Measuring Financial Distress and Predicting Corporate Bankruptcy: An Index Approach

Abstract

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.

A Business Failure Index Using Rank Transformation

“…Accordingly, scholars have suggested adopting a different approach, based on an aggregate index to predict corporate failure; for example, Liao and Mehdian (2016) have documented the higher predictive ability of their ijef.ccsenet.org…”

“…Actually, an aggregate index could be useful in order to determine the overall position of each firm, at the same time obtaining a ranking of the different entities in term of financial health (Cabaleiro et al, 2013;Liao & Mehdian, 2016). Furthermore, using many indicators separately could be too costly and problematic to manage, while a composite indicator can easily summarise multi-dimensional issues, supporting decision-makers (Saisana & Tarantola, 2002).…”

“…Moreover, rank transformations have been used successfully in many applications, among the other predicting corporate bond ratings (Perry et al, 1985;Perry & Cronan, 1986;Skomp et al, 1986), assessing corporate strategic risk (Collins & Ruefli, 1992), forecasting the likelihood of corporate failure (Perry et al, 1986;Kane et al, 1996). Liao and Mehdian (2016) used the rank transformations for estimating an aggregate bankruptcy index based on five financial ratios, in order to predict the propensity of financial failure and corporate bankruptcy. They ranked individual financial ratios into deciles with the least distressed firms taking the value of 10, and the most distressed taking the value of 1.…”

“…Because of all this, our study aims to apply rank transformations to financial ratios for estimating a business failure index that can be used as a measure of firms' global performance in order to improve the predictive usefulness of standard failure prediction models. In particular, the aggregate index computed takes into account a larger number of ratios compared with those considered in Liao and Mehdian (2016), so that it is possible to capture which ratios are more likely to influence the risk of failure. Moreover, for comparison and assessment of the robustness of our approach, we estimated the distress probability by logit regression, using the same set of financial ratios to compare the predictive accuracy of two approaches.…”

Construction Industry and Payment Discipline in the Czech Republic

“…Salamon and Mesko (2013) discussed in their research papers, how one of the key reasons for the lack of payment discipline is poor business ethics and considering the results of their research, they suggest the improvement of payment dimensions by weakening of ethical climate. Liao and Mehdian (2016) mentioned, during the last six decades and, following the research papers by Beaver (1968) and Altman (1968), how a voluminous amount of literature has been developed in finance and accounting to measure financial distress and to predict the bankruptcy of companies (see for example Altman, 1973;Jones, 1987;Altman et al, 1994;Zmijewski, 1984;Beaver et al, 2005;Branch, 2015, Taffler, 1982;Agarwal and Taffler, 2008). A great variety of models for diagnosis and prediction of the business financial situation is used.…”