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Contribution of Macroeconomic Factors to the Prediction of Small Bank Failures

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Contribution of Macroeconomic Factors to the Prediction of Small Bank Failures

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... To the best of our knowledge, very few studies have examined the credit risk determinants of cooperative banks in Italy thus far. The existing literature has focused mainly on the effects of asset quality on cooperative banks' failure, such as the capital adequacy ratio (Deelchand & Padgett, 2009;Fiordelisi & Mare, 2013;Mare, 2015) and its nonmonotonic effect (Porath, 2006), the loan loss provision; inefficiency and bank size (Alessi, Di Colli, & Lopez, 2014;Deelchand & Padgett, 2009;Mare, 2015;Skała, 2014); non-performing loans (Skała, 2014); the liquidity ratio (Fiordelisi & Mare, 2013;Mare, 2015); and efficiency (Fiordelisi & Mare, 2013). Recently, several predictive models have been estimated for Italian cooperative banks. ...
... To the best of our knowledge, very few studies have examined the credit risk determinants of cooperative banks in Italy thus far. The existing literature has focused mainly on the effects of asset quality on cooperative banks' failure, such as the capital adequacy ratio (Deelchand & Padgett, 2009;Fiordelisi & Mare, 2013;Mare, 2015) and its nonmonotonic effect (Porath, 2006), the loan loss provision; inefficiency and bank size (Alessi, Di Colli, & Lopez, 2014;Deelchand & Padgett, 2009;Mare, 2015;Skała, 2014); non-performing loans (Skała, 2014); the liquidity ratio (Fiordelisi & Mare, 2013;Mare, 2015); and efficiency (Fiordelisi & Mare, 2013). Recently, several predictive models have been estimated for Italian cooperative banks. ...
... To the best of our knowledge, very few studies have examined the credit risk determinants of cooperative banks in Italy thus far. The existing literature has focused mainly on the effects of asset quality on cooperative banks' failure, such as the capital adequacy ratio (Deelchand & Padgett, 2009;Fiordelisi & Mare, 2013;Mare, 2015) and its nonmonotonic effect (Porath, 2006), the loan loss provision; inefficiency and bank size (Alessi, Di Colli, & Lopez, 2014;Deelchand & Padgett, 2009;Mare, 2015;Skała, 2014); non-performing loans (Skała, 2014); the liquidity ratio (Fiordelisi & Mare, 2013;Mare, 2015); and efficiency (Fiordelisi & Mare, 2013). Recently, several predictive models have been estimated for Italian cooperative banks. ...
Article
This paper analyzes the drivers of financial distress that were experienced by small Italian cooperative banks during the latest deep recession, focusing mainly on the importance of bank capital as a predictor of bankruptcy for Italian nonprofit banks. The analysis aims to build an early-warning model that is suitable for this type of bank. The results reveal non-monotonic effects of bank capital on the probability of failure. In contrast to distress models for for-profit banks, non-performing loans, profitability, liquidity, and management quality have a negligible predictive value. The findings also show that unreserved impaired loans have an important impact on the probability of bank distress. Moreover, the loan-loss ratio provision on substandard loans constitutes a suitable antibody against bank distress. Overall, the results are robust in terms of both the methodology (i.e., frequentist and Bayesian approaches) and the sample used (i.e., cooperative banks in Italy and euro-area countries).
... On the other hand, great losses have also characterized the period following the global financial havoc, questioning the diversification strategies particularly of cooperative banks' central institutions. The protracted slowdown in economic activity in Italy has increased the risk of failure for Italian cooperative banks (Mare 2015). Austrian cooperative banks have been affected by the diversification strategy involving an expansion of operations in Central and Eastern Europe (Brazda et al. 2016). ...
... Memmel et al. (2015) show that five common risk drivers (nationwide loss rate, difference in the portfolio composition, maturity, region and exposure to export-oriented industries) are able to explain almost 8% of the time variation in individual credit related write-down for German regionally active banks. Mare (2015) finds that the interstate deposit rate and the unemployment rate are significant variables to predict Italian cooperative banks' default. Applying their model to forecast the distress of Italian cooperative banks based on CAMEL ratios, Forgione and Migliardo (2018) find that banks with high loan to deposit ratio and located in the south are most vulnerable. ...
... However, since 2009 we notice an upward trend and at least half of the Italian banks in our sample are now more exposed to credit risk compared to German banks. As suggested in Mare (2015) for the overall bank solvency, a tentative explanation is that the slowdown in economic activity has taken a toll on Italian cooperative banks soundness. This is also true for the overall banking system where the percentage of outstanding non-performing loan rate was 18.1 percent in December 2015. ...
Article
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This study sheds light on risk exposures of cooperative banks in Austria, Germany and Italy. We investigate how major risk elements of banks in these countries have evolved over time, across countries and institutions. Cooperative banks’ exposure to risk is analyzed looking at aggregate risk categories. In detail, we address the questions of (a) how single risk categories can be assessed in a consistent way, (b) how the different risk categories behaved over time, (c) what factors drive the diverse risks and (d) if there are similarities of risk characteristics for specific clusters of cooperative banks. We find that credit, interest rate and residual risk have a high degree of commonality. Liquidity risk is somehow dissociated from the other categories. Nevertheless, the risk behavior appears to vary over time and for different countries. This feature is relevant for the prudent management of cooperative banks and for the assessment of systemic financial risk.
... Second such models would have a built-in facility to stress test PD estimates across the portfolio. There are few studies that have incorporated a macro-dependent hazard into the equations (Mare, 2012;Nam, Kim, Park, & Lee, 2008;Qu, 2008). In this paper we control for macro conditions, inflation and interest rate changes, over the sample period. ...
... However, as acknowledged by the same author, the direction of the relationship inflation-probability of default has not been unequivocally established due to the 'complexity of inflation's effect on the economy.' 21 Mare (2012), on the other hand, develops a failure prediction model for banks and founds that the measure of inflation employed is positively related to the probability of default. His rationale is that high inflation is rather the consequence of a generally weak macroeconomic environment, which in turn increases the number of banking crises. ...
Article
Using a sample of 23,218 company-year observations of listed companies during the period 1980–2011, the paper investigates empirically the utility of combining accounting, market-based and macro-economic data to explain corporate credit risk. The paper develops risk models for listed companies that predict financial distress and bankruptcy. The estimated models use a combination of accounting data, stock market information and proxies for changes in the macro-economic environment. The purpose is to produce models with predictive accuracy, practical value and macro dependent dynamics that have relevance for stress testing. The results show the utility of combining accounting, market and macro-economic data in financial distress prediction models for listed companies. The performance of the estimated models is benchmarked against models built using a neural network (MLP) and against Altman's (1968) original Z-score specification.
... The empirical literature on this topic has returned conflicting results. Mare 48 showed a positive and statistically significant relationship between Z Score and bank size among a sample of Italian BCCs. Similar results can be found in Chiaramonte et al. 49 and Barra and Zotti. ...
... As in Mare, 48 Chiaramonte et al., 49 and Barra and Zotti, 50 but contrary to Mare and Gramlich, 51 the coefficient of Size is positive in both the dynamic and SDPD models. Therefore, the larger the bank size of a bank, the more sound is its finances. ...
Article
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In this article we consider the effects of the inclusion of spatial dependence in the empirical model measuring small cooperative banks' risk performance. In the presence of cross-sectional dependence, spatial analysis deals with co-movement among geographical units, allowing for the evaluation of spillover effects and improving econometric models. The article makes several contributions to the literature. First, we support the hypothesis that the inclusion of spatial terms improves small bank soundness models. Second, with the Z Score used as a proxy for bank soundness, we indirectly test the impacts of relationship lending on small firms, which is a classic tool adopted by small banks to assess the creditworthiness of small firms. Third, since we control for banks' market power, we expand the literature on the relationship between bank risk and market competitive pressure. Finally, we find empirical evidence that bank size does affect the financial standing of small banks.
... Their results suggest that addbacks are positively associated with failure and that this relationship holds in cases in which the add-backs are very likely to increase a bank's total regulatory capital. Mare (2015) is focused on Italian cooperative banks using annual financial statements and a set of macroeconomic variables over the period 1993-2011 to compute the hazard rate separately for bankrupt institutions and for those subject to merger, acquisition, and voluntary closure based on the Shumway model. His results show that bank failure is better captured when we account for the state of the economy both at the national and the regional levels and that voluntary closures and acquisitions are linked to bank distress. ...
... The definition of distress in Poghosyan and Cihak (2011) relies on one of the following keywords: rescue, bailout, financial support, liquidity support, government guarantee, and distressed merger. The study of Mare (2015) defines a bank in default as one entering into special administration (i.e., conservatorship) under which the distressed bank remains alive as a going-concern entity, or compulsory liquidation which is a gone-concern action. Further, Mare states on p.34 that "distress may be resolved through a private solution (i.e., merger and acquisition), take over, bail out, or closure of the failing bank." ...
Article
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During the global financial crisis, a large number of banks worldwide either failed or received financial aid thus inflicting substantial losses on the system. We contribute to the early warning literature by constructing a dynamic competing risks hazard model that explores the joint determination of the probability of a distressed bank to face a licence withdrawal or to be bailed out. The underlying patterns of distress are analysed based on a broad range of bank-level and environmental factors. We find that institutions with inadequate capital, illiquid and risky assets, poor management, low levels of earnings and high sensitivity to market conditions have a higher probability to go bankrupt. Bailed out banks, on the other hand, face both capital and liquidity shortages, experience low earnings, and are highly exposed to market products; however, neither the managerial expertise, nor the quality of assets is relevant to the odds of bailout. We further document that large and complex banks are less likely to fail and more likely to be bailed out and also that authorities are more prone to provide support to a distressed bank, which is well-connected with politicians and political parties and less prone to let it go bankrupt. Importantly, our model outperforms the commonly used logit model in terms of forecasting power in all the in- and out-of-sample tests we conduct.
... Vyšší inflace přitom na nedokonalých úvěrových trzích omezuje regulaci zadluženosti firem (Wadhwani, 1986). Ta je nedílně spojena s nestabilitou bankovních institucí a nabídkou úvěrů (Mare, 2015. ...
... Recent examples include Arena (2005) and Davis and Karim (2008) who use logit models; Vazquez and Federico (2012) who use a probit model and Klomp and Haan (2012) who use a principal component factor approach. Scoring models have been extended in different ways: interesting developments include the incorporation of macroeconomic components (see, e.g., Kanno, 2013;Kenny, Kostka, & Masera, 2013;Koopman, Lucas, & Schwaab, 2011;Mare, 2012); and the explicit consideration of the credit portfolio, as in the symbol model of De Lisa, Zedda, Vallascas, Campolongo, and Marchesi (2011) À that allows stress tests of banking asset quality and capital, as emphasized in the recent paper by Halaj (2013). The problem with scoring models is that they are mostly based on balance sheet data, which are different from the market and has a low frequency of update (annual or, at best, quarterly) and depends on subjective strategic choices. ...
Article
Full-text available
The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.
... As in Merton's reduced form model, the main intuition behind these models is to decompose failure risk into an idiosyncratic component, that can be studied using microeconomic data, and a systematic component, that can be addressed with macroeconomic data. See, for example, the papers by Koopman, Lucas and Schwaab (2012) and, more recently, Mare (2012) and Kanno (2012) who applied this kind of models respectively, to the Italian and Japanese banking systems. An interesting, and complementary approach, is suggested in Kenny, Kostka and Masera (2013) who suggest employing economists' opinions as expert assessments of risks. ...
Technical Report
Full-text available
The recent European sovereign debt crisis clearly illustrates the importance of measuring the contagion effects of bank failures. Indeed, to better understand and monitor contagion risk, the European Central Bank is about to assume the supervision of the largest banks in each of the member states. This paper focuses precisely on the estimation of the intensity of contagion of bank failures. Using a binary spatial autoregressive model, which allows for the estimation of spatial or network interdependence with binary dependent variables (Fleming 2004; Calabrese and Elkink 2014), we estimate the contagion parameter for banks within the Eurozone, between 1996 and 2012. The paper further investigates the pattern of European bank defaults by using different specifications of the linear model, and of the interbank connectivity matrix. We provide evidence of high levels of systemic risk due to contagion.
... Scoring models have been extended in dierent ways: interesting developments include the incorporation of macroeconomic components (see e.g. [38] [45] , [34] and [36]) and the explicit consideration of the credit portfolio, as in the Symbol model of [41], that allows stress tests of banking asset quality and capital, as emphasized in the recent paper by [30]. The problem with scoring models is that they are mostly based on balance sheet data, which have, dierently from the market, a low frequency of update (annual or, at best, quarterly) and do depend on subjective strategic choices. ...
Article
Full-text available
The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian graphical Gaussian models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models, that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the on-going banking union process in Europe. From a computational viewpoint, we develop a novel Markov Chain Monte Carlo algorithm based on Bayes factor thresholding.
... As in Merton's reduced form model, the main intuition behind these models is to decompose failure risk into an idiosyncratic component, that can be studied using microeconomic data, and a systematic component, that can be addressed with macroeconomic data. See, for example, the papers by Koopman et al. (2012) and, more recently, Mare (2012) and Kanno (2013) who applied this kind of models respectively, to the Italian and Japanese banking systems. An interesting, and complementary approach, is suggested in Kenny et al. (2012) who suggest employing economists' opinions as expert assessments of risks. ...
Article
Full-text available
The paper proposes a novel model for the prediction of bank failures, on the basis of both macroeconomic and bank-specific microeconomic factors. As bank failures are rare, in the paper we apply a regression method for binary data based on extreme value theory, which turns out to be more effective than classical logistic regression models, as it better leverages the information in the tail of the default distribution. The application of this model to the occurrence of bank defaults in a highly bank dependent economy (Italy) shows that, while microeconomic factors as well as regulatory capital are significant to explain proper failures, macroeconomic factors are relevant only when failures are defined not only in terms of actual defaults but also in terms of mergers and acquisitions. In terms of predictive accuracy, the model based on extreme value theory outperforms classical logistic regression models.
... Scoring models have been extended in different ways: interesting developments include the incorporation of macroeconomic components (see e.g. Kanno, 2013;Koopman, Lucas, and Schwaab, 2011;Mare, 2012 andKenny, Kostka, andMasera, 2013) and the explicit consideration of the credit portfolio, as in the Symbol model of De Lisa, Zedda, Vallascas, Campolongo, and Marchesi (2011), that allows stress tests of banking asset quality and capital, as emphasized by Halaj (2013). Recent extensions are aimed at overcoming the rare nature of bank defaults: noticeable examples include Betz, Oprica, Peltonen, and Sarlin (2014) and Calabrese and Giudici (to appear). ...
Article
Financial network models are a useful tool to model interconnectedness and systemic risks in banking and finance. Recently, graphical Gaussian models have been shown to improve the estimation of network models and, consequently, the interpretation of systemic risks. This paper provides a novel graphical Gaussian model to estimate systemic risks. The model is characterised by two main innovations, with respect to the recent literature: it estimates risks considering jointly market data and balance sheet data, in an integrated perspective; it decomposes the conditional dependencies between financial institutions into correlations between countries and correlations between institutions, within countries. The model has been applied to study systemic risks among the largest European banks, with the aim of identifying central institutions, more subject to contagion or, conversely, whose failure could result in further distress or breakdowns in the whole system. The results show that, in the transmission of systemic risk, there is a strong country effect, that reflects the weakness or the strength of the underlying economies. Besides the country effect, the most central banks are those larger in size.
... They reported that LLR is influenced positively with inefficiency and bank size, while negatively with capital adequacy ratio and loans to total assets ratio. Mare (2012) identified factors that are responsible for the bankruptcy of small banks. His research focused on PD of 434 Italian cooperative banks in 20 Italian regions for the period 1993-2011. ...
Article
Full-text available
The article focuses on the credit risk of cooperative banks in Greece. The main objective is to define which factors are responsible for variations in loan quality during the period 2003-2014. Loan quality is measured by Loan Loss Reserves Ratio (LLR) and dynamic regression techniques are implemented for the econometric estimations. The outlined results suggest that the macroeconomic environment (i.e. public debt, local unemployment, economic activity and inflation) and the accounting ratios (i.e. past loan quality and profitability) seem to be the explanatory variables of problem loans.
... Other authors analyze the combination of bank-specific and macro factors as determinants of bank failures (e.g. Gonzalez-Hermosillo, 1999;Mare, 2015). ...
... It was found that by taking macroeconomic variables the accuracy level of estimating potential default increased significantly. Mare (2015) analyzed the impact of economic conditions on the small bank failure in Italy by taking variables like interbank deposit rate, region unemployment. The result found increase in failure risk of banks with deteriorating economic conditions. ...
Article
Full-text available
The worldwide financial crises highlighted the serious deficiencies in risk models used for credit risk management. Previous studies show a trend of changes in the choices of variables in the default prediction models. Initially researchers have used only accounting variables while framing various models but now several other factors within and outside organizational variables such as market and macroeconomic variables also being taken into consideration for designing various models. Much of the previous work in financial distress prediction focuses on static predictions and uses static variables in estimating the predictive model. Therefore, there is a need of the models which can make dynamic predictions with the use of dynamic variables. This advance warning will help management to take appropriate steps and decisions to avoid financial distress which will help to mitigate the cost associated with financial distress and resulting business failure.
... Using CAMEL factors (Capital adequacy, Asset quality, Management, Earnings performance and Liquidity) from the bank failure literature, we examine the characteristics of credit unions that failed between 2003 and 2015. Recent studies evaluating the determinants of distress in institutions with mutual ownership structures like that of the credit unions evaluated in this paper employ similar techniques and find that such CAMEL factors are helpful in characterizing distress in these firms ( Fiordelisi and Mare, 2013;Mare, 2015;Francis, 2014). We extend these analyses to consider recent work on policymaker preferences (i.e., relative aversion with respect to missed 'failures' and 'false alarms') to help in evaluating the performance of our failure model. ...
Article
Full-text available
Using regulatory data on credit unions, this paper provides empirical evidence on the determinants of credit union failure in the United Kingdom. We find that a small set of financial attributes related to capital adequacy, asset quality, earnings performance and liquidity is useful for early identification of troubled credit unions. In and out-of-sample results indicate that this parsimonious set of firm-level characteristics, augmented with national and regional unemployment rates, reliably identifies failures while keeping false alarm rates at modest levels. The results provide support for establishing early warning criteria for supervisory use in monitoring credit unions.
... A difficult situation can lead to poor structure of sales, a level of sales prices inconsistent with the market and therefore, insufficient profitability (Liou and Smith, 2007). The interest for a better understanding of the mechanisms of failure is often accentuated in times of economic recession (Lev, 1974;Delion, 2008;Blot and Le Bayon, 2009;Mare, 2012). Mensh (1984) shows that the increase in failure during an economic recession is linked to factors external to the company. ...
Article
Full-text available
A number of authors suggested that the impact of the macroeconomic factors on the incidence of the financial distress, and afterward in case of failure of companies. However, macroeconomic factors rarely, if ever, appear as variables in predictive models that seek to identify distress and failure; modellers generally suggest that the impact of macroeconomic factors has already been taken into account by financial ratio variables. This article presents a systematic study of this domain, by examining the link between the failure of companies and macroeconomic factors for the French companies to identify the most important variables and to estimate their utility in a predictive context. The results of the study suggest that several macroeconomic variables are strictly associated to the failure, and have a predictive value by specifying the relation between the financial distress and the failure.
... We use return on assets (ROA -net incometo-total assets) to proxy bank profitability. Whilst, less profitable banks face incentives to take risks in an attempt to boost profitability (Mare, 2015;Poghosyan and Čihak, 2011), profitable banks could use their resources to increase risky lending. We proxy credit risk using the ratio of nonperforming loans-to-gross loans (NPLs). ...
Preprint
We offer early evidence on how negative interest rate policy affects bank risk-taking. We identify a dichotomy between monetary policy and prudential regulation. Our primary result suggests NIRP produced an unintended outcome, which we measure as a 10 per cent reduction in banks' holdings of risky assets. It infers that banks deleverage their balance sheets and invest in safer, liquid assets to meet new and binding capital and liquidity requirements. We find risk-taking behaviour is sensitive to capitalisation and banks with stronger capital ratios take more risks. Similarly, tighter prudential requirements could inadvertently retard economic growth should poorly capitalised banks reduce investment in riskier assets in favour of zero risk-weighted assets, such as, sovereign bonds to comply with risk-based capital requirements. Risk-taking is greater in less competitive markets because stronger market power insulates net interest margins and profitability. We obtain our results from a sample of 2,371 banks from 33 OECD countries between 2012 and 2016, and a difference-indifferences framework.
... Another macroeconomic indicator that is included in the final model is Inflation. A high inflation rate increases the prices of goods and services, thereby leading to an increase in the number of firms that end up in financial difficulties (for further details, see Demirgüç-Kunt and Detragiache (1998); Rinaldi and Sanchis-Arellano (2006); Mare (2015), andĆurak et al. (2013)). ...
Article
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Any critical analysis of the corporate financial distress of listed firms in international exchange would be incomplete without a serious dissection at the industry level, because of the different levels of risks concerned. This paper considers the financial distress of listed firms at the industry level in Vietnam over the last decade. Two periods are considered, namely during the Global Financial Crisis (GFC) (2007–2009) and post-GFC (2010–2017). The logit regression technique is used to estimate alternative models based on accounting and market factors. The paper also extends the analysis to include selected macroeconomic factors that are expected to affect the corporate financial distress of listed firms at the industry level in Vietnam. The empirical findings confirm that the corporate financial distress prediction model, which includes accounting factors with macroeconomic indicators, performs much better than alternative models. In addition, the evidence confirms that the GFC had a damaging impact on each sector, with the Health & Education sector demonstrating the most impressive recovery post-GFC, and the Utilities sector recording a dramatic increase in bankruptcies post-GFC.
... The regressions also include a measure of profitability (Profitability). On the one hand, less profitable banks face incentives to take risks in an attempt to boost profitability (Mare, 2015;Poghosyan and Čihak, 2011). On the other hand, profitable banks could use their resources to increase risky lending. ...
Article
Full-text available
This paper analyses the impact of the Banking Union on European bank credit risk. Specifically, we investigate the effect that the establishment of the Single Supervisory Mechanism has had on the credit risk of the banks it supervises in comparison to financial institutions that are still supervised by National Supervisory Authorities. We analyse a sample of 746 European banks over the period 2011-2018, by means of a difference-in-differences methodology. We provide empirical evidence that Single Supervisory Mechanism supervised banks reduced credit risk exposure compared to banks supervised by National Supervisory Authorities, suggesting that the Banking Union has successfully reduced the riskiness of the European banking sector. Our results passed a battery of robustness tests that support the reliability of our analysis. Our contribution sheds light on the benefits of centralised versus decentralised supervision, on the effectiveness of the current supervisory system in Europe, and on its impact on European bank risk.
... Non-financial variables, such as firms' strategies, corporate governance, and macroeconomic indicators, which are not embodied in accounting data can exert significant influence in predicting the risk of failure. In turn, various authors have investigated the usefulness of models that, while based on financial ratios, also include complementary non-financial information (Brédart, Séverin, & Veganzones, 2021;Ciampi, 2015;Mare, 2015). Such non-financial variables, particularly those related to firms' characteristics, are difficult to gather though and can rather be inefficient, such that the marginal gain in accuracy obtained by inclusion is minimal compared with the marginal cost of collecting the information. ...
Article
This paper studies the relationship between corporate failure forecasting and earnings management variables. Using a new threshold model approach that separates samples into different regimes according to a threshold variable, the authors examine regimes to evaluate the prediction capacities of earnings management variables. By proposing this threshold model and applying it innovatively, this research reveals boundaries within which earnings management variables can yield superior corporate failure forecasting. The inclusion of earnings management variables in corporate failure models improves failure prediction capacities for firms that manipulate substantial earnings. Furthermore, an accruals-based variable improves predictions of failed firms, but the real activities-based variable improves predictions of non-failed firms. These findings highlight the importance of indicators of the magnitude of earnings management and the tools used to improve the performance of corporate failure models. The proposed model can determine the predictive power of particular explanatory variables to forecast corporate failure.
... The European Union (EU), or the group of developed markets in Europe, is well-researched (e.g., Betz et al., 2014;Calabrese & Giudici, 2015;Mare, 2015;Westgaard & der Wijst, 2001), as are other developed markets (e.g., Vazquez & Federico, 2015;Wang et al., 2019). On the other hand, emerging markets are much less covered, potentially because data are not readily available. ...
Article
Bank survival is essential to economic growth and development because banks mediate the financing of the economy. A bank's overall condition is often assessed by a supervisory rating system called CAMELS, an acronym for the components Capital adequacy, Asset quality, Management quality, Earnings, Liquidity, and Sensitivity to market risk. Estimates of the impact of CAMELS components on bank survival vary widely. We perform a metasynthesis and metaregression analysis (MRA) using 2120 estimates collected from 50 studies. In the MRA, we account for uncertainty in moderator selection by employing Bayesian model averaging. The results of the synthesis indicate an economically negligible impact of CAMELS variables on bank survival; in addition, the effect of bank‐specific, (macro)economic, and market factors is virtually absent. The results of the heterogeneity analysis and publication bias analysis are consistent in terms that they do not find an economically significant impact of the CAMELS variables. Moreover, best practice estimates show a small economic impact of CAMELS components and no impact of other factors. The study concludes that caution should be exercised when using CAMELS rating to predict bank survival or failure.
... For instance, Berger, Imbierowicz, and Rauch (2016) find that bank failures are strongly influenced by ownership structure and Jin, Kanagaretnam, and Lobo (2011) conclude that audit quality variables are also important. Other factors that have been reported to play a role, include macroeconomic conditions ( Mare, 2015 ), banking regulation ( Acharya, 2009 ), the conditions in the banking and financial markets ( Avino, Conlon, & Cotter, 2019 ;Sun, Wu, & Zhao, 2018 ), and political factors ( Liu & Ngo, 2014 ), among others. ...
Article
Bank failure prediction models usually combine financial attributes through binary classification approaches. In this study we extend this standard framework in three main directions. First, we explore the predictive power of attributes that describe the diversification of banking operations. Second, we consider the prediction of failure in a multi-period context. Finally, an enhanced ordinal classification framework is introduced, which considers multiple instances of failed banks prior to failure (up to three years prior to bankruptcy). Various ordinal models are developed using techniques from multiple criteria decision analysis, statistics, and machine learning. Moreover, ensemble models based on error-correcting output codes are examined. The analysis is based on a sample consisting of approximately 60,000 observations for banks in the United States over the period 2006-2015. The results show that diversification attributes improve the predictive power of bank failure prediction models, mainly for mid to long-term prediction horizons. Moreover, ordinal classification models provide a better description of the state of the banks prior to failure and are competitive to standard binary classification models.
... These authors also express doubt about the capacity of models to predict failure with evidence only from financial ratios, noting the many possible causes of bankruptcy. Some subsequent studies have investigated complementary factors, such as market (Doumpos et al., 2017;Tian et al., 2015), corporate governance (Ciampi, 2015;Liang et al., 2016), DEA efficiency (Ouenniche & Tone, 2017;Tsolas, 2015), and economic (Fabling & Grimes, 2005;Mare, 2015) variables. The inclusion of such variables provides greater discriminatory power and improves model accuracy, but they tend to be difficult to gather or are available only for specific firms. ...
Article
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This study investigates whether earnings management in its two forms (accruals and real activities manipulation) can improve bankruptcy prediction models. In particular, it examines whether special information extracted from earnings management, including potential manipulations of the reported earnings found in financial statements, might improve the accuracy of bankruptcy prediction models. It applies earnings management–based models, based on financial ratios and earnings management variables, to a sample of 6,000 French small and medium-size enterprises, then compares the classification rates obtained by these models with a model based solely on financial ratios. This study thus makes several contributions by (1) investigating novel predictors, accruals, and real activities manipulation variables, in the context of bankruptcy prediction modeling; (2) enabling analyses of the contribution of earnings management–based variables, in the form of static and dynamic indicators, to model performance; (3) revealing the influence of these variables on the forecasting horizon of bankruptcy prediction models (one- to three-year horizon); and (4) establishing that earnings management information provides a complementary explanatory variable for enhancing model performance.
... High unemployment rate affects productivity and economic growth. The inclusion of this variable is because unemployment is related to the degree of solvency or hazard which measure the stability of the banking sector in a country (Charpe and Flaschel, 2013;Mare, 2015). In the other viewpoint, unemployment caused lower rate of non-performing loans as people who are unemployed definitely could not borrow (Meng, Hoang, & Siriwardana, 2013) and they do not contribute to the instability of the bank. ...
Article
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This study investigates the effect of fertility on financial stability and its determinant particularly therelevance of demographic changes. This is motivated by the huge impact of demographic changes(increasing ageing population and low fertility level). Population ageing and low fertility tend to lowerboth labour- force participation and saving rates (change bank business model), thereby raising concernson a future slowing economic growth and financial instability. The system GMM results show that thefertility level somehow acts as a buffer and reflects to the degree of stability to the financial system. Anincrease in fertility and old-age population will contribute to lowering the financial stability. As a matterof policy implication, the nations, financial sectors, and economies should take pro-active active stepsand enhance policies in handling the inter-related issue of the ageing population, decreasing fertility, andfinancial stability especially in developed countries, but not necessarily to overlook the impact of theissues in developing countries. Keywords: Demographic change, old-age population, fertility, financial stability
... Owing to the bank has typical procyclical characteristics, that is, the credit of the banking industry and macroeconomic conditions have similar trends. e empirical research by Mare [24] also shows the necessity of considering macroeconomics when studying the risk of the banking system. Furthermore, Nickell et al. [25] and Pesola [26] have found that macroeconomic fluctuations are closely related to the credit risk of the banking system. ...
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The stability of banking system has caused wide concerns since the global financial crisis. The present paper focuses on studying the stability of a banking network system in the case that the banking network system suffers the dynamic macroeconomic fluctuation shocks. We firstly construct a banking network system with a scale-free structure, then let the banking network system suffer the dynamic macroeconomic fluctuation (here, we consider three kinds of situations; the macro economy fluctuates in downward, upward, and random trends, respectively). Then, we study the stability of the banking network system under each situation. Firstly, the results show that the scale-free topology has an important effect on the stability of the banking network system at each macroeconomic fluctuation situation. Secondly, the investment payback period and the ratio of investment to deposit have a large effect on the stability of the banking network system in the cases of dynamic macroeconomic fluctuation. In addition, our further studies find that there is an optimal ratio of the investment to deposit under various macroeconomic fluctuation scenarios, which divides the banking system into stable and unstable regions. Finally, we discuss the regulation strategies for banks, which may provide decision supports for the relevant regulatory authority.
... Finally, economic variables that reflect macroeconomic factors also have been contemplated, due to their potential influence on the accuracy of predictive models. Mare (2015) shows that variations in economic cycles relate positively to failure probability. Nonetheless, the gain in accuracy obtained by including these variables may be minimal. ...
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This article studies bank failures in twenty-one emerging market countries in the 1990s. By using a competing risk hazard model for bank survival, we show that a government is less likely to take over or close a failing bank if the banking system is weak. This Too-Many-to-Fail effect is robust to controlling for macroeconomic factors, financial crises, the Too-Big-to-Fail effect, domestic financial development, and concerns due to systemic risk and information spillovers. The article also shows that the Too-Many-to-Fail effect is stronger for larger banks and when there is a large government budget deficit. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.
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In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks and forecasted asset return correlations. Importantly, using realized correlations estimated from high-frequency equity return data can significantly improve the accuracy of forecasted correlations. Our stress testing methodology, using an integrated micro–macro model, takes into account dynamic linkages between the health of major US banks and macro-financial conditions. Our results suggest that the theoretical insurance premium that would be charged to protect against losses that equal or exceed 15% of total liabilities of 12 major US financial firms stood at $110 billion in March 2008 and had a projected upper bound of $250 billion in July 2008.
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On-site examinations are regulators' primary tool for monitoring the financial condition of federally insured depository institutions. In this paper, we assess the speed with which the information content of the supervisory rating assigned during bank exams - the CAMEL rating - decays. This is an important issue because cost and regulatory burden considerations often cause CAMEL ratings to be assigned relatively infrequently. As a benchmark for information content, we use econometric forecasts of bank failures generated by applying a probit model to publicly available accounting data. When compared with all CAMEL ratings available at a given point in time, the econometric forecasts provide a more accurate indication of failure. Further analysis reveals that this overall finding reflects the tendency for a CAMEL rating's information content to deteriorate noticeably beginning in the second or third quarter after the rating initially was assigned.
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There has been a notable debate in the banking literature on the impact of bank competition on financial stability. While the dominant view sees a detrimental impact of competition on the stability of banks, this view has recently been challenged by Boyd and De Nicolo (2005) who see the reverse effect. The aim of this paper is to contribute to this literature by providing the first empirical investigation of the role of bank competition on the occurrence of bank failures. We analyze this issue based on a large sample of Russian banks over the period 2001-2007 and in line with the previous literature we employ the Lerner index as the metric of bank competition. Our findings clearly support the view that tighter bank competition enhances the occurrence of bank failures. The normative implication of our findings is therefore that measures that increase bank competition could undermine financial stability.
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Cooperative banks are small credit institutions, and they are more likely than commercial banks to default in periods of financial stability. Focusing on Italy (one of the largest cooperative banking markets), we analyze the contribution of efficiency to the estimation of the probability of default of cooperative banks. We estimate several measures of bank efficiency, and we run a discrete-time survival model to determine whether different managerial abilities play different roles in predicting bank failures. We show that higher efficiency levels (both in cost minimization and revenue and profit maximization) have a positive and statistically significant link with the probability of survival of cooperative banks. We also find that capital adequacy reduces the probability of default, supporting the view that higher capital buffers provide additional loss absorbency and reduce moral hazard problems.
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The financial crisis and recession that began in 2007 brought a sharp increase in the number of bank failures in the United States. This article investigates characteristics of banks that failed and regional patterns in bank failure rates during 2007-10. The article compares the recent experience with that of 1987-92, when the United States last experienced a high number of bank failures. As during the 1987-92 and prior episodes, bank failures during 2007-10 were concentrated in regions of the country that experienced the most serious distress in real estate markets and the largest declines in economic activity. Although most legal restrictions on branch banking were eliminated in the 1990s, the authors find that many banks continue to operate in a small number of markets and are vulnerable to localized economic shocks.
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The paper develops an early-warning model for predicting vulnerabilities leading to distress in European banks using both bank and country-level data. As outright bank failures have been rare in Europe, we introduce a novel dataset that complements bankruptcies and defaults with state interventions and mergers in distress. The signals of the early-warning model are calibrated not only according to the policymaker’s preferences between type I and II errors, but also to take into account the potential systemic relevance of each individual financial institution. The key findings of the paper are that complementing bank-specific vulnerabilities with indicators for macro-financial imbalances improves model performance and yields useful out-of-sample predictions of bank distress during the current financial crisis.
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There is general agreement among bank researchers that revenues from fee-based activities are more volatile than revenues from more traditional interest-based (loans and deposits) activities. We test whether noninterest income was a determining factor in the hundreds of U.S. commercial bank failures during the financial crisis. We separate noninterest income into three categories: fee income from traditional banking activities like deposit accounts and lines of credit; fee-for-service income from nontraditional activities like brokerage and insurance, and stakeholder income from nontraditional activities that require banks to make asset investments. We find that fee-for-service income significantly and substantially reduced the probability that healthy banks failed or became financially distressed, while stakeholder income significantly and substantially increased the probability that distressed banks failed. These results indicate that the risk-return characteristics of noninterest income vary in idiosyncratic ways that have not been recognized or accounted for in prior research. Our results also suggest that bank capital charges should be larger, and supervisory responses at distressed banks should be prompter, for banks that engage in what we define here as stakeholder activities.
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This paper empirically analyzes the contribution of microeconomic and macroeconomic factors in five recent episodes of banking system problems in the U.S. Southwest (1986-92), Northeast (1991-92), and California (1992-93); Mexico (1994-95); and Colombia (1982-87). The paper finds that a low capital equity and reserve coverage of problem loans ratio is a leading indicator of bank distress, signaling a high likelihood of near-term failure. Distress is shown to be a function of the same fundamental macro-micro sources of risk that determine bank failures. Focusing on distress has the advantage that the fragility of the banking system can be assessed before a crisis actually occurs.
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The idea that regional economic performance affects bank health is intuitive and broadly consistent with the aggregate banking data. That said, micro-level research on this relationship provides a mixed picture of the importance, size, and timing of regional variables for bank performance. This paper helps rec oncile the heterogeneous findings of previous research by: (1) employing a unique "composite measure" of regional economic performance that combines several regional indicators into a single index; (2) constructing bank-specific measures of regional economic conditions, based on bank deposit shares, that account for banks' presence in several states; and (3) estimating models for all banks and intra- and inter- state banks separately. Empirical results based on this ba nk-specific composite regional measure point to a tractable link between regional economic performance and bank health. The importance of regional variables holds for both intra- and inter-s tate banks. Out-of- sample forecasts indicate that the composite index also helps tie down the relative riskiness of bank portfolios across states. Finally, although interstate banks do seem to diversify away some of their portfolio risk, our analysis sugge sts it is too soon to c onclude that interstate banks are immune from regional influences.
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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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I argue that hazard models are more appropriate than single-period models for forecasting bankruptcy. Single-period models are inconsistent, while hazard models produce consistent estimates. I describe a simple technique for estimating a discrete-time hazard model. I find that about half of the accounting ratios that have been used in previous models are not statistically significant. Moreover, market size, past stock returns, and idiosyncratic returns variability are all strongly related to bankruptcy. I propose a model that uses both accounting ratios and market-driven variables to produce out-of-sample forecasts that are more accurate than those of alternative models.
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This paper provides a selected review of a large number of empirical studies on the relationship between business cycle and bank stability, both from a micro and an aggregate perspective. While not exhaustive, it tries to identify the common patterns of bank fragility, considering the evidence arising from works on banking sector crises, early warning systems and procyclicality of banks' behaviour. All these studies, even if starting from different points of view, have made it clear that the analysis of macroeconomic variables is of some help for banking supervisors in order to fully assess banks' health.
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The purpose of this study is to present the Cox proportional hazards model and to apply this model to the prediction of bank failures. The Cox model, which has been used extensively in biomedical applications, has not been previously employed in the finance literature. The principal advantage of the Cox model over other classification techniques is that it models the expected time to failure. Results of the study indicate that total classification accuracy of the Cox model is similar to that of discriminant analysis, although the Cox model produces somewhat lower type I errors. In a comparison of actual and predicted times to failure, the Cox model tends to identify bankruptcies prior to the actual failure date.
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The recent period of crisis in credit markets has highlighted the crucial role of bank risk taking. Our paper assesses the inter-temporal relationships among bank efficiency, capital and bank risk-taking in the EU-26 commercial banking industry between 1995 and 2007. Our results support the bad management-, luck-, cost and revenue skimping hypotheses. Overall, our paper provides evidence that higher performance (enhanced efficiency) has been not related to higher managerial skills, rather to cost and revenue skimping and bad management.
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This paper tests empirically the proposition that bank fragility is determined by bank-specific factors, macroeconomic conditions, and potential contagion effects. The methodology allows the variables that determine bank failure to differ from those that influence banks' time to failure (or survival rate). Based on the indicators of fragility of individual banks, we construct an index of fragility for the banking system. The framework is applied to the Mexican financial crisis that began in 1994. For Mexico, bank-specific variables and contagion effects explain the likelihood, whereas macroeconomic variables largely determine the timing, of bank failure.
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We assess the inter-temporal relationship between bank efficiency, capital and risk in a sample of European commercial banks employing several definitions of efficiency, risk and capital and using the Granger-causality methodology in a panel data framework. Our results suggest that lower bank efficiency with respect to costs and revenues Granger-causes higher bank risk and that increases in bank capital precede cost efficiency improvements. We also find that more efficient banks eventually become better capitalized and that higher capital levels tend to have a positive effect on efficiency levels. These results are generally confirmed by a series of robustness tests. The results have potentially important implications for bank prudential supervision and underline the importance of attaining long-term efficiency gains to support financial stability objectives.
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The recent crisis highlighted, once again, the importance of early warning models to assess the soundness of individual banks. In the present study, we use six quantitative techniques originating from various disciplines to classify banks in three groups. The first group includes very strong and strong banks; the second one includes adequate banks, while the third group includes banks with weaknesses or serious problems. We compare models developed with financial variables only, with models that incorporate additional information in relation to the regulatory environment, institutional development, and macroeconomic conditions. The accuracy of classification of the models that include only financial variables is rather poor. We observe a substantial improvement in accuracy when we consider the country-level variables, with five out of the six models achieving out-of-sample classification accuracy above 70% on average. The models developed with multi-criteria decision aid and artificial neural networks achieve the highest accuracies. We also explore the development of stacked models that combine the predictions of the individual models at a higher level. While the stacked models outperform the corresponding individual models in most cases, we found no evidence that the best stacked model can outperform the best individual model.
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Outright bank failures without prior indication of financial instability are very rare. In fact, banks can be regarded as troubled to varying degrees before outright closure. But failure studies usually neglect the ordinal nature of bank distress. We distinguish four different kinds of increasingly severe events on the basis of the distress database of the Deutsche Bundesbank. Only the worst distress event entails a bank to exit the market. Since the four categories of hazard functions are not proportional, we specify a generalized ordered logit model to estimate respective probabilities of distress simultaneously. We find that the likelihood of ordered distress events changes differently in response to given changes in the financial profiles of banks. Consequently, bank failure studies should account more explicitly for the different shades of distress. This allows an assessment of the relative importance of financial profile components for different degrees of bank distress.
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Motivated by the liberalisation and harmonisation of financial systems in Europe, we investigate whether the observed shift into non-interest income activities improves performance of small European credit institutions. Using a sample of 755 small banks for the period 1997–2003, we find no direct diversification benefits within and across business lines and an inverse association between non-interest income and bank performance. Our findings are robust to a set of sensitivity analyses using alternative samples and controlling for the regulatory environment. Furthermore, the results provide circumstantial evidence for the presence of economies of scale. The absence of benefits of diversification confirms findings for other banking markets and suggests small European banks enter lines of business where they currently lack expertise and experience. These results have implications for bank supervisors, regulators and bank managers.
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This paper develops and tests logit and discriminant models that could aid regulatory agencies, as well as bank examiners, investors, analysts and others in identifying the potential failures in the banking industry. As a secondary objective, the paper assesses the comparative abilities of logit and discriminant analyses in distinguishing failed from non-failed banks. The models are validated by two alternate means. Classification accuracies and validation tests indicate that the logit and discriminant models developed are useful in predicting potential failures, and that their weighted efficiencies compare favorably to models developed in the past.
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This paper considers the joint role of macroeconomic, structural and bank-specific factors in explaining the occurrence of banking problems in the nineteen Eastern European transition countries over the last decade. With data at the individual bank level we show, using a discrete time survival model, that all three factors interact in their impact and have a rich dynamic profile, which underlines the highly volatile cycles challenging the stability of banks in this region. A fragile funding basis accompanied by high exposure to market risk in an environment of reforms and macroeconomic disturbances is the typical precursor of bank distress.
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Despite the extensive literature on prediction of banking crises by Early Warning Systems (EWSs), their practical use by policy makers is limited, even in the international financial institutions. This is a paradox since the changing nature of banking risks as more economies liberalise and develop their financial systems, as well as ongoing innovation, makes the use of EWS for informing policies aimed at preventing crises more necessary than ever. In this context, we assess the logit and signal extraction EWS for banking crises on a comprehensive common dataset. We suggest that logit is the most appropriate approach for global EWS and signal extraction for country-specific EWS. Furthermore, it is important to consider the policy maker's objectives when designing predictive models and setting related thresholds since there is a sharp trade-off between correctly calling crises and false alarms.
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The number of US community banks is falling rapidly. Is this reduction being driven in part by banks’ desire to geographically diversify to reduce their vulnerability to local economic shocks? A comparison of the performance of banks in counties that suffered economic shocks in the 1990s with similar banks in counties that did not suffer economic shocks shows that banks withstand local economic shocks quite well. This result suggests that the geographic concentration risk that community banks must bear to focus on relationship lending is small and is not an important factor contributing to the decline of community banks.
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The paper examines the informational content of market data for long-term horizons in models, which predict bank failure. Univariate results document patterns such as declining prices, negative returns, declining dividends, and rising return volatility, up to 4 years before failure. Multivariate analysis shows that market information improves the failure predictive content of traditional models, which are based on accounting data. Out-of-sample predictions show that the use of stock market data does improve the forecast of bank failure. Furthermore, the persistence of this contribution generally increases with greater distances from the date of failure documenting the forward-looking nature of financial markets.
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Historically, unusually strong increases in credit and asset prices have tended to precede banking crises. Could the current crisis have been anticipated by exploiting this relationship? We explore this question by assessing the out-of-sample performance of leading indicators of banking system distress developed in previous work, also extended to incorporate explicitly property prices. We find that they are fairly successful in providing a signal for several banking systems currently in distress, including that of the United States. We also consider the complications that arise in calibrating the indicators as a result of cross-border exposures, so prominent in the current episode.
The recent financial crisis has highlighted once more that interconnectedness in the financial system is a major source of systemic risk. I suggest a practical way to levy regulatory capital charges based on the degree of interconnectedness among financial institutions. Namely, the charges are based on the institution’s incremental contribution to systemic risk. The imposition of such capital charges could go a long way towards internalizing the negative externalities associated with too-connected-to-fail institutions and providing managerial incentives to strengthen an institution’s solvency position, and avoid too much homogeneity and excessive reliance on the same counterparties in the financial industry.
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I argue that hazard models are more appropriate than single-period models for forecasting bankruptcy. Single-period models are inconsistent, while hazard models produce consistent estimates. I describe a simple technique for estimating a discrete-time hazard model. I find that about half of the accounting ratios that have been used in previous models are not statistically significant. Moreover, market size, past stock returns, and idiosyncratic returns variability are all strongly related to bankruptcy. I propose a model that uses both accounting ratios and market-driven variables to produce out-of-sample forecasts that are more accurate than those of alternative models. Copyright 2001 by University of Chicago Press.
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This paper studies the features of de novo Co-operative Credit Banks (CCBs) established in Italy during the 1990s. It shows that de novo CCBs in the start-up period are endowed with a higher default risk than long-run incumbent CCBs. Split-population duration models distiguish the determinants of duration and probability of default. The focus is on those determinants related to market structure. We find that duration is positively related to the market share of large banks. Conversely, duration is higher when there are no incumbent CCBs in the same market. Survival probability is directly related to the local level of GDP.
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A potentially troubling characteristic of the U.S. banking industry is the geographic concentration of many community banks* offices and operations. If geographic concentration of operations exposes banks to local market risk, we should observe a widespread decline in their financial performance following adverse local economic shocks. In addition, geographic diversification should help banks reduce risk significantly. By analyzing the performance of geographically concentrated U.S. community banks exposed to severe unemployment shocks in the 1990s, I find that banks are not systematically vulnerable to local economic deterioration. Indeed, differences in performance at banks in counties that suffered economic shocks relative to those that did not suffer economic shocks are either statistically insignificant or economically small. These findings suggest that banks are unlikely to engage in mergers and acquisitions primarily to reduce local market risk because that risk source is already low. This result bodes well for the continued existence of geographically concentrated community banks, though scale and scope economies will continue to reduce their numbers relative to larger banks.
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An explanation of how a Cox proportional hazards model can be used to identify both failed and healthy banks with a high degree of accuracy using a relatively small set of publicly available data.
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This paper explores a new approach to early warning systems for commercial banks. Factor analysis and logit estimation are used to measure the condition of individual institutions and to assign each of them a probability of being a problem bank. The model employs widely used financial ratios and information taken from bank examinations. The factors produced by the model for use in the logit estimation are very similar to the CAMEL rating system used by bank examiners. Empirical results show that the combination of factor analysis and logit estimation is a promising method of evaluating bank condition.
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This paper uses bank-level data from recent banking crises in East Asia and Latin America to address the following two questions: (1) To what extent did individual bank conditions explain the failures (2) In terms of their fundamentals, was it mainly the weak banks ex ante that failed in the crisis countries The results show that for the two regions, bank-level fundamentals significantly affect the likelihood of collapse for these banks. Systemic shocks (both macroeconomic and liquidity) that triggered the crises mainly destabilized the weak banks ex ante, particularly in East Asia, which raises questions about the role that regional differences play for the degree of banking sector resilience to systemic shocks in the financial and macroeconomic environment.