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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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Listed:
  • 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.

Suggested Citation

  • Chin-Shien Lin & Haider A. Khan & Ying-Chieh Wang & Ruei-Yuan Chang, 2006. "A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?," CARF F-Series CARF-F-065, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  • Handle: RePEc:cfi:fseres:cf065
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    File URL: http://www.carf.e.u-tokyo.ac.jp/pdf/workingpaper/fseries/66.pdf
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    References listed on IDEAS

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    Cited by:

    1. Mustapha Djennas & Mohamed Benbouziane & Meriem Djennas, 2011. "An Approach of Combining Empirical Mode Decomposition and Neural Network Learning for Currency Crisis Forecasting," Working Papers 627, Economic Research Forum, revised 09 Jan 2011.
    2. 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.
    3. Markus Behn & Carsten Detken & Tuomas Peltonen & Willem Schudel, 2017. "Predicting Vulnerabilities in the EU Banking Sector: The Role of Global and Domestic Factors," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 147-189, December.
    4. Yanping Zhao & Jakob Haan & Bert Scholtens & Haizhen Yang, 2014. "Leading Indicators of Currency Crises: Are They the Same in Different Exchange Rate Regimes?," Open Economies Review, Springer, vol. 25(5), pages 937-957, November.
    5. 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.
    6. Cumperayot, Phornchanok & Kouwenberg, Roy, 2013. "Early warning systems for currency crises: A multivariate extreme value approach," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 151-171.
    7. Ping Wang & Tomoe Moore, 2014. "The determinants of vulnerability to currency crises: country-specific factors versus regional factors," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(4), pages 619-640, November.

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