IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/24656.html
   My bibliography  Save this paper

Neuro-Fuzzy approach for the predictions of economic crisis

Author

Listed:
  • Giovanis, Eleftherios

Abstract

In this paper we present the neuro-fuzzy technology for the prediction of economic crisis of USA economy. Our findings support ANFIS models to traditional discrete choice models of Probit and Logit, indicating that the last models are not very useful for forecasting purposes. We have developed a MATLAB routine to show how ANFIS procedure works and it is provided for replications, further research applications and experiments, for modifications, expansions and improvements. We propose the use of both models, because with discrete choice models we can examine and investigate the effects of the inputs or the independent variables, while we can simultaneously use ANFIS for forecasting purposes. The wise option and the most appropriate scientific action is to combine both models and not taking only one of them.

Suggested Citation

  • Giovanis, Eleftherios, 2008. "Neuro-Fuzzy approach for the predictions of economic crisis," MPRA Paper 24656, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:24656
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/24656/1/MPRA_paper_24656.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    3. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    4. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    5. Barry Eichengreen & Andrew K. Rose, 1998. "Staying Afloat When the Wind Shifts: External Factors and Emerging-Market Banking Crises," NBER Working Papers 6370, National Bureau of Economic Research, Inc.
    6. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    7. Asli Demirgüç-Kunt & Enrica Detragiache, 1998. "The Determinants of Banking Crises in Developing and Developed Countries," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 81-109, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eleftherios Giovanis, 2012. "Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA," Economic Analysis and Policy, Elsevier, vol. 42(1), pages 79-96, March.
    2. Eleftherios Giovanis, 2010. "Application of logit model and self‐organizing maps (SOMs) for the prediction of financial crisis periods in US economy," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 2(2), pages 98-125, June.
    3. Mariano Roberto S & Gultekin Bulent N & Ozmucur Suleyman & Shabbir Tayyeb & Alper C. Emre, 2004. "Prediction of Currency Crises: Case of Turkey," Review of Middle East Economics and Finance, De Gruyter, vol. 2(2), pages 1-21, August.
    4. Reuven Glick & Michael M. Hutchison, 1999. "Banking and currency crises; how common are twins?," Proceedings, Federal Reserve Bank of San Francisco, issue Sep.
    5. Mahir Binici & Aytül Ganioglu, 2021. "Net external position, financial development, and banking crisis," Empirical Economics, Springer, vol. 61(3), pages 1225-1251, September.
    6. Pavel Trunin & M. Kamenskih, 2007. "Monitoring Financial Stability In Developing Economies (Case of Russia)," Research Paper Series, Gaidar Institute for Economic Policy, issue 111.
    7. Mark Illing & Ying Liu, 2003. "An Index of Financial Stress for Canada," Staff Working Papers 03-14, Bank of Canada.
    8. Jason Furman & Joseph E. Stiglitz, 1998. "Economic Crises: Evidence and Insights from East Asia," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(2), pages 1-136.
    9. Schmidt Paul-Günther, 2001. "Ursachen systemischer Bankenkrisen: Erklärungsversuche, empirische Evidenz und wirtschaftspolitische Konsequenzen," ORDO. Jahrbuch für die Ordnung von Wirtschaft und Gesellschaft, De Gruyter, vol. 52(1), pages 239-280, January.
    10. Roy, Saktinil & Kemme, David M., 2012. "Causes of banking crises: Deregulation, credit booms and asset bubbles, then and now," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 270-294.
    11. Glick, Reuven & Hutchison, Michael, 2005. "Capital controls and exchange rate instability in developing economies," Journal of International Money and Finance, Elsevier, vol. 24(3), pages 387-412, April.
    12. Thanh C. Nguyen & Vítor Castro & Justine Wood, 2022. "Political environment and financial crises," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 417-438, January.
    13. Andre Cartapanis, 2004. "Le declenchement des crises de change : qu'avons-nous appris depuis dix ans ?," Economie Internationale, CEPII research center, issue 97, pages 5-48.
    14. Peter Sarlin & Dorina Marghescu, 2011. "Visual predictions of currency crises using self‐organizing maps," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(1), pages 15-38, January.
    15. Ari, Ali, 2008. "An Early Warning Signals Approach for Currency Crises: The Turkish Case," MPRA Paper 25858, University Library of Munich, Germany, revised 2009.
    16. Yong Ma & Yulu Chen, 2014. "Financial Imbalance Index as a New Early Warning Indicator: Methods and Applications in the Chinese Economy," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 22(6), pages 64-86, November.
    17. Jing, Zhongbo, 2015. "On the relation between currency and banking crises in developing countries, 1980–2010," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 267-291.
    18. Gian Maria Milesi Ferretti & Assaf Razin, 2000. "Current Account Reversals and Currency Crises: Empirical Regularities," NBER Chapters, in: Currency Crises, pages 285-323, National Bureau of Economic Research, Inc.
    19. Lin, Chin-Shien & Khan, Haider A. & Chang, Ruei-Yuan & Wang, Ying-Chieh, 2008. "A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1098-1121, November.
    20. Elisabetta Falcetti & Merxe Tudela, 2008. "What do Twins Share? A Joint Probit Estimation of Banking and Currency Crises," Economica, London School of Economics and Political Science, vol. 75(298), pages 199-221, May.

    More about this item

    Keywords

    Economic crisis; ANFIS; Neuro-Fuzzy; fuzzy rules; triangle function; Gaussian function; Generalized Bell function forecasting; discrete choice models; Logit; Probit; economy of USA; MATLAB;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:24656. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.