Thesis_gd_final_vers.. - Vernimmen

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1) Introduction The subprime crisis, which has been widely considered as the worst banking and economic crisis since the Great Depression in the 1930s, has created the opportunity to revive the analysis of bank failures and develop new failure prediction models. Indeed, recent research on bank failures is based on past periods: the 1980s and the beginning of the 1990s for Cole and Wu (2009), from the mid-1980s to the mid-1990s for Wheelock and Wilson (2000), the first part of the 1990s for Curry Elmel and Fissel (2004). Most of the failure prediction models built are thus based on the bankruptcies which occurred at the time of the Savings and Loans crisis or in the couple of year after. To our knowledge, the only study that looked at bank failures after the end of the long deregulation process opened at the end of the 1970s, is King, Nuxoll and Yeager (2006). They find that the dramatic regulatory financial and technological changes which have happened in the banking system have modified causes of bank failures, based on the comparison of failed banks over 1984-1994 and 1995-2003. They also underline that despite the development of new dynamic statistic models, the research into bank financial distress and failures has slowed. Even though the number of failures remains high after a peak in 2009, which shows that bank failures caused by the subprime crisis are not over, we think it is time to look at the determinants of the failure of US banks which have already failed. We decided to perform a classical mean difference test on several variable to determine whether failed banks have been differing from safe banks and consequently determine meaningful inputs to a dynamic logistic model very similar to the one first introduced by Shumway (2001) for corporate failure prediction, which has then been tested by Cole and Wu (2009) for bank failure prediction. However, contrary to them we do not include macro-economic data which is an interesting possibility offered by dynamic models. Moreover we rely exclusively on bank accounting data, as using market data would have dramatically reduced our bank sample 1 . Both Shumway (2001) and Cole and Wu (2009) develop mathematical arguments proving the superiority of dynamic logistic models using data panels on logit or probit, one-period models which only rely on cross-sectional data. First, this comes from the fact that dynamic models use much more observations which improves the accuracy of estimates and coefficients. Second, dynamic models allow to capture how long a bank is at risk of failure, whereas this is not taken into account by one-period models which produces biased and inconsistent estimates. As the crisis revealed the depth of the changes caused by deregulation, financial innovation and technological progress to the US banking and financial system, we devote the next section to a 1 Curry, Elmer and Fissel (2004) report that at the end of 2008 only 400 banks were listed on a total number of banks close to 8,000. - 3 -
description and analysis of the evolution of the US banking system based on a review of literature. The third section presents our data, the treatment we made and the variables we will use in the comparison of failed and safe banks. The fourth section presents the results of the comparison of failed and safe banks on 25 criteria and builds a profile of failed banks. The fifth section presents the results of our dynamic logistic regression and the extent to which it verifies our hypotheses on the determinants of bank failures during the crisis. Finally the sixth section concludes. 2) The evolution of the US banking system before the crisis In a few decades, US banks experienced dramatic changes and transformations which were essentially fuelled by the combined impacts of deregulation and technological changes. This resulted among other things in the boom of financial markets and the development of securitization which lead to the 2007-2008 credit bubble and the following crisis. In this section we describe the main evolutions of the US banking industry in order to better understand the nature of the banking industry in the 2000s and the causes of bank failures during the crisis. This will help us determine the variables which we will use to compare failed and non-failed banks, as well as potential inputs to the dynamic logistic model. a) Deregulation and its impact on US banks i) A short history of deregulation At the beginning of the 80s, the US banking industry was essentially made of small depository institutions which dominated real estate lending, consumer lending and small business lending. In their article on community banks, DeYoung and al. (2004) explain that the US banking industry had been shaped by the regulation of the 1920s and 1930s following the Great Depression. According to DeYoung and al. (2004), commercial banking was the largest intermediary in the US financial system with nearly 60% of intermediated assets when including thrifts and other depository institutions. In this heavily regulated context, banks were protected from geographical competition by the McFadden Act of 1927 which prohibited interstate branching 2 . There was only one loophole in the McFadden Act, which allowed cross-border banking through multibank holding companies. However, prior to the Banking Act of 1956 exploiting this loophole required state approval. 2 However the Mc Fadden Act did not put the same constraints on wholesale banking. Commercial and industrial loans could be delivered on a national basis through local loan production offices, as long as these offices did not engage in deposit-taking. - 4 -
Page 1 and 2: Student: Guillaume Despagne Tutor:
Page 3: Student: Guillaume Despagne The det
Page 7 and 8: anching allowed statewide consolida
Page 9 and 10: US banks decreased by almost 30% 4
Page 11 and 12: monitoring costs incurred in the fr
Page 13 and 14: i) Technological changes in the ban
Page 15 and 16: Progress in both financial and info
Page 17 and 18: were responsible for much of the fi
Page 19 and 20: eceivables with $3.5 billion, other
Page 21 and 22: Beyond a credit boom which benefitt
Page 23 and 24: not have been profitable if they ha
Page 25 and 26: We observe 10,504 individual banks
Page 27 and 28: We selected data from the income st
Page 29 and 30: deposits in denomination higher tha
Page 31 and 32: Table 4: Variables used in the comp
Page 33 and 34: failure risk. On the other side, th
Page 35 and 36: Table 5: Univariate tests of equali
Page 41 and 42: decline is explained first by the i
Page 43 and 44: ely more on alternative funding sou
Page 45 and 46: growth rates, which cannot be expla
Page 47 and 48: Table 12: Dynamic logistic regressi
Page 49 and 50: models 1, 2 and 3, exposure to real
Page 51 and 52: crisis breaks out, are the exposure
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Curry, J., Elmer, P., Fissel, G., 2
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Appendix 1: individual banks droppe
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265818 KOREA EXCH BK NY BR 1921111
Page 61 and 62:
776006 BANCO NACION ARGENTINA NY BR
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