Bankruptcy prediction: the case of Belgian SMEs

Cultrera, Loredana; Brédart, Xavier

doi:10.1108/raf-06-2014-0059

Cited by 77 publications

(66 citation statements)

References 36 publications

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“…[28] using NN, the Cox model and Logit managed to correctly predict 81.20% of bankruptcy cases, using a sample of French firms for their models. [12] developed a Logit bankruptcy prediction model based on Belgian firms, including control variables such as the size and age. The results showed that ratios of profitability and liquidity increase the accuracy of bankruptcy prediction.…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

“…This has also resulted in a new area of research, given the need to create models to predict bankruptcy, not just for a given country, but also to explain the common features shared by companies in the same geographical setting [8]. However, when creating models that attempt to offer rigorous predictions of bankruptcy, the majority of these have centred on companies in a single country or industry [9–12] or have focused on comparing the results of different predictive models, but without considering the creation of a global model [8]. …”

Section: Introductionmentioning

confidence: 99%

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A Global Model for Bankruptcy Prediction

2016

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The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

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Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Global Model for Bankruptcy Prediction

2016

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“…The abundance of scientific literature reveals the necessity for a reliable, precise and relevant bankruptcy prediction model. According to Aruldoss et al (2015), Cultrera and Bredart (2016), Salehi and Pour (2016), the information rendered by bankruptcy forecasting models is bounded to separate industries. Alaka et al (2018), Schonfeld et al (2018), Svabova and Kliestik (2018), and Slefendorfas (2016) believe that the traditional bankruptcy prediction models are not suited to analyzing modern enterprises because the dynamic macroeconomic environment and business are interdependent.…”

Section: Introductionmentioning

confidence: 99%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

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“…Lee and Han [25] used non-financial information, such as type of business, number of employees, number of exports, and technical manpower ratings, to build a prediction model along with financial information. In addition to the financial ratio, the types of non-financial information used for insolvent prediction were based on research focusing on industry sector, region, and company age [26,27]; the quality of accounting information, firm owners' personal credit performance, and management quality [28]; context-based feature set designing industry-representative models [19]; and indicators such as value-added rate and labor productivity [29]. The prediction rates for these studies ranged from 74.1% to 93%.…”

Section: Research On Predicting Company Insolvencymentioning

confidence: 99%

Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques

2020

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The government makes great efforts to maintain the soundness of policy funds raised by the national budget and lent to corporate. In general, previous research on the prediction of company insolvency has dealt with large and listed companies using financial information with conventional statistical techniques. However, small- and medium-sized enterprises (SMEs) do not have to undergo mandatory external audits, and the quality of accounting information is low due to weak internal control. To overcome this problem, we developed an insolvency prediction model for SMEs using data mining techniques and technological feasibility assessment information as non-financial information. We divided the dataset into two types of data based on three years of corporate age. The synthetic minority over-sampling technique (SMOTE) was used to solve the data imbalance that occurred at this time. Six insolvency prediction models were created using logistic regression, a decision tree, an artificial neural network, and an ensemble (i.e., boosting) of each algorithm. By applying a boosted decision tree, the best accuracies of 69.1% and 82.7% were derived, and by applying a decision tree, nine and seven influential factors affected the insolvency of SMEs established for fewer than three years and more than three years, respectively. In addition, we derived several insolvency rules for the two types of SMEs from the decision tree-based prediction model and proposed ways to enhance the health of loans given to potentially insolvent companies using these derived rules. The results of this study show that it is possible to predict SMEs’ insolvency using data mining techniques with technological feasibility assessment information and find meaningful rules related to insolvency.

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Bankruptcy prediction: the case of Belgian SMEs

Cited by 77 publications

References 36 publications

A Global Model for Bankruptcy Prediction

A Global Model for Bankruptcy Prediction

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques

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