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

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  • Giovanis, eleftheios

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

Suggested Citation

  • Giovanis, eleftheios, 2008. "A Neuro-Fuzzy Approach in the Prediction of Financial Stability and Distress Periods," MPRA Paper 24659, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:24659
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    References listed on IDEAS

    as
    1. Wen-Ying Cheng & Ender Su & Sheng-Jung Li, 2006. "A Financial Distress Pre-Warning Study by Fuzzy Regression Model of TSE-Listed Companies," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 2(2), pages 75-93.
    2. Harlan Platt & Marjorie Platt, 2002. "Predicting corporate financial distress: Reflections on choice-based sample bias," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 26(2), pages 184-199, June.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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    More about this item

    Keywords

    Financial distress; ANFIS; Neuro-Fuzzy; Fuzzy rules; Fuzzy membership functions; triangle; Gaussian; MALTAB;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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