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Using Neural Networks to Support Early Warning System for Financial Crisis Forecasting

  • Kyong Joo Oh
  • Tae Yoon Kim
  • Hyoung Yong Lee
  • Hakbae Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

This study deals with the construction process of a daily financial condition indicator (DFCI), which can be used as an early warning signal using neural networks and nonlinear programming. One of the characteristics in the proposed indicator is to establish an alarm zone in the DFCI, which plays a role of predicting a potential financial crisis. The previous financial condition indicators based on statistical methods are developed such that they examine whether a crisis will be break out within 24 months. In this study, however, the alarm zone makes it possible for the DFCI to forecast an unexpected crisis on a daily basis and then issue an early warning signal. Therefore, DFCI involves daily monitoring of the evolution of the stock price index, foreign exchange rate and interest rate, which tend to exhibit unusual behaviors preceding a possible crisis. Using nonlinear programming, the procedure of DFCI construction is completed by integrating three sub-DFCIs, based on each financial variable, into the final DFCI. The DFCI for Korean financial market will be established as an empirical study. This study then examines the predictability of alarm zone for the financial crisis forecasting in Korea.

Keywords

Financial Market Financial Crisis Early Warning Early Warning System Stable Period 
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 2005

Authors and Affiliations

  • Kyong Joo Oh
    • 1
  • Tae Yoon Kim
    • 2
  • Hyoung Yong Lee
    • 3
  • Hakbae Lee
    • 4
  1. 1.Dept. of Information and Industrial EngineeringYonsei UniversitySeoulKorea
  2. 2.Dept. of StatisticsKeimyung UniversityDaeguKorea
  3. 3.Dept. of Management EngineeringKorea Advanced Institute of Science and TechnologySeoulKorea
  4. 4.Dept. of StatisticsYonsei UniversitySeoulKorea

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