Application of Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA
In this paper discrete choice models, Logit and Probit
are examined in order to predict the economic recession or expansion
periods in USA. Additionally we propose an adaptive neuro-fuzzy
inference system with triangular membership function. We examine
the in-sample period 1947-2005 and we test the models in the out-of
sample period 2006-2009. The forecasting results indicate that the
Adaptive Neuro-fuzzy Inference System (ANFIS) model outperforms
significant the Logit and Probit models in the out-of sample period.
This indicates that neuro-fuzzy model provides a better and more
reliable signal on whether or not a financial crisis will take place.
[1] A. Demirguc-kunt and E. Detragiache, "The Determinants of Banking
Crises in Developing and Developed Countries", IMF Staff Papers, Vol.
45, No. 1, pp. 81-109, 1998
[2] B. Eichengreen and A.K. Rose, "Staying Afloat When the Wind Shifts:
External Factors and Emerging-Market Banking Crises", NBER
Working Papers 6370, National Bureau of Economic Research,
Cambridge, MA, 1998
[3] J. Frankel and A.K. Rose, "Currency Crashes in emerging Markets: An
Empirical Treatment", International Finance Discussion Papers 534,
Board of Governors of the Federal Reserve System, Washington. D.C,
1996
[4] R. Glick and A.K. Rose, "Contagion and trade: Why currency crises are
regional?", NBER Working Papers 6806, National Bureau of Economic
Research, Cambridge, MA, 1998
[5] R. Glick and A.K. Rose, "Money and Credit, Competitiveness and
Currency Crises in Asia and Latin America", Center for Pacific Basin
Money and economic Studies, Federal Reserve Bank of San Francisco
papers BP99-01,1999
[6] L.G. Kaminsky and C.M. Reinhart, "The Twin Crises: The Causes of
Banking and Balance of Payments Problems", Federal Reserve Board
Discussion Papers 544. Board of Governors of the Federal Reserve
System.Washington. D.C., 1996
[7] L.G. Kaminsky and C.M. Reinhart, "Leading Indicators of Currency
Crises", IMF Staff Papers, Vol. 45, No. 1, pp. 1-48, 1998
[8] L. Salchenberger, E. Cinar N. and Lash, "Neural networks: A new tool
for predicting thrift failures", Decision Sciences, Vol. 23, pp. 899-916,
1992
[9] P.K. Coats and F.L. Fant, "Recognizing financial distress patterns using
a neural network tool", Financial Management, Vol. 22, pp. 142-155,
1993
[10] G. Zhang, Y. Hu and E.B. Patuwo, "Artificial neural networks in
bankruptcy prediction: General framework and cross-validation
analysis", European Journal of Operation Research, Vol. 116, pp. 16-
32, 1999
[11] P.L Brockett, L.L. Golden, J. Jang, and C. Yang, "A comparison of
neural network, statistical methods and variable choice for life insurers-
financial distress prediction", Journal of Risk and Insurance, Vol. 73,
No. 3, pp. 397-419, 2006
[12] E. Giovanis, "Application of Logit Model and Self-Organizing Maps
(SOM) for the prediction of Financial Crisis Periods in US Economy",
Journal of Financial Economic Policy, Vol. 2, No. 2, pp. 98-125, 2010
[13] H. Ni and H. Yin, "Exchange rate prediction using hybrid neural
networks and trading indicators", Neurocomputing, Vol. 72, No. 13-15,
pp. 2815-2823, 2009
[14] W.H. Greene, Econometric Analysis, Sixth Edition, Prentice Hall: New
Jersey, 2008, pp. 666-668
[15] J.-S.R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference
Systems," IEEE Trans. on Systems, Man, and Cybernetics, Vol. 23, No.
3, pp. 665-685, 1993
[16] J.-S. R. Jang and C.-T. Sun, "Neuro-fuzzy Modeling and Control,"
Proceedings of the IEEE, Vol. No. 83, No. 3, 378-406, March, 1995
[17] L. Khan, S. Anjum and R. Bada, "Standard Fuzzy Model Identification
using Gradient Methods," World Applied Sciences Journal, Vol. 8, No.
1, pp. 01-09, 2010
[18] M.A. Denai, F. Palis and A. Zeghbib, ANFIS Based Modelling and
Control of Non-Linear systems: A Tutorial, IEEE Conf. on Systems,
Man, and Cybernetics, Vol. 4, pp. 3433-3438, 2004
[19] L.H. Tsoukalas, and R.E. Uhrig, Fuzzy and Neural Approaches in
Engineering, First Edition, John Wiley & Sons, 1997, pp. 445-470.
[1] A. Demirguc-kunt and E. Detragiache, "The Determinants of Banking
Crises in Developing and Developed Countries", IMF Staff Papers, Vol.
45, No. 1, pp. 81-109, 1998
[2] B. Eichengreen and A.K. Rose, "Staying Afloat When the Wind Shifts:
External Factors and Emerging-Market Banking Crises", NBER
Working Papers 6370, National Bureau of Economic Research,
Cambridge, MA, 1998
[3] J. Frankel and A.K. Rose, "Currency Crashes in emerging Markets: An
Empirical Treatment", International Finance Discussion Papers 534,
Board of Governors of the Federal Reserve System, Washington. D.C,
1996
[4] R. Glick and A.K. Rose, "Contagion and trade: Why currency crises are
regional?", NBER Working Papers 6806, National Bureau of Economic
Research, Cambridge, MA, 1998
[5] R. Glick and A.K. Rose, "Money and Credit, Competitiveness and
Currency Crises in Asia and Latin America", Center for Pacific Basin
Money and economic Studies, Federal Reserve Bank of San Francisco
papers BP99-01,1999
[6] L.G. Kaminsky and C.M. Reinhart, "The Twin Crises: The Causes of
Banking and Balance of Payments Problems", Federal Reserve Board
Discussion Papers 544. Board of Governors of the Federal Reserve
System.Washington. D.C., 1996
[7] L.G. Kaminsky and C.M. Reinhart, "Leading Indicators of Currency
Crises", IMF Staff Papers, Vol. 45, No. 1, pp. 1-48, 1998
[8] L. Salchenberger, E. Cinar N. and Lash, "Neural networks: A new tool
for predicting thrift failures", Decision Sciences, Vol. 23, pp. 899-916,
1992
[9] P.K. Coats and F.L. Fant, "Recognizing financial distress patterns using
a neural network tool", Financial Management, Vol. 22, pp. 142-155,
1993
[10] G. Zhang, Y. Hu and E.B. Patuwo, "Artificial neural networks in
bankruptcy prediction: General framework and cross-validation
analysis", European Journal of Operation Research, Vol. 116, pp. 16-
32, 1999
[11] P.L Brockett, L.L. Golden, J. Jang, and C. Yang, "A comparison of
neural network, statistical methods and variable choice for life insurers-
financial distress prediction", Journal of Risk and Insurance, Vol. 73,
No. 3, pp. 397-419, 2006
[12] E. Giovanis, "Application of Logit Model and Self-Organizing Maps
(SOM) for the prediction of Financial Crisis Periods in US Economy",
Journal of Financial Economic Policy, Vol. 2, No. 2, pp. 98-125, 2010
[13] H. Ni and H. Yin, "Exchange rate prediction using hybrid neural
networks and trading indicators", Neurocomputing, Vol. 72, No. 13-15,
pp. 2815-2823, 2009
[14] W.H. Greene, Econometric Analysis, Sixth Edition, Prentice Hall: New
Jersey, 2008, pp. 666-668
[15] J.-S.R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference
Systems," IEEE Trans. on Systems, Man, and Cybernetics, Vol. 23, No.
3, pp. 665-685, 1993
[16] J.-S. R. Jang and C.-T. Sun, "Neuro-fuzzy Modeling and Control,"
Proceedings of the IEEE, Vol. No. 83, No. 3, 378-406, March, 1995
[17] L. Khan, S. Anjum and R. Bada, "Standard Fuzzy Model Identification
using Gradient Methods," World Applied Sciences Journal, Vol. 8, No.
1, pp. 01-09, 2010
[18] M.A. Denai, F. Palis and A. Zeghbib, ANFIS Based Modelling and
Control of Non-Linear systems: A Tutorial, IEEE Conf. on Systems,
Man, and Cybernetics, Vol. 4, pp. 3433-3438, 2004
[19] L.H. Tsoukalas, and R.E. Uhrig, Fuzzy and Neural Approaches in
Engineering, First Edition, John Wiley & Sons, 1997, pp. 445-470.
@article{"International Journal of Business, Human and Social Sciences:56525", author = "Eleftherios Giovanis", title = "Application of Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA", abstract = "In this paper discrete choice models, Logit and Probit
are examined in order to predict the economic recession or expansion
periods in USA. Additionally we propose an adaptive neuro-fuzzy
inference system with triangular membership function. We examine
the in-sample period 1947-2005 and we test the models in the out-of
sample period 2006-2009. The forecasting results indicate that the
Adaptive Neuro-fuzzy Inference System (ANFIS) model outperforms
significant the Logit and Probit models in the out-of sample period.
This indicates that neuro-fuzzy model provides a better and more
reliable signal on whether or not a financial crisis will take place.", keywords = "ANFIS, discrete choice models, financial crisis, USeconomy", volume = "4", number = "7", pages = "1695-6", }
