The problem of banks bankruptcy risk forecasting under uncertainty is considered. For its solution, the application of computational intelligence methods fuzzy neural networks ANFIS and TSK and inductive modeling method FGMDH was suggested and explored. Experimental investigations were carried out and estimation of the efficiency of the suggested methods was performed at the problems of bankruptcy risk forecasting for Ukrainian and leading European banks. The efficiency comparison with classic statistical methods such as ARMA, logit, and probit models was fulfilled. The comparative experiments with rating system CAMELS and matrix method were carried out. In general, the comparative analysis had shown that fuzzy forecasting methods and techniques give better results than conventional crisp methods for forecasting bankruptcy risk. On the whole, the conclusions of experiments with European banks completely confirmed the conclusions of experiments with Ukrainian banks. But at the same time, the crisp methods are more simple in implementation and demand less time for their adjustment. The set of informative bank financial factors for bankruptcy risk forecasting was determined and estimated.
Part of the book: Accounting and Finance
This paper is devoted to the investigation and application of fuzzy inductive modeling method group method of data handling (GMDH) in problems of forecasting in the financial sphere. GMDH method belongs to self-organizing methods and allows to discover internal hidden laws in the appropriate object area. The advantage of GMDH algorithms is the possibility of constructing optimal models. In the generalization of GMDH in case of uncertainty, new method fuzzy GMDH is described which enables to construct fuzzy models almost automatically. The algorithms of fuzzy GMDH for different membership functions are considered. The extensions of fuzzy GMDH for different partial descriptions—orthogonal polynomials of Chebyshev and trigonometric polynomials of Fourier—are considered. The problem of adaptation of fuzzy models obtained by FGMDH is considered, and the corresponding adaptation algorithm is described. The experimental investigations of the suggested FGMDH in the problem of forecasting macroeconomic indicators of Ukraine are carried out, and comparison with classic GMDH and neural network BP is performed.
Part of the book: Introduction to Data Science and Machine Learning