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A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

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Author Info

  • Chin-Shien Lin

    (National Chung Hsing University)

  • Haider A. Khan

    (GIGS, University of Denver)

  • Ying-Chieh Wang

    (Providence University)

  • Ruei-Yuan Chang

    (Providence University)

Abstract

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

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Bibliographic Info

Paper provided by Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo in its series CARF F-Series with number CARF-F-065.

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Length: 33 pages
Date of creation: Apr 2006
Date of revision:
Handle: RePEc:cfi:fseres:cf065

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References

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Cited by:
  1. Zeynep KARACOR & Korhan GOKMENOGLU, 2012. "Predictability Of Financial Crises: Testing K.R.L. Model In The Case Of Turkey," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 2, pages 5-16, June.
  2. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.

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