Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach

Abstract

The majority of classification models developed have used a pool of financial ratios combined with statistical variable selection techniques to maximize the accuracy of the classifier constructed. Rather than follow this approach, this article seeks to provide an explicit economic basis for the selection of variables for inclusion in bankruptcy models. This search to develop an economic theory of bankruptcy augments the existing bankruptcy prediction literature. Variables which occur in bankruptcy probability expressions derived from the solution of a stochastic optimizing model of firm behaviour are 'proxied' by variables constructed from financial statement data. The random nature of the lifetime of a single firm provides the rationale for the use of duration or hazard-based statistical methods in the validation of the derived bankruptcy probability expressions. Results of the validation exercise confirm that the majority of variables included in the empirical hazard formulation behave in a way that is consistent with the model of the firm. The results highlight the need for developments in the measurement of earnings dispersion.

Accounting and non-accounting determinants of default: An analysis of privately-held firms

“…These specific models are structurally embedded in the dated bankruptcy prediction models (Beaver, 1966;Karels and Prakash, 1987) and their contextual applications generate different implications depending on the question under study (Kolari et al, 2002;Rashad and El-Sheshai, 1980). But the general consensus is that scholars should develop models for different types of corporate failures that are specific to the country contexts wherever required and meet end user needs (Platt and Platt, 2002;Peat, 2007).…”

Dynamic forecasts of financial distress of Australian firms

“…Therefore, survival analysis is a natural choice for bankruptcy prediction since it allows the estimation of the probability that a firm survives or goes bankrupt at each point in time t over the forecast period, given the 'random nature of the lifetime' of a company (Peat, 2007: 303). From 2000 onwards there has been growing use of survival analysis in financial distress modelling (Bonfim, 2009;Campbell et al, 2008;Chava and Jarrow, 2004;Cole and Wu, 2009;Nam et al, 2008;Partington et al, 2001;Peat, 2007;Shumway, 2001;Wong et al, 2007).…”

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

“…If y > 0 the company is classified as bankrupt, otherwise the company is considered healthy. It is interesting to note that, as was also pointed out by Peat in [30], and common sense dictates, debt contributes positively to bankruptcy probability, while working capital contributes negatively (and the possibility to cancel debt, represented by x 7 ).…”

“…Thus, although it has been a widely studied problem, research on this topic is still being carried out (for instance, recent papers by Hu et al [29] and Peat [30] approach the problem from the soft computing and classical economical analysis perspective); researchers seek generic applications with a higher predictive ability. Classically, methods such as discriminant analysis and logit have been used [31], [32], but soft computing methods such as artificial neural nets (ANNs) have proved to have a higher predictive ability than traditional statistical methods [33].…”