A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods

Giovanis, eleftheios (2008): A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods. Unpublished.

Abstract

The purpose of this paper is to present a neuro-fuzzy approach of financial distress pre-warning model appropriate for risk supervisors, investors and policy makers. We examine a sample of the financial institutions and electronic companies of Taiwan Security Exchange (TSE) from 2002 through 2008. We present an adaptive neuro-fuzzy system with triangle and Gaussian membership functions. We conclude that neuro-fuzzy model presents almost perfect forecasts for financial distress periods as also very high forecasting performance for financial stability periods, indicating that ANFIS technology is more appropriate for financial credit risk control and management and for the forecasting of bankruptcy and distress periods. On the other hand we propose the use of both models, because with Logit and generally 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 and the most scientific option are to combine both models and not taking only one of them

• Item Type: MPRA Paper
• Language: English
• Keywords: Financial distress; ANFIS; Neuro-Fuzzy; Fuzzy rules; Fuzzy membership functions; triangle; Gaussian; MALTAB
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Other Model Applications; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C63 - Computational Techniques; Simulation Modeling ; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C25 - Discrete Regression and Qualitative Choice Models; Discrete Regressors

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