The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher

The purpose of this study is to demonstrate potential problems associated with the use of bankruptcy prediction models in current research. The tests in this study demonstrate the problems that may arise when bankruptcy prediction models are inappropriately applied. This analysis evaluated the Zmijewski (1984) and Ohlson (1980) models using time periods, industries, and financial distress situations other than those used to originally develop the models. The findings indicated that both models were sensitive to time periods. That is, the accuracy of the models declined when applied to time periods different from those used to develop the models. The findings also suggest that the accuracy of each model continues to decline moving from the 1988–1991 to the 1992–1999 sample period. Additionally, Ohlson's (Zmijewski's) model was (was not) sensitive to industry classifications. The findings of this study also suggest that the Ohlson and Zmijewski models are not sensitive to financial distress situations other than those used to develop the models. Thus, the models appear to be more generally useful for predicting financial distress, not just bankruptcy.In sum, the results of this study suggest that researchers should use bankruptcy prediction models cautiously. Applying the models to time periods and industries other than those used to develop the models may result in a significant decline in the models' accuracies. Additionally, some bankruptcy prediction models may be more appropriate for evaluating various forms of financial distress as opposed to just bankruptcy. To avoid erroneous applications of bankruptcy prediction models in the future, it is necessary for researchers not only to understand the uses of prediction models, but also to understand the limitations of the models.     

THE IMPLICATIONS OF DISTRESS PREDICTION MODELS FOR CORPORATE LENDING

Three literatures on financial distress prediction are reviewed and evaluated in terms of their usefulness in credit analysis. These are (1) bankruptcy prediction models initiated by Altman, (2) behavioural studies of the ability of loan officers and others to make accurate predictions of bankruptcy and (3) attempts to simulate loan officers' judgements. It is concluded that techniques based on judgement simulation are likely to be the most effective decision aids.     

Accounting Quality and Loan Pricing: The Effect of Cross-country Differences in Legal Enforcement

This study examines whether the strength of legal enforcement at the country level plays a role in the value-relevance of accounting quality for loan pricing determination, using an international sample of firms reporting under IFRS. The underlying hypothesis is that stronger vs. weaker enforcement should affect the informativeness of financial statements, due to their increased credibility, and thus results in a stronger influence of accounting quality on loan pricing, in case this information is considered more reliable by potential lenders. Evidence indicates that accounting quality is consequential for the determination of loan spread only in combination with the level of legal enforcement, and this only holds for the countries with stronger legal enforcement. This evidence indicates that financial statement quality information is value-relevant and has a significant impact on the determination of loan pricing only if this information is considered to be credible enough by loan providers in a country, and this is the case when legal enforcement is stronger.     

Bankruptcy classification errors in the 1980s: An empirical analysis of Altman's and Ohlson's models

Empirical accounting researchers often use Altman's (1968) and Ohlson's (1980) bankruptcy prediction models as indicators of financial distress. While these models performed relatively well when they were estimated, we show that they do not perform as well in more recent periods (in particular, the 1980s), even when the coefficients are re-estimated. When we compare the performance of Ohlson's original model to our re-estimated version of his model and to that of Altman's original and re-estimated models, we find that Ohlson's original model displays the strongest overall performance. Given that Ohlson's original model is frequently used in academic research as an indicator of financial distress, its strong performance in this study supports its use as a preferred model.     

Can Loan Valuation Adjustment (LVA) approach immunize collateralized debt from defaults?

This study focuses on structuring tangible asset backed loans to inhibit their endemic option to default. We adapt the pragmatic approach of a margin loan in the configuring of collateralized debt to yield a quasi‐default‐free facility. We link our practical method to the current Basel III (2017) regulatory framework. Our new concept of the Loan Valuation Adjustment (LVA) and novel method to minimize the LVA converts the risky loan into a quasi risk‐free loan and achieves value maximization for the lending financial institution. As a result, entrepreneurial activities are promoted and economic growth invigorated. Information asymmetry, costly bailouts and resulting financial fragility are reduced while depositors are endowed with a safety net equivalent to deposit insurance but without the associated moral hazard between risk‐averse lenders and borrowers.     

Accounting-based versus market-based cross-sectional models of CDS spreads

Models of financial distress rely primarily on accounting-based information (e.g. [Altman, E., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23, 589–609; Ohlson, J., 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 19, 109–131]) or market-based information (e.g. [Merton, R.C., 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29, 449–470]). In this paper, we provide evidence on the relative performance of these two classes of models. Using a sample of 2860 quarterly CDS spreads we find that a model of distress using accounting metrics performs comparably to market-based structural models of default. Moreover, a model using both sources of information performs better than either of the two models. Overall, our results suggest that both sources of information (accounting- and market-based) are complementary in pricing distress.     

Asset Liquidity,Moral Hazard,and Bank Loan Rescheduling

We analyze the bank's decision to reschedule or to foreclose on a loan in default and the borrower's decision to divert lender-financed assets to personal use, i.e., to consume the assets. We show that the debt of borrowers in financial distress that have substantial intangible or highly specialized assets—i.e., illiquid assets—is likely to be rescheduled. Alternatively, banks will likely foreclose on borrowers in distress that have assets that are difficult to monitor. It is the interaction of the asset's liquidity and the borrower moral hazard that helps determine the nature of the equilibrium. When the condition of the borrower upon default is observable, we find that suboptimal foreclosures are possible but reschedulings are always optimal; when the borrower's condition is private information, however, reschedulings may also be suboptimal. Additionally, borrowers whose lenders foreclose are more impaired then those whose debt is rescheduled. Finally, we show that randomization of the rescheduling/foreclosure decision by the bank and the decision to consume by the borrower may be optimal for particular assets.     

An Empirical Investigation of Firm Longevity: A Model of the Ex Ante Predictors of Financial Distress

Empirical models of a potential failure process that incorporate distress states between the extremes of corporate health and bankruptcy are uncommon. We depict financial distress as a series of financial events that reflect varied stages of corporate adversity. Our intent is to provide information regarding the influence of certain risk dimensions and firm-specific attributes on distressed firm survival over time. Within a theorized distress framework, we utilize the techniques of survival analysis to longitudinally track firms, grouped a priori according to an initial decline in operating cash flows. We find that the event of default has a significant positive association with business failure. Further, we document that the significant accounting covariates tend to change conditional on a firm having progressed through the diverse stages of distress. These findings accentuate the heterogeneous nature of financial distress and potential business failure.     

Is there any dependence between consumer credit line utilization and default probability on a term loan? Evidence from bank-customer data

We study the relationship between two financial instruments through the simultaneous analysis of personal credit line utilization and default probability on a personal term loan. We model both dependent variables in a system of simultaneous equations and find strong evidence of dependence between the two financial instruments. Individuals in the default state draw their credit line by 9 percentage points more and, depending on the specification, a 10 percentage point increase in credit line utilization decreases the default probability by 0.09 to 0.41 percentage points, on a base default rate of 1.08%. This provides evidence that borrowers may use the liquidity of the credit line to pay down the term loan in periods of financial distress and suggests that banks should manage both financial instruments simultaneously.     

Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks

This is an extension of prior studies that have used artificial neural networks to predict bankruptcy. The incremental contribution of this study is threefold. First, we use only financially stressed firms in our control sample. This enables the models to more closely approximate the actual decision processes of auditors and other interested parties. Second, we develop a more parsimonious model using qualitative ‘bad news’ variables that prior research indicates measure financial distress. Past research has focused on the ‘usefulness’ of accounting numbers and therefore often ignored non‐accounting variables that may contribute to the classification accuracy of the distress prediction models. In addition, rather than use multiple financial ratios, we include a single variable of financial distress using the Zmijewski distress score that incorporates ratios measuring profitability, liquidity, and solvency. Finally, we develop and test a genetic algorithm neural network model. We examine its predictive ability to that of a backpropagation neural network and a model using multiple discriminant analysis. The results indicate that the misclassification cost of the genetic algorithm‐based neural network was the lowest among the models. Copyright © 2001 John Wiley & Sons, Ltd.     

The effect of borrowers' accounting conservatism on lenders' loan loss provisions: Evidence from China's banking industry

This paper investigates how borrowers' accounting conservatism affects lenders' loan loss provisions in the Chinese banking context. We predict that when borrowers' financial statements are more conservative, lenders receive borrowers' bad news in a timelier manner and set aside more loan loss provisions. The empirical results confirm that borrowers' accounting conservatism is positively associated with lenders' loan loss provisions, as the former affects the latter via its impact on loan classification, and this positive association is more pronounced when information asymmetry is higher. In heterogeneity tests, we find that this positive association is stronger for non-state-owned, listed, and less prudent lenders and also varies across debt contract characteristics. Collectively, the results of this study offer insights into how lenders accrue loan losses when borrowers' financial reporting is more conservative.     

Financial distress and bankruptcy prediction among listed companies using accounting,market and macroeconomic variables

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.     

Relative default rates on corporate loans and bonds

We use two alternative matched-set methodologies to examine for differences in loan and bond default rates among US non-financial corporate issuers. Under both methodologies, the data indicate that loan default rates are roughly 20% lower than the bond default rates due to issuers that default on their bonds but avoid bankruptcy and avoid defaulting on their loans. For a small number of European issuers, the data suggest a similar reduction in loan default rates relative to bond default rates. However, the European results differ qualitatively from the US results, due likely to differences in US and European bankruptcy regimes, as well as the larger role of bank debt on most European issuers’ balance sheets. These results have important implications for investors, bank supervisors, and rating agencies that assess the relative expected credit losses on loans versus bonds.     

系统性风险与企业财务危机预警——基于前沿机器学习的新视角

本文采用Logit回归模型以及随机森林模型、梯度提升模型等前沿机器学习方法,深入考察系统性风险指标对我国企业财务危机的预测能力。结果表明,系统性风险对中下游企业的财务危机具有显著的预测能力,而基于因子分析构建的系统性风险指标,结合随机森林模型可取得更好的预测效果。本文进一步区分财务危机的不同成因并发现,基于随机森林模型和Logit回归模型的预测框架能够对我国大多数财务危机事件进行有效预警。在此基础上,本文对我国上市企业监管提出相关建议,从而为完善金融风险处置机制提供一定参考。     

Predicting Bankruptcy Resolution

This study examines classification and prediction of the bankruptcy resolution event. Filing of bankruptcy is resolved through one of three alternative resolutions: acquisition, emergence or liquidation. Predicting the final bankruptcy resolution has not been examined in the prior accounting and finance literature. This post-bankruptcy classification and prediction of the final resolution is harder than discriminating between healthy and bankrupt firms because all filing firms are already in financial distress. Motivation for predicting the final resolution is developed and enhanced. A sample of 237 firms filing for bankruptcy is used. Classification and prediction accuracies are determined using a logit model. A ten-variable, three-group resolution logit model, which includes five accounting and five non-accounting variables is developed. The model correctly classifies 62 percent of the firms, significantly better than a random classification. We conclude that non-accounting data add relevant information to financial accounting data for predicting post bankruptcy resolution. Further, public policy implications for investors, researchers, bankruptcy judges, claimants and other stakeholders are discussed.     

Liquidity and asset prices: A new monetarist approach

When lenders cannot force borrowers to repay debts, assets are often pledged to secure loans. In this paper borrowers lose collateral once they renege on debts, and exclusion of defaulters occurs probabilistically, with a higher probability implying better enforcement. Increased efficiency in enforcement reduces asset prices, while raising loan-to-value ratios. If the rise in loan-to-value ratios is the dominant effect, aggregate liquidity and output increase with the advance in enforcement. Inflation raises the repayment cost by increasing the loan rate, while raising the default cost through exclusion. Consequently, inflation raises loan-to-value ratios and output only when enforcement is sufficiently efficient.     

Market‐based estimates of implicit government guarantees in European financial institutions

I exploit the price differential of credit default swap (CDS) contracts written on debts with different levels of seniority to measure the implicit government guarantees enjoyed by European financial institutions from 2005 to 2013. I determine that the aggregate guarantee increased substantially during the recent financial crises and peaked at an average of 89 bps in 2011. My analysis suggests that the extent of implicit support depends on the type of financial institutions and there exists a eurozone effect. Further investigation of feedback relationship shows that the guarantee implicitly offered by a government positively ‘Granger causes’ the sovereign's default risk.     

Credit Contagion from Counterparty Risk

Standard credit risk models cannot explain the observed clustering of default, sometimes described as "credit contagion." This paper provides the first empirical analysis of credit contagion via direct counterparty effects. We find that bankruptcy announcements cause negative abnormal equity returns and increases in CDS spreads for creditors. In addition, creditors with large exposures are more likely to suffer from financial distress later. This suggests that counterparty risk is a potential additional channel of credit contagion. Indeed, the fear of counterparty defaults among financial institutions explains the sudden worsening of the credit crisis after the Lehman bankruptcy in September 2008.     

Loan Covenants and Relationship Banking in MBOs

This paper examines the role of accounting-based covenants and other sources of information in signalling financial distress in UK MBOs. Using an in-depth questionnaire and follow-up interviews to investigate the perceptions of senior UK MBO lenders, we find that: MBO loan agreements contain more covenants than general corporate lending agreements; monthly management accounts and telephone communication are more frequent first indicators of distress than are accounting-based covenant breaches; lenders with specialist MBO lending units are more likely to waive covenant breaches and less likely to recall loans in default than those without such units; syndicate members find both information flows prior to breach and subsequent action taken to be less effective than do syndicate leaders or sole lenders; and the presence of a specialist MBO lending unit provides the skills and reputation needed to establish a high degree of trust between the banks on the one hand and the MBOs and the equity houses on the other, but there is wide variety in the ways that banks manage these relationships. These findings confirm the expectation that the relatively more acute adverse selection and moral hazard problems inherent in MBO lending increase the demand for monitoring via covenants, and that the closer the lender/borrower relationship, the more effective the monitoring.     

Evidence on the insurance effect of bankruptcy exemptions

Defaulting on one’s debts when experiencing an adverse event can help insure households against unexpected income risks. But the effect of allowing default on debt levels is ambiguous, not least because lenders may restrict access to credit. Hence the optimal punishment for bankruptcy is unclear. The US states allows householders to keep a variety of assets when filing for bankruptcy – the ‘bankruptcy exemptions’ – and these exemptions change substantially between states and over time. By exploiting these differences this study shows that raising the exemptions (i) marginally decreases the level of unsecured debt that household’s hold; (ii) makes consumption smoother. Thus, despite the effect on debt levels, the generous provisions to debtors in arrears may be helping US households to insure themselves against income risk.