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Developing an early warning system to predict currency crises

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  • Sevim, Cuneyt
  • Oztekin, Asil
  • Bali, Ozkan
  • Gumus, Serkan
  • Guresen, Erkam

Abstract

The purpose of this paper is to develop an early warning system to predict currency crises. In this study, a data set covering the period of January 1992–December 2011 of Turkish economy is used, and an early warning system is developed with artificial neural networks (ANN), decision trees, and logistic regression models. Financial Pressure Index (FPI) is an aggregated value, composed of the percentage changes in dollar exchange rate, gross foreign exchange reserves of the Central Bank, and overnight interest rate. In this study, FPI is the dependent variable, and thirty-two macroeconomic indicators are the independent variables. Three models, which are tested in Turkish crisis cases, have given clear signals that predicted the 1994 and 2001 crises 12months earlier. Considering all three prediction model results, Turkey’s economy is not expected to have a currency crisis (ceteris paribus) until the end of 2012. This study presents uniqueness in that decision support model developed in this study uses basic macroeconomic indicators to predict crises up to a year before they actually happened with an accuracy rate of approximately 95%. It also ranks the leading factors of currency crisis with regard to their importance in predicting the crisis.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:237:y:2014:i:3:p:1095-1104
    DOI: 10.1016/j.ejor.2014.02.047
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    2. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    3. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2016. "Short term prediction of extreme returns based on the recurrence interval analysis," Papers 1610.08230, arXiv.org.
    4. Cueyt SEVIM & Taylan Taner DOGAN, 2016. "Turkiye Ekonomisinde Ihracat ve Doviz Kuru Oynakligi Iliskisi," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 16(2), pages 303-318.
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    10. Huang, Yan & Kou, Gang & Peng, Yi, 2017. "Nonlinear manifold learning for early warnings in financial markets," European Journal of Operational Research, Elsevier, vol. 258(2), pages 692-702.
    11. Asil Oztekin, 0. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 0, pages 1-16.

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