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.     

Strategic Bankruptcies and Price Reactions to Bankruptcy Filings

This study investigates whether the stock market differentiates between firms that file bankruptcy petitions for strategic reasons and firms that file bankruptcy petitions for financial reasons. We perform both univariate and regression tests on a sample of 245 firms that filed Chapter 11 bankruptcy petitions between 1981 and 1996. After controlling for bankruptcy outcome, probability of bankruptcy, firm financial condition, and firm size, we find that, in the period around bankruptcy filing, firms that file bankruptcy petitions for financial reasons have significantly larger stock price declines than firms that file bankruptcy petitions for strategic reasons.     

Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings

We examine whether the language used in 10‐K filings reflects a firm's risk of bankruptcy. Our sample contains 424 bankrupt U.S. companies in the period 1994–2015 and we use propensity score matching to find healthy matches. Based on a logit model of failing and vital firms, our findings indicate that firms at risk of bankruptcy use significantly more negative words in their 10‐K filings than comparable vital companies. This relationship holds up until three years prior to the actual bankruptcy filing. With our investigation, we confirm the results from previous accounting and finance research. 10‐K filings contain valuable information beyond the reported financials. Additionally, we show that 10‐Ks filed in the year of a firm's collapse contain an increased number of litigious words relative to healthy businesses. This indicates that the management of failing firms is already dealing with legal issues when reporting financials prior to bankruptcy. Our results suggest that analysts ought to include the presentation of financials in their assessment of bankruptcy risk as it contains explanatory and predictive power beyond the financial ratios.     

Why and How UK Firms Hedge

This paper attempts to differentiate among the theories of hedging by using disclosures in the annual reports of 400 UK companies and data collected via a survey. I find, unlike many previous US studies, strong evidence linking the decision to hedge and the expected costs of financial distress. The tests show that this is mainly because my definition of hedging includes all hedgers and not just derivative users. However, when the tests employ the same hedging definition as previous US studies, financial distress cost factors still appear to be more important for this sample than samples of US firms. Therefore, a secondary explanation for the strong financial distress results might be due to differences in the bankruptcy codes in the two countries, which result in higher expected costs of financial distress for UK firms. The paper also examines the determinants of the choice of hedging method distinguishing between non‐derivative and derivatives hedging. My evidence shows that larger firms, firms with more cash, firms with a greater probability of financial distress, firms with exports or imports and firms with more short‐term debt are more likely to hedge with derivatives. Thus, differences in opportunities, in incentives for reducing risk and in the types of financial price exposure play an important role in how firms hedge their risks.     

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。     

A comparison of the relative costs of financial distress models: artificial neural networks,logit and multivariate discriminant analysis

This study uses two artificial neural networks (ANNs), categorical learning/instar ANNs and probabilistic (PNN) ANNs, suitable for classification and prediction type issues, and compares them to traditional multivariate discriminant analysis (MDA) and logit to examine financial distress one to three years prior to failure. The results indicate that traditional MDA and logit perform best with the lowest overall error rates. However, when the relative error costs are considered, the ANNs perform better than traditional logit or MDA. Also, as the time period moves farther away from the eventual failure date, ANNs perform more accurately and with lower relative error costs than logit or MDA. This supports the conclusion that for auditors and other evaluators interested in early warning techniques, categorical learning network and probabilistic ANNs would be useful. © 1997 John Wiley & Sons, Ltd.     

Modeling default risk with support vector machines

Predicting default risk is important for firms and banks to operate successfully. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so-called Support Vector Machine (SVM) to predict the default risk of German firms. Our analysis is based on the Creditreform database. In all tests performed in this paper the nonlinear model classified by SVM exceeds the benchmark logit model, based on the same predictors, in terms of the performance metric, AR. The empirical evidence is in favor of the SVM for classification, especially in the linear non-separable case. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of SVM parameters. In terms of the empirical results obtained by SVM, the eight most important predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Some of the financial ratios selected by the SVM model are new because they have a strong nonlinear dependence on the default risk but a weak linear dependence that therefore cannot be captured by the usual linear models such as the DA and logit models.     

Inter-firm linkages and the wealth effects of financial distress along the supply chain

Extant research examines the extent to which bankruptcy has intra-industry valuation consequences. This study broadens the investigation by examining the wealth effects of distress and bankruptcy filing for suppliers and customers of filing firms. On average, important wealth effects occur prior to and at bankruptcy filings and extend beyond industry competitors along the supply chain. Specifically, distress related to bankruptcy filings is associated with negative and significant stock price effects for suppliers. Supplier wealth effects are more negative when intra-industry contagion is more severe. We also investigate the importance of industry structure, specialized product nature, and leverage on supply chain effects.     

The financial distress pricing puzzle in banking firms

This paper examines whether the financial distress pricing puzzle observed for non-financial firms is also observed for financial firms and how this puzzle differs according to the extent of short-sale constraints. By using the eight distress measures developed for financial firms, we find that there is a strong negative relation in the cross-section between financial distress and subsequent bank stock returns, regardless of adjustment for risk. However, this distress pricing puzzle is statistically significant only for high short-sale constrained banks, but not for low short-sale constrained banks. Thus, short-sale constraints are at least one non-risk attribute that causes the distress pricing puzzle for financial firms. We also find that despite its simple form, compared to the other complex distress measures, non-performing loans (NPLs) are the most informative in predicting future bank stock returns as well as bankruptcy and failure.     

Firms’ performance under different bankruptcy systems: a Europe–USA empirical analysis

This study empirically analyses the effect that the bankruptcy law has on firms’ performance based on its financial situation. To do this, we considered the different types of efficiency and their influence on firms’ value. The study was carried out for Germany, Spain, the United States, France and the United Kingdom. We applied System‐GMM estimation to dynamic panel data. The main results show that under creditor‐oriented systems, there is a decrease in the value of both financially distressed firms and those filing for bankruptcy.     

Beyond bankruptcy: Does the US bankruptcy code provide a fresh start to entrepreneurs?

This paper assesses the extent to which the US bankruptcy system is effective in providing small businesses a “fresh start” after a bankruptcy filing. I use data from the 1993, 1998 and 2003 National Survey of Small Business Finances to explore how firms fare after a bankruptcy filing. On the positive side, previously bankrupt firms are not any more burdened than the average small firm by problems relating to profitability, cash flow, health insurance costs, or taxes. Further, the fact that these firms are surviving several years after the filing is itself a testament to the efficient functioning of the US bankruptcy system. It suggests that the bankruptcy system goes a long way toward helping businesses recover after a bankruptcy filing.     

Direct Bankruptcy Costs: Evidence from the Trucking Industry

Direct costs of bankruptcy are measured for a sample of firms in the trucking industry that petitioned for bankruptcy protection from 1970 to 1985. Average direct bankruptcy costs represent 9.12 percent of the book value of total assets as of the year before filing. These costs are large compared with those reported in prior studies: 3.39 percent of book value of assets for retail firms, 4.31 percent for industrials, and .53 percent for railroads. We also find evidence of substantial economies of scale in bankruptcy costs in the trucking sample.     

BANKRUPTCY DISCRIMINATION WITH REAL VARIABLES

This paper reconsiders the accepted usage of nondeflated financial ratios in statistical models to differentiate between failed and nonfailed firms. Non-deflated ratios are hypothesized to inadequately reflect inter-temporal macroeconomic fluctuations that affect the ability of firm's to survive. Using a sample of 124 oil and gas companies between the period 1982–1988, the going concern assumption is evaluated with statistical logit models using either nondeflated or deflated financial ratios. Deflated company ratios are created by transforming data with price indices or by creating market value ratios. Empirical results suggest that a superior bankruptcy early warning model is developed for the oil and gas industry by creating real financial and reserve ratios and by introducing external factors, such as oil prices, interest rates and accounting method, as independent predictors. Overall classification accuracy is approximately 95 percent.     

On the Efficiency of Bankruptcy Law: Empirical Evidence in Spain

The current economic crisis is showing one of the main problems that many companies in financial distress have to face, namely, the impact of bankruptcy law in relation to companies and firms. This paper aims to analyze the bankruptcy law ex‐ante efficiency when companies are in financial distress. To test it out, two research questions are submitted: (i) Is solvency, the criterion used in the Spanish law, the best one to assess the relative significance of the main indicators, which determine bankrupt firms? (ii) Is the Spanish bankruptcy law efficient according to solvency or are there better criteria? To answer them, a logistic regression model is conducted. The sample embraces 1,387 firms in Spain, the data being obtained from 12 Commercial Justice Courts complemented with financial information. The main conclusion is that the solvency criterion is adequate to classify bankrupt companies although currently Spanish Bankruptcy law is not as efficient as it could be. Additionally, the relevant companies' indicators, which explain the financial distress procedure, are presented. Copyright © 2013 INSOL International and John Wiley & Sons, Ltd     

An Early Warning System of Financial Distress Using Multinomial Logit Models and a Bootstrapping Approach

This study adopts multinomial logit models to separately measure the extent to which financial ratios and corporate governance signal the likelihood of "slight distress events" and "reorganization and bankruptcy." The results show that corporate governance variables are closely related to the occurrence of "slight distress events." The estimated misclassification costs of the 1,000 resamples generated through bootstrapping procedures are statistically lower for a model that makes use of corporate governance (CG model) than one without corporate governance (non-CG model) at all cutoff points in 2009, and cutoff points from 0.11 to 0.27 in 2008. Since corporate governance is incrementally useful in predicting financial distress, the CG model's predictive ability improves as two corporate governance factors are considered: ownership ratio of insiders and pledge-ownership ratio of insiders.     

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.     

REORGANIZATION AND FINANCIAL DISTRESS: AN EMPIRICAL INVESTIGATION

Prior studies on financial distress focus on the restructuring of one aspect of the firm. By examining various forms of restructuring, we provide empirical evidence that asset restructuring and governance restructuring play significant roles before bankruptcy filing. Our analysis shows that financial restructuring before bankruptcy is influenced by the holdout problem among creditor groups. Evidence suggests that the fraudulent conveyance provision does not pose a serious impediment to divestitures during the two years before bankruptcy. The evidence also indicates that Chapter 11 reorganization is lenient toward management. Although Chapter 11 allows the firm to breach burdensome executory contracts with employees, our findings suggest that union busting is not an important part of the reorganization process. Finally, we identify various financial characteristics to predict the different types of restructuring a firm may undertake.     

Investing in Bankrupt Firms

We examine the investment characteristics of firms electing to enter bankruptcy, between 1973 and 1982. Comparisons are made before and after the Bankruptcy Reform Act of 1978. Our results indicate that the 1978 Act had no significant impact on bankruptcy decisions or resolutions for actively traded firms. Trading in bankrupt firms' securities is becoming more common, but no abnormal returns appear to be available. Systematic risk does not change significantly with the filing of bankruptcy, but there is a significant increase in return variance. The financial markets also react to various announcements of stages in the reorganization process.     

Effect of cash flow risk on corporate failures,and the moderating role of earnings management and abnormal compensation

In this study, we find that United States firms' average cash flow risk (CFR) shows a significantly increasing trend over the past four decades or so. This does not portend well considering the significance of cash flows in maintaining a firm's financial health and going concern status. The CFR also increases dramatically for firms approaching financial distress or bankruptcy, suggesting its important role in predicting a firm's failure. Empirically, we find that CFR has a strong positive effect on a firm's financial distress likelihood. We also find that the association between CFR and financial distress is negatively moderated in firms with high earnings management and abnormal compensation. The results suggest that managers in firms with high CFR are more likely to use heuristics in form of earnings management. Thus, supporting the upper echelons theory related to managers under performance pressure. Meanwhile, consistent with the notion in the agency theory that financial incentives serve as effective monitoring mechanisms, compensation packages can incentivize better risk management practices and decrease the likelihood of a firm's failure. Our findings are also robust to alternative definitions of a firm's failure: financial constraints, presumed debt covenant violation and legal bankruptcy filings.