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Modern Methods of Bankruptcy Prediction
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Modern Methods of Bankruptcy Prediction

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 73147

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Błażej Prusak

E-Mail Website
Guest Editor
Faculty of Management and Economics, Gdańsk University of Technology, Gdańsk, Poland
Interests: corporate bankruptcy prediction; institutional aspects of corporate bankruptcy; risk management; business valuation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Successes and failures are constant elements of the functioning of markets and have accompanied them from their very beginning. In many countries, not only is corporate bankruptcy provided for by law, but it is also possible to declare the bankruptcy of a natural person (so-called personal or consumer bankruptcy). The consequences of financial failure are enormous for creditors, shareholders, investors, employees and even a country’s economy. Hence, research has been conducted for decades to develop more effective models of bankruptcy prediction. Despite growing interest in bankruptcy forecasting models, many questions remain unanswered. Moreover, the current global coronavirus pandemic has dramatically increased the risk of insolvencies throughout the world. Thus, the issue of forecasting bankruptcy is still a very important problem in the area of finance. With the development of new statistical methods and IT tools, bankruptcy prediction has become more effective, but scientists are still looking for more sophisticated solutions.

This Special Issue aims at collecting a number of new contributions, both at the theoretical level and in terms of applications. We expect publications of a theoretical and empirical nature, which will be an important contribution to the development of literature on this issue.

The topics covered in this Special Issue will include (but are not limited to) the following areas: the search for new tools in forecasting business and personal bankruptcy, the development of bankruptcy prediction models for specific organizations such as e.g.: small and medium sized enterprises, banks, social entities, insurance companies, tourism enterprises, football clubs etc., the problem of selecting a learning sample and explanatory variables to bankruptcy models, the search for macroeconomic, governance and sectoral factors in the process of bankruptcy forecasting, the dynamization of models and the problem of prolonging the forecasting horizon, the impact of differences between national and international accounting standards on the accuracy and correctness of analyses in the field of bankruptcy prediction, the impact of specific local factors on the selection of explanatory variables to models, advantages and limitations of bankruptcy prediction models, and the comparison of the effectiveness of expert assessments with the results obtained using models.

Dr. Błażej Prusak

Guest Editor

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Keywords

  • New tools in corporate bankruptcy prediction
  • Comparative analysis of corporate bankruptcy prediction methods
  • Local versus global and sectoral versus universal business failure prediction models
  • Symptoms of business failure
  • Bankruptcy trajectories

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Published Papers (13 papers)

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Research

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16 pages, 638 KiB  
Article
Approval of an Arrangement in the Restructuring Proceedings and the Financial Condition of Companies Listed on the Stock Exchanges in Warsaw. Is There Any Relationship?
by Błażej Prusak and Paweł Galiński
J. Risk Financial Manag. 2021, 14(11), 523; https://doi.org/10.3390/jrfm14110523 - 1 Nov 2021
Cited by 2 | Viewed by 1984
Abstract
This paper attempts to identify the financial indicators differentiating companies that are insolvent or at risk of insolvency and have successfully entered into an arrangement with their creditors from those that have not. In addition, a two-factor model for predicting the odds of [...] Read more.
This paper attempts to identify the financial indicators differentiating companies that are insolvent or at risk of insolvency and have successfully entered into an arrangement with their creditors from those that have not. In addition, a two-factor model for predicting the odds of an arrangement has been proposed. The research was conducted using a population of companies listed on stock exchanges in Warsaw that initiated restructuring proceedings between October 2004 and 31 December 2020. Binary logistic regression was used as the research method. The research shows that the financial health of public companies in Poland, as measured by various financial indicators, has little impact on the effectiveness of a debtor–creditor arrangement. The main measure showing discriminatory features between groups of successful and unsuccessful entities is the share of short-term liabilities in total liabilities. A statistically significant influence was also recorded for the indicator showing the share of short-term receivables in the total assets. Furthermore, a statistically significant discriminatory power for both groups was recorded for the indicator showing the ratio of inventories to sales revenue. However, in this situation the selection of this measure was due to the industry diversity of the research population. The identification of these determinants and the proposed model may help courts and supervisors to divide insolvent companies into those that should be subject to liquidation or restructuring procedures at an early stage of the proceedings. Consequently, this can significantly reduce the direct and indirect costs of implementing bankruptcy proceedings. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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13 pages, 265 KiB  
Article
Failure Prediction in the Condition of Information Asymmetry: Tax Arrears as a Substitute When Financial Ratios Are Outdated
by Oliver Lukason and Germo Valgenberg
J. Risk Financial Manag. 2021, 14(10), 470; https://doi.org/10.3390/jrfm14100470 - 6 Oct 2021
Cited by 3 | Viewed by 1809
Abstract
This paper aims to study the usefulness of applying tax arrears in failure prediction, when annual reports to calculate financial ratios are outdated. Three known classification methods from the failure prediction literature are applied to the whole population dataset from Estonia, incorporating various [...] Read more.
This paper aims to study the usefulness of applying tax arrears in failure prediction, when annual reports to calculate financial ratios are outdated. Three known classification methods from the failure prediction literature are applied to the whole population dataset from Estonia, incorporating various tax arrears variables and financial ratios. The results indicate that accuracies remarkably exceeding those of models based on financial ratios can be obtained with variables portraying the average, maximum, and duration contexts of tax arrears. The main contribution of the current study is that it provides a proof of concept that accounting for the dynamics of payment defaults can lead to useful prediction models in cases in which up-to-date financial reports are not available. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
19 pages, 2091 KiB  
Article
The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy
by Róbert Štefko, Jarmila Horváthová and Martina Mokrišová
J. Risk Financial Manag. 2021, 14(5), 220; https://doi.org/10.3390/jrfm14050220 - 13 May 2021
Cited by 13 | Viewed by 3035
Abstract
The paper deals with the issue of analyzing the financial failure of businesses. The aim was to select key performance indicators entering the DEA model. The research was carried out on a sample of 343 Slovak heat management companies. When addressing the research [...] Read more.
The paper deals with the issue of analyzing the financial failure of businesses. The aim was to select key performance indicators entering the DEA model. The research was carried out on a sample of 343 Slovak heat management companies. When addressing the research problem, we made use of multidimensional scaling (MDS) and principal component analysis (PCA), which pointed out the areas of financial health of companies that may predict their financial failure. The core of our interest and research was the data envelopment analysis (DEA) method, which represents a more exact approach to the assessment of financial health. The important finding is that the statistical graphical methods—PCA and MDS—are very helpful in identifying outliers and selecting key performance indicators entering the DEA model. The benefit of the paper is the identification of companies that are at risk of bankruptcy using the DEA method. The originality is the selection of key inputs and outputs to the DEA model by the PCA method. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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15 pages, 486 KiB  
Article
Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison
by Jakub Horak, Jaromir Vrbka and Petr Suler
J. Risk Financial Manag. 2020, 13(3), 60; https://doi.org/10.3390/jrfm13030060 - 24 Mar 2020
Cited by 49 | Viewed by 6073
Abstract
Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool [...] Read more.
Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool to assess the future development of any company in the market. The objective of this paper is to create a model for predicting potential bankruptcy of companies using suitable classification methods, namely Support Vector Machine and artificial neural networks, and to evaluate the results of the methods used. The data (balance sheets and profit and loss accounts) of industrial companies operating in the Czech Republic for the last 5 marketing years were used. For the application of classification methods, TIBCO’s Statistica software, version 13, is used. In total, 6 models were created and subsequently compared with each other, while the most successful one applicable in practice is the model determined by the neural structure 2.MLP 22-9-2. The model of Support Vector Machine shows a relatively high accuracy, but it is not applicable in the structure of correct classifications. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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28 pages, 1187 KiB  
Article
Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis
by Nicoleta Bărbuță-Mișu and Mara Madaleno
J. Risk Financial Manag. 2020, 13(3), 58; https://doi.org/10.3390/jrfm13030058 - 18 Mar 2020
Cited by 39 | Viewed by 14080
Abstract
Assessment and estimation of bankruptcy risk is important for managers in decision making for improving a firm’s financial performance, but also important for investors that consider it prior to making investment decision in equity or bonds, creditors and company itself. The aim of [...] Read more.
Assessment and estimation of bankruptcy risk is important for managers in decision making for improving a firm’s financial performance, but also important for investors that consider it prior to making investment decision in equity or bonds, creditors and company itself. The aim of this paper is to improve the knowledge of bankruptcy prediction of companies and to analyse the predictive capacity of factor analysis using as basis the discriminant analysis and the following five models for assessing bankruptcy risk: Altman, Conan and Holder, Tafler, Springate and Zmijewski. Stata software was used for studying the effect of performance over risk and bankruptcy scores were obtained by year of analysis and country. Data used for non-financial large companies from European Union were provided by Amadeus database for the period 2006–2015. In order to analyse the effects of risk score over firm performance, we have applied a dynamic panel-data estimation model, with Generalized Method of Moments (GMM) estimators to regress firm performance indicator over risk by year and we have used Tobit models to infer about the influence of company performance measures over general bankruptcy risk scores. The results show that the Principal Component Analysis (PCA) used to build a bankruptcy risk scored based on discriminant analysis indices is effective for determining the influence of corporate performance over risk. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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16 pages, 1244 KiB  
Article
Comparison of Prediction Models Applied in Economic Recession and Expansion
by Dagmar Camska and Jiri Klecka
J. Risk Financial Manag. 2020, 13(3), 52; https://doi.org/10.3390/jrfm13030052 - 10 Mar 2020
Cited by 8 | Viewed by 4615
Abstract
As a rule, the economy regularly undergoes various phases, from a recession up to expansion. This paper is focused on models predicting corporate financial distress. Its aim is to analyze impact of individual phases of the economic cycle on final scores of the [...] Read more.
As a rule, the economy regularly undergoes various phases, from a recession up to expansion. This paper is focused on models predicting corporate financial distress. Its aim is to analyze impact of individual phases of the economic cycle on final scores of the prediction models. The prediction models may be used for quick, inexpensive evaluation of a corporate financial situation leading to business risk mitigation. The research conducted is drawn from accounting data extracted from the prepaid corporate database, Albertina. The carried-out analysis also highlights and examines industry specifics; therefore, three industry branches are under examination. Enterprises falling under Manufacture of metal products, Machinery, and Construction are categorized into insolvent and healthy entities. In this study, 18 models are selected and then applied to the business data describing recession and expansion. The final scores achieved are summarized by the main descriptive statistics, such as mean, median, and trimmed mean, followed by the absolute difference comparing expansion and recession. The results confirm the expectations, assuming that final scores with higher values describe better corporate financial standing during the expansion phase. Similar results are achieved for both healthy and insolvent enterprises. The paper highlights exceptions and offers possible interpretations. As a conclusion, it is recommended that users need to respect the current phase of the economic cycle when interpreting particular results of the prediction models. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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14 pages, 278 KiB  
Article
Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya
by Daniel Ogachi, Richard Ndege, Peter Gaturu and Zeman Zoltan
J. Risk Financial Manag. 2020, 13(3), 47; https://doi.org/10.3390/jrfm13030047 - 4 Mar 2020
Cited by 25 | Viewed by 8562
Abstract
Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining [...] Read more.
Predicting bankruptcy of companies has been a hot subject of focus for many economists. The rationale for developing and predicting the financial distress of a company is to develop a predictive model used to forecast the financial condition of a company by combining several econometric variables of interest to the researcher. The study sought to introduce deep learning models for corporate bankruptcy forecasting using textual disclosures. The study constructed a comprehensive study model for predicting bankruptcy based on listed companies in Kenya. The study population included all 64 listed companies in the Nairobi Securities Exchange for ten years. Logistic analysis was used in building a model for predicting the financial distress of a company. The findings revealed that asset turnover, total asset, and working capital ratio had positive coefficients. On the other hand, inventory turnover, debt-equity ratio, debtors turnover, debt ratio, and current ratio had negative coefficients. The study concluded that inventory turnover, asset turnover, debt-equity ratio, debtors turnover, total asset, debt ratio, current ratio, and working capital ratio were the most significant ratios for predicting bankruptcy. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
35 pages, 5218 KiB  
Article
An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship
by Tomasz Pisula
J. Risk Financial Manag. 2020, 13(2), 37; https://doi.org/10.3390/jrfm13020037 - 19 Feb 2020
Cited by 18 | Viewed by 5030
Abstract
This publication presents the methodological aspects of designing of a scoring model for an early prediction of bankruptcy by using ensemble classifiers. The main goal of the research was to develop a scoring model (with good classification properties) that can be applied in [...] Read more.
This publication presents the methodological aspects of designing of a scoring model for an early prediction of bankruptcy by using ensemble classifiers. The main goal of the research was to develop a scoring model (with good classification properties) that can be applied in practice to assess the risk of bankruptcy of enterprises in various sectors. For the data sample, which included 1739 Polish businesses (of which 865 were bankrupt and 875 had no risk of bankruptcy), a genetic algorithm was applied to select the optimum set of 19 bankruptcy indicators, on the basis of which the classification accuracy of a number of ensemble classifier model variants (boosting, bagging and stacking) was estimated and verified. The classification effectiveness of ensemble models was compared with eight classical individual models which made use of single classifiers. A GBM-based ensemble classifier model offering superior classification capabilities was used in practice to design a scoring model, which was applied in comparative evaluation and bankruptcy risk analysis for businesses from various sectors and of different sizes from the Podkarpackie Voivodeship in 2018 (over a time horizon of up to two years). The approach applied can also be used to assess credit risk for corporate borrowers. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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10 pages, 274 KiB  
Article
Cross-Country Application of Manufacturing Failure Models
by Sebastian Klaudiusz Tomczak and Piotr Staszkiewicz
J. Risk Financial Manag. 2020, 13(2), 34; https://doi.org/10.3390/jrfm13020034 - 18 Feb 2020
Cited by 6 | Viewed by 2654
Abstract
The post-Altman models suffer from moral amortization. This paper asks whether models developed in one country can be applied in other economies. One of the characteristics of the prediction model is that a date drives the estimation. Thus, the estimated model based on [...] Read more.
The post-Altman models suffer from moral amortization. This paper asks whether models developed in one country can be applied in other economies. One of the characteristics of the prediction model is that a date drives the estimation. Thus, the estimated model based on one economy is not necessarily applicable to other economies. To verify such a statement, we carried out a literature review to identify the manufacturing models constructed during the last 30 years that were reported in reputable scientific journals. Our literature comprised 75 papers, and with the application of the citation count and citation mining, we selected a sample and traced the selected papers to the cross-country application. Our results indicated an existing gap in the cross-economy validation of existing manufacturing models. Our study has implications for policy, as the application of the prediction models to cross-economies’ consolidated financial statements is biased. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
13 pages, 209 KiB  
Article
Tax Arrears Versus Financial Ratios in Bankruptcy Prediction
by Oliver Lukason and Art Andresson
J. Risk Financial Manag. 2019, 12(4), 187; https://doi.org/10.3390/jrfm12040187 - 11 Dec 2019
Cited by 12 | Viewed by 4118
Abstract
This paper aims to compare the usefulness of tax arrears and financial ratios in bankruptcy prediction. The analysis is based on the whole population of Estonian bankrupted and survived SMEs from 2013 to 2017. Logistic regression and multilayer perceptron are used as the [...] Read more.
This paper aims to compare the usefulness of tax arrears and financial ratios in bankruptcy prediction. The analysis is based on the whole population of Estonian bankrupted and survived SMEs from 2013 to 2017. Logistic regression and multilayer perceptron are used as the prediction methods. The results indicate that closer to bankruptcy, tax arrears’ information yields a higher prediction accuracy than financial ratios. A combined model of tax arrears and financial ratios is more useful than the individual models. The results enable us to outline several theoretical and practical implications. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
15 pages, 2654 KiB  
Article
Dynamic Bankruptcy Prediction Models for European Enterprises
by Tomasz Korol
J. Risk Financial Manag. 2019, 12(4), 185; https://doi.org/10.3390/jrfm12040185 - 9 Dec 2019
Cited by 29 | Viewed by 6780
Abstract
This manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for [...] Read more.
This manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for European enterprises. To conduct this objective, four forecasting models are developed with the use of four different methods—fuzzy sets, recurrent and multilayer artificial neural network, and decision trees. Such a research approach will answer the question of whether changes in indicators are relevant predictors of a company’s coming financial crisis because declines or increases in values do not immediately indicate that the company’s economic situation is deteriorating. The research relies on two samples of firms—the learning sample of 50 bankrupt and 50 non-bankrupt enterprises and the testing sample of 250 bankrupt and 250 non-bankrupt firms. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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20 pages, 11686 KiB  
Article
ISA 701 and Materiality Disclosure as Methods to Minimize the Audit Expectation Gap
by Tomasz Iwanowicz and Bartłomiej Iwanowicz
J. Risk Financial Manag. 2019, 12(4), 161; https://doi.org/10.3390/jrfm12040161 - 16 Oct 2019
Cited by 4 | Viewed by 7131
Abstract
Purpose: The main purpose of this paper is to determine how particular audit firms deal with ISA 701 requirements and the society expectations towards reporting the materiality levels. Additionally, the aim of this paper is to range the assertions in terms of the [...] Read more.
Purpose: The main purpose of this paper is to determine how particular audit firms deal with ISA 701 requirements and the society expectations towards reporting the materiality levels. Additionally, the aim of this paper is to range the assertions in terms of the frequency of their occurrence. Design/methodology/approach: The tested sample consisted of 317 companies listed on Warsaw (158 companies) or London (159 companies) stock exchange. The analysis was divided into companies from the following ten market indexes (WIGs): construction, IT, real estate, food, media, oil and gas, mining, energy, automotive and chemicals. The research was executed based on the analysis of annual consolidated financial statements (annual reports) and independent auditor reports that were published by in-scope entities for the latest twelve-months period available as at the date of the research (mostly periods ended on 31 December 2017 and 31 March 2018). All values were denominated to euro (EUR) with use of average exchange rates published by the National Bank of Poland. All performed analyses and developed charts were supported by Microsoft Power BI data analysis tool. Findings: The general conclusion, which may be drawn from this research, is that implementation of ISA 701 and materiality disclosure limited the audit expectation gap. Detailed observations are described throughout the paper and summarized in the conclusions section. Originality/value: This study extends the prior research by providing various dimensions of the analysed matters. It contributes to understanding of the audit expectation gap and investigates on methods of minimizing it. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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Review

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20 pages, 364 KiB  
Review
A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary
by Tamás Kristóf and Miklós Virág
J. Risk Financial Manag. 2020, 13(2), 35; https://doi.org/10.3390/jrfm13020035 - 19 Feb 2020
Cited by 23 | Viewed by 5496
Abstract
The article provides a comprehensive review regarding the theoretical approaches, methodologies and empirical researches of corporate bankruptcy prediction, laying emphasis on the 30-year development history of Hungarian empirical results. In ex-socialist countries corporate bankruptcy prediction became possible more than 20 years later compared [...] Read more.
The article provides a comprehensive review regarding the theoretical approaches, methodologies and empirical researches of corporate bankruptcy prediction, laying emphasis on the 30-year development history of Hungarian empirical results. In ex-socialist countries corporate bankruptcy prediction became possible more than 20 years later compared to the western countries, however, based on the historical development of corporate bankruptcy prediction after the political system change it can be argued that it has already caught up to the level of international best practice. Throughout the development history of Hungarian bankruptcy prediction, it can be tracked how the initial, small, cross-sectional sample and classic methodology-based bankruptcy prediction has evolved to today’s corporate rating systems meeting the requirements of the dynamic, through-the-cycle economic capital calculation models. Contemporary methodological development is characterized by the domination of artificial intelligence, data mining, machine learning, and hybrid modelling. On the basis of empirical results, the article draws several normative proposals how to assemble a bankruptcy prediction database and select the right classification method(s) to accomplish efficient corporate bankruptcy prediction. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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