Abstract
The present paper studies the evolution of a set of USA firms during the years 1993–2002. The firms that faced a difficult economic and financial situation in 1993 were considered to be in a distress situation. The aim of this study is to explore if the evolution of this situation depends on the initial features of the distress or if it concerns certain firms' characteristics. If the evolution is independent from the above, the management decisions become crucial in critical times. For the analysis we used a Multidimensional Scaling methodology where the firms are represented in a consensus map according to symptom variables, reaction variables, and recovering variables.
Highlights
The research on financial distress has been closely tied to the determination of failure prediction models
The use of the 7 original variables applied to classify the firms in financial distress situation is in agreement with the approach proposed by Smith and Graves 3 to test the role of severity of a distressed state in the turnaround process
We used Multidimensional Scaling to detect and to analyze the existence of similarities between economic and financial structures of the companies being in a “crisis situation”
Summary
The research on financial distress has been closely tied to the determination of failure prediction models. Failure is considered to be the result of an evolutionary process, where the underlying idea is the possibility that the crisis can be anticipated 1 Pioneer prediction models such as the one proposed by Altman 2 built the basics of the research based on prediction. Those researches were mainly centered on minimizing classification errors and maximizing goodness of fit measures using certain variables throughout a wide period of time. The existence of an error in the classification of those companies, which did not fail even though were being described as failed, was considered as a failure of the proposed model These results leave an open door to consider the possibility that the companies can survive a difficult situation or even subsist in a permanent crisis situation. Prediction models provide some essential information in order to take actions against the given default probability, and warn about a future outcome which, in many cases, may not even take place
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