Artificial neural networks for developing early warning system for banking system: Indian context

Abstract

This study attempts to develop an early warning system for the Indian banking sector using artificial neural networks (ANNs) by considering important economic variables based on detailed literature review. It takes into account an Elman recurrent neural network and a multilayered feedforward backpropagation network (MLFN). The ANNs are evaluated based on their accuracy and calibration using quadratic probability score (QPS) and global squared bias (GSB) for both within the sample and out of the sample. The scores depict results with Elman recurrent network outperforming the MLFN. The uniqueness of this study lies in using and identifying pertinent macroeconomic variables to anticipate the banking sector fragility for the Indian economy using sequential feature selection algorithms. The ANN models are found to be appropriate and useful for policy planners to foresee the possibility of the occurrence of banking fragility and take proactive corrective measures to minimise and safeguard the economy from adverse implications of banking crisis.