Cash Flow in Bankruptcy Prediction

Gombola, Michael; Haskins, Mark E.; Ketz, J. Edward; Williams, David D.; Haskins, Ethelbert W.

doi:10.2307/3666109

Cited by 98 publications

(61 citation statements)

References 17 publications

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“…It was used by many authors (Deakin (1972), Edmister (1972), Blum (1974), Eisenbeis (1977), Taffler and Tisshaw (1977), Altman et al (1977), Micha (1984, Gombola et al (1987), Lussier (1995), Altman et al (1995)). However, in most of these studies, authors pointed out that two basic assumptions of MDA are often violated when applied to the default prediction problems 9 .…”

Section: Default Prediction Methodologiesmentioning

confidence: 99%

The value of non-financial information in SME risk management

Altman¹,

Sabato²,

Wilson³

2010

JCR

298

302

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Within the commercial client segment, small business lending is gradually becoming a major target for many banks. The new Basel Capital Accord has helped the financial sector to recognize small and medium sized enterprises (SMEs) as a client, distinct from the large corporate. Some argue that this client base should be treated like retail clients from a risk management point of view in order to lower capital requirements and realize efficiency and profitability gains. In this context, it is increasingly important to develop appropriate risk models for this large and potentially even larger portion of bank assets. So far, none of the few studies that have focused on developing credit risk models specifically for SMEs have included non-financial information as predictors of the company credit worthiness. For the first time, in this study we have available non-financial, regulatory compliance and 'event' data to supplement the limited accounting data which are often available for non-listed firms. We employ a sample consisting of over 5.8 million sets of accounts of unlisted firms of which over 66,000 failed during the period 2000-2007. We find that qualitative data relating to such variables as legal action by creditors to recover unpaid debts, company filing histories, comprehensive audit report/opinion data and firm specific characteristics make a significant contribution to increasing the default prediction power of risk models built specifically for SMEs.JEL classification: G33, G32, M13

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Section: Default Prediction Methodologiesmentioning

confidence: 99%

The value of non-financial information in SME risk management

Altman¹,

Sabato²,

Wilson³

2010

JCR

298

302

View full text Add to dashboard Cite

show abstract

“…9 The original Z-score model (Altman, 1968) For many years thereafter, MDA was the prevalent statistical technique applied to the default prediction models. It was used by many authors (Deakin (1972), Edmister (1972), Blum (1974), Eisenbeis (1977), Taffler and Tisshaw (1977), Altman et al (1977), Bilderbeek (1979), Micha (1984), Gombola et al (1987), Lussier (1995), Altman et al (1995)). However, in most of these studies, authors pointed out that two basic assumptions of MDA are often violated when applied to the default prediction problems 10 .…”

Section: Default Prediction Studiesmentioning

confidence: 99%

Modeling Credit Risk for SMEs: Evidence from the US Market

Altman

Sabato²

2005

SSRN Journal

124

217

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Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs. JEL classification: G21; G28

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“…PCA is mainly used in combination with neural networks (Mensah, 1984;Zavgren, 1985;Gombola et al 1987;Skogsvik 1990;Alici, 1996;Sharma and Iselin, 2003;Shin and Lee, 2003;Wang, 2004;Canbas et al, 2005;Min and Lee, 2005;Tang and Chi, 2005;Shin et al, 2006;Sookhanaphibarn et al, 2007;Yao, 2007. Ravi andPramodh, 2008;Chen and Du 2009;Balas et al, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

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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Cash Flow in Bankruptcy Prediction

Cited by 98 publications

References 17 publications

The value of non-financial information in SME risk management

The value of non-financial information in SME risk management

Modeling Credit Risk for SMEs: Evidence from the US Market

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

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