The objective of this study is to introduce a more refined methodological approach for signalling corporate collapse. The proposed methodological approach provides informed stakeholders in a corporation with a tool that would help them signal impending collapse with a higher degree of accuracy than the existing mainstream methodology. By doing so, the proposed methodological approach helps stakeholders take appropriate measures, if possible, to save their company from collapse. The motivation behind this study emanates from a need in the literature in relation to coming up with a new methodological approach that is superior to what is available. For example, Jones and Hensher (2004), one of the most recent studies in the field, stated that over the past three decades there has been a conspicuous absence of modelling innovation in the literature on financial distress prediction, as well as a failure to keep abreast of important methodological developments emerging in other fields of the social sciences. Specifically, this study introduces a new ratio-based multivariate methodological approach for signalling corporate collapse, called Multi-Level Modelling (MLM). Moreover, this study demonstrated that MLM provides informed stakeholders in a corporation with a tool that would help them signal impending collapse with a higher degree of accuracy than Multiple Discriminant Analysis (MDA), which is the mainstream benchmark methodological approach. By doing so, MLM helps stakeholders take appropriate measures, if possible, to save their company from collapse. The empirical results depicted the superiority of MLM over MDA. MLM generated better overall predictive power and dramatically reduced the occurrence of Type I error (classifying a collapsed company as non-collapsed). Moreover, MLM achieved those results while at the same time capturing variations in industry sectors among the data sample of companies. This is something that MDA was not capable of.

Thesis (Ph. D.)--University of Washington, 1989. / Vita. Includes bibliographical references (leaves [183]-197).

Thesis (PhD) - Swinburne University of Technology, 2007. / Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Research Psychology, Swinburne University of Technology - 2007. Typescript. Includes bibliographical references (p. 401-429).

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (Ph. D.)--Florida State University, 2005. / Advisors: Dr. Pamela Peterson-Drake and Dr. Gary A. Benesh, Florida State University, College of Business, Dept. of Finance. Title and description from dissertation home page (viewed June 8, 2005). Document formatted into pages; contains ix,118 [i.e. 115] pages. Includes bibliographical references.

D.Com. (Business Management) / Please refer to full text to view abstract

M.A. / The rate of change in the business arena is continuously increasing and companies are constantly seeking innovative products, services and knowledge-enabled processes to enable them to compete effectively against each other. IT plays an important role as an enabler of these processes. The problem is that the implementations of IT projects, commissioned to support and enable the business functions, often fail. The high rate of failing IT projects is a real and relevant concern of the business environment. Companies are wasting a significant portion of their resources on failed IT projects. More than often, IT projects fail to realize their intended purpose of reducing operating costs, increasing worker productivity, increasing cycle times and eventually increasing companies' market share. Management of these companies continues to complain about costly IT expenditure returning only a portion of the expected return on investment. The phenomenon of IT project failure has been in existence for many years and has in more recent years become a relevant topic calling for investigation and further study. Accordingly, this document is the culmination of the results of a study of the reasons why IT projects fail and offers possible measures to be taken to counter the failure of IT projects. The most significant and relevant reasons for IT project failure are listed in this document. These reasons span from the internal project environment through to the external project environment. Aligning these reasons with the environment of any anticiPated IT project will establish a base from which a successful IT project could be initiated. It is hoped that this document will serve as valuable input to the decision makers in the IT and business arenas that need to be made aware of the reasons for IT project failure in order for them to better manage their IT projects.

This dissertation consists of three essays exploring market reactions to business failure. In the first essay, the filing strategies are divided into three basic types, voluntary, involuntary and prepackaged. The second essay provides insight into industry wide factors impacting assimilation of information by the market. The third essay provides a view of the GARCH-M model in measuring a risk premium as a firm approaches bankruptcy.

Credit has become very important in the global economy (Cynamon and Fazzari, 2008). The Altman (1968) failure prediction model, or derivatives thereof, are often used in the identification and selection of financially distressed companies as it is recognized as one of the most reliable in predicting company failure (Eidleman, 1995). Failure of a firm can cause substantial losses to creditors and shareholders, therefore it is important, to detect company failure as early as possible. This research report empirically tests the Altman (1968) failure prediction model on 227 South African JSE listed companies using data from the 2008 financial year to calculate the Z-score within the model, and measuring success or failure of firms in the 2009 and 2010 years. The results indicate that the Altman (1968) model is a viable tool in predicting company failure for firms with positive Z-scores, and where Z-scores do not fall into the range of uncertainty as specified. The results also suggest that the model is not reliable when the Z–scores are negative or when they are in the range of uncertainty (between 2.99 and 1.81). If one is able to predict firm failure in advance, it should be possible for management to take steps to avert such an occurrence (Deakin, 1972; Keasey and Watson, 1991; Platt and Platt, 2002).

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