Thesis_gd_final_vers.. - Vernimmen

growth rates, which cannot be explained by the development of new businesses as both the asset
mix and the revenue mix are staying relatively constant overtime. Consequently the growth comes
from existing activities and especially lending. This strongly growing lending activity may be a hint
of the riskier profile of failed banks. Indeed, a very strong growth in loans issuance can conduct to
less careful applicant screening, to lowering credit standards, thus favouring lending volume over
risk and quality. Moreover lending expansion into new geography or previously un-addressed
riskier customer segments in which the bank did not have any informational advantage may have
resulted in the bank attracting marginally riskier borrowers, which then increased the risks on the
balance sheet. The fact that failed banks were holding riskier loans and were having borrowers of
lower credit quality is reflected by the strong rise of the level of non-performing loans when the
crisis starts. As a consequence, failed banks were much more severely hit by the crisis because of
their riskier assets. Losses on their loan portfolios brought them progressively to financial distress
and then to bankruptcy. Concerning the financing, failed banks have been exposed to higher
liquidity risk because they relied more on non-core funding sources such as brokered deposits,
alternative funding and to a lesser extent federal funds. This riskier financing structure may be a
direct consequence of the strong growth of their total assets. Because it is difficult to increase
rapidly core deposits or equity, fast growing banks favour non-core funding.
5) Developing a new bank failure prediction model
a) Methodology
We use a dynamic logistic regression using our data panel to assess the importance of our different
variables in explaining bankruptcy. Shumway (2001) who was among the first to apply a dynamic
model to corporate bankruptcy prediction develops a mathematical demonstration proving that
one-period logit model are giving inconsistent variable coefficients and that they have a poor
prediction ability compared to dynamic logistic regression. To our knowledge, dynamic models
have been only applied to predict bank failures by Cole and Wu (2009), who demonstrate the
superior in-sample as well as out of sample prediction accuracy of a dynamic logistic regression.
Cole and Wu (2009) use data from Call Reports between 1980 and 1992, that they divide into two
intervals 1980-1989 which is their sample period, and 1989-1992 which is used for the out of
sample prediction. They compare the results of the logistic regression with the results of a probit
model using the same variables. They find that a dynamic logistic regression yields much more
accurate results than the one-period probit model. They also develop a second dynamic logistic
regression model adding to the accounting variables from the Call Reports variables which are
linked to economic data such as interest rate and GDP evolution, but they do not find that the
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