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A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods

Giovanis, Eleftherios A Study of Panel Logit Model and Adaptive Neuro-Fuzzy Inference System in the Prediction of Financial Distress Periods. World Academy of Science, Engineering and Technology, 40. pp. 646-652. ISSN 2010-376X (2010)

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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.

Item Type: Article
Uncontrolled Keywords: ANFIS, Binary logistic regression, Financial distress, Panel data
Subjects: D History General and Old World > DS Asia
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Research Area: Economics and Institutional Change
Depositing User: Ms T. Iannizzi
Date Deposited: 09 Jul 2013 14:36
Last Modified: 09 Jul 2013 14:36
URI: http://eprints.imtlucca.it/id/eprint/1635

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