, and this may harm its reliability as an EWS. The main purpose of this article is to propose and discuss a way of smoothing DFCI, i.e., it will be tuned using long-term (monthly or quarterly) fundamental economic variables. It turns out that such a tuning procedure could reveal influential macroeconomic variables on financial markets. Since tuning DFCI is done by the method of fitting various types of data simultaneously, neural networks are employed. Tuning the DFCI for the Korean financial market is given as an empirical example.


Financial Market Financial Crisis Transition Period Early Warning System Macroeconomic Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adei, H., Hung, S.: Machine Learning: Neural Networks, Genetic Algorithsms, and Fuzzy Systems. Wiley, New York (1995)Google Scholar
  2. 2.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by back propagation. In: Rumelhart, D.E., McClelland, J.L. and PDP research group (eds.) Parallel Distributed Processing, MIT Press, Cambridge (1986)Google Scholar
  3. 3.
    Draper, N.R., Smith, H.: Applied Regression Analysis. Wiley, New York (1981)MATHGoogle Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)MATHGoogle Scholar
  5. 5.
    Holland, J.H.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. University of Michigan Press, Michigan (1975)Google Scholar
  6. 6.
    Khalid, A.M., Kawai, M.: Was financial market contagion the source of economic crisis in Asia? Evidence using a multivariate VAR model. Journal of Asian Economics 14, 131–156 (2003)CrossRefGoogle Scholar
  7. 7.
    Kaminsky, G.L., Reinhart, C.M.: The twin crises: The causes of banking and balance-of-payments problems. American Economic Review 89, 473–500 (1999)CrossRefGoogle Scholar
  8. 8.
    Kim, T.Y., Hwang, C., Lee, J.: Korea financial condition indicator using a neural network trained on the 1997 crisis. Journal of Data Science 2, 371–381 (2004a)Google Scholar
  9. 9.
    Kim, T.Y., Oh, K.J., Sohn, I., Hwang, C.: Usefulness of artificial neural networks for early warning system of economic crisis. Expert Systems with Applications 26, 585–592 (2004b)Google Scholar
  10. 10.
    Kim, T.Y., Oh, K.J., Kim, C., Do, J.D.: Artificial Neural Networks for Non-Stationary Time Series. Neurocomputing 61, 439–447 (2004c)CrossRefGoogle Scholar
  11. 11.
    Oh, K.J., Kim, T.Y., Lee, H.Y., Lee, H.: Using Neural Networks to support Early Warning System for Financial Crisis Forecasting. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 284–296. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Oh, K.J., Kim, T.Y., Kim, C.: An Early Warning System for detection of Financial Crisis using Financial Market Volatility. Expert Systems 23, 83–98 (2006)CrossRefGoogle Scholar
  13. 13.
    Peters, E.E.: Chaos and Order in the Capital Markets. Wiley, New York (1991)Google Scholar
  14. 14.
    Rosenblatt, F.: Principles of Neurodynamics. Spartan, New York (1962)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyong Joo Oh
    • 1
  • Tae Yoon Kim
    • 2
  • Chiho Kim
    • 3
  • Suk Jun Lee
    • 1
  1. 1.Department of Information and Industrial EngineeringYonsei UniversitySeoulKorea
  2. 2.Department of StatisticsKeimyung UniversityDaeguKorea
  3. 3.Korea Deposit Insurance CorporationSeoulKorea

Personalised recommendations