Special Issue "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: 30 January 2022.

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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
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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

Published Papers (11 papers)

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Research

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Article
The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy
J. Risk Financial Manag. 2021, 14(5), 220; https://doi.org/10.3390/jrfm14050220 - 13 May 2021
Viewed by 535
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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Article
Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison
J. Risk Financial Manag. 2020, 13(3), 60; https://doi.org/10.3390/jrfm13030060 - 24 Mar 2020
Cited by 7 | Viewed by 1153
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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Article
Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis
J. Risk Financial Manag. 2020, 13(3), 58; https://doi.org/10.3390/jrfm13030058 - 18 Mar 2020
Cited by 10 | Viewed by 1919
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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Article
Comparison of Prediction Models Applied in Economic Recession and Expansion
J. Risk Financial Manag. 2020, 13(3), 52; https://doi.org/10.3390/jrfm13030052 - 10 Mar 2020
Cited by 5 | Viewed by 1252
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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Article
Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya
J. Risk Financial Manag. 2020, 13(3), 47; https://doi.org/10.3390/jrfm13030047 - 04 Mar 2020
Cited by 2 | Viewed by 2179
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)
Article
An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship
J. Risk Financial Manag. 2020, 13(2), 37; https://doi.org/10.3390/jrfm13020037 - 19 Feb 2020
Cited by 6 | Viewed by 1446
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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Article
Cross-Country Application of Manufacturing Failure Models
J. Risk Financial Manag. 2020, 13(2), 34; https://doi.org/10.3390/jrfm13020034 - 18 Feb 2020
Cited by 4 | Viewed by 844
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)
Article
Tax Arrears Versus Financial Ratios in Bankruptcy Prediction
J. Risk Financial Manag. 2019, 12(4), 187; https://doi.org/10.3390/jrfm12040187 - 11 Dec 2019
Cited by 6 | Viewed by 1248
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)
Article
Dynamic Bankruptcy Prediction Models for European Enterprises
J. Risk Financial Manag. 2019, 12(4), 185; https://doi.org/10.3390/jrfm12040185 - 09 Dec 2019
Cited by 4 | Viewed by 1631
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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Article
ISA 701 and Materiality Disclosure as Methods to Minimize the Audit Expectation Gap
J. Risk Financial Manag. 2019, 12(4), 161; https://doi.org/10.3390/jrfm12040161 - 16 Oct 2019
Viewed by 1620
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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Review
A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary
J. Risk Financial Manag. 2020, 13(2), 35; https://doi.org/10.3390/jrfm13020035 - 19 Feb 2020
Cited by 7 | Viewed by 1255
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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