@article{(Open Science Index):https://publications.waset.org/pdf/7348,
	  title     = {A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods},
	  author    = {Ε. Giovanis},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {4},
	  number    = {4},
	  year      = {2010},
	  pages     = {423 - 429},
	  ee        = {https://publications.waset.org/pdf/7348},
	  url   	= {https://publications.waset.org/vol/40},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 40, 2010},
	}