Abstract
Using a unique data set on bank distress, this paper provides novel empirical evidence on the determinants of bank soundness in the European Union (EU) as a whole. The estimation results are consistent with the hypothesis that bank risks have converged across EU members, providing empirical support for introduction of a more centralized system of financial regulation in the EU. We show that asset quality and earning profile of banks are important determinants of bank distress next to leverage, suggesting that these should be central in EU-wide financial regulation and supervision. We find that market discipline, both by depositors and by stock market participants, plays a role in the EU, supporting the notion that transparency and dissemination of financial information would contribute to the financial soundness of banks. Our data also point to the presence of contagion effects, relatively higher fragility of concentrated banking sectors, and hazards associated with high ratios of wholesale funding.
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Notes
Most of the literature on bank distress focuses on the United States, which had numerous bank failures that provide a rich data set for a “forensic” examination of the determinants of distress (see, e.g., Lane et al. 1986, Cole and Gunther 1995, Calomiris and Mason 2000, Estrella et al. 2000, and Wheelock and Wilson 2000). Among individual country studies outside the United States, see Gonzalez-Hermosillo et al. (1997) for Mexico, Persons (1999) for Thailand, and Kraft and Galac (2007) for Croatia, and Kick and Koetter (2007) for Germany. Cross-country studies are Bongini et al. (2002) for five East Asian countries, Arena (2008) for five East Asian and three Latin Americal countries, and Mannassoo and Mayes (2009) for 19 Eastern European transition countries (country coverage in the latter study partially overlaps with ours, but does not cover the post-2004 period).
Romania and Bulgaria are excluded, since they joined the EU only in 2007. As regards the “new EU member states” that entered the EU in 2004, the benchmark specification includes all their observations, because their economies were characterized by a high degree of integration with the “old” EU countries even prior to their entry. This is confirmed by one of the robustness checks, in which we exclude pre-2004 observations in these countries.
The Factiva contains a collection of 14,000 sources, including the Wall Street Journal, the Financial Times, Dow Jones and Reuters newswires, and the Associated Press, as well as Reuters Fundamentals, and D&B company profiles (for details, see www.factiva.com).
The data set on distress events starts and ends one year later than the financial data set, because we examine the relationship between lagged financial variables and observed distress.
To correct for some banks “flying under the radar,” we carried out NewsPlus/Factiva searches for the names of all the EU banks in our list (without any accompanying keywords), and, as a robustness check, we rerun our models excluding observations on banks for which these searches returned no hits.
Also, it is a definition that is relatively consistent across the EU, because the NewsPlus/Factiva database covers reasonably well the main business media across the EU countries. In contrast, internal supervisory definitions of banks in distress differ across the EU countries.
The number of banks is smaller than the number of distress events, since some banks experienced multiple distress events over time.
In the baseline estimate, we lagged the explanatory variables by one period, i.e., 1 year. As a robustness check, we also experimented with 2-year and 3-year lags. These checks yielded results that were very similar, but weaker in terms of statistical significance (especially for the 3-year lags), suggesting that the predictive power of the explanatory variables declines as we attempt to predict failures further into the future.
In addition to the logit model, we have also considered using a survival time model. However, given that our panel data set has a rather large number of banks (5,708) combined with a relatively short time span (11 years) and observations of distress (79). most of the dependent variable in the survival analysis (time until failure) is censored. In such setting, the survival time model has a relatively low value added, essentially only confirming that factors that tend to decrease the probability of distress in a bank also increase that bank’s survival time.
Relatedly, Gropp and Heider (2008), examining a sample of banks and nonbank corporations in Europe and the United States, and using a simple leverage ratio, are unable to detect first order effects of capital regulation (imposed on the risk-weighted capital adequacy ratio) on the capital structure of banks. They find that the standard cross-sectional determinants of firms’ capital structures valid for nonbank corporations also apply to large, publicly traded banks.
We performed two robustness checks. First, we defined “similar size” as ±100 million Euro rather than ±200 million euro. Second, we used the share of loans to total assets, with a ± 5 percent band, as an alternative measure of similarity. Our estimation results do not change when these alternative definitions of similarity are used to evaluate the impact of contagion (the results are available upon request).
To alleviate the impact of extreme observations and errors in the sample, all these independent variables are winsorized at the 1 percent level.
Observations for individual banks may be correlated. To take this into account, we drop the standard assumption that errors are independent within each bank and use a variance-covariance matrix that is robust to clustering of errors.
The results might have been different if we used a more direct measure of cost efficiency of a bank, a measure generated by the stochastic frontier analysis. However, introducing such a measure would make the model substantially more complex to implement and to explain to an outsider, which would not be in line with the intended uses of the model. For the same reason, the cost-to-income ratio that we employ is a widely used measure of bank’s managerial quality (see, e.g. Mannasoo and Mayes 2009).
Unfortunately, bank balance sheets in BankScope are not available at a higher frequency. However, in the next section, as part of the robustness checks, we introduce another variable characterizing the liquidity exposure in a bank, namely the share of wholesale financing, and this variable does have a significant impact on the PD.
This is consistent with the theoretical prediction of the model by Hellmann et al. (2000).
This result is robust to alternative measures of contagion dummy. Instead of defining similarity among banks as asset size within EUR ±200 million we re-estimated the model by (i) defining “similar size” as EUR ±100 million and (ii) employing the share of loans to total assets, with a ±5 percent band, as an alternative measure of similarity. The estimation results do not change substantially (the results are available upon request).
Since the model fit (pseudo R-squared) does not improve after this sample reduction, we proceed with using the total sample in our subsequent estimations.
There are 21 repetitive distress bank-year observations in total. The remaining 54 distress events correspond to the number of distressed banks in the sample.
To keep Table 4 legible, we show just the three macroeconomic factors discussed in the previous paragraph. We also tested the other macroeconomic variables that come out in the studies on systemic distress, such as Čihák and Schaeck (2007), and they were not significant. Results are available upon request.
Results for this iteration of the robustness check are not shown in Table 4, but are available upon request.
The intercept becomes insignificant when macroeconomic variables enter the specification, which may reflect a complex relationship between the contagion dummy, macroeconomic shocks and the baseline hazard.
Listed banks were identified from BankScope by their International Securities Identification Number (ISIN). Daily series of bank stock prices and the FTSE-100 index are taken from Datastream. The market information variable takes a value of zero for the nonlisted banks. Because the logit estimate is based on annual data, we use yearly averages of the daily stock price data. We also experimented with different approaches to mapping the daily data into yearly data, but this had little impact on the results.
Panel data estimation results can also be considered as yet another exercise to examine robustness of the baseline model with respect to the estimation method.
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The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. We would like to thank Ales Bulir, Jakob De Haan, Enrica Detragiache, Michaela Erbenová, Luc Everaert, Olivier Frecaut, Tomislav Galac, Thomas Harjes, Heiko Hesse, Luc Laeven, Klaus Schaeck, Iman van Lelyveld, Thomas Walter, and participants of an IMF seminar, Deutsche Bundesbank conference at the Technical University of Dresden, CREI conference at the Pompeu Fabra University, EBC conference at the Tilburg University, Finlawmetrics conference at the Bocconi University, for useful comments.
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Poghosyan, T., Čihak, M. Determinants of Bank Distress in Europe: Evidence from a New Data Set. J Financ Serv Res 40, 163–184 (2011). https://doi.org/10.1007/s10693-011-0103-1
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DOI: https://doi.org/10.1007/s10693-011-0103-1