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

Chin-Shien Lin, Haider Khan (), Ying-Chieh Wang and Ruei-Yuan Chang
Additional contact information
Chin-Shien Lin: National Chung Hsing University
Ying-Chieh Wang: Providence University
Ruei-Yuan Chang: Providence University

No CIRJE-F-411, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

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.

Pages: 33pages
Date: 2006-04
New Economics Papers: this item is included in nep-cba, nep-cmp, nep-ecm, nep-fmk, nep-ict and nep-ifn
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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http://www.cirje.e.u-tokyo.ac.jp/research/dp/2006/2006cf411.pdf (application/pdf)

Related works:
Journal Article: A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively? (2008)
Working Paper: A New Approach to Modeling Early Warning Systems for Currency Crises: can a machine-learning fuzzy expert system predict the currency crises effectively? (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2006cf411

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