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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)

Abstract

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.

Keywords

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.

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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

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