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

Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.

Economic policy uncertainty is an important factor determining the external environment of an enterprise, and it affects firm behavior, with consequences including financial distress risk. Using A-share listed companies on the Shanghai and Shenzhen Stock Exchanges in China from 2007 to 2017, this paper finds a negative relationship between economic policy uncertainty and distress risk. A mediating-effect test indicates that if uncertainty is high, enterprises will reduce the risk of financial distress by reducing investment expenditure and increasing cash holdings. Our paper adds to the literature on factors driving distress risk and the economic consequences of economic policy uncertainty, and it provides a basis for enterprises to respond to changes in policies.

This study compares the accuracy and efficiency of five different estimation methods for predicting financial distress of small and medium-sized enterprises. We apply different methods for a large set of financial and non-financial variables, using filter and wrapper selection, to predict bankruptcy up to 10 years before the event in an open, European economy. Our findings show that logistic regression and neural networks are superior to other approaches. We document how the cost-return ratio considerably affects the location of optimal cut-off points and attainable profit in credit decisions. Once a loan provider selects a particular prediction model, an effort should be made to find the optimal cut-off score to maximize the efficiency of the technique. Indeed, this often involves determining several cut-off levels where the portfolio of products and services exhibits different cost-return characteristics.

Over the last 30 years, the distressed debt market has come a long way and is now a legitimate investment asset class, albeit with periodic dramatic activity. Despite the benign credit cycle in US markets since the last great financial crisis, there are still more than 200 financial institutions in the United States, and a large number operating in other countries, such as Italy, Brazil, and India, specializing in investment in distressed and defaulted bonds and nonperforming loans. We document this novel and intriguing investment market, with a discussion of size, strategies, and performance. We also present new empirical results on pre- and postdefault experience, leveraging our unique databases on bond and loan prices and our indexes of performance of defaulted bonds and bank loans. The results show that the investment performance in distressed debt is not particularly impressive over the entire sample (1987–2016). For the last 10 years (2006–2016), however, the results are much better for overall outperformance, especially those using several strategies with respect to the seniority of the debt and market timing. This is due perhaps to favorable changes for creditors in the US bankruptcy code in 2005.

A key goal of Chapter 11 is to provide economically viable firms an opportunity to reorganize, while liquidating those that are not viable. This chapter describes recent evidence on bankruptcy outcomes and postbankruptcy performance, and their relevance to the debate over the efficiency of Chapter 11. When a firm enters Chapter 11, the expected outcome in most cases is to confirm a plan under which the firm is reorganized (or sometimes sold) as a going concern. An important concern in interpreting any analysis of postbankruptcy performance based only on firms that survive Chapter 11 is the fact that firms' asset composition changes significantly before and during bankruptcy. Early critics of the Chapter 11 process have primarily argued that the current U.S. bankruptcy system is biased toward allowing inefficient firms to reorganize.

Enterprise valuation plays a central role in negotiations over how and when to restructure a distressed firm. This chapter provides some explanations as to why such large differences in valuation arise. It reviews the models commonly used to value distressed firms, based on accepted practices. The two most widely used approaches to valuation are “relative valuation” models, where value is derived from the pricing of comparable assets, and discounted cash flow (DCF) models. The “comparable company” approach, sometimes also referred to as a “trading multiples” valuation, estimates the value of the target firm by applying valuation multiples of peer firms to the target. The first key input to the DCF model is the projection of free cash flows. Parties to a valuation dispute will sometimes refer to observed trading prices of claims to support their position on value.