Bank Default Prediction: A Comparative Model using Principal Component Analysis

Abstract

Bank default prediction continues to draw attention given the ongoing effects of the recent financial crisis. Seminal works have found that structural models are better predictors of default. In this paper I argue that accounting models predictive ability have been weakened due to the multicollinearity problem and propose principal component analysis to improve the accounting model. The paper then compares accounting and structural default prediction models using a logit analysis and further evaluates the performance of a combination of accounting and structural default models to predict default. The paper uses panel data on US banks from the Federal Deposit Insurance Corporation database between 1995-2012 and the analysis is developed on 519 defaulted bank years and 5,965 non defaulted bank years. The accounting model is improved and outperforms the structural model; the study also finds that a combination of both models performs better than any one model at predicting default in the US banking system.