A Note on Selecting a Response Measure for Financial Distress

Since 1966, researchers have examined financial distress prediction models to determine the usefulness of accounting information to lenders. These researchers primarily used legal bankruptcy as the response variable for economic financial distress, or included legal bankruptcy with other events in dichotomous prediction models. However, theoretical models of financial distress normally define financial distress as an economic event, the inability to pay debts when due (insolvency). This study uses a loan default/accommodation response variable as a proxy for the inability to pay debts when due. The purpose of this note is to empirically test whether or not using the inability of a firm to pay debts when due, loan default/accommodation, as a response measure produces different results than using legal bankruptcy as the response measure. The study's empirical results show that legal bankruptcy and loan default/accommodation financial distress prediction models produce different statistical results, thus suggesting that the responses measure different constructs. A loan default/accommodation model also fits the data better than a bankrupt model. Our results suggest that a loan default/accommodation response may be a more appropriate measure to determine which accounting information is most useful to lenders in evaluating a firm's credit risk.     

Industry contagion in loan spreads

Spreads on new and renegotiated corporate loans are significantly higher when the loan originates (or is renegotiated) in the two years surrounding bankruptcy filings by industry rivals. This industry-specific contagion is particularly severe in the middle of industry bankruptcy waves. Furthermore, this contagion in loan spreads is mitigated in concentrated industries, consistent with the hypothesis and evidence in Lang and Stulz (1992) that bankruptcy filings in concentrated industries can have positive consequences for rivals (increased market share and/or power). There is also some evidence that contagion affects non-spread terms in loan contracts.     

Bankruptcy,overlapping directors,and bank loan pricing

Using a sample of loan facilities borrowed by firms that share directors with bankrupt firms, this study investigates whether the overlapping directors are a transmission channel of the bankruptcy contagion effect in the bank loan market and, if so, what the underlying mechanism is. We find that firms are charged higher loan spreads in the period following the bankruptcy filing of a firm with a common director and that overlapping directors are a relevant channel for the bankruptcy contagion effect, in addition to other channels identified in literature. We also find that the negative contagion effect on loan pricing is most likely driven by the overlapping directors' reputation loss due to their involvement in bankruptcy events, and not by competing hypotheses, such as director distraction and director career concern/experience. Further analyses reveal that the adverse contagion impact on loan spreads is more pronounced when overlapping directors have greater influence over corporate policies or when their reputation is more seriously damaged. Meanwhile, the contagion effect is mitigated when interlocked firms have a higher-quality board. These results further support our evidence of the director reputation loss hypothesis. We strengthen the identification strategy to establish causality. In sum, our study identifies common directors as a channel of bankruptcy contagion effects on loan pricing and director reputation loss as an underlying mechanism.     

基于非线性组合模型的P2P网贷平台危机预警研究

针对P2P网贷平台现金流较大、利润率较低和财务数据获取困难的特点,构建基于平台交易真实数据的危机预警评价指标体系和组合预测模型.将传统的财务评价指标转换成网贷平台交易数据指标,运用邻域粗糙集属性约简的方法对采集的数据指标进行降噪和约减处理,再基于机器学习理论引入神经网络、支持向量机和Logit回归等模型对数据进行训练.通过分组进行单模型和组合模拟预测,提高了新的破产指标下各模型预测的准确率.     

Implications of Debt Renegotiation for Optimal Bank Policy and Firm Behavior

This paper analyzes the problems associated with the renegotiation of debt contracts involving a bank (the lender) and a firm (the borrower) when the latter is operated by a risk averse manager. Firms undertake risky projects with loan capital borrowed from the bank. When a firm cannot pay off a loan it is technically bankrupt. Both the borrower and the lender may however experience a Pareto-improvement in their positions by renegotiating the loan. By renegotiating the terms of the debt the financially distressed firm can avoid the stigmatization of bankruptcy and the bank can avoid the costs of seizing the borrower's assets. However, our main finding is that, from the bank's point of view, renegotiating as a policy of recovering loan payments may be inefficient in practice because of false bankruptcy claims and moral hazard problems associated with exposure of the borrowing firm to the risk of default. We present a solution to the false bankruptcy claim problem that involves a mixe d strategy between asset seizure by the bank and debt renegotiation.     

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.     

The Impact of Receiving Debtor-in-Possession Financing on the Probability of Successful Emergence and Time Spent Under Chapter 11 Bankruptcy

The ability to obtain financing is a critical element in attempting to successfully reorganise a firm which has declared Chapter 11 bankruptcy. Debtor-in-possession (DIP) financing has become an increasingly popular method in recent years. This paper examines whether receiving DIP financing is related to successful reorganisations and a shortened duration under Chapter 11 bankruptcy proceedings. This study finds that there is an increase in realised returns to equity at the announcement of DIP loan agreements which is positive and statistically significant. It is also found that DIP-financed firms have a reduced probability of liquidation, and shorter time spent under bankruptcy proceedings.     

An Evaluation of the Corporate Takeover Model Using Neural Networks

Neural networks have been found to be promising in financial prediction tasks like bankruptcy and loan defaults. Their use in the capital markets is relatively new, although they have been used with some success in picking undervalued stocks. Accurate prediction of corporate takeover targets results in high financial payoffs. Researchers have used statistical procedures like logistic regression with little success in predicting corporate takeover targets. We use neural networks that are capable of producing complex mapping functions to predict mergers. We develop several neural network models carefully controlling for overfitting. Our results indicate that although neural networks map the data very well, they do not predict merger targets significantly better than logistic regression. This strongly suggests that the financial models used to predict mergers are inadequate. Firms should approach the development of merger prediction models cautiously and identify other factors that are more likely to predict mergers. Attempts to apply better analysis techniques to existing models will most likely produce similar results.     

Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach

The main purpose of this paper is to evaluate the data mining applications, such as classification, which have been used in previous bankruptcy prediction studies and credit rating studies. Our study proposes a multiple criteria linear programming (MCLP) method to predict bankruptcy using Korean bankruptcy data after the 1997 financial crisis. The results, of the MCLP approach in our Korean bankruptcy prediction study, show that our method performs as well as traditional multiple discriminant analysis or logit analysis using only financial data. In addition, our model??s overall prediction accuracy is comparable to those of decision tree or support vector machine approaches. However, our results are not generalizable because our data are from a special situation in Korea.     

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.     

Economic policy uncertainty and bankruptcy filings

Applying machine learning techniques to predict bankruptcy in the sample of French, Italian, Russian and Spanish firms, the study demonstrates that the inclusion of economic policy uncertainty (EPU) indicator into bankruptcy prediction models notably increases their accuracy. This effect is more pronounced when we use novel Twitter-based version of EPU index instead of original news-based index. We further compare the prediction accuracy of machine learning techniques and conclude that stacking ensemble method outperforms (though marginally) machine learning methods, which are more commonly used for bankruptcy prediction, such as single classifiers and bagging.     

The determinants of bank loan recovery rates

Using Moody’s Ultimate Recovery Database, we estimate a model for bank loan recoveries using variables reflecting loan and borrower characteristics, industry and macroeconomic conditions, and several recovery process variables. We find that loan characteristics are more significant determinants of recovery rates than are borrower characteristics prior to default. Industry and macroeconomic conditions are relevant, as are prepackaged bankruptcy arrangements. We examine whether a commonly used proxy for recovery rates, the 30-day post-default trading price of the loan, represents an efficient estimate of actual recoveries and find that such a proxy is biased and inefficient.     

PROBABILISTIC APPROACHES FOR CREDIT SCREENING AND BANKRUPTCY PREDICTION

Three probabilistic neural network approaches are used for credit screening and bankruptcy prediction: a logistic regression neural network (LRNN), a probabilistic neural network (PNN) and a semi‐supervised expectation maximization‐based neural network. Using real‐world bankruptcy prediction and credit screening datasets, we compare the three probabilistic approaches using various performance criteria of sensitivity, specificity, accuracy, decile lift and area under receiver operating characteristics (ROC) curves. The results of our experiments indicate that the PNN outperforms the other two techniques for decile lift and specificity performance metric. Using the area under ROC curve, we find that for bankruptcy prediction data the PNN outperforms the other two approaches when false positive rates (FPRs) are less than 40 %. LRNN outperforms the other two techniques for FPRs higher than 40 % for bankruptcy data. We observe that the LRNN results are very sensitive to the ratio of examples belonging to two classes in training data and there is a tendency to overfit training data. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Empirical comparison of hazard models in predicting SMEs failure

This study aims to shed light on the debate concerning the choice between discrete-time and continuous-time hazard models in making bankruptcy or any binary prediction using interval censored data. Building on the theoretical suggestions from various disciplines, we empirically compare widely used discrete-time hazard models (with logit and clog-log links) and the continuous-time Cox Proportional Hazards (CPH) model in predicting bankruptcy and financial distress of the United States Small and Medium-sized Enterprises (SMEs). Consistent with the theoretical arguments, we report that discrete-time hazard models are superior to the continuous-time CPH model in making binary predictions using interval censored data. Moreover, hazard models developed using a failure definition based jointly on bankruptcy laws and firms’ financial health exhibit superior goodness of fit and classification measures, in comparison to models that employ a failure definition based either on bankruptcy laws or firms’ financial health alone.     

Implications of bank regulation for loan supply and bank stability: a dynamic perspective

ABSTRACT

We show that internal funds play a particular role in the regulation of bank capital, which has not received much attention, yet. A bank's decision on loan supply and capital structure determines its immediate bankruptcy risk as well as the future availability of internal funds. These internal funds in turn determine a bank's future costs of external finance and its future vulnerability to bankruptcy risks. Using a partial equilibrium model, we study how internal funds affect these intra- and intertemporal links. Moreover, our positive analysis identifies the effects of risk-weighted capital-to-asset ratios, liquidity coverage ratios and regulatory margin calls on the dynamics of internal funds and thus loan supply and bank stability. Only regulatory margin calls or large liquidity coverage ratios achieve bank stability for all risk levels, but for large risks a bank will stop credit intermediation.     

Intra‐industry bankruptcy contagion: Evidence from the pricing of industry recovery rates

How does bankruptcy contagion propagate among industry peers? We study the debt recovery channel of industry contagion by examining whether the cost of a company's debt is affected by the observed recovery rates of its bankrupt industry peers. Our results show that lower industry recovery rates are associated with higher loan spreads, but only when the contracts were originated during industry bankruptcy waves. Consistent with the debt recovery channel of industry contagion, we find that the negative effects of industry recovery rates are significantly stronger under situations where the effect is expected to be more salient.     

An empirical comparison of bankruptcy models

Four types of bankruptcy prediction models based on financial statement ratios, cash flows, stock returns, and return standard deviations are compared. Based on a sample of bankruptcies from 1980 to 1991, results indicate that no existing model of bankruptcy adequately captures the data. During the last fiscal year preceding bankruptcy, none of the individual models may be excluded without a loss in explanatory power. If considered in isolation, the cash flow model discriminates most consistently two to three years before bankruptcy. By comparison, the ratio model is the best single model during the year immediately preceding bankruptcy. Quasi-jack-knifing procedures suggest that none of the models can reliably predict bankruptcy more than two years in advance.     

Bankruptcy and Institutions: Theory and Empirical Evidence from Korea and the United States

Using a simple z-score bankruptcy model, this article explores the relationship between bankruptcy threshold and institutions. The z-score threshold for bankruptcy is found to be higher in countries with stronger institutions. To test this claim, a cross-section data set of 86 Korean firms and 60 US firms from 1991 to 2001, extracted from a panel data set, is used. The empirical finding that the z-score bankruptcy threshold in the United States (which has better quality of institutions than does Korea) is higher than that in Korea is consistent with the prediction of the model. Additionally, having examined bankruptcy laws of the two countries, it is found that filing a petition for bankruptcy is easier and debtors rights are better protected in the United States than in Korea, which suggests that the bankruptcy laws of Korea and the United States may be partially responsible for the difference in the z-score threshold for bankruptcy.