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The Bankruptcy System's Chapter 22 Recidivism Problem: How Serious is It?
Abstract
This paper is adapted from the keynote address from the Eastern Finance Association's 2014 meeting in Pittsburg, Pennsylvania. We highlight a recidivism problem: about 15% of debtors who emerge as continuing entities under Chapter 11, or are acquired as part of the bankruptcy process, ultimately file for bankruptcy protection again (18.25% when considering only those firms which emerge as a continuing, independent entity). We argue that the “Chapter 22” issue should not be dismissed by the bankruptcy community just because no interested party objects during the confirmation hearing. Applying the Z”-Score model to a large sample of Chapter 11 cases reveals highly different and significant expected survival profiles at emergence. Credible distress prediction techniques can effectively predict the future success of firms emerging from bankruptcy and be used by the bankruptcy court to assess the feasibility of the reorganization plan, a requirement mandated by the Bankruptcy Code. Branch reviews, discusses, and critiques in this follow-up article to Altman's original thesis.
... This notion motivates our study as investors as well as management could benefit from the information contained in the stock price. In addition, researchers have documented that approximately 15 percent of firms that have reorganized and emerged as continuing entities under Chapter 11 ultimately file for bankruptcy protection again (Altman, 2014;Altman and Branch, 2015). This recidivism problem is costly to taxpayers and can be mitigated with an improved bankruptcy process. ...
... The Bankruptcy Abuse Prevention and Consumer Protection Act of 2005 states that in order for a reorganization plan to be confirmed, the court must make an independent finding that it is feasible and that further reorganization is unlikely. However, unless convincing evidence of the lack of feasibility is presented by interested parties, bankruptcy courts appear to believe that sanctioning the plan as presented is the only and best option (Altman and Branch, 2015). Altman et al. (2009) document that firms that filed second bankruptcy petitions were both significantly less profitable and more highly leveraged than those that emerged and continued as going concerns. ...
... The tendency for companies to emerge from Chapter 11 with too much debt and too little profitability causes concern for the bankruptcy process. Altman (2014) and Altman and Branch (2015), when studying the recidivism of bankrupt firms filing multiple times, suggest that a credible distress prediction technique can effectively predict the future success of firms emerging from bankruptcy and can be used by bankruptcy courts to assess the feasibility of reorganization plans. Franks and Loranth (2013) document how the allocation of control rights between secured and unsecured creditors in bankruptcy and the compensation scheme of the agent managing the bankruptcy process influences outcomes. ...
Purpose
The purpose of this paper is to investigate if the volatility of stock prices in the days surrounding the Chapter 11 bankruptcy process predicts a firm’s likelihood to successfully restructure and emerge from bankruptcy.
Design/methodology/approach
The authors use a sample of Chapter 11 cases between 1980 and 2016 that have available stock price data surrounding the bankruptcy filing dates. Following Goyal and Wang (2013), the KMV–Merton model is utilized to estimate the probability that a firm successfully emerges from its restructuring process. In order to interpret the market’s assessment about a firm, the authors use the analogy of a European call option to derive the assessment of the firm’s prospects as the probability that it will emerge from bankruptcy. This estimated probability of emergence is compared to actual outcomes of bankruptcy cases and tested for significance using various regression techniques.
Findings
This study exploits the information found in stock prices surrounding the bankruptcy process and finds that volatility after, but not before, filing for bankruptcy significantly predicts a firm’s likelihood to emerge. In addition, the market-based probability of emergence has better predictive power on the recovery rates of unsecured creditors than measures based on financial statements.
Originality/value
Predictors of bankruptcy have been extensively studied by scholars over the decades, with early studies focusing on accounting-based measures and recent studies incorporating market-driven variables. However, in recent years, studies have begun to assess bankrupt firms’ ability to reorganize and successfully emerge from bankruptcy. This study contributes to the recent literature investigating market-based predictors of successful emergence.
... A separate trend in forecasting corporate bankruptcy is the predicting of repeated corporate bankruptcy (e.g. [2,7,17]). To justify the usefulness of conducting this type of research, arguments presented by the authors of the paper [2] can be cited. ...
... [2,7,17]). To justify the usefulness of conducting this type of research, arguments presented by the authors of the paper [2] can be cited. According to them, about 18.25% of companies in the USA land in court once again (or more than once) after a court has declared their bankruptcy open for arrangement. ...
... An alternative approach to the one proposed in the paper [2] is a statistical evaluation of the financial standing of companies in the years following the declaration of bankruptcy compared with the situation of financially sound companies. The examination of companies' paths to get out of the insolvency problem may be a source of valuable information, useful for the assessment of the likelihood that other bankrupt enterprises achieve success as a result of the execution of restructuring proposals. ...
In the literature devoted to applications of multivariate statistical analysis to finance, the issue of bankruptcy forecasting is dealt with at length, but few papers concern the statistical evaluation of financial standing of companies after they have been declared bankrupt. The examination of their way out from the insolvency problem may be a source of valuable information, useful for the assessment of the probability that other bankrupt enterprises achieve success as a result of the execution of restructuring proposals. The purpose of this article is to present a proposal to use selected classification methods when studying the financial standing of companies after the declaration of bankruptcy in comparison with the situation of financially sound companies. The logit model and the classification tree were used to classify companies. The evaluation of the classification efficiency was based on the following measures: sensitivity, specificity and AUC. In the study, both univariate (Tukey’s criterion) and multivariate (projection depth function) methods for detecting outliers were considered. The study covered construction companies in Poland in the years 2005–2009.
... Using firms' balance sheets and income statements, we calculate 50 financial ratios commonly used in the literature when predicting corporate bankruptcy (Beaver 1966;Altman 1968;Ohlson 1980;Dimitras et al. 1996;Dimitras et al. 1999;Balcaen and Ooghe 2006;Agarwal and Taffler 2008;Amendola et al. 2011;Jackson and Wood 2013;Wang et al. 2014;Altman and Branch 2015;Kim et al. 2016;Veganzones and Séverin 2018). We then restricted the number of variables to the most noteworthy 30 variables. ...
This study addresses a significant gap in the literature by comparing the effectiveness of traditional statistical methods with artificial intelligence (AI) techniques in predicting bankruptcy among small and medium-sized enterprises (SMEs). Traditional bankruptcy prediction models often fail to account for the unique characteristics of SMEs, such as their vulnerability due to lean structures and reliance on short-term credit. This research utilizes a comprehensive database of 7104 Belgian SMEs to evaluate these models. Belgium was selected due to its unique regulatory and economic environment, which presents specific challenges and opportunities for bankruptcy prediction in SMEs. Our findings reveal that AI techniques significantly outperform traditional statistical methods in predicting bankruptcy, demonstrating superior predictive accuracy. Furthermore, our analysis highlights that a firm’s position within the Global Value Chain (GVC) impacts prediction accuracy. Specifically, firms operating upstream in the production process show lower prediction performance, suggesting that bankruptcy risk may propagate upward along the value chain. This effect was measured by analyzing the firm’s GVC position as a variable in the prediction models, with upstream firms exhibiting greater vulnerability to the financial distress of downstream partners. These insights are valuable for practitioners, emphasizing the need to consider specific performance factors based on the firm’s position within the GVC when assessing bankruptcy risk. By integrating both AI techniques and GVC positioning into bankruptcy prediction models, this study provides a more nuanced understanding of bankruptcy risks for SMEs and offers practical guidance for managing and mitigating these risks.
... Using firms' balance sheets and income statements, we calculate 50 financial ratios commonly used in the literature when predicting corporate bankruptcy (Beaver, 1966;Altman, 1968;Ohlson, 1980;Dimitras et al., 1996;Dimitras et al., 1999;Balcaen and Ooghe, 2006;Agarwal and Taffler, 2008;Amendola et al., 2011;Jackson and Wood, 2013;Wang et al., 2014;Altman and Branch, 2015;Kim et al., 2016, Veganzones andSéverin, 2018). Among these, several variables possess the same numerator or denominator, so we check for multicollinearity and remove all variables with a variance inflation factor (VIF) greater than 10 (O' Brien, 2007). ...
This study addresses a gap in the literature by conducting a comparative analysis of statistical and artificial intelligence techniques for predicting bankruptcy in small and medium-sized enterprises (SMEs). Using a comprehensive database of 7,104 Belgian SMEs, our findings demonstrate that intelligent techniques offer superior predictive accuracy for bankruptcy. Notably, our results indicate that a firm's position in the global value chain significantly affects prediction accuracy, with lower performance for firms operating upstream in the production process. This suggests that bankruptcy risk may spread upward along the value chain. This insight is particularly valuable for practitioners, as it highlights the importance of focusing on specific performance factors based on the type of firms they manage, advise, or collaborate with.
... Others check how restructuring proceedings affected share prices (Ahmad et al., 2018;Komera & Jijo Lukose, 2013;Prusak & Potrykus, 2021). Altman tried to predict subsequent financial problems of companies that had been restructured (Altman & Branch, 2015;Altman et al., 2009). Several authors also aimed at identifying financial factors affecting the duration of a company with financial problems (Ayadi et al., 2021;Cepec & Grajzl, 2020); still other research focused on identifying determinants of a successful or unsuccessful restructuring process and forecasting the result (Ahmad, 2019). ...
Deteriorating financial condition of a company may lead to insolvency. As a result,
the company may be declared bankrupt or undergo restructuring. The first goal of the study described in the paper is to compare the financial condition of Poland-based non-financial companies undergoing restructuring and bankruptcy processes. In the empirical study, a tool for forecasting the future financial situation of a company was constructed. The second goal is the assessment of the effectiveness of restructuring processes on the basis of a comparative analysis of companies subjected to varios forms of this procedure. An attempt was made to identify the determinants of the success or failure of the restructuring process.The study was based on the information from the Coface Poland, EMIS Professional and the Court and Commercial Gazette (Pol. ‘Polski Monitor Sądowy i Gospodarczy’) databases. The empirical research was conducted on a random sample of financial data of 1740 non-financial companies (580 companies that were declared bankrupt, 580 companies undergoing a restructuring, and 580 companies in a good financial condition) in 2015–2019. The Kruskal-Wallis test, Dunn’s test, Mann-Whitney’s test and the random forest classifier were used for the purpose of the study.
Companies that were declared bankrupt or underwent a restructuring process have more in common with each other than with companies efficiently operating in the market. It was possible to create a classifier which enabled forecasting whether a company will have financial problems. The results of the study demonstrated that the efficiency of the restructuring process does not depend on financial factors. Moreover, restructuring often fails to protect companies from bankruptcy and does not have a significant impact on the financial condition of restructured entities.
... Other studies examined the reorganization outcome and the factors that influence this outcome (Altman et al., 2009;Altman, 2014a,b;Altman and Branch, 2015;Altman et al., 2019) but most of them used univariate analysis, multivariate discriminant analysis, and conditional probability models that have some common problems. First, most classical methods assume a dichotomous dependent variable (failed and non-failed firms). ...
This paper investigates the survival prospects of reorganized companies in France. It seeks to identify the determinants that accelerate or reduce the time to failure of reorganized firms in the French context. Our findings reveal that the company's size, profitability, liquidity, the industry profitability, and inflation rate have a positive effect while the leverage and the variation of short-term interest rate have a negative effect on the survival of reorganized firms in France. The study also shows that the failure process of a reorganized firm is similar to that of a new firm.
... In addition, in evidence (e.g., Smith and Dion 2008) supports that conclusion. Finally, Altman and Branch (2015), argue that using a distressed prediction model, like Z-and Z"-scores, to assess the likelihood whether an emerging firm from bankruptcy will or will not subsequently file for bankruptcy again, is a prudent strategy for bankruptcy courts. More details on this and other studies we have conducted on the Chapter 22 phenomena can be found in Altman et al. (forthcoming). ...
Fifty years ago, I published the initial, classic version of the Z-score bankruptcy prediction models. This multivariate statistical model has remained perhaps the most well-known, and more importantly, most used technique for providing an early warning signal of firm financial distress by academics and practitioners on a global basis. It also has been used by scholars as a benchmark of credit risk measurement in countless empirical studies. Practical applications of the Altman Z-score model have also been numerous and can be divided into two main categories: (1) from an external analytical standpoint, and (2) from an internal to the distressed firm viewpoint. This paper discusses a number of applications from the former’s standpoint and in doing so, we hope, also provides a roadmap for extensions beyond those already identified.
... Backward stepwise selection consists in starting with all potential predictors, testing the deletion of each potential predictor using a chosen model fit criterion, deleting the potential predictor (if any) whose loss gives the most statistically insignificant alteration of the model fit, and repeating this process until no further potential predictors can be deleted without a statistically significant loss of fit. Referring to prior works in the literature, this study includes 30 financial predictors that are commonly used and selected in predicting corporate bankruptcy (Agarwal & Taffler, 2008;Altman, 1968;Altman & Branch, 2015;Amendola, Restaino, & Sensini, 2011;Balcaen & Ooghe, 2006;Beaver, 1966;Dimitras, Zanakis, &Zopoudinis, 1996;Jackson & Wood, 2013;Kim et al., 2016;Ohlson, 1980;Wang et al., 2014). These indicators are reported in Table 1. ...
The main purpose of this paper is to identify the financial antecedents of corporate bankruptcy by employing a robust variable selection procedure. Based on a sample of 1,338 Belgian private firms, a backward stepwise logistic regression technique is employed. The findings indicate that the likelihood of corporate bankruptcy is higher at lower levels of solvency, liquidity and profitability. Furthermore, our results reveal that corporate bankruptcy is more likely in older and larger firms.
... Since the fundamental paper of Beaver (1966), which proposes for the first time the use of financial indicators as bankruptcy predictors, and the even more essential work of Altman (1968), which extended the previous intuition to a multivariate framework, there have been many contributions in this field (Agarwal and Taffler, 2008;Aloy Niresh, J. and Pratheepan, T., 2015;Altman et al., 2014;Altman E. I. and Branch B., 2015;Balcaen S. and Ooghe H., 2006;Becchetti and Sierra, 2003;Bellovari et al., 2007;Dimitras et al., 1996;Gunathilaka, C., 2014;Jackson R. and Wood,Morris A., 2013;Platt and Platt, 2002;Poddighe and Madonna, 2006;Sanchez J.A. and Sensini L., 2013). Some authors have studied the role of the financial variables (Keasey and Watson, 1987;Amendola, et al., 2010) as predictors. ...
This paper investigates the performance of forecasting models for default risk referring to the annual balance sheet information of Italian firms. One of the main issues in bankruptcy predictions is related to the selection of the best set of indicators. Therefore, our main research question concerns the identification of the determinants of corporate financial distress, comparing the performance of innovative selection techniques. Furthermore, several aspects related to the default risk analysis have been considered, namely the nature of the numerical information and the sample design. The proposed models take in consideration the above-mentioned issues and the empirical results, elaborated on a data set of financial indices expressly derived from annual reports of the industrial firms. These reports provide evidence in favor of our proposal over the traditional ones.
... Since the fundamental paper of Beaver (1966), which proposes for the first time the use of financial indicators as bankruptcy predictors, and the even more essential work of Altman (1968), which extended the previous intuition to a multivariate framework, there have been many contributions in this field (Agarwal and Taffler, 2008;Aloy Niresh, J. and Pratheepan, T., 2015;Altman et al., 2014;Altman E. I. and Branch B., 2015;Balcaen S. and Ooghe H., 2006;Becchetti and Sierra, 2003;Bellovari et al., 2007;Dimitras et al., 1996;Gunathilaka, C., 2014;Jackson R. and Wood,Morris A., 2013;Platt and Platt, 2002;Poddighe and Madonna, 2006;Sanchez J.A. and Sensini L., 2013). Some authors have studied the role of the financial variables (Keasey and Watson, 1987;Amendola, et al., 2010) as predictors. ...
This paper investigates the performance of forecasting models for default risk referring to the annual balance sheet information of Italian firms. One of the main issues in bankruptcy predictions is related to the selection of the best set of indicators. Therefore, our main research question concerns the identification of the determinants of corporate financial distress, comparing the performance of innovative selection techniques. Furthermore, several aspects related to the default risk analysis have been considered, namely the nature of the numerical information and the sample design. The proposed models take in consideration the above-mentioned issues and the empirical results, elaborated on a data set of financial indices expressly derived from annual reports of the industrial firms. These reports provide evidence in favor of our proposal over the traditional ones.
... During the last six decades and following the seminal papers by Beaver (1968) and Altman (1968), a voluminous body of literature has been developed in finance and accounting to measure financial distress and to predict the bankruptcy of the firm (i.e., Altman, 1968; Beaver, 1968; Altman, 1973; Altman et al., 1977; Jones, 1987; Altman et al., 1994; Mensah, 1984; Scott, 1981; Zmijewski, 1984; Hillegeist et al., 2004; Jones and Hensher, 2004; Beaver and McNichols, 2005; Altman and Branch, 2015; see also Bellovary et al., 2007 for a review of studies on the topic). In a notable portion of these studies, the authors utilize, in general, parametric-based and/or non-parametric-based methodologies. ...
In this paper, we follow Anderson et al. (2009) and suggest a simple approach to employ a set of financial ratios as inputs to estimate an aggregate bankruptcy index (ABI). This index is a within sample measure, ranges between 0 and 1, and ranks the firms on the basis of their relative financial distress. ABI can be used to predict the propensity of financial failure and corporate bankruptcy. For the purpose of comparison and assessment of the robustness of this index, we estimate Z-score by multivariate discriminant analysis, using the same set of financial ratios to compare the predictive accuracy of two approaches.
We find that, to some extent, ABI can predict the bankruptcy of the firms more accurately than Z-score. The empirical results of the paper suggest that ABI has relatively robust predictive power and, therefore, can be applied together with other, based on parametric and non-parametric models to predict corporate bankruptcy.
... Altman and Branch (2015) highlight the recidivism problem where firms that have successfully undergone Chapter 11 reorganization and emerged as continuing entities or are acquired as part of the bankruptcy process refile for bankruptcy protection.5 The estimation results are available upon request. ...
We examine market reactions to the financial distress announcements of listed firms in Malaysia. The sample consists of 236 financially distressed companies between 2001 and 2014. We investigate whether the market differentiates between the subsequently re-emerged and subsequently delisted firms at the time of financial distress announcements. The results suggest that there is evidence of differing reactions to distress announcements according to firms' outcomes. These results suggest that, at the time of financial distress announcements, the capital market differentiates firms based on the expected outcomes of the distress, showing that the market has insights into the expected outcomes of the financial distress.
This paper studies the long‐term effect of hedge fund activism on distressed firms by tracing the post‐emergence performance of firms that successfully resolved distress. We find that the firms restructured with hedge funds' intervention, compared to their counterparts that emerged without such intervention, are more likely to lose their public status, enjoy higher financial stability, and invest more. Notably, the gap in financial strength lasts at least 3 years after emergence. These findings suggest that the efficiency gains brought by hedge fund activism during the restructuring process tend to positively impact the restructured firms' financial soundness in the post‐intervention period.
This paper provides a new approach to developing a firm's distress and recovery prediction score. This score was designated the FL-Score and was structured from the interaction between financial and economic components. The tests were conducted from a sample of U.S. non-financial public firms from 2002 to 2019, using both more traditional statistical models, such as logit regressions, and machine learning techniques. The results show that the FL-Score is robust in predicting companies' distress and recovery, even for particular cases of distress, e.g., pure economic distress, pure financial distress, and mixed distress. Different profiles of failure risks were identified according to firm size, and an inverse relationship was also identified between the risks of distress, measured by the FL-Score, and the use of financial derivatives by the firm as a way to mitigate distress or accelerate recovery. The study also presents relevant considerations regarding using metrics related to the current and expected generation of economic value for the modeling of distress and recovery prediction scores.
The bankruptcy system plays an important role in resolving financial distress and reallocating resources in the economy. While many believe that transparency is central to an efficient bankruptcy system, the Bankruptcy Code lacks clear standards for disclosure and financial reporting. In this paper, I study the effects of information disclosure on bankruptcy outcomes by exploiting two sources of plausibly exogenous variation in disclosure during a bankruptcy: the random assignment of bankruptcy judges who may differ in interpreting the disclosure requirements of the law, and a significant change in regulation that increased disclosure by certain creditors but not others. I find evidence that disclosure can both improve asset allocation choices and facilitate more efficient bargaining between creditors. I also highlight two frictions—compliance costs and proprietary costs—that may inhibit transparency and make mandatory disclosure undesirable. My findings provide some of the first evidence on the role of information disclosure in corporate bankruptcy, and I discuss their implications for policy and for the broader corporate disclosure literature.
The aim of this paper is to present the results of an assessment of the financial condition of companies from the construction industry after the announcement of arrangement bankruptcy, in comparison to the condition of healthy companies. The logistic regression model estimated by means of the maximum likelihood method and the Bayesian approach were used. The first achievement of our study is the assessment of the financial condition of companies from the construction industry after the announcement of bankruptcy. The second achievement is the application of an approach combining the classical and Bayesian logistic regression models to assess the financial condition of companies in the years following the declaration of bankruptcy, and the presentation of the benefits of such a combination. The analysis described in the paper, carried out in most part by means of the ML logistic regression model, was supplemented with information yielded by the application of the Bayesian approach. In particular, the analysis of the shape of the posterior distribution of the repeat bankruptcy probability makes it possible, in some cases, to observe that the financial condition of a company is not clear, despite clear assessments made on the basis of the point estimations.
Giełda papierów wartościowych jest ważnym wyznacznikiem kondycji gospodarki, stąd spółki giełdowe są przedmiotem publicznego zainteresowania. Województwa leżące bezpośrednio przy wschodniej granicy Polski charakteryzują się niższym poziomem uprzemysłowienia od średniej krajowej. Przedmiotem artykułu jest analiza zmian kondycji finansowej spółek giełdowych z tego regionu w latach 2013–2017. Wyniki badań wskazują, iż spółki te nie są zagrożone bankructwem – charakteryzują się przeciętną sytuacją finansową, stałą w badanym okresie.
Purpose
Periods of financial distress represent an episode during the firm’s life that requires an effective governance structure in the interests of shareholders. Changes in corporate governance structure are examined as firms approach and emerge from Chapter 11 bankruptcy. The purpose of this paper is to posit that firms alter their governance toward a more effective framework during restructuring, leading to emergence as a better performing firm.
Design/methodology/approach
The data set includes large firms that filed for bankruptcy between 1998 and 2013. Financial and governance characteristics prior to filing are compared to traits following emergence. The likelihood of emerging from bankruptcy is tested based on governance characteristics prior to filing and the change in these characteristics during bankruptcy.
Findings
The results show that firms use the bankruptcy process to significantly change their governance characteristics. These changes include smaller boards, greater board independence, unitary boards, the separation of the CEO and chairman positions, and changes in the ownership structure. Despite these changes, performance following emergence does not improve, and the changes in governance structure do not alter the likelihood that the firm will emerge.
Originality/value
This study, unlike previous studies, takes a broad look at governance characteristics for firms before and after bankruptcy. The findings imply that “better” governance, as defined in the literature, is not necessarily the pathway to better performance as many posit. The factors that influence the likelihood of emerging from bankruptcy and post-emergence performance require further study.
The paper deals with payment discipline in the building industry in the Czech Republic. The aim of this paper is to identify, compare and evaluate the financial situation of building companies with different payment practices in the Czech Republic in the period 2010 – 2014. The paper uses enterprise and statistical methods. The payment discipline of companies is expressed by a payment index, which is constructed by Bisnode. The results are based on analysis of 1374 companies which are operated in the building industry. The analysis shows that payment habits increase with higher index IN05, Taffler´s model, index IN99 and with larger size of the firms. Payment habits of building companies are very good. However, about 2 % of solvent companies have poor payments habits.
Before they emerge from bankruptcy, U.S. debtor companies are required to provide financial and operational projections to the Bankruptcy Court. The projections are of interest to numerous parties - analysts, advisors, creditors, and securityholders - but the results of this study suggest that the projections are frequently (and often, greatly) overstated.
This study presents surprising new statistical evidence that contributes to the current “over-heated” academic debate about the Delaware courts’ role in Chapter 11 failure. In 2001, Professor LoPucki published an influential article suggesting that when large corporations file for bankruptcy under Chapter 11, they fail at a dramatically higher rate in Delaware courts than in other jurisdictions. He attributed this to corruption. His article enraged many academics and practitioners, and ignited many articles in the past two decades. This study presents startling evidence that while Chapter 11s filed in Delaware courts did have much higher failure rates from 1991-1996, after 1996, the failure rates for Delaware-cases and non-Delaware cases have been converging. In fact, in very recent years, failure rates have been approximately the same for Delaware cases and non-Delaware cases. While this study suggests that the debate about Delaware’s relevance is now obsolete, it also offers important insight into Professor LoPucki’s theory, as well as his critics’ arguments.
In the early 1990s, Delaware replaced New York as the jurisdiction of choice for the bankruptcy reorganization of large, public companies. In an empirical study of 188 companies emerging from bankruptcy reorganization from 1983 through 1996, the authors found that the refiling rates for public companies reorganized in Delaware and New York were about five to seven times the refiling rates for companies reorganized in other courts. Nine of the thirty large, public companies emerging in Delaware from 1991 to 1996 (30%) have already refiled. New York rates were higher during the period of New York's dominance than during the period of Delaware's dominance and higher for the judge who attracted cases to New York in the 1980s than for other judges on the New York court. Delaware and New York rates were highest for prepackaged bankruptcies; the opposite was true for other courts' rates. The authors argue that Delaware refiling rates are probably higher than efficient. They conclude that New York, and later Delaware, each won its dominance of bankruptcy reorganization at least in part by confirming plans likely to lead to the necessity for further reorganization. Based on their conclusion that companies probably are seeking inefficient reorganizations in Delaware, the authors speculate that companies may also be seeking inefficient incorporations there. The authors also found that public companies emerging from bankruptcy are more than four times as likely to file for bankruptcy reorganization as are public companies generally. The refiling rate for emerging companies is double the background rate for the first year after confirmation. It climbs to more than four times the background rate by the third year after confirmation and then declines to about three times the background rate by the eighth year after confirmation.
High yield bond defaults in 2003 declined significantly from record 2002 levels closing the year at 137.4 billion; the vast majority was used for refinancing existing loan and bond issues.Based on our mortality rate methodology and assuming different measures of credit risk ofrecent new issuance, we expect default rates to continue their decline in 2004 to between3.2% - 3.8%, with rates increasing in 2005 to above 4.0%.
In this article we introduce a scoring system (EMS Model) for Emerging Corporate Bonds. The scoring system provides an empirically based tool for the investor to use in making relative value determinations. The EMS Model is an enhanced version of the statistically proven Z-Score model. Unlike the original Z-Score model, our approach can be applied to nonmanufacturing companies, and manufacturers, and is relevant for privately held and publicly owned firms.The adjusted EMS Model incorporates the particular credit characteristics of emerging markets companies, and is best suited for assessing relative value among emerging markets credits. The model combines fundamental credit analysis and rigorous benchmarks together with analyst-enhanced assessments to reach a modified rating, which can then be compared to agency ratings (if any) and market levels. We have included a summary of Mexican companies for which we have applied the EMS Model. We have included in this a description of Mexican company credits, first from prior to the Mexican crisis (1994) then followed, in some cases, to a more recent date.
Assistant Professor of Finance, New York University. The author acknowledges the helpful suggestions and comments of Keith V. Smith, Edward F. Renshaw, Lawrence S. Ritter and the Journal' reviewer. The research was conducted while under a Regents Fellowship at the University of California, Los Angeles.
Despite the long experience in the U.S. with restructuring companies in bankruptcy, there remains a persistent tendency for companies to emerge from Chapter 11 with too much debt and too little profitability. In this article, the author uses a variant of his well-known “Z-Score” bankruptcy prediction model to assess the future viability of companies when emerging from bankruptcy, including the likelihood that they will file again—a surprisingly common phenomenon that is now referred to as “Chapter 22.”
The author reports that those companies that filed second bankruptcy petitions were both significantly less profitable and more highly leveraged than those that emerged and continued as going concerns. Indeed, the average financial profile and bond rating equivalent for the “Chapter 22” companies on emerging from their first bankruptcies were not much better than those of companies in default.
The authors findings also suggest that a credible corporate distress prediction model could be used as an independent, unbiased method for assessing the future viability of proposed reorganization plans. Another potential application of the model is by the creditors of the “old” company when assessing the investment value of the new package of securities, including new equity, offered in the plan.
This article examines the performance of 197 public companies that emerged from Chapter 11. Over 40 percent of the sample firms continue to experience operating losses in the three years following bankruptcy; 32 percent reenter bankruptcy or privately restructure their debt. The continued involvement of prebankruptcy management in the restructuring process is strongly associated with poor postbankruptcy performance. The substantial number of firms emerging from Chapter 11 that are not viable or need further restructuring provides little evidence that the process effectively rehabilitates distressed firms and is consistent with the view that there are economically important biases toward continuation of unprofitable firms. Copyright 1995 by American Finance Association.
This study develops an alternative way to measure default risk and suggests an appropriate method to assess the performance of fixed-income investors over the entire spectrum of credit-quality classes. The approach seeks to measure the expected mortality of bonds and the consequent loss rates in a manner similar to the way actuaries assess mortality of human beings. The results show that all bond ratings outperform riskless Treasuries over a ten-year horizon and that, despite relatively high mortality rates, B-rated and CCC-rated securities outperform all other rating categories of the first four years after issuance, with BB-rated securities outperforming all others thereafter. Copyright 1989 by American Finance Association.
University of Rochester. The author wishes to thank M. Gruber, R. Hamada, F. Jen, M. Jensen, E. H. Kim, E. Kitch, M. Scholes, J. Siegel, C. Smith, B. Stone, H. Stoll, and J. Williams for their comments on previous drafts. I am indebted to N. Gonedes and especially M. Miller for their encouragement and for their criticisms of previous versions of the paper.
In this paper, empirical evidence with respect to both the direct and indirect costs of bankruptcy is assessed. This should be of interest for three related reasons. First, there is a need to provide further evidence as to the size of bankruptcy costs. Second, for the first time a proxy methodology for measuring indirect costs of bankruptcy is presented and actually measured. Third, a simple format for measuring the present value of expected bankruptcy costs is compared with the present value of expected tax benefits from interest payments on leverage. This comparison has important implications for the continuing debate as to whether or not an optimum capital structure exists for corporations.
Defaults and returns in the high yield bond market: The year 2013 in review and outlook
- E.I. Altman
- B. Kuehne
Investment decisions under chapter 11 bankruptcy
- E S Hotchkiss
Post-bankruptcy performance: Evidence from 25 years of chapter 11
- E S Hotchkiss
- Mooradian
Til debt do us. Part: Serial defaulters in the U.S. show lower recoveries and higher losses
- Vazza D.
The Future of Market Flows
- E.I. Altman
- J. Hartzell
- M. Peck
What's new with chapter 22?
- Gutzeit G.
Till debt do us. Part: A study of serial defaults
- D. Vazza
- D. Aurora
- J. Torres
- N. Kraemer
The Chapter after Chapter 11
- J.T. Lee
- J. Cunney
- Altman
Revisiting the recidivism – chapter 22 phenomenon in the U.S. bankruptcy system
- Altman E.I.
The role of distressed debt markets, hedge funds and recent trends in bankruptcy on the outcomes of chapter 11 reorganizations
- Altman E.I.
Till debt do us. Part: A study of serial defaults
- Vazza D.
Post-bankruptcy performance: Evidence from 25 years of chapter 11 Working paper
- E S R Hotchkiss
- Mooradian