Empirical evidence from the corporate collapse literature suggesting re-examination of accounting standards in relation to valuation of assets

This paper examines the recent spectacular corporate collapses of Parmalat in Europe, Enron and WorldCom in the USA and HIH in Australia and argues for a re-examination of corporate governance regulations, particularly in relation to accounting standards regarding the valuation of assets. The recommendation that is put forward in this regard is based upon empirical evidence arising from further examination of the empirical results in (Hossari and Rahman, 2004). Specifically, the recommendation is based upon the realization that, among the 48 financial ratios across the 50-plus refereed studies, five financial ratios, all of which contained assets as one of the variables, were a relatively robust indicator of corporate collapse. The five ratios are: Net Income/Total Assets, Current Assets/Current Liabilities, Total Liabilities/Total Assets, Working Capital/Total Assets, and Earnings Before Interest and Taxes/Total Assets. This paper suggests that it's not the failure of the corporate collapse prediction models, rather it's the erosion of the reliability of some key input data, namely assets and the valuation thereof, that is largely responsible for the apparent failure of these models in capturing impending collapses, such as those that we witnessed in the recent past. Such empirical findings support the argument that assets are soft targets for misrepresentation, because of the leeway granted in accounting standards with regards to their valuation.

Methodological shifts in ratio-based modelling of corporate collapse

The year 1968 saw a major shift from univariate to multivariate methodological approaches to ratio-based modelling of corporate collapse. This was facilitated by the introduction of a new statistical tool called Multiple Discriminant Analysis (MDA). However, it did not take long before other statistical tools were developed. The primary objective for developing these tools was to enable deriving models that would at least do as good a job asMDA, but rely on fewer assumptions. With the introduction of new statistical tools, researchers became pre-occupied with testing them in signalling collapse. lLTUong the ratio-based approaches were Logit analysis, Neural Network analysis, Probit analysis, ID3, Recursive Partitioning Algorithm, Rough Sets analysis, Decomposition analysis, Going Concern Advisor, Koundinya and Purl judgmental approach, Tabu Search and Mixed Logit analysis. Regardless of which methodological approach was chosen, most were compared to MDA. This paper reviews these various approaches. Emphasis is placed on how they fared against MDA in signalling corporate collapse.

A comprehensive formal ranking of the popularity of financial ratios in multivariate modeling of corporate collapse

This paper provides a fonnal ranking of the popularity of financial ratios in modeling corporate collapse. The analysis identified 48 financial ratios and ranked them according to their usefulness as portrayed in 53 studies that have utilized such ratios in modeling corporate collapse. The methodologies adopted in those studies are predominantly of the "multivariate" type. The 53 studies extend from 1966 to 2002, inclusive.

A dynamic ratio-based model for signalling corporate collapse

The recognition of behavioural elements in finance has caused major shifts in the analytic framework pertaining to ratio-based modeling of corporate collapse. The modeling approach so far has been based on the classical rational theory in behavioural economics, which assumes that the financial ratios (i.e., the predictors of collapse) are static over time. The paper argues that, in the absence of rational economic theory, a static model is flawed, and that a suitable model instead is one that reflects the heuristic behavioural framework, which is what characterises behavioural attributes of company directors and in turn influences the accounting numbers used in calculating the financial ratios. This calls for a dynamic model: dynamic in the sense that it does not rely on a coherent assortment of financial ratios for signaling corporate collapse over multiple time periods. This paper provides empirical evidence, using a data set of Australian publicly listed companies, to demonstrate that a dynamic model consistently outperforms its static counterpart in signaling the event of collapse. On average, the overall predictive power of the dynamic model is 86.83% compared to an average overall predictive power of 69.35% for the static model.

Financial fraud and the use of ratios in corporate collapse prediction models

This paper highlights the prevalence and extent of financial fraud amongst collapsed corporations. In doing so, it examines the recent spectacular corporate collapses of Parmalat in Europe, Enron and WoridCom in the USA and HIH in Australia. A new methodology that provides empirical evidence to the financial fraud claims found in the literature, is then put forward. The proposed methodology argues that if financial fraud was a possibility amongst collapsed corporations, then two premises ought to be observed in the literature on ratio based multivariate modelling for predicting corporate collapse. First, in the absence of financial fraud, we expect the models to consistently predict corporate collapse with a high degree of accuracy; particularly, as one approaches the incident of collapse. Second, if financial fraud takes place and statement figures are distorted, then we expect the financial ratios, which are the predictor variables in these models, to lose relevance and therefore their use in models will be short-lived. Empirical support from Hossari and Rahman (2004) and Hossari and Rahman (2005) is presented as evidence to the two premises.

Benchmarking new statistical techniques in ration-based modelling of corporate collapse

Up until 1979, Multiple Discriminant Analysis (MDA) was the primary multivariate methodological approaches to ratio-based modelling of corporate collapse. However, as new statistical tools became available, researchers started testing them with the primary objective of deriving models that would at least do as good a job as MDA, but that rely on fewer assumptions. Regardless of which methodological approach was chosen, most were compared to MDA. This paper analyses 84 studies on ratio based modelling of corporate collapse over the period 1968 to 2004. The results indicate that when MDA was not the primary methodology it was the benchmark of choice for comparison; thereby, demonstrating its importance as a foundation multivariate methodological approach in signalling corporate collapse.

Modelling corporate collapse: definitional issues of the collapse event

This paper investigates problems associated with interpretations of corporate collapse, and argues for a unified legal, rather than financial, definition of the event. In the absence of a formal definition of the event of corporate collapse, the integrity of sample selection becomes questionable; moreover, comparisons between empirical studies becomes less useful, if not altogether futile, due to the lack of a common ground in the basic building block. Upon close examination of 84 studies on ratio-based modeling of corporate collapse, between 1968 and 2004, this paper finds evidence in favor of a legal interpretation of the event of corporate collapse. Specifically, studies that adopted a legal definition are five times as many as those that opted for a financial explanation.

An empirical evaluation of sampling controversies in ratio-based modelling of corporate collapse

Empirical investigations regarding ratio-based modelling of corporate collapse have been on going for decades. With any study of an empirical nature, a data sample is a necessary prerequisite. It allows testing the performance of the prediction model, thereby establishing its practical relevance. However, it is necessary to first ensure that the data sample used satisfies certain conditions, and these have lead to some choice controversies. This paper considers the controversial issues that arise in data sampling, provides a critical evaluation of these issues, and makes choice recommendations on the controversial aspects, by empirically examining the literature.

A heuristic framework for ratio-based modelling of corporate collapse

This paper draws on empirical evidence to demonstrate that a heuristic framework signals collapse with significantly higher accuracy than the traditional static approach. Using a sample of 494 US publicly listed companies comprising 247 collapsed matched with 247 financially healthy ones, a heuristic framework is decisively superior the closer one gets to the event of collapse, culminating in 12.5% more overall accuracy than a static approach during
the year of collapse. An even more dramatic improvement occurs in relation to reduction of Type I error, with a heuristic framework delivering an improvement of 66.7% over its static counterpart.

Absence of industry effect in modelling corporate collapse in Australia

This paper investigates whether or not an industry effect is present when modelling corporate collapse in Australia. The investigation is motivated by a lack of consistency in the literature regarding such an effect. Moreover, this paper makes a unique contribution by applying an innovative methodological approach, called Multi-Level Modelling (MLM), for model derivation. Unlike the traditional two-step methodology used so far in the literature, MLM carries out model derivation and tests for an industry effect in a single step. Finally, the effectiveness of MLM is demonstrated using a sample of Australian publicly listed companies during the period 1989 to 2005; empirical results point to the absence of an industry effect.

Signalling corporate collapse using a dual classification scheme:Australian evidence

Regardless of the technical procedure used in signalling corporate collapse, the bottom line rests on the predictive power of the corresponding statistical model. In that regard, it is imperative to empirically test the model using a data sample of both collapsed and non-collapsed companies. A superior model is one that successfully classifies collapsed and non-collapsed companies in their respective categories with a high degree of accuracy. Empirical studies of this nature have thus far done one of two things. (1) Some have classified companies based on a specific statistical modelling process. (2) Some have classified companies based on two (sometimes – but rarely – more than two) independent statistical modelling processes for the purposes of comparing one with the other. In the latter case, the mindset of the researchers has been – invariably – to pitch one procedure against the other. This paper raises the question, why pitch one statistical process against another; why not make the two procedures work together? As such, this paper puts forward an innovative dual-classification scheme for signalling corporate collapse: dual in the sense that it relies on two statistical procedures concurrently. Using a data sample of Australian publicly listed companies, the proposed scheme is tested against the traditional approach taken thus far in the pertinent literature. The results demonstrate that the proposed dual-classification scheme signals collapse with a higher degree of accuracy.

Enhancing procedural efficiency in multiple-industry ratio-based modeling of corporate collapse

This paper utilizes a methodological approach called Multi-Level Modeling (MLM) that addresses two major shortcomings in the two step analytic process that is traditionally adopted in the pertinent literature for modeling corporate collapse; thereby, enhancing procedural efficiency. The robustness of MLM vis-à-vis the traditional two-step procedure is ascertained using a data sample of Australian
publicly listed companies, equally split between collapsed and non collapsed, during the period 1989 to 2006. The results indicate that not only does MLM improve procedural efficiency, it does so while
enhancing the robustness of signaling corporate collapse; in particular, MLM signals collapse with an overall 6.6% increase in accuracy.

Optimising errors in signaling corporate collapse using MCCCRA

Purpose – The purpose of this paper is to put forward an innovative approach for reducing the variation between Type I and Type II errors in the context of ratio-based modeling of corporate collapse, without compromising the accuracy of the predictive model. Its contribution to the literature lies in resolving the problematic trade-off between predictive accuracy and variations between the two types of errors.

Design/methodology/approach – The methodological approach in this paper – called MCCCRA – utilizes a novel multi-classification matrix based on a combination of correlation and regression analysis, with the former being subject to optimisation criteria. In order to ascertain its accuracy in signaling collapse, MCCCRA is empirically tested against multiple discriminant analysis (MDA).

Findings – Based on a data sample of 899 US publicly listed companies, the empirical results indicate that in addition to a high level of accuracy in signaling collapse, MCCCRA generates lower variability between Type I and Type II errors when compared to MDA.

Originality/value – Although correlation and regression analysis are long-standing statistical tools, the optimisation constraints that are applied to the correlations are unique. Moreover, the multi-classification matrix is a first in signaling collapse. By providing economic insight into more stable financial modeling, these innovations make an original contribution to the literature.