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Bankruptcy Prediction
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Bankruptcy Prediction

A special issue of International Journal of Financial Studies (ISSN 2227-7072).

Deadline for manuscript submissions: closed (15 November 2018) | Viewed by 42333

Special Issue Editor

Prof. Dr. Małgorzata Iwanicz-Drozdowska

E-Mail Website
Guest Editor
Institute of Finance, Warsaw School of Economics, 02-554 Warsaw, Poland
Interests: management of financial institutions; risk management; financial safety net; financial stability; financial education

Special Issue Information

Dear Colleagues,

I am pleased to invite you to submit your research to the special issue of “International Journal of Financial Studies” on bankruptcy prediction. In 1968 prof. Edward Altman from NYU-Stern published his world-wide known Z-Score methodology, which has been in use over last 50 years. Therefore, this is a great opportunity to present in the special issue various perspectives of bankruptcy prediction looking back at the history, as well as looking forward to identify new challenges. Recent years have brought a lot of studies on application of data mining and artificial intelligence techniques in bankruptcy prediction, however the traditional approaches, such as logistic regression still prove to be efficient. We invite papers presenting single- and cross-country studies, comparing different estimation techniques, analyzing bankruptcy prediction for various industries, including financial industry, or referring to the future challenges.

Prof. Dr. Małgorzata Iwanicz-Drozdowska
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Financial Studies is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bankruptcy
  • Failure
  • Default
  • Financial distress
  • Estimation techniques

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Published Papers (5 papers)

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Research

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13 pages, 514 KiB  
Article
On Unbalanced Sampling in Bankruptcy Prediction
by Marek Gruszczyński
Int. J. Financial Stud. 2019, 7(2), 28; https://doi.org/10.3390/ijfs7020028 - 5 Jun 2019
Cited by 7 | Viewed by 4130
Abstract
The paper discusses methodological topics of bankruptcy prediction modelling—unbalanced sampling, sample bias, and unbiased predictions of bankruptcy. Bankruptcy models are typically estimated with the use of non-random samples, which creates sample choice biases. We consider two types of unbalanced samples: (a) when bankrupt [...] Read more.
The paper discusses methodological topics of bankruptcy prediction modelling—unbalanced sampling, sample bias, and unbiased predictions of bankruptcy. Bankruptcy models are typically estimated with the use of non-random samples, which creates sample choice biases. We consider two types of unbalanced samples: (a) when bankrupt and non-bankrupt companies enter the sample in unequal numbers; and (b) when sample composition allows for different ratios of bankrupt and non-bankrupt companies than those in the population. An imbalance of type (b), being more general, is examined in several sections of the paper. We offer an extended view of the relationship between the biased and unbiased estimated probabilities of bankruptcy—probability of default (PD). A common error in applications is neglecting the possibility of calibrating the PD obtained from a bankruptcy model to the unbiased PD that is population adjusted. We show that Skogsviks’ formula of 2013 coincides with prior correction known for the logit model. This, together with solutions for other binomial models, serves as practical advice for obtaining the calibration of unbiased PDs from popular bankruptcy models. In the final section, we explore sample bias effects on classification. Full article
(This article belongs to the Special Issue Bankruptcy Prediction)
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23 pages, 1662 KiB  
Article
Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence
by Nora Muñoz-Izquierdo, María-del-Mar Camacho-Miñano, María-Jesús Segovia-Vargas and David Pascual-Ezama
Int. J. Financial Stud. 2019, 7(2), 20; https://doi.org/10.3390/ijfs7020020 - 8 Apr 2019
Cited by 24 | Viewed by 7384
Abstract
Despite the number of studies on bankruptcy prediction using financial ratios, very little is known about how external audit information can contribute to anticipating financial distress. A handful of papers have shown that a combination of ratios and audit data is significant for [...] Read more.
Despite the number of studies on bankruptcy prediction using financial ratios, very little is known about how external audit information can contribute to anticipating financial distress. A handful of papers have shown that a combination of ratios and audit data is significant for predictive purposes, but only one recent paper provided a predictive accuracy of 80% solely by using the disclosures contained in audit reports. This study was complemented by simplifying the analysis of audit reports for prediction purposes and the same predictive accuracy was achieved. By applying three artificial intelligence techniques (PART algorithm, random forest, and support vector machine), the predictive ability of more easily extracted information from the report was examined and a practical implication suggested for each user. Simply by (1) finding the audit opinion, (2) identifying if a matter section exists, and (3) the number of comments disclosed, any user may predict a bankruptcy situation with the same accuracy as if they had scrutinized the whole report. In addition, an extended literature review is included, on previous studies on the interaction between bankruptcy prediction and the external audit information. Full article
(This article belongs to the Special Issue Bankruptcy Prediction)
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17 pages, 2957 KiB  
Article
Analysis of Bankruptcy Threat for Risk Management Purposes: A Model Approach
by Monika Wieczorek-Kosmala, Joanna Błach and Joanna Trzęsiok
Int. J. Financial Stud. 2018, 6(4), 98; https://doi.org/10.3390/ijfs6040098 - 17 Dec 2018
Cited by 5 | Viewed by 4188
Abstract
In previous works, the importance of risk management implementation was addressed with regard to the problem of bankruptcy threat, with the explanation of risk impact on higher bankruptcy costs or the underinvestment problem. However, the evaluation of the impact of risk outcomes is [...] Read more.
In previous works, the importance of risk management implementation was addressed with regard to the problem of bankruptcy threat, with the explanation of risk impact on higher bankruptcy costs or the underinvestment problem. However, the evaluation of the impact of risk outcomes is technically linked to risk frequency and risk severity as the two dimensions of the risk map. The purpose of our study is to advocate two additional dimensions that incorporate liquidity and/or debt capacity constraint in the aftermath of risk occurrence. In the conceptual dimension, we propose a model that may support the appropriate design of risk management methods, by scaling a company’s ability to self-resist the risk outcomes. The study provides the empirical illustration of the frequency of the distinguished patterns of risk self-resistance. It was found that most frequently companies face the limited ability to self-resist risk outcomes, due to high debt capacity and high liquidity constraints. We also found statistically significant interdependencies between the company’s sector and the risk self-resistance. It supports the conclusion that the level of liquidity and debt capacity constraints and thus the ability to retain risk outcomes is sector-specific. It has important implications for the effective design of risk management methods. Full article
(This article belongs to the Special Issue Bankruptcy Prediction)
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15 pages, 1849 KiB  
Article
Applications of Distress Prediction Models: What Have We Learned After 50 Years from the Z-Score Models?
by Edward I. Altman
Int. J. Financial Stud. 2018, 6(3), 70; https://doi.org/10.3390/ijfs6030070 - 2 Aug 2018
Cited by 37 | Viewed by 12792
Abstract
Fifty years ago, I published the initial, classic version of the Z-score bankruptcy prediction models. This multivariate statistical model has remained perhaps the most well-known, and more importantly, most used technique for providing an early warning signal of firm financial distress by academics [...] Read more.
Fifty years ago, I published the initial, classic version of the Z-score bankruptcy prediction models. This multivariate statistical model has remained perhaps the most well-known, and more importantly, most used technique for providing an early warning signal of firm financial distress by academics and practitioners on a global basis. It also has been used by scholars as a benchmark of credit risk measurement in countless empirical studies. Practical applications of the Altman Z-score model have also been numerous and can be divided into two main categories: (1) from an external analytical standpoint, and (2) from an internal to the distressed firm viewpoint. This paper discusses a number of applications from the former’s standpoint and in doing so, we hope, also provides a roadmap for extensions beyond those already identified. Full article
(This article belongs to the Special Issue Bankruptcy Prediction)
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Review

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28 pages, 382 KiB  
Review
Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries
by Błażej Prusak
Int. J. Financial Stud. 2018, 6(3), 60; https://doi.org/10.3390/ijfs6030060 - 22 Jun 2018
Cited by 49 | Viewed by 13005
Abstract
In developed countries, the first studies on forecasting bankruptcy date to the early 20th century. In Central and Eastern Europe, due to, among other factors, the geopolitical situation and the introduced economic system, this issue became the subject of researcher interest only in [...] Read more.
In developed countries, the first studies on forecasting bankruptcy date to the early 20th century. In Central and Eastern Europe, due to, among other factors, the geopolitical situation and the introduced economic system, this issue became the subject of researcher interest only in the 1990s. Therefore, it is worthwhile to analyze whether these countries conduct bankruptcy risk assessments and what their level of advancement is. The main objective of the article is the review and assessment of the level of advancement of bankruptcy prediction research in countries of the former Eastern Bloc, in comparison to the latest global research trends in this area. For this purpose, the method of analyzing scientific literature was applied. The publications chosen as the basis for the research were mainly based on information from the Google Scholar and ResearchGate databases during the period Q4 2016–Q3 2017. According to the author’s knowledge, this is the first such large-scale study involving the countries of the former Eastern Bloc—which includes the following states: Poland, Lithuania, Latvia, Estonia, Ukraine, Hungary, Russia, Slovakia, Czech Republic, Romania, Bulgaria, and Belarus. The results show that the most advanced research in this area is conducted in the Czech Republic, Poland, Slovakia, Estonia, Russia, and Hungary. Belarus Bulgaria and Latvia are on the other end. In the remaining countries, traditional approaches to predicting business insolvency are generally used. Full article
(This article belongs to the Special Issue Bankruptcy Prediction)
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