 A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress PeriodsMPRA Paper from University Library of Munich, Germany 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 Keywords: Financial distress; ANFIS; Neuro-Fuzzy; Fuzzy rules; Fuzzy membership functions; triangle; Gaussian; MALTAB (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:24659 Access Statistics for this paper More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC. |
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