Speaker’s name: Dimitra Michala
Affiliation: University of Luxembourg, Luxembourg School of Finance
Place of work: Luxembourg
Position: PhD Candidate, Research Assistant
Address: 4, rue Albert Borschette, L – 1246, Luxembourg
Tel: ++352 46 66 44 6805
Fax: ++352 46 66 44 6835
Email: dimitra.michala@uni.lu
Title of proposed paper: SME distress forecasting with macroeconomic dependencies
Authors: Dimitra Michala1, Theoharry Grammatikos, Sara Ferreira Filipe
1
Corresponding author.
Extended abstract
The study addresses empirically the issue of distress forecasting for small and mediumsized enterprises (SMEs). Applying the multi-period logit technique, we develop distress
prediction hazard models for non-financial SMEs using a large sample from seven European
countries over the three-year period 2007-2009. We examine the macroeconomic
vulnerabilities of SMEs for the overall sample as well as for the individual country
subsamples. Finally, we explore the performance of our models with both in-sample and outof-sample tests.
The paper contributes to the existing literature in several ways. First, despite the
importance of SMEs to the economy (they account for 95% of all enterprises in the OECD
countries and 99% in Europe), they remain largely unexplored by the academia due to the
challenges in their credit risk modeling. This study improves our understanding of SMEs by
adopting the strict European Commission definition (less than 250 employees and either
annual turnover up to €50m or total assets up to €43m) and using a data set that includes a
very high number of micro companies (75% of our sample). This is particularly important for
Europe, where one out of ten SMEs is a micro company. Second, to our knowledge, this is
the first study that examines distress for SMEs in a multi-country setting, allowing for
interesting cross-country comparisons. Third, it is also the first study that extensively
explores and uncovers the sensitivities of SMEs to the macroeconomic conditions. As a result,
apart from finding distress probabilities for individual firms, our models can capture changes
in the overall distress rates during upturns and downturns, identify possible sources of distress
correlation and, finally, serve as input to a stress testing framework or a portfolio credit risk
model.
For private firms, such as SMEs, structural market-based models for credit risk, such as
Merton’s (1974) distance-to-default model cannot be applied since market data are not
available for them. Instead, credit scoring approaches, such as Altman’s z-score (Altman,
1968) are the most common. Credit scoring approaches use mainly accounting information
(financial ratios) to predict distress. Recently, Grunert et al. (2005) note the importance of
qualitative information in distress prediction (such as size, legal form, bank relationships) and
Altman et al. (2010) develop a credit-scoring model that incorporates such kind of “soft”
information for SMEs. The latest stand in distress prediction includes models that examine
macroeconomic variables in credit-scoring approaches (Nam et al. 2008) but, to our
knowledge, there is no study that explores particularly the relationship between distress risk
for SMEs and the macro environment.
Concerning the estimation methodologies, multiple discriminant analysis, which had been
popular in early studies, was gradually replaced by probit and (mostly) logit analysis.
Recently, duration (hazard) analysis is replacing them. In our study, we follow Shumway
(2001) and develop a discrete-time hazard model that we estimate with the multi-period logit
technique. Shumway proves that a discrete-time hazard model with time-varying covariates
and a multi-period logit model have the same likelihood functions. He shows that such a
hazard model can be estimated using the logit technique. He also compares static (singleperiod) and multi-period logit models and finds that the second are superior to the first. In a
static logit model, one firm-year observation for each healthy firm is randomly selected from
the available firm-years. For distressed firms, the firm-year immediately prior to distress is
non-randomly selected. In a multi-period logit model though, data on each firm, on each
available year are used, as if each firm-year is an independent observation. Shumway
demonstrates that multi-period logit models produce unbiased and consistent estimates
because they account for the changes in the firms’ financial conditions (due to the panel
structure of the data). They are also more accurate and efficient than static ones because they
use more data for their estimation (multiple firm-year stacked observations). Following
Shumway, we assume that the probability of distress over the next year (the hazard rate)
follows the logistic distribution:
ℎ(𝑡/𝑥𝑖,𝑡−1 ) = 𝑃(𝑌𝑖,𝑡 = 1/𝑥𝑖,𝑡−1 ) = 1/(1 + exp(−𝛽𝑥𝑖,𝑡−1 − 𝛾𝑦𝑖,𝑡−1 ), where:
1. 𝑌𝑖,𝑡 is an indicator that equals one if the firm is distressed in year 𝑡,
2. 𝑥𝑖,𝑡−1 is a vector of firm-specific variables (quantitative and qualitative) known at the end
of the previous year 𝑡, and
3. 𝛾𝑦𝑖,𝑡−1 is the baseline hazard function that includes some other time-dependent variables
𝑦𝑖,𝑡−1 which can be common across firms (in our case macroeconomic variables)2.
The only adjustment needed in the estimation procedure is the correction of standard errors
to account for the dependence between firm-years of the same firm. For this reason, we
calculate cluster-corrected standard errors (Wooldridge, 2002).
Our data set consists of 1,100,776 firm-year observations (448,124 firms) out of which
31,440 distressed, from seven European countries, namely Czech Republic, France, Germany,
Poland, Portugal, Spain and United Kingdom, covering the period 2007-2009. The hold-out
sample consists of 122,100 firm-year observations (49,726 firms) out of which 3,476
distressed. We collect the firm-specific variables from Orbis Europe database, available from
Bureau Van Dijk, and the macroeconomic variables from public data available from Eurostat
2
The baseline hazard expresses the hazard rate in the absence of firm-specific covariates.
and the ECB. The financial data are annual and we take averages or annualize the
macrovariables when needed.
We classify a firm-year as distressed (𝑌𝑖,𝑡 = 1) if:
1. It is the last firm-year for which we have available financial statements before the firm
leaves the sample, and;
2. The firm appears with one of the statuses defaulted, in receivership, in liquidation or it
appears as active but has negative equity in the year before leaving the sample,
according to Boss’s (2010) definition of stock-based insolvency.
In selecting the variables, we take into account the stability, fit and parsimony of the
models as well as the statistical significance of the estimated coefficients. Specifically, we
select the best performing accounting ratios among 70 ratios from nine categories
(profitability, liquidity, coverage, leverage, activity, cash flow, growth, asset utilization and
employees efficiency) after performing univariate analysis, correlation analysis and a forward
stepwise selection procedure. We also control for firm size, legal form (three legal form
dummies) and industry conditions (five wide industry group dummies). Finally, we select the
most explanatory macrovariables among 17 variables from three categories (business cycle,
bank lending conditions and financial market).
We evaluate the performance of the distress prediction models with two widely used
methods, the Hosmer and Lemeshow groupings based on estimated distress probabilities and
the area under the Receiver Operating Characteristic (ROC) curve and perform both insample and out-of-sample tests.
We find that, for most countries, the macroeconomic conditions, especially the GDP
growth, have significant predictive power for SMEs’ distress risk and considering such
patterns can lead to improvements compared to using only firm-specific information. Apart
from the GDP growth, we also find the economic sentiment, the volatility of the exchange
rate, the interest rate, the balance of payments as a percentage of the GDP and the oil price to
improve predictive power in some countries. Nevertheless, controlling for industry effects by
including industry dummies does not provide improvements in most countries, especially
when we have already accounted for the prevailing macro conditions. Country-specific
models yield better results than the generic model fitted with data from all countries. This is
mainly due to the vulnerabilities to different macro factors among the different countries.
Finally, macroeconomic variables demonstrate high correlations among them and usually one
or two are enough to lead to improvements in the models.
The results of the study can be useful to lending specialists, private equity investors,
banks, regulators, auditors, consultants, analysts, managers, governments and others. Some
limitations though are that the time-series dimension of our study is small and that we do not
account for feedback effects between firm-specific information and the macroeconomy.