Abstract: The prediction of firm financial distress has been largely investigated since the 1930’s and several
approaches have been proposed.
The most used statistical models for both theoretical and empirical studies are the discriminant analysis,
logistic and probit regression, survival analysis. The dependent variable of these models is binary, indicating
whether the firm is distressed or not, and its knowledge is a prerequisite for estimating the probability of
default. However, the binary indicator is usually known only when the failure already occurs, and
consequently it is impossible to bring about the actions to restore firms’ financial situation.
Due to the global financial crisis, a renewed attention from financial institutions, academics, and
practitioners on corporate distress analysis and forecasting can be observed. As highlighted by a recent
proposal of an EU Directive on “preventive restructuring frameworks, second chance and measures to
increase the efficiency of restructuring, insolvency and discharge procedures”, the crucial point is the
implementation of an early warning system, able to prevent the distress or accelerate the liquidation of the
distressed firm, preserving its asset value.
The aim of this study is to overcome the drawback of the knowledge of the binary variable, by creating an
aggregate failure index (AFI) based on relevant financial ratios. Our model recognizes several statuses
(active; active but with financial difficulties; bankrupt; liquidation) comparing the different situations a firm
can face with. Through the identification of a threshold, the model allows to classify and predict on time
the business failure. This procedure, evaluated by some accuracy measures, could be a suitable instrument
for preventing financial distress and avoiding firms’ crisis.
Presented by:
Marialuisa Restaino, University of Salerno
Date & time:
May 24, 2017 12:00 pm - May 24, 2017 1:00 pm
Venue:
Large Seminar Room: 2N2.4.16
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