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In this paper, we attempt to study the effectiveness of various simple machine learning methods in the prediction of bank failures. From a raw dataset of 10,938 US banks during the period of 2000–2020, we find that machine learning approaches do not really outperform the benchmark of conventional statistical method, logistic regression. However, using PCA to retain relevant variance in variables significantly improve the performance of machine learning methods and raise the out-of-sample accuracy of those method to over 70% to over 80%. Of all the machine learning methods used in this paper, the simple KNN seems to be the best model in forecasting bank failure in the United States. [ABSTRACT FROM AUTHOR]

Utilizing a simple time‐varying hazard model, we incorporate nationwide and state‐level economic variables with banking‐industry and bank‐level data to examine U.S. bank failures during 1977–2019. We find that bank‐level financial conditions are more essential in predicting bank failure, although macro factors affect the failure likelihood of vulnerable banks. We also find that banking‐industry market variables are significant predictors. Unlike bank systemic funding cost whose predictive power is subsumed in the presence of macroeconomic variables, banking‐industry market performance has a significantly independent predictive power on bank failure. This finding is novel to existing literature of bank‐failure forecast and has important policy implications. [ABSTRACT FROM AUTHOR]

This study investigates the predictors of financial distress of banks in Sub-Saharan Africa. Specifically, we examine the relationship between bank financial distress and the 5Cs (i.e., Character, Capacity, Capital, Condition, and Collateral). We use logistic regression and panel data from 228 listed and non-listed Sub-Sahara Africa Banks over the period 2006 to 2016 to test the hypotheses. We find that the rating measures of capacity (cost to income), capital (leverage), and condition (loan loss reserves to gross loan and inflation) positively affect the financial distress of the banks in Sub-Saharan Africa. Control of corruption decreases the probability of financial distress; however, the collateral and character indicators do not predict the financial distress of the banks. This study adds to the debate on how Character, Capacity, Capital, Condition, and Collateral affect bank financial distress in Sub-Saharan Africa, a region with high bank insolvency but research remains scant. [ABSTRACT FROM AUTHOR]

The unexpected collapse of Silicon Valley Bank (SVB) on March 10, 2023, sent shockwaves through the tech industry. SVB, a long-standing financial institution serving tech startups for nearly four decades, suddenly shifted from solvency to insolvency within 48 hours, marking the second-largest bank failure in U.S. history. This event provides an excellent opportunity to test the Efficient Market Hypothesis (EMH), particularly its semi-strong form, which posits that rapid information absorption prevents significant stock price gains in response to new information. This research uses the standard event study methodology in the finance literature to investigate SVB's collapse and its impact on the stock prices of 30 banks traded on the New York Stock Exchange. The study aims to discern whether stock returns exhibited reactions prior to, on, or after the public announcement of SVB's failure, thereby assessing market efficiency. Using historical stock and S&P 500 index data, the study analyzes holding period returns, performs regression analyses for pre-event periods, and calculates average excess returns. Results indicate statistically significant negative impacts on stock prices surrounding and on the event date. Furthermore, consistent with behavioral finance theory, a decline in adjusted stock prices approximately 7 days before the event suggests anticipatory market behavior, in line with semi-strong market efficiency. SVB's case emphasizes the role of external factors, regulatory changes, and industry concentrations in shaping market responses. This research contributes empirical evidence to the discourse on market efficiency, highlighting the need for a nuanced understanding of market behaviors during crises. Lessons from SVB's collapse will inform regulatory and risk management strategies, impacting future discussions on market efficiency. Likewise, the study results support the semi-strong form efficient market hypothesis and suggest the possibility of trading on this information up to 7 days prior to the announcement consistent with the behavioral finance literature (Bacon & Howell 2021). This study provides valuable insights into market dynamics during unprecedented events, influencing future discussions on regulation, risk management, and market efficiency. [ABSTRACT FROM AUTHOR]

Several empirical studies have identified unique characteristics of banks that subsequently failed during the Great Financial Crisis. The notion is that by identifying these risk characteristics we are better able to monitor and regulate the risks to banks during the next crisis. A concern is bank failure is a relatively rare event, therefore inferences based on a single model specification can be sensitive to the choice of variables. We re-examine three studies (DeYoung and Torna in J Financ Intermed 22:397–421, 2013; Jin et al. in J Bank Finance 35:2811–2819, 2011; Ng and Roychowdhury in Rev Acc Stud 19:1234–1279, 2014) of bank failures during the Great Financial Crisis to determine whether these authors' main findings are robust to accounting for uncertainty in the model's specification. Our results indicate their results are not robust and that the causes of bank failures during the Great Financial Crisis are similar to those of past periods of crisis and are driven by traditional measures of risk. [ABSTRACT FROM AUTHOR]

We analysed the capacity for banks to fail in Indonesia. A “failed bank” can be decoded as a bank facing financial difficulties and possibility of collapse. It is no longer feasible for the LPP (Banking Supervisory Agency) to address bank failures under its current authority. In Indonesia, bank failures are managed by the Deposit Insurance Corporation (IDIC), helped by rules from Bank Indonesia and the Financial Services Authority (OJK). Under Article 5 of Law no. 24 of 2004 regarding the Deposit Insurance Corporation, one of the jobs of IDIC is to develop, specify and enforce a procedure for the liquidation of failing banks that do not have a systemic effect and address failing banks that do have a systemic effect. The definition of systemic effect is when a bank’s failure will have an extraordinary impact on the availability of funds and the smoothness and sustainability of the economy. While a non-systemic effect is bank’s failure that does not meet the standards noted above. The implication of our research is to provide an understanding that assistance for failing banks in Indonesia is taken over by the IDIC which will form an entity called a bridge bank.

State-chartered commercial bank – Silicon Valley Bank (SVB) was the primary subsidiary of SVB Financial Group. In March 2023, after central bank–endorsed interest rate hikes during the 2021–2023 inflation surge, there was a bank run on its deposits, which led to its collapse. This marked the third-largest bank failure in U.S. history and took place alongside the March 2023 United States bank failures. This paper evaluated the differential effect of Silicon Valley Bank failure on the stock market. Two objectives were pivotal, namely the examination of differential stock price performance of SVB prior to failure. Secondly is the examination of the differential effect of SVB failure on the US S&P500 stock price performance. Applying a t-test statistics for difference in means, the results show a significant difference in mean stock price of SVB between a month and two weeks period before SVB failure at P

This study investigates whether the ratio of long-term investment to total assets, the ratio of cash on hand to total assets, and the ratio of price-to-earnings are risk indicators for bank failures. Silicon Valley Bank (SVB), Signature Bank, and Silvergate Capital Corp., which experienced bank failure, and banks that are among the 20 largest banks in the USA are analyzed with the panel data method. Analyses were made using quarterly data between 2003Q4 and 2022Q4. It is revealed that the long-term investment to total assets ratio increases the bank failure risk. The risk of bank failure varies negatively with the cash on hand to total assets ratio. Bank failure risk rises as the price-to-earnings ratio rises. In terms of revealing the factors influencing the risk of bank failure and possible consequences, it is expected that the findings obtained could contribute to the literature.

Russia has witnessed a high number of bank failures over the last two decades. Using monthly data for 2002–2020, spanning four election cycles (2004, 2008, 2012, 2018), we test the hypothesis that bank failures are less likely before presidential elections. We find that, in general, bank failures are less likely to occur in the 12 months leading up to an election. However, we do not observe that bank failures during electoral cycles are more pronounced for banks associated with greater political costs (financial troubles) than for other reasons (illegal activities). Overall, our results provide mixed evidence that political cycles matter for the occurrence of bank failures in Russia. [ABSTRACT FROM AUTHOR]

The failure of First Republic Bank (FRB) – the largest bankruptcy in the US banking system since the GFC – may send chilling effects through different asset markets in the US and even worldwide. This paper aims to clarify the effects of FRB failure on several assets and figure out potential safe havens for investors during the 2023 US banking turmoil. Results from the event study approach reveal that eleven particular assets are relatively safe during the current turbulence. Several important implications are made for investors, portfolio managers, and policymakers.

This paper reiterates the importance of corporate governance in banks. Failure prediction studies have mainly relied on using financial ratios as predictors. The most suitable financial predictors for banks are financial ratios following the CAMEL rating system. Also, corporate governance has been proven to be an important aspect of banks, especially after the financial crisis. Given its importance, the novelty of this paper is to test the ability of corporate governance to increase the accuracy and extend the time-horizon of bank failure prediction in the US market. Using discriminant analysis, we predict the failure of banks insured by the Federal Deposit Insurance Corporation from 2010 to 2018. Using financial and non-financial predictors, we find that combining CAMEL ratios with corporate governance variables not only increases the accuracy of prediction but also extends the time horizon to three years before failure. We also show that bank earnings is a more significant predictor than capital structure and asset quality. The results further reveal that CEO compensation, voting rights and institutional ownership are significant predictors. These results are robust when using logit regression and out-of-sample examination. This study shows that corporate governance plays a key role in the success or failure of banks. The regulatory implication of this paper is that more attention needs to be directed to corporate governance and earnings aspects of banks rather than focusing on capital structure. [ABSTRACT FROM AUTHOR]

This study investigates whether the ratio of long-term investment to total assets, the ratio of cash on hand to total assets, and the ratio of price-to-earnings are risk indicators for bank failures. Silicon Valley Bank (SVB), Signature Bank, and Silvergate Capital Corp., which experienced bank failure, and banks that are among the 20 largest banks in the USA are analyzed with the panel data method. Analyses were made using quarterly data between 2003Q4 and 2022Q4. It is revealed that the long-term investment to total assets ratio increases the bank failure risk. The risk of bank failure varies negatively with the cash on hand to total assets ratio. Bank failure risk rises as the price-to-earnings ratio rises. In terms of revealing the factors influencing the risk of bank failure and possible consequences, it is expected that the findings obtained could contribute to the literature. [ABSTRACT FROM AUTHOR]

One of the major goals of bank supervisors is to predict bank distress events. As the environment changes, it is crucial to reassess and improve the models used in monitoring banks. The financial soundness of banks is traditionally assessed based on accounting ratios. However, the incorporation of market information in these models may significantly improve its ability to predict bank distress. The present paper has two main objectives, the first is to assess if market information adds value to accounting-based monitoring models when the purpose is to detect bank distress situations. Further, it also seeks to understand if the predictive power of market signals increased with transparency requirements. To accomplish this purpose, a total of 81 distress events from a sample of 248 European banks between 2008 and 2020 were analyzed. First, a logit univariate analysis was used to evaluate the relevance of each accounting and market variable. Then, the optimal multivariate accounting-based model to predict distress events was constructed using a stepwise approach. Finally, the previous model was extended to include the relevant market variables. The results support the use of market variables in bank monitoring models. Further, the present study provides evidence that the predictive power of market variables increased after the strengthening of the information requirements set by the Basel agreements. It can be concluded that the results support the use of market information for banking supervisory purposes, especially, in transparent markets. [ABSTRACT FROM AUTHOR]

This research investigates the bank failures in the United States in March 2023, concentrating on the impact of held-to-maturity debt instruments in the event and the implications for accounting methods. Our research deciphers the alleged "accounting loophole" (Farrell, 2023) associated with these securities and provides an in-depth analysis of the associated accounting treatment. We analyze the accounting treatment using the Accounting Standards Codification (ASC) and International Financial Reporting Standards (IFRS). Furthermore, our study employs automated machine learning techniques and the local interpretable model-agnostic explanations (LIME) method to identify key accounting features that could explain bank failures. The research identifies five essential accounting aspects, two of which are related to held-to-maturity assets. The findings underscore the importance of these accounting features in evaluating financial institutions, thereby providing valuable insights for stakeholders, decision-makers, and future research. Our research also advocates for increased transparency and accuracy in accounting practices, via ASC 825 (Financial Accounting Standards Board [FASB], n.d.-a), particularly related to the fair value of held-to-maturity securities. [ABSTRACT FROM AUTHOR]

Riverbank failure is an essential source of river sediment and a major cause of river meandering. The Bank Stability and Toe Erosion Model (BSTEM) is a widely used simulation tool owing to its simplicity and clear presentation of results. However, the three core issues in the simulation by the software, time‐step determination, top‐of‐toe (TT) selection, and the iterative algorithm, still lack an in‐depth discussion to ensure the rationality and stability of the simulation results. In this study, the Jing‐150 cross‐section of the Jingjiang River was chosen as an example to illustrate these three problems. For the first problem, which is the choice of the time step, the simulation results become stable and close to the measured value as the time‐step decreases. After determining a suitable time step, the difference between adopting and modifying the default TT of the BSTEM was analysed. The results show that adopting the default TT causes chaos in the distribution of profile nodes, leading to an overly large bank failure width, whereas the modified TT node distribution is normal and the bank failure width is close to the measured value. For the third problem, the original iteration routine (OR) may cause the output bank profile to be unstable and cause a deviation between the bank width and measured value. The negative impact of unstable bank profiles diminishes as the time‐step decreases. However, the workload for reducing the time step is enormous. Therefore, an improved routine (IR) was proposed. With IR, the relative error between the simulated and measured values of the failure width was within 15% when the SD of the water stage was reduced to 0.2478. A similar result could only be obtained when the SD of the water stage was less than 0.099 for OR. This means that to obtain the same simulation accuracy for the failure width, IR can use a larger time step than OR, thereby reducing the workload of the simulation. [ABSTRACT FROM AUTHOR]

State-chartered commercial bank - Silicon Valley Bank (SVB) was the primary subsidiary of SVB Financial Group. In March 2023, after central bank-endorsed interest rate hikes during the 2021-2023 inflation surge, there was a bank run on its deposits, which led to its collapse. This marked the third-largest bank failure in U.S. history and took place alongside the March 2023 United States bank failures. This paper evaluated the differential effect of Silicon Valley Bank failure on the stock market. Two objectives were pivotal, namely the examination of differential stock price performance of SVB prior to failure. Secondly is the examination of the differential effect of SVB failure on the US S&P500 stock price performance. Applying a t-test statistics for difference in means, the results show a significant difference in mean stock price of SVB between a month and two weeks period before SVB failure at P<0.05, with higher variance within the week before failure. Furthermore, the t-test results disclose a highly significant difference in US S&P500 mean stock price between February 2023 (a month before failure) and March 2023 (within the month of failure) at P<0.001 with a high variance in March; however, this effect did not last long. Accordingly, the paper concludes that the failure of a prominent financial institution may trigger ripples in a major stock market index. Additionally, investors should be cautious of an unprecedent and abnormal higher variance in a corporate stock price as this might be an ominous indicator of potential financial failure. This paper contributes a current information for managers and particularly for speculative investors for decisions on selling short or long. It recommends further research to examine the effect of SVB failure and FDIC intervention on stock markets. [ABSTRACT FROM AUTHOR]

This study investigates whether the ratio of long-term investment to total assets, the ratio of cash on hand to total assets, and the ratio of price-to-earnings are risk indicators for bank failures. Silicon Valley Bank (SVB), Signature Bank, and Silvergate Capital Corp., which experienced bank failure, and banks that are among the 20 largest banks in the USA are analyzed with the panel data method. Analyses were made using quarterly data between 2003Q4 and 2022Q4. It is revealed that the long-term investment to total assets ratio increases the bank failure risk. The risk of bank failure varies negatively with the cash on hand to total assets ratio. Bank failure risk rises as the price-to-earnings ratio rises. In terms of revealing the factors influencing the risk of bank failure and possible consequences, it is expected that the findings obtained could contribute to the literature. [ABSTRACT FROM AUTHOR]

Russia has witnessed a high number of bank failures over the last two decades. Using monthly data for 2002-2020, spanning four election cycles, we test the hypothesis that bank failures are less likely before presidential elections. We find that bank failures are less likely to occur in the twelve months leading up to an election. However, we do not observe election cycles in bank failures are more pronounced for banks associated with greater political costs. Overall, our results provide mixed evidence that political cycles matter for the occurrence of bank failures in Russia. [ABSTRACT FROM AUTHOR]

This paper examined bank-specific performance indicators and macroeconomic factors affecting the short-term financing obligation of Nigerian banks from 2010 to 2019. The data for the study are sourced annually from the financial statements of the selected Deposit Money Banks and the Central Bank of Nigeria Statistical Bulletin. The panel unit root and co-integration tests are employed to ascertain the sustainability of the bank-specific performance indicators. The models for the industry were cast in a host of panel frameworks such that we estimated the static and dynamic panel models. The study observed that the capital adequacy ratio, which is the short-term financing obligation of Nigerian banks was elastic to bank profitability positively. In addition, interbank call rate, bank size, and oil price positively influence the capital adequacy ratio over time, whereas loan-to-deposit ratio, inflation and exchange rate exacerbate the capital adequacy ratio. Consequently, we canvass those Nigerian banks should reduce dividend payouts and increase retained profits as a buffer against exposed risks. [ABSTRACT FROM AUTHOR]

For decades, the prediction of bank failure has been a popular topic in credit risk and banking studies. Statistical and machine learning methods have been working well in predicting the probability of bankruptcy for different time horizons prior to the failure. In recent years, bank efficiency has attracted much interest from academic circles, where low productivity or efficiency in banks has been regarded as a potential reason for failure. It is generally believed that low efficiency implies low-quality management of the organisation, which may lead to bad performance in the competitive financial markets. Previous papers linking efficiency measures calculated by Data Envelopment Analysis (DEA) to bank failure prediction have been limited to cross sectional analyses. A dynamic analysis with the updated samples is therefore recommended for bankruptcy prediction. This paper proposes a nonparametric method, Malmquist DEA with Worst Practice Frontier, to dynamically assess the bankruptcy risk of banks over multiple periods. A total sample of 4426 US banks over a period of 15 years (2002–2016), covering the subprime financial crisis, is used to empirically test the model. A static model is used as the benchmark, and we introduce more extensions for comparisons of predictive performance. Results of the comparisons and robustness tests show that Malmquist DEA is a useful tool not only for estimating productivity growth but also to give early warnings of the potential collapse of banks. The extended DEA models with various reference sets and orientations also show strong predictive power. [ABSTRACT FROM AUTHOR]

Auditor credibility is important in the banking industry due to the opacity of bank assets and the use of financial statements by external parties to facilitate monitoring. Depositors monitor and discipline bank behavior, but they can also contribute to the spread of shocks from one bank to another. We argue that depositors perceive bank failure as an audit failure, which reduces their assessment of auditor credibility. We document that exposure to failure through the audit firm is associated with lower uninsured deposit growth following the failure, consistent with depositors perceiving failures as a negative signal of auditor credibility. We further document that this association is stronger when depositors perceive connection to failure to reflect a pervasive issue within the audit firm. Collectively, our results suggest that depositors consider accounting signals at other banks in assessing financial reporting credibility. JEL Classifications: G21; M41; M42. [ABSTRACT FROM AUTHOR]

This paper investigates whether or not banks in the MENA region are susceptible to failures. Two z score models are investigated in predicting the health of ninety banks across ten countries. Using discriminant and regression analysis, one can determine which ratios are statistically significant in predicting the health of the selected banks and which zone they belong to safe, grey, or distressed zone. The study spans the years 2006 to 2016. The goal of this study is to compare two z scores to assess if banks within MENA are subject to failure. According to the findings of this study, the Z score developed by El Ansary may be a better way for emerging economies to measure the indicators that rigger banks' risk level. [ABSTRACT FROM AUTHOR]

Abstract Soil slides can occur when the water level in a lake or a reservoir is lowered. This may take place in situations when a reservoir is flushed to remove sediments. The current study describes a three-dimensional numerical model used for the simulation of reservoir flushing that includes the slide movements. The geotechnical failure algorithms start with modelling the groundwater levels at the banks of the reservoir. A limit equilibrium approach is further used to find the location of the slides. The actual movement of the sediments is computed by assuming the soil to be a viscous liquid and by solving the Navier–Stokes equations. The resulting bed elevation changes from the slides are computed in adaptive grids that change as a function of water level, bed erosion and slide movements. The numerical model is tested on the Bodendorf reservoir in Austria, where field measurements are available of the bank elevations before and after a flushing operation. The results from the numerical simulations are compared with these observations. A parameter test shows that the results are very sensitive to the cohesion and less sensitive to the E and G modules of the soil.

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. [ABSTRACT FROM AUTHOR]

This paper examines the restructuring of the Spanish financial system. This study is justified by the massive economic and social impact of this process in Spain. Based on the annual accounts and the annual reports of Spanish credit institutions, a model was created to predict the possibility of bank failure or bailout. The variables were selected following a review of the literature. They included the legal form of the credit institution (savings bank versus bank), leverage, real estate investment, gross operating margin, staff costs and non-performing loans. Two variables that had not previously been used in studies of this type were also included in the model: risk-weighted assets and coverage (as part of the provisions for non-performing loans). Fuzzy-set qualitative comparative analysis (fsQCA) was used for the analysis. Use of fsQCA is another novel aspect of this study. This technique has never been used to predict corporate bankruptcy or the failure of credit institutions. Proposals were made and tested using data on Spanish banks and savings banks for the year 2010, a critical year for the Spanish financial system. The results yield several interesting conclusions. First, greater leverage increases the probability of survival, contrary to the opinion of most scholars. This result shows that the problem facing Spanish credit institutions was the closure of international markets, not the level of debt. Another key conclusion is that financial institutions with higher staff costs have a greater chance of survival. Therefore, the widespread policy of reducing human resource costs should be reconsidered. Finally, the risk-weighted assets measure offers an excellent predictor of failure of credit institutions, as well as providing other benefits, which substantially increase the value of this measure. [ABSTRACT FROM AUTHOR]

This paper examines the career of economist Prof. George Kaufman, whose work combined and benefited both academia and the financial industry, helping both better understand the problems of financial stability, financial regulation, and financial sector competition. In doing so, he brought together great minds from both fields and helped many economists progress in their careers. Prof. Kaufman promulgated the idea that government policy needed to foster two simple goals: proper incentives and competitive markets. He believed the combination would produce a richer society and more stable economy. He created the U.S. Federal Reserve's Conference on Bank Structure and Competition, the leading conference in the world addressing financial regulatory issues for 50 years. Prof. Kaufman also founded the U.S. Shadow Financial Regulatory Committee in 1986 in the midst of the thrift crisis, helping to solve that crisis and inspiring the creation of other shadow financial regulatory committees around the world. Through his activities, he left behind an outstanding legacy of leadership, research, and opinion in various economic fields, providing platforms upon which many scholars will build in the coming decades. [ABSTRACT FROM AUTHOR]

Following the collapse of Saambou bank in February 2002, contagion rapidly spread amongst South African small and medium-sized banks. By the end of 2003, half of the country's banks had deregistered. The paper constructs a unique monthly bank-level data set to show that the banks that failed were those with short-term liabilities from other financial institutions. An initial delay in providing liquidity to solvent banks in distress and raising interest rates may have exacerbated the crisis. The need for prompt, swift action echoes lessons from banking panics throughout history. [ABSTRACT FROM AUTHOR]

Bank failure is a major concern for the authoritative bodies and market participants because of the possible contagion across the financial sector and the subsequent collapse of the economic system. This paper investigates the impact of managerial ability on bank failure. We find that more ably managed banks experienced lower failure probability. Further analyses provide support to this finding and reveal that more ably managed banks are associated with more adequate capital, better asset quality, greater efficiency in the allocation of resources, higher liquidity and lower risk. [ABSTRACT FROM AUTHOR]

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Soil slides can occur when the water level in a lake or a reservoir is lowered. This may take place in situations when a reservoir is flushed to remove sediments. The current study describes a three-dimensional numerical model used for the simulation of reservoir flushing that includes the slide movements. The geotechnical failure algorithms start with modelling the groundwater levels at the banks of the reservoir. A limit equilibrium approach is further used to find the location of the slides. The actual movement of the sediments is computed by assuming the soil to be a viscous liquid and by solving the Navier–Stokes equations. The resulting bed elevation changes from the slides are computed in adaptive grids that change as a function of water level, bed erosion and slide movements. The numerical model is tested on the Bodendorf reservoir in Austria, where field measurements are available of the bank elevations before and after a flushing operation. The results from the numerical simulations are compared with these observations. A parameter test shows that the results are very sensitive to the cohesion and less sensitive to the E and G modules of the soil. [ABSTRACT FROM AUTHOR]

Past financial crises have illustrated the importance of recognising the combinations of factors that can cause financial distress in the banking industry. Accordingly, this study uses fuzzy-set qualitative comparative analysis (fsQCA) to identify the combinations of factors that lead to bank failure. The data consist of 30 annual financial ratio series for 156 U.S. banks over a 15-year period (2001–2015). Identifying combinations of conditions that can produce bank failure is crucial to help regulators and bank managers. The fsQCA presented in this paper sheds light on the relationships between combinations of conditions and bank failure, providing a solution comprising two sufficient and disjunctive sets of conditions that lead to bank failure. This solution includes financial indicators of performance, asset quality, efficiency and capitalisation. The robustness of the two sets of conditions is assessed using several tests. [ABSTRACT FROM AUTHOR]

Focusing on the recent banking crisis, our article examines whether accounting-based proxies for the Federal Deposit Insurance Corporation’s proprietary CAMELS measurement system effectively predict the likelihood of failure for most regulated, depository institutions with U.S. operations (in and out of sample testing). This article also utilizes regulatory enforcement actions as both an explanatory and predicted variable. The CAMELS ratings from bank examinations are not released to the public because of regulatory concerns over potential bank runs. To the extent that such ratings provide timely information to banks, our lagged proxies are less likely to be effective predictors of bank failure. However, we find that proxies for each of the six categories of CAMELS—capital adequacy, asset quality, management, earnings, liquidity, and sensitivity to interest rates—are significantly associated with the probability of bank failure when examined individually. Nearly all measures maintain their significance when examined collectively. These results should aid investors, customers of commercial banks, and also regulators to better assess the risk of bank distress in the future. [ABSTRACT FROM PUBLISHER]

The Great Financial Crisis shows that bank failure in the United States, while rare, is a concern during uncertain times. Interest here is in the ability to predict future failures at the start of a crisis, when the recent past has few events on which to base inferences. I show that policy makers using estimates based on the Savings and Loans crisis would identify in early 2009 that 2.0% of banks were in critical condition and 7.0% were unhealthy. This is comparable to the 1.7% of banks that failed within a year and the 3.9% of banks that would fail during the crisis. [ABSTRACT FROM AUTHOR]