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

Academics and practitioners are increasingly concerned about global zombieism, a term used to describe insolvent firms that survive with the support of financial institutions, investors, or governments, particularly during unusual market conditions. Using dual-filters of interest coverage ratio and an empirically validated default prediction model, we propose a new measure to gauge the extent of zombieism in the world’s 20 largest economies. The average zombie share of listed firms has increased significantly since 1990, to about 7% in 2020. Zombie firms are typically found among small and medium-sized enterprises. Economic growth, industry compositions, and lenient monetary policies have strong explanatory power for global zombieism. We show that the presence of zombie firms generates significant market congestion, limiting the growth of healthy firms. We also find that the development of global corporate bond markets contributes to zombie firm growth. Leveraging staggered bankruptcy reforms as an exogenous variation, we find that these reforms lower zombie ratio by 1.4% points. The reduction is more substantial if the bankruptcy law becomes more creditor-friendly. Having failed to recover, zombie firms can survive for an average of 5 years before declaring bankruptcy, being delisted, or being acquired. Bankruptcy reforms accelerate the dissolution of zombie status.

We estimate the impact of the Covid-19 pandemic on credit risk changes on a large sample of SME firms. The Altman Z"-Score model, which has proven to be a powerful and robust bankruptcy prediction model across many industries and countries, is used to assess over 1,000 SMEs from seven Polish industrial sectors. Specifically, we assess the vulnerability of the sampled firms to credit downgrades, including the likelihood of becoming insolvent and filing for bankruptcy, over the expected downturn in the real economy. Based on scenario analysis on individual firm financial data, we analyse rating transitions under multiple potential scenarios, focusing on the deterioration of the SME firms' profits, and working capital including an increase in current liabilities. We find that the impact on companies from the various rating equivalent groupings are quite diverse and cannot be explained only by the firms' industrial sector. Of particular importance is the proportion of firms whose credit quality deterioration could result in insolvency. What is perhaps surprising is that the most resilient companies with respect to credit downturns are, apart from the AAA/AA+ and AA/AA-rated, those which initially were assigned to the most risky (CCC) credit rating equivalent class. And, those that were assigned to lower investment grade classes were amongst the least resilient.

The Omega Score, a novel small and medium-sized enterprise (SME) default predictor developed by Altman et al. in 2022, combines indicators related to financial ratios, payment behavior, and management and employees variables that play an important role in predicting SME defaults. Built with machine-learning techniques and rich dataset information, the Omega Score can be used to categorize an SME into one of the following three groups: healthy, moderate-risk, and high-risk. The Omega Score can be utilized by financial institutions to reduce lending errors and minimize loan defaults, support policy makers in implementing effective restructuring policies, assist credit analytics firms in assessing creditworthiness, assist investors in allocating funds, and asset managers to support decision-making processes.

SME default prediction is a long-standing issue in the finance and management literature. Proper estimates of the SME risk of failure can support policymakers in implementing restructuring policies, rating agencies and credit analytics firms in assessing creditworthiness, public and private investors in allocating funds, entrepreneurs in accessing funds, and managers in developing effective strategies. Drawing on the extant management literature, we argue that introducing management-and employee-related variables into SME prediction models can improve their predictive power. To test our hypotheses, we use a unique sample of SMEs and propose a novel and more accurate predictor of SME default, the Omega Score, developed by the Least Absolute Shortage and Shrinkage Operator (LASSO). Results were further confirmed through other machine-learning techniques. Beyond traditional financial ratios and payment behavior variables, our findings show that the incorporation of change in management, employee turnover, and mean employee tenure significantly improve the model's predictive accuracy.

In response to the global concern of the Evergrande debt crisis, we document several findings of the crisis's contagion effect. First, although most real estate companies have strong financial fundamentals, the high default risk among large firms sends an alarming signal. Second, the spillover effect to peer developers is stronger in the credit market than in the stock market. Finally, the systemic financial risk of the banking sector remains low during the crisis. Overall, while there is a low probability of a wide-ranging financial crisis, a debt crisis in the real estate sector might be on the horizon.

We study whether public announcements (through delisting warnings) of financial distress of some firms in an industry affect the conditional accounting conservatism of intra-industry non-distressed firms. We hypothesize that the lenders of non-distressed firms perceive higher riskiness and demand for stricter debt covenants and more efficient monitoring of debt contracts when some firms show signals of financial distress in that industry. Intra-industry non-distressed firms increase their levels of conditional conservatism to meet the lenders’ demands for stricter monitoring of debt contracts and to reduce debt costs. Using the delisting warning data from the Chinese stock exchanges, we find that financial distress announcements lead to increases in conditional conservatism of non-distressed firms in that industry. We provide new evidence for the spillover effects of financial distress within an industry and the usefulness of conditional conservatism in debt contracts.

We estimate the impact of the COVID-19 pandemic on credit risk changes on a large sample of Polish SME firms. The Altman Z"-Score model, which has proven to be a powerful and robust bankruptcy prediction model across many industries and countries, is used to assess over 1,000 SMEs from seven Polish industrial sectors. Specifically, we assess the vulnerability of the sampled firms to credit downgrades, including the likelihood of becoming insolvent and filing for bankruptcy, over the expected downturn in the real economy. Based on scenario analysis on individual firm financial data, we analyze rating transitions under multiple potential scenarios, focusing on the deterioration of the SME firms' profits, working capital including an increase in current liabilities. Our modelling provides Bond Rating Equivalents (BREs) to capture changes in credit quality under the different scenarios. We find that the impact on companies from the various rating equivalent groupings are quite diverse and cannot be explained only by the firms’ industrial sector. Of particular importance is the proportion of firms whose credit quality deterioration could result in insolvency. What is perhaps surprising is that the most resilient companies with respect to credit downturns, are apart from the AAA/AA+ and AA/AA- rated, those which initially were assigned to the most risky (CCC) credit rating equivalent class. And, those that were assigned to lower investment grade classes were amongst the least resilient. We explain and comment upon this seemingly counter-intuitive result in our analysis.