Thesis_gd_final_vers.. - Vernimmen

6) Conclusion
The subprime crisis has been the culminating point of a major evolution phase of the US banking
industry which has been driven by a long deregulation movement, the rise of information
technologies, and the implementation of new financial products. While major changes such as
securitization are helpful to understand the credit and real estate bubbles and their subsequent
bursts, or even Lehman Brothers or Bear Stearns bankruptcies, they seem to be less pertinent in
explaining the determinants of US regional bank failures.
Failed US regional banks have been caught in a credit crisis. Based on our results, we think that
the banks with both the riskiest loan categories allocation and the worst loan quality have failed.
We find that before the crisis failed banks were normal or difficult to distinguish from safe
banks back in 2005 and 2006 based on capital ratios or profitability measures. It is true that they
were exposed to a stronger liquidity risk. But we interpret their financing structure as the
consequence of their stronger asset growth. The beginning of the crisis is linked to a progressive
deterioration of their capital ratios, non-performing loans, and profitability measures. This
deterioration tends to accelerate as bankruptcy gets closer. These symptoms, as we call them, are
the direct explanation of bankruptcy, and are well captured by our dynamic logistic regression
model. A profitability measure such the net income ratio, an asset quality measure such as the nonperforming
loan ratio or a liquidity measure such as the level of non-core deposits had already
been found significant by Cole and Wu (2009) who examine bank failures between 1890 and 1995.
The variables that measure the degradation of a bank situation and lead to bankruptcy are thus
consistent over time and remain the same despite different environments. However these
symptoms are often observable not very long before failure.
Moreover we try to determine the causes of bank failures, i.e. characteristics that were
observable before the crisis and could have been an early-warning of bankruptcy risk. Our
hypothesis is that failing banks asset-mix overweighted loans in general and real estate loans more
specifically to the detriment of securities. This is not well captured by our first logistic regression
models, and we added a “crisis” effect, simply simulated through a crisis variable, which partly
confirms our hypotheses but do not prove statistically significant. We then performed dynamic
regressions on the level on non-performing loans and net interest income that verifies that the
asset allocation of failed banks is partly explaining the evolution of these two direct drivers of
bankruptcy. Based on the results of our regression we can say that other elements explain the
degradation of profitability and asset quality ratios over time. We think that more detailed asset
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