Predicting Bank Failure in Nigeria using Principal Component Analysis and D Score Model
In this study, we coupled principal component analysis with discriminant model to predict the probability of bank failure in Nigeria. Our empirical analysis reveals that the warning signal so developed produces a robust result with high prediction accuracy. This is a very promising result as it indicates its invaluable usefulness for regulators in assessing the health status of banks of interest. The analysis of the regression model indicates that the measures of profitability, liquidity, credit risk and capital adequacy are the key predictive financial ratios. In other words, differences in profitability, liquidity, credit risk (asset quality) and capital adequacy (sustenance) are found to be the major distinguishing characteristics between the non failed (healthy) and failed banks. However, variables for management quality and other bank characteristics like economic conditions and staff productivity are potentially not important predictors of financial problems in Nigerian banks but might make a difference for the group of banks that are facing difficulties. The research methodology employed in this study could be applied to other financial and non financial sectors of the economy. Keywords Bank failure prediction, D score model, principal component analysis, early warning signal, Nigerian banking crisis