A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods
The purpose of this paper is to present two different
approaches of financial distress pre-warning models appropriate for
risk supervisors, investors and policy makers. We examine a sample
of the financial institutions and electronic companies of Taiwan
Security Exchange (TSE) market from 2002 through 2008. We
present a binary logistic regression with paned data analysis. With
the pooled binary logistic regression we build a model including
more variables in the regression than with random effects, while the
in-sample and out-sample forecasting performance is higher in
random effects estimation than in pooled regression. On the other
hand we estimate an Adaptive Neuro-Fuzzy Inference System
(ANFIS) with Gaussian and Generalized Bell (Gbell) functions and
we find that ANFIS outperforms significant Logit regressions in both
in-sample and out-of-sample periods, indicating that ANFIS is a
more appropriate tool for financial risk managers and for the
economic policy makers in central banks and national statistical
services.
[1] E. I. Altman, "Financial ratios, discriminate analysis and the prediction
of corporate bankruptcy", Journal of Finance, vol. 23, No. 4, pp. 589-
609, 1968
[2] W. Y. Cheng, and S. H. Li, "A prediction validity meta-analysis study
of impact factors on financial distress pre-warning model", Joint
Conference on Business Evaluation, pp. 66-73, 2003
[3] H. D. Platt, and M. B. Platt, "Predicting corporate financial distress:
Reflections on choice-based sample bias", Journal of Economics and
Finance, vol. 26, pp. 184-199, 2002
[4] W. Y. Cheng, S. H. Li, and H. H. Yeh, "An evaluation model study on
financial administration stages of the electronic stock listing in the
Taiwan Security Exchange", Journal of Risk Management, vol. 8, No. 1,
pp. 71-96, 2006
[5] G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial
neural networks: The state of the art", International Journal of
Forecasting, vol. 14, pp. 35-62, 1998
[6] D. E. O-leary, "Using neural networks to predict corporate failure",
International Journal of Intelligent Systems in Accounting, Finance and
Management, vol. 7, pp. 187-197, 1998
[7] W. Y Cheng, E. Su and S. J. Li, "A Financial distress pre-warning study
by fuzzy regression model of TSE-listed companies", Asian Academy of
Management Journal of Accounting and Finance, vol. 2, No. 2, pp. 75-
93, 2006
[8] W.H. Greene, Econometric Analysis, Sixth Edition, Prentice Hall: New
Jersey, 2008
[9] J.-S.R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference
Systems," IEEE Transactions on Systems, Man, and Cybernetics, vol.
23, no. 3, pp. 665-685, 1993
[10] J.-S. R. Jang and C.-T. Sun, "Neuro-fuzzy Modeling and Control,"
Proceedings of the IEEE, vol. 83, no. 3, 378-406, March, 1995
[11] Moore, E. H. (1920), "On the reciprocal of the general algebraic matrix,"
Bulletin of the American Mathematical Society, 26, 394-395
[12] Penrose, R. (1955), "A generalized inverse for matrices," Proceedings of
the Cambridge Philosophical Society, 51, 406-413
[13] Petrou, M. and P. Bosdogianni, (2000), Image Processing: The
Fundamentals, John Wiley
[14] 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
[15] 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
[16] L.H. Tsoukalas, and R.E. Uhrig, Fuzzy and Neural Approaches in
Engineering, First Edition, John Wiley & Sons, 1997, pp. 445-470
[17] J. A. Gentry, P. Newbold, and W. Whitford, "Classifying bankrupt firms
with funds flow components", Journal of Accounting Research, pp. 46-
160, 1985
[18]`Y.L. Chiung, and C.H. Chang, "Application of Company Financial Crisis
Early Warning Model-Use of ÔÇÿFinancial Reference Database", World
Academy of Science, Engineering and Technology, vol. 53, pp. 74-79,
2009
[1] E. I. Altman, "Financial ratios, discriminate analysis and the prediction
of corporate bankruptcy", Journal of Finance, vol. 23, No. 4, pp. 589-
609, 1968
[2] W. Y. Cheng, and S. H. Li, "A prediction validity meta-analysis study
of impact factors on financial distress pre-warning model", Joint
Conference on Business Evaluation, pp. 66-73, 2003
[3] H. D. Platt, and M. B. Platt, "Predicting corporate financial distress:
Reflections on choice-based sample bias", Journal of Economics and
Finance, vol. 26, pp. 184-199, 2002
[4] W. Y. Cheng, S. H. Li, and H. H. Yeh, "An evaluation model study on
financial administration stages of the electronic stock listing in the
Taiwan Security Exchange", Journal of Risk Management, vol. 8, No. 1,
pp. 71-96, 2006
[5] G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial
neural networks: The state of the art", International Journal of
Forecasting, vol. 14, pp. 35-62, 1998
[6] D. E. O-leary, "Using neural networks to predict corporate failure",
International Journal of Intelligent Systems in Accounting, Finance and
Management, vol. 7, pp. 187-197, 1998
[7] W. Y Cheng, E. Su and S. J. Li, "A Financial distress pre-warning study
by fuzzy regression model of TSE-listed companies", Asian Academy of
Management Journal of Accounting and Finance, vol. 2, No. 2, pp. 75-
93, 2006
[8] W.H. Greene, Econometric Analysis, Sixth Edition, Prentice Hall: New
Jersey, 2008
[9] J.-S.R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference
Systems," IEEE Transactions on Systems, Man, and Cybernetics, vol.
23, no. 3, pp. 665-685, 1993
[10] J.-S. R. Jang and C.-T. Sun, "Neuro-fuzzy Modeling and Control,"
Proceedings of the IEEE, vol. 83, no. 3, 378-406, March, 1995
[11] Moore, E. H. (1920), "On the reciprocal of the general algebraic matrix,"
Bulletin of the American Mathematical Society, 26, 394-395
[12] Penrose, R. (1955), "A generalized inverse for matrices," Proceedings of
the Cambridge Philosophical Society, 51, 406-413
[13] Petrou, M. and P. Bosdogianni, (2000), Image Processing: The
Fundamentals, John Wiley
[14] 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
[15] 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
[16] L.H. Tsoukalas, and R.E. Uhrig, Fuzzy and Neural Approaches in
Engineering, First Edition, John Wiley & Sons, 1997, pp. 445-470
[17] J. A. Gentry, P. Newbold, and W. Whitford, "Classifying bankrupt firms
with funds flow components", Journal of Accounting Research, pp. 46-
160, 1985
[18]`Y.L. Chiung, and C.H. Chang, "Application of Company Financial Crisis
Early Warning Model-Use of ÔÇÿFinancial Reference Database", World
Academy of Science, Engineering and Technology, vol. 53, pp. 74-79,
2009
@article{"International Journal of Business, Human and Social Sciences:56426", author = "Ε. Giovanis", title = "A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods", abstract = "The purpose of this paper is to present two different
approaches of financial distress pre-warning models appropriate for
risk supervisors, investors and policy makers. We examine a sample
of the financial institutions and electronic companies of Taiwan
Security Exchange (TSE) market from 2002 through 2008. We
present a binary logistic regression with paned data analysis. With
the pooled binary logistic regression we build a model including
more variables in the regression than with random effects, while the
in-sample and out-sample forecasting performance is higher in
random effects estimation than in pooled regression. On the other
hand we estimate an Adaptive Neuro-Fuzzy Inference System
(ANFIS) with Gaussian and Generalized Bell (Gbell) functions and
we find that ANFIS outperforms significant Logit regressions in both
in-sample and out-of-sample periods, indicating that ANFIS is a
more appropriate tool for financial risk managers and for the
economic policy makers in central banks and national statistical
services.", keywords = "ANFIS, Binary logistic regression, Financialdistress, Panel data", volume = "4", number = "4", pages = "381-7", }