| Summary: | The recent financial crisis continues to draw attention in the literature given its deep impact. This dissertation investigates three main areas associated with the crisis. Firstly it focuses on bank default prediction models asking whether structural or accounting models can better predict default. In the second instance we investigate the credit rating agencies culpability in the financial crisis by attempting to trace the transmission from sovereign debt ratings to bank credit ratings, an area that is sparse in the literature. Finally we investigate the classification of bank ratings using four statistical techniques altering the independent variables with financial variables and principal components to assess which statistical method and technique is better able to classify ratings. In the first instance the analysis compares accounting and structural default prediction models using a logit analysis to predict default. The paper uses panel data on US banks from the Federal Deposit Insurance Corporation database between 1993-2015 and the analysis is developed on 536 defaulted bank years and 25,614 non-defaulted bank years. The dissertation goes on to evaluate the impact of sovereign credit ratings on the ratings assigned to banks. Using data on Euro zone countries by credit rating agencies Moody's, Standard and Poor's and Fitch between 2003-2013, I find that multiple notch sovereign downgrades do influence bank downgrades particularly in the crisis period. The study suggests that while a bank's financial fundamentals do play an important role in rating assignments the rating change of the sovereign provides stimulus for the amount of notches the bank is downgraded by. In the final chapter the empirical results suggest that the multiple discriminant analysis statistical model is the better classifier of bank credit ratings for all three rating agencies.
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