Predicting Bank Failures: A Comparison of On- and Off-Site Monitoring Systems

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.     

Banks and markets in a monetary economy

Modern financial sectors consist of banks, asset markets and a central bank. This paper builds a model where these institutions provide different financial services, and their interaction supports efficient allocations. When one institution is missing equilibria are, by construction, inefficient. The paper analyzes how interest rates and asset prices depend on the structure of the financial sector and characterizes the central bank policy that supports efficient allocations. The analysis relies on the difference between liquidity and real shocks, and relates the notion of liquidity used in this paper to the one adopted in other studies.     

Twenty‐five years of the Taffler z‐score model: Does it really have predictive ability?

Although copious statistical failure prediction models are described in the literature, appropriate tests of whether such methodologies really work in practice are lacking. Validation exercises typically use small samples of non‐failed firms and are not true tests of ex ante predictive ability, the key issue of relevance to model users. This paper provides the operating characteristics of the well‐known Taffler (1983) UK‐based z‐score model for the first time and evaluates its performance over the 25‐year period since it was originally developed. The model is shown to have clear predictive ability over this extended time period and dominates more naïve prediction approaches. This study also illustrates the economic value to a bank of using such methodologies for default risk assessment purposes. Prima facie, such results also demonstrate the predictive ability of the published accounting numbers and associated financial ratios used in the z‐score model calculation.     

Incentives to innovate and financial crises

In this paper I develop a model of a competitive financial system with unrestricted but costly entry and an endogenously determined number of competing financial institutions (“banks” for short). Banks can make standard loans on which plentiful historical data are available and unanimous agreement exists on default probabilities. Or banks can innovate and make new loans on which limited historical data are available, leading to possible disagreement over default probabilities. In equilibrium, banks make zero profits on standard loans and positive profits on innovative loans, which engenders innovation incentives for banks. But innovation brings with it the risk that investors could disagree with the bank that the loan is worthy of continued funding and hence could withdraw funding at an interim stage, precipitating a financial crisis. The degree of innovation in the financial system is determined by this trade-off. Welfare implications of financial innovation and mechanisms to reduce the probability of crises are discussed.     

Internal Capital Markets and Capital Structure: Bank Versus Internal Debt

We argue that domestic business groups are able to actively optimise the internal/external debt mix across their subsidiaries. Novel to the literature, we use bi‐level data (i.e. data from both individual subsidiary financial statements and consolidated group level financial statements) to model the bank and internal debt concentration of non‐financial Belgian private business group affiliates. As a benchmark, we construct a size and industry matched sample of non‐group affiliated (stand‐alone) companies. We find support for a pecking order of internal debt over bank debt at the subsidiary level which leads to a substantially lower bank debt concentration for group affiliates as compared to stand‐alone companies. The internal debt concentration of a subsidiary is mainly driven by the characteristics of the group's internal capital market. The larger its available resources, the more intra‐group debt is used while bank debt financing at the subsidiary level decreases. However, as the group's overall debt level mounts, groups increasingly locate bank borrowing in subsidiaries with low costs of external financing (i.e. large subsidiaries with important collateral assets) to limit moral hazard and dissipative costs. Overall, our results are consistent with the existence of a complex group wide optimisation process of financing costs.     

A network model of systemic risk: stress testing the banking system1

Although there are many definitions of systemic risk, most agree that it manifests itself by an initial shock that results in the failure of one or more banks and then spreads out to the entire system by a contagion mechanism which can result in the failure of more banks in the system. Assuming that bank failures in the initial shock are randomly dependent on the failure probabilities of the individual banks and that the ensuing contagion process is deterministic, depending on interbank exposures, in this paper we propose a network model to analyse systemic risk in the banking system that, in contrast to other proposed models, seeks to obtain the probability distribution of losses for the financial system resulting from the shock/contagion process. Thus, calculating the probabilities of joint failures by simulation and assuming that the matrix of bilateral interbank exposures is known, we represent systemic risk in the financial system by means of a graph and use discrete modelling techniques to characterize the dynamics of contagion and corresponding losses within the network. The probability distribution of losses, risk profile for the Mexican banking system, is obtained through an efficient, complete enumeration procedure of all possible bank default events in the system. This, in turn, allows the use of the wide variety of well-established risk measures to describe the fragility of the financial system. Additionally, the model allows us to perform stress tests along both the bank default probabilities and the interbank exposures and is used to assess the risk of the Mexican banking system. Copyright © 2009 John Wiley & Sons, Ltd.     

Modelling Credit Risk for SMEs: Evidence from the U.S. Market

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyse its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analysed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 U.S. firms (with sales less than $65 million) over the period 1994–2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 per cent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs.     

A STOCHASTIC SETTING TO BANK FINANCIAL PERFORMANCE FOR REFINING EFFICIENCY ESTIMATES

This study contributes to develop a framework to measure the financial performance of banks in a stochastic setting. The framework comprises several steps, the first of which is the development of a financial performance measurement model to evaluate a bank's financial performance using a set of factors from the CAMEL (Capital adequacy, Assets, Management Capability, Earning and Liquidity) system. Second, the stochastic setting of the efficiency measurement is handled using the data collection budget allocation approach, whereby Monte Carlo simulations are used to analyse additional generated data and a genetic algorithm is used to refine the accuracy of the efficiency estimates. The results show that the accuracy of the model is greatly improved using the proposed approach. In contrast to the conventional deterministic model, the proposed framework is more useful to managers in determining the bank's future financial operations to improve the overall financial soundness of the bank. Copyright © 2014 John Wiley & Sons, Ltd.     

Hostile bank takeover offers : Analysis and implications

A comparison of the financial characteristics of banks involved in hostile takeover bids with a control group of nonhostile bank mergers indicates: (1) hostile targets experience abnormal returns that are significantly greater than for the targets of nonhostile bank mergers; (2) hostile bidders experience negative abnormal returns that are insignificantly different than for bidders involved in nonhostile bank mergers; (3) hostile bank acquisition announcements produce positive net wealth effects which are larger than the wealth effects of nonhostile acquisitions; (4) a Logit regression model using financial ratios, stock price data, and ownership data is able to distinguish between hostile and nonhostile targets.     

DECOMPOSITION MEASURES AND THE PREDICTION OF FINANCIAL FAILURE

Lev has indicated that the decomposition measures of failed firms are larger than those of non-failed firms and concludes that decomposition measures may be usefully included in financial failure prediction models. This paper extends the use of decomposition measure concepts for financial failure prediction. Firstly, on a univariate basis, attributes of decomposition measures are tested for discriminating ability between failed and non-failed companies. Secondly, all decomposition measures tested are used to derive a discriminant analysis model for failure prediction. The paper concludes that (1) the stability and size of some balance sheet derived decomposition measures discriminate between failed and non-failed companies as far as four years before failure, and (2) a discriminant analysis model of balance sheet derived decomposition measures is not successful at predicting financial failure.     

Are regulatory capital adequacy ratios good indicators of bank failure? Evidence from US banks

Motivated by massive bank failures during the financial crisis, this paper examines whether capital adequacy ratios required by regulators are associated with bank failure. It investigates whether the association is affected by the bank's proximity to the minimum required capital ratios. If results show a significant association between regulatory capital and failure of banks falling below the minimum capital ratios, then the ratios are set at an adequate level. Examining a sample of 560 US bank holding companies for the period 2003–2009, results reveal that the association between the core (Tier 1) capital ratio and bank failure becomes significant only if the bank holding company has a Tier 1 capital ratio of less than 6%. This is the level below which US bank regulators do not regard banks as being well capitalized. During the financial crisis period of 2007–2009, there is a significant association only when the criterion is set at or above 8%. Market-based probability of default is more significantly associated with failure relative to Tier 1 capital ratio. The findings of this paper are relevant to regulatory policy discussions and Basel III deliberations on capital adequacy at times of financial turmoil.     

Ability of accounting and audit quality variables to predict bank failure during the financial crisis

We examine the ability of selected accounting and audit quality variables measured in a period prior to the financial crisis (i.e., the four quarters of 2006), to predict banks that subsequently failed during the financial crisis. We employ two sets of samples from the US: a troubled banks sample that includes banks that failed in or after 2007 as well as banks classified as being troubled based on profitability, loan quality, and balance sheet position in 2007, and a full sample that includes all banks with available required data. Using the troubled banks sample, we identify six reliable predictors of bank failure: auditor type, auditor industry specialization, Tier 1 capital ratio, proportion of securitized loans, growth in loans, and loan mix. For the larger full sample of banks, we identify the following ten predictors of bank failure: auditor type, Tier 1 capital ratio, proportion of securitized loans, nonperforming loans, loan loss provisions, growth in commercial loans, growth in real estate loans, growth in overall loans, loan mix, and whether the bank is a public bank.     

The Application of Corporate Governance Indicators With XBRL Technology to Financial Crisis Prediction

ABSTRACT

The widespread adoption of eXtensible Business Reporting Language (XBRL) suggests that intelligent software agents can now use financial information disseminated on the Web with high accuracy. Financial data have been widely used by researchers to predict financial crises; however, few studies have considered corporate governance indicators in building prediction models. This article presents a financial crisis prediction model that involves using a genetic algorithm for determining the optimal feature set and support vector machines (SVMs) to be used with XBRL. The experimental results show that the proposed model outperforms models based on only one type of information, either financial or corporate governance. Compared with conventional statistical methods, the proposed SVM model forecasts financial crises more accurately.     

DEBT FORGIVENESS AND STOCK PRICE REACTION OF LENDING BANKS: THEORY AND EVIDENCE FROM JAPAN

We provide a simple model for analyzing how debt forgiveness affects the stock price of a lending bank. Our model shows that although debt forgiveness increases shareholder wealth of a bank in healthy financial condition, it decreases shareholder wealth of a bank in unhealthy financial condition. We empirically investigate the announcement effect of debt forgiveness on bank stock prices in Japanese markets. On average, lending banks experience a significant negative announcement effect with respect to debt forgiveness. Consistent with the prediction of the model, we find a negative relation between the announcement effect and the net bad loan ratio as a proxy of the unhealthiness of the financial condition of the bank.     

Ratings assignments: Lessons from international banks

This paper estimates ordered logit models for bank ratings which include a country index to capture country-specific variation. The empirical findings support the hypothesis that the individual international bank ratings assigned by Fitch Ratings are underpinned by fundamental quantitative financial analyses. Also, there is strong evidence of a country effect. Our model is shown to provide accurate predictions of bank ratings for the period prior to the 2007–2008 banking crisis based upon publicly available information. However, our results also suggest that quantitative models are unlikely to predict ratings with complete accuracy. Furthermore, we find that both quantitative models and rating agencies are likely to produce highly inaccurate predictions of ratings during periods of financial instability.     

Will TLAC regulations fix the G-SIB too-big-to-fail problem?

The efficacy of the Financial Stability Board's proposed requirement for minimum “total loss absorbing capacity” (TLAC) at global systemically important banks (G-SIBs) is assessed using a stylized model of a bank holding company and an equilibrium asset pricing model to value financial claims. I identify a number of G-SIB strategies that satisfy minimum TLAC requirements but fail to reduce implicit safety net subsidies that accrue to G-SIB shareholders or increase the resources available to recapitalize a failing G-SIB subsidiary. To meet the FSB's stated goals, TLAC requirements must impose minimum TLAC at all subsidiaries and restrict how TLAC funds can be invested. An equivalent, but much simpler solution is to significantly increase regulatory capital requirements on systemically important bank subsidiaries.     

Failed bank takeovers and financial stability

Current discussion about the design of bank resolution frameworks suggests that the takeover of a failed bank by an incumbent one has two effects on financial stability. First, the incumbent takeover may boost financial stability by providing bankers with incentives to be solvent so as to profit from their competitors’ failure. Second, the incumbent takeover may spoil financial stability by creating “Systemically Important Financial Institutions”. The innovation of this paper is to capture these two effects in a theoretical model. We show that when incumbent bankers are impatient enough (i.e., they have high discount rates), the second effect prevails over the first one. We discuss the implications of this result for the design of bank resolution policies.     

Bank funding structures and risk: Evidence from the global financial crisis

This paper analyzes the evolution of bank funding structures in the run up to the global financial crisis and studies the implications for financial stability, exploiting a bank-level dataset that covers about 11,000 banks in the U.S. and Europe during 2001–09. The results show that banks with weaker structural liquidity and higher leverage in the pre-crisis period were more likely to fail afterward. The likelihood of bank failure also increases with pre-crisis bank risk-taking. In the cross-section, the smaller domestically-oriented banks were relatively more vulnerable to liquidity risk, while the large cross-border (Global) banks were more vulnerable to solvency risk due to excessive leverage. In fact, a 3.5 percentage point increase in the pre-crisis capital buffers of Global banks would have caused a 48 percentage point in their probability of failure during the crisis. The results support the proposed Basel III regulations on structural liquidity and leverage, but suggest that emphasis should be placed on the latter, particularly for the systemically-important institutions. Macroeconomic and monetary conditions are also shown to be related with the likelihood of bank failure, providing a case for the introduction of a macro-prudential approach to banking regulation.     

我国个人银行结算账户开户数量合理性实证研究

随着金融供给侧结构性改革和普惠金融进程的不断深入,我国金融服务质量不断提升,银行服务的可获得性和服务质量均得到明显改善。个人银行账户分类改革实施以来,个人多元化支付需求得到充分满足,银行账户风险得到有效控制。但在市场机制作用下,科学合理的银行账户数量对刺激金融服务需求、实现普惠金融、控制金融风险将起到推动作用。本文将针对市场环境下,通过建立科学的分析框架对个人银行账户开立需求与合理开户数量进行关联分析,选取层次分析法AHP和BP神经网络组合,训练得出“个人银行账户开立需求-个人银行账户合理数量”预测模型。最终根据不同个体银行账户开户需求系数得到个人银行开户指导数量并提出数量管理实施路径和相关政策建议。     

Monetary policy and financial (in)stability: An integrated micro–macro approach

Evidence on central banks’ twin objective, monetary and financial stability, is scarce. We suggest an integrated micro–macro approach with two core virtues. First, we measure financial stability directly at the bank level as the probability of distress. Second, we integrate a microeconomic hazard model for bank distress and a standard macroeconomic model. The advantage of this approach is to incorporate micro information, to allow for non-linearities and to permit general feedback effects between financial distress and the real economy. We base the analysis on German bank and macro data between 1995 and 2004. Our results confirm the existence of a trade-off between monetary and financial stability. An unexpected tightening of monetary policy increases the probability of distress. This effect disappears when neglecting microeffects and non-linearities, underlining their importance. Distress responses are largest for small cooperative banks, weak distress events, and at times when capitalization is low. An important policy implication is that the separation of financial supervision and monetary policy requires close collaboration among members in the European System of Central Banks and national bank supervisors.