Page 76 - Contributed Paper Session (CPS) - Volume 4

CPS2129 Matilde Bini et al.
recession, begun in 2011 and it endured until 2014. The aim of this work is to
collect evidence on the Italian manufacturing system with the following goals:
to calculate the main financial ratios related to firms’ riskiness and distress risk
trend by means of the book-value data (Zeli & Mariani, 2009; Zeli, 2014); to
detect the guide-variables outlining the firms’ riskiness and distress risk trend
in the period 2008-2017 (Di Clemente, 2008; Kudlyak & Sánchez, 2017); and
to investigate the riskiness-distress risk trend for manufacturing industries and
try to understand the effects of the Great Recession on this important business
indicator trend (Graham et al. 2011; Giroud & Mueller, 2015). To perform this
analysis a Latent Growth Curve Model is proposed, using an important Italian
private database containing the book-value data of the joint-stock company
Italian firms.

2. Measures of firms’ riskiness-distress
A large part of literature is aimed to properly classify the “signal” of
bankruptcy coming principally from book values and standard financial
statements (Altman et al., 1994; Bottazzi et. Al, 2011). A lot of indicators are
considered in literature, among these, the most largely used ones cover area
of economic enterprises’ accounting related with financial distress such as:
liquidity, leverage, profitability.
The interest coverage compares net profits to interest on loans and
thereby expresses the firm’s vulnerability linked to liquidity. It can be seen as
a short-term risk indicator (the lower interest cover index, the higher the
probability of financial distress). The leverage indicates the best indicators of
the financial distress, because the possibility to pay back the debts decreases
when the leverage increases (the lower leverage, the lower the probability of
financial distress). Profitability, measured by means of ROE, assesses the ability
to achieve a minimum profit share level, after covering costs.
In literature there are three approaches to bankruptcy prediction:
accounting approach, analytical approach and statistical one. This last
approach offers many statistical models that use balance sheet data. Statistical
procedures (multiple discriminant analysis, logit or probit) were the most used
methods in this kind of problem. Among them the factorial and latent analysis
can be applied. A latent growth curve model is, innovatively, proposed to
analyse riskiness-distress trend for manufacturing firms in 2008-2017 period.
Unlike traditional longitudinal data analysis techniques, LGM allows
researchers to make inferences about individual level effects as well as group
effects.

3. The Data
We utilize an important private database AIDA (Analisi Informatizzata delle
Aziende Italiane) containing the book-value data of the joint-stock company

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