Bank failure prediction is important to bank regulators, including central banks and financial ministries. Off-site surveillance systems or early warning models use financial statements or other rating systems to assess the financial status of the banks. Statistical based bank failure prediction models assume a normal probability distribution and equal dispersion on the data that is often violated. Neural network (NN) based models do not make these assumptions about the data. NNs give better prediction results, however, they lack explanatory capabilities. Fuzzy-neural networks (FNN), allow explanatory capabilities and give better performance. In this paper, several bank failure prediction models using the pseudo outer product-based FNN with a compositional rule of inference and singleton fuzzifier (POPFNN-CRI(S)) are proposed. The system uses financial covariates derived from publicly available financial statements to predict failing banks. The performances of the models are assessed through the classification of 3636 US banks for one-year and two-year periods. As it is not possible to obtain all financial statements, we also propose a model to aid in the reconstruction of missing financial data using POPFNN-CRI(S). To complete the study, the effect that missing data has on the outcome of bank failure prediction is examined.
Discussion(0)
No comments yet. Be the first to comment.