Edward I. Altman’s research while affiliated with New York University and other places

Bubble theories are becoming quite common these days for several asset classes, and in important growth areas of the world, like China, India and the U.S. Are we in the midst of an inflating credit bubble and, if so, when is it likely that the bubble will burst? Contrarily, are we experiencing an extended period of opportunistic debt financing? The evidence we have compiled leads us to conclude that, indeed, a bubble is building, but it is not likely to explode dramatically, with a significant increase in corporate bond and loan defaults, until at least late 2016 or more likely in 2017–2018. We believe that if not for the enormous credit stimuli by all of the major Central Banks of the world, the most recent benign credit cycle, one of over six years now, would be over, and a new stressed cycle would be starting. That is, the match (cycle) is now in “extra-time.”

Bubble theories and concerns are becoming quite common these days for several asset classes, prompting discussions and warnings, including those from regulators. We now come to some key questions—are we in the midst of an inflating credit bubble and, if so, when is it likely that the bubble will burst? Contrarily, are we experiencing an extended period of opportunistic debt financing—a theory made popular amongst corporate finance theorists going back to at least the 1960s and 1970s. The evidence we have compiled leads us to conclude that, indeed, a bubble is building, but it is not likely to explode dramatically, with a significant increase in corporate bond and loan defaults, until at least late 2016 or more likely in 2017–2018. Fear, however, of a potential crisis in credit and equity markets, or major dislocations in important industries or countries, may contribute to periods of negative price movements and increased volatility in these, and other, asset classes before the bubble actually bursts.

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.

Reviews on financial distress prediction models indicate that these techniques give highly reliable estimates of probabilities of default (PDs) and loss given default (LGD) only for relatively short horizons, rarely beyond two years. Major stakeholders, e.g. investors and bank risk and capital analysts, therefore, have such models sanctioned by portfolio managers and regulators for the same short horizons; for example, the Basel Committee on Banking Supervision recommends PD and LGD estimates for one year. This is especially the case when financial variables make up the sole or primary estimates, and only a bit longer reliable estimators when these models include non-financial variables as additional early warning signals. Beyond three years, such models, regardless of their structure, rarely give reliable estimates, perhaps not much better than flipping a coin. The objective of this study is to assess the predictive ability of both financial and non-financial variable constructs for longer term horizons of up to ten years based on rigorous post-development distress and non-distress financial events in the Finnish environment. Our model, built with cross-section data from 2003, analyses results for 2004-2013. Results show that measures of solvency, turnover, industry risk, payment behaviour, and board member characteristics can be significant predictors of bankruptcies for as long as ten years. The most accurate long-range prediction results combine financial and non-financial variables. Subsequent tests should attempt to extend such models in a multi-country setting, whether or not bankruptcy regimes are similar across national borders.

This article analyzes the returns of distressed high-yield corporate bond portfolios based on the volatility characteristics of their corresponding option-adjusted spreads (“OAS”). Applying Capital Asset Pricing Model (“CAPM”) theory to a portfolio of distressed bonds generates different results depending on whether overall high-yield market OAS volatility is considered. CAPM expectations (higher risk investments demand higher expected returns) are not generally fulfilled in both time-independent and time-dependent space after normalizing the data for overall high-yield market OAS volatility, i.e. the lowest OAS volatility portfolios outperform the highest volatility portfolios. We provide evidence that this phenomenon is a result of the idiosyncratic characteristics of the securities that comprise the lowest OAS volatility portfolios, which generate higher returns because they experience lower default rates and higher terminal values relative to the securities in the highest OAS volatility portfolios. Al...

High-yield bond default rates on US, Canadian and Mexican high-yield bonds decreased slightly in 2013 and remained well below historical averages. The rate decreased from 1.62% at year-end 2012 to 1.04% for all of 2013. Defaults include straight corporate bonds whose firms went bankrupt, missed an interest payment and did not cure it within the grace or forbearance period, or completed a distressed exchange. The 2013 rate is based on a mid-year market size of $1.39 trillion, up by a sizeable$180 billion from a year earlier. In all, $14.5 billion of defaults were recorded in 2013 (Table 8.1). The historical weighted-average annual default rate is 3.61% over the 43-year period (1971–2013). This weighted-average rate is down compared to 3.82% at the end of 2012. Our weights are based on the par value of high-yield bonds outstanding in each year. The arithmetic annual average default rate dropped to 3.14% from 3.19% one year earlier.

Bubble theories and concerns are becoming quite common these days for several asset classes, prompting discussions and warnings, including those from regulators. We now come to some key questions – are we in the midst of an inflating credit bubble and, if so, when is it likely that the bubble will burst? Contrarily, are we experiencing an extended period of opportunistic debt financing – a theory made popular amongst corporate finance theorists going back to at least the 1960s and 1970s. The evidence we have compiled leads us to conclude that, indeed, a bubble is building, but it is not likely to explode dramatically, with a significant increase in corporate bond and loan defaults, until at least late 2015 or more likely in 2016–2017. Fear, however, of a potential crisis in credit and equity markets may contribute to periods of negative price movements and increased volatility in these, and other, asset classes before the bubble actually bursts.

The purpose of this paper is firstly to review the literature on the efficacy and importance of the Altman Z-Score bankruptcy prediction model globally and its applications in finance and related areas. This review is based on an analysis of 33 scientific papers published from the year 2000 in leading financial and accounting journals. Secondly, we use a large international sample of firms to assess the classification performance of the model in bankruptcy and distressed firm prediction. In all, we analyze its performance on firms from 31 European and three non-European countries. This kind of comprehensive international analysis has not been presented thus far. Except for the U.S. and China, the firms in the sample are primarily private and cover non-financial companies across all industrial sectors. Thus, the version of the Z-Score model developed by Altman (1983) for private manufacturing and non-manufacturing firms (Z"-Score Model) is used in our testing. The literature review shows that results for Z-Score Models have been somewhat uneven in that in some studies the model has performed very well, whereas in others it has been outperformed by competing models. None of the reviewed studies is based on a comprehensive international comparison, which makes the results difficult to generalize. The analysis in this study shows that while a general international model works reasonably well, for most countries, with prediction accuracy levels (AUC) of about 75%, and exceptionally well for some (above 90%), the classification accuracy may be considerably improved with country-specific estimation especially with the use of additional variables. In some country models, the information provided by additional variables helps boost the classification accuracy to a higher level.

Outcomes Of Chapter 11 CasesPostbankruptcy Operating PerformancePostbankruptcy Stock PerformanceImplications For Chapter 11

Bankruptcy and Reorganization TheoryBankruptcy FilingsThe Bankruptcy Industry PlayersThe DebtorsChapter 22 Debtors and Bankruptcy SuccessReasons For Corporate FailuresThe Judicial System