Data system for assessing probability of failure in SME reorganization

Laitinen, Erkki K.

doi:10.1108/02635570810897964

Cited by 22 publications

(32 citation statements)

References 25 publications

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“…This result is consistent with the previous research on Finnish reorganization proceedings (Sundgren 1998;Bergström et al 2004;Laitinen 2008Laitinen , 2009). It reflects a high rate of filtering failure comparable with that of Chapter 11 (Jensen-Conklin 1992).…”

Section: Resultssupporting

confidence: 95%

“…However, the viability measures showed that only 31-38% of the non-bankrupted reorganization firms are viable. This is consistent with prior findings that about 67% of the corporated reorganization firms (limited companies) which continue with a confirmed plan will fail during the program period (Laitinen 2008(Laitinen , 2009). The present sample consists mainly of limited companies.…”

Section: Resultssupporting

confidence: 92%

“…The fourth hypothesis H4 assumed that ex ante viability assessment based on financial information only is not efficient in discriminating between bankrupted and non-bankrupted reorganization firms. However, H5 assumed that the assessment based on combined information is efficient in this task (Fisher and Martel 2004;Laitinen 2008Laitinen , 2009. Empirical evidence strongly supported these hypotheses.…”

Section: Resultsmentioning

confidence: 79%

“…In Finland, most firms applying for reorganization are not bankrupted prior to filing. However, prior studies show that most of them are financially heavily distressed and nonfinancial variables are needed to predict the outcome of proceedings (Laitinen 2008(Laitinen , 2009). Therefore, the fourth research hypothesis (H4) for reorganization firms assumes that financial variables are not efficient in discriminating between bankrupted and non-bankrupted reorganization firms (filers).…”

Section: Hypothesesmentioning

confidence: 99%

“…Most studies on the area are based on financial variables only (Balcaen and Ooghe 2006). Only rarely non-financial variables are used in analyses (Laitinen 1999;Barniv et al 2002;Fisher and Martel 2004;Back 2005;Lensberg et al 2006;Laitinen 2008Laitinen , 2009.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assessing viability of Finnish reorganization and bankruptcy firms

Laitinen

2010

Eur J Law Econ

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Section: Resultssupporting

confidence: 95%

Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 79%

Section: Hypothesesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing viability of Finnish reorganization and bankruptcy firms

Laitinen

2010

Eur J Law Econ

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Human resources and corporate failure prediction modeling: Evidence from Belgium

Brédart

Séverin

Veganzones³

2021

Journal of Forecasting

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This paper analyzes the prediction performance of human resources (HR) variables in corporate failure modeling. We define corporate failure as a two-phase process from financial distress to bankruptcy, so that we can determine the prediction power of HR variables along a firm's phase in the financial deterioration process. We demonstrate the use of HR variables and their application to a two-phase corporate failure model, providing first evidence for the predictive power of HR variables. The experimental results, based on realworld datasets from Belgium, show that HR variables used in conjugation with accounting-based information improve the accuracy of prediction modeling. However, the predictive power of HR variables varies in different phases of corporate failure with better prediction accuracy during the initial symptoms of corporate failure (i.e., financial distress). Findings show that our proposed model predicted financial distress with 84.1%, whereas the accuracy decreased to 83.3% when predicting bankruptcy. Besides, they also show that, on average, the inclusion of HR variables improves the global accuracy of the prediction models of 3.8% and allows to decrease Type I error of 5%.

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Corporate failure prediction using threshold‐based models

Veganzones

2022

Journal of Forecasting

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Corporate failure prediction literature indicates that models' performance depends on more than the complexity of the prediction method. Recent advances cite the relevance of sampling approaches to model performance.Therefore, this study proposes a novel approach that implements threshold models for corporate failure prediction efforts. Threshold models estimate asymptotically conservative confidence regions, in which the samples are split by size. Then, single-based classifiers and a combination of multiple classifiers are employed in each region to estimate the prediction accuracy. This article offers a comparison of the proposed threshold-based corporate failure model with two benchmark models, previously used in studies in the same context.The empirical results show that the proposed threshold-based model clearly outperforms conventional models, in particular with the combination of multiple classifiers. The superiority of the threshold-based model stems from its ability to discern failed firms, which represent the most important class in financial terms. This study thus provides initial evidence of the utility of threshold models in corporate failure prediction efforts.

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Data system for assessing probability of failure in SME reorganization

Cited by 22 publications

References 25 publications

Assessing viability of Finnish reorganization and bankruptcy firms

Assessing viability of Finnish reorganization and bankruptcy firms

Human resources and corporate failure prediction modeling: Evidence from Belgium

Corporate failure prediction using threshold‐based models

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