The prediction of bankruptcy of small- and medium-sized industrial firms

Pompe, Paul P. M.; Bilderbeek, J.

doi:10.1016/j.jbusvent.2004.07.003

Cited by 169 publications

(113 citation statements)

References 19 publications

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“…The validity test on the SE discriminant function gives a global accuracy level of 80.58% with 18.74% of Type 1 errors and 20,10% Type 2 errors, while the same test on the SMLE discriminant function gives 20,80% Type 1 errors, 22,20%% Type 2 errors and an overall accuracy of 78.5%. These results confirm H1: small firms have their own specific structural and strategic characteristics, which are unlike those of large firms (Ciampi, 1994;Ciampi, 2015;Pompe & Bilderbeek, 2005) and consequently their credit risk profiles are significantly different from larger companies. Financial institutions should therefore use credit rating models specifically built for SEs in order to maximize their capacity to create value for their shareholders (Altman & Sabato, 2007).…”

Section: Methodssupporting

confidence: 76%

The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

Ciampi¹

2017

IJBM

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The existing literature has proved the effectiveness of financial ratios for company default prediction modelling. However, such researches rarely focus on small enterprises (SEs) as specific units of analysis. The aim of this paper is to demonstrate that SE default prediction should be modelled separately from that of large and medium-sized firms. In fact, a multivariate discriminant analysis was applied to a sample of 2,200 small manufacturing firms located in Central Italy and a SE default prediction model was developed based on a selected group of financial ratios and specifically constructed to capture the specificities of SEs' risk profiles. Subsequently, the prediction accuracy rates obtained by this model were compared with those obtained from a second model based on a sample of 3,200 manufacturing firms situated in Central Italy which belong to all dimensional classes. The findings are the following: 1) evaluating the probability of default of SEs separately from that of larger firms improves prediction performance; 2) the predictive power of the discriminant function improves if it takes into account the different profiles of firms operating in different industry sectors; 3) this improvement is much greater for SEs compared to larger firms.

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Section: Methodssupporting

confidence: 76%

The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

Ciampi¹

2017

IJBM

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“…This finding may be explained in two different ways. First, it could potentially qualify the low predictive power of the bankruptcy variables selected, even if this goes against the empirical literature on the subject (in particular, the findings of Altman, 1968;Frydman et al, 1985;Ooghe and Van Wymeersch, 2000;Pompe and Bilderbeek, 2005). Then, the relevance of using this methodology in the context of bankruptcy prediction could also be questioned.…”

Section: Resultsmentioning

confidence: 99%

“…Representation on the first two axes (for axes 2 and 3 also) pointed out a high concentration of companies at the origin of the axes, not allowing for healthy firms to be adequately discriminated from failing ones, from the variables initially selected. However, the choice of variables should not be called into question because, on the one hand, they are used in many articles in the literature (Altman, 1968;Taffler, 1982;Ooghe and Van Wymeersch, 2000;Pompe and Bilderbeek, 2005), and on the other hand, the discriminating power of these variables was confirmed using logistic regression (most variables one year, three years and five years before bankruptcy were significant). The principal components analysis method associated with the Ward criterion and mobile centres applied to the prediction of bankruptcy was not found to be effective for determining a sufficient number of classes composed of a majority of healthy or failing firms.…”

Section: Discussionmentioning

confidence: 99%

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Predicting Bankruptcy of Belgian SMEs: A Hybrid Approach Based on Factorial Analysi

Cultrera¹,

Croquet²,

Jospin³

2017

IBR

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The aim of this paper is to verify the relevance of technical data analysis which seems to be useful for identifying predictors of bankruptcy of Belgian SMEs. To do so, a sample of 1,860 Belgian companies, including healthy and bankrupt firms, was used. The sample was constituted using Belfirst software (2015). A mixed method data analysis, coupling the Ward aggregation criterion, the method of mobile centres and principal component analysis, was performed on the variables commonly cited in the literature as predictive of corporate bankruptcies. The results of this study show that the use of these methods is not relevant in the context of bankruptcy prediction using this sample, but the results of the logistic regressions did not question the discriminatory power of the introduced active variables.

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“…Ohlson 1980, Altman 2000, Becchetti and Sierra 2003, Pompe and Bilderbeek 2005, the public sector has failed to draw such a great attention from researchers. Consequently, the academic literature in the field is primarily directed towards the private sector both in terms of theoretical and empirical approaches.…”

Section: Overview Of the Previous Studiesmentioning

confidence: 99%

Predicting the Risk of Encountering Financial Difficulties by the Example of Estonian Municipalities

Lohk¹,

Siimann²

2016

Proceedings of the 5th International Conference on Accounting, Auditing, and Taxation (ICAAT 2016)

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The objective of the study is to determine the financial and non-financial indicators which describe the financial difficulties encountered by Estonian municipalities and to prepare a model to predict the risk of encountering financial difficulties. In order to achieve the objective, the study employs multidimensional statistical analysis methods: discriminant analysis and logit analysis. The predictive model is based on the financial and non-financial indicators of Estonian municipalities for the years 2004-2014 and the risk of encountering financial difficulties is defined as the value of the variable primary operating outcome to depreciation of tangible fixed assets of the population over the years 2011-2014. The classification accuracy of the discriminant model comprising seven independent variables found as a result of the analysis is 88% and the same indicator for the logit model is 87%. The variable with the highest descriptive capacity in predicting the risk of encountering financial difficulties is the ratio of primary operating outcome to the primary operating revenues. The methods of the study and the predictive model prepared make a significant contribution to the development of this field of study in Estonia and are applicable also to other countries where municipalities operate under similar financial management principles.

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The prediction of bankruptcy of small- and medium-sized industrial firms

Cited by 169 publications

References 19 publications

The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms

Predicting Bankruptcy of Belgian SMEs: A Hybrid Approach Based on Factorial Analysi

Predicting the Risk of Encountering Financial Difficulties by the Example of Estonian Municipalities

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