scholarly journals Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries

2019 ◽  
Vol 10 (4) ◽  
pp. 743-772 ◽  
Author(s):  
Maria Kovacova ◽  
Tomas Kliestik ◽  
Katarina Valaskova ◽  
Pavol Durana ◽  
Zuzana Juhaszova
Keyword(s):  
Cluster Analysis ◽  
Correspondence Analysis ◽  
Prediction Models ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Individual Country ◽  
Explanatory Variables ◽  
Visegrad Group ◽  
Visegrád Countries ◽  
Specific Country

Research background: Since the first bankruptcy prediction models were developed in the 60’s of the 20th century, numerous different models have been constructed all over the world. These individual models of bankruptcy prediction have been developed in different time and space using different methods and variables. Therefore, there is a need to analyse them in the context of various countries, while the question about their suitability arises. Purpose of the article: The analysis of more than 100 bankruptcy prediction models developed in V4 countries confirms that enterprises in each country prefer different explanatory variables. Thus, we aim to review systematically the bankruptcy prediction models developed in the countries of Visegrad four and analyse them, with the emphasis on explanatory variables used in these models, and evaluate them using appropriate statistical methods. Methods: Cluster analysis and correspondence analysis were used to explore the mutual relationships among the selected categories, e.g. clusters of explanatory variables and countries of the Visegrad group. The use of the cluster analysis focuses on the identification of homogenous subgroups of the explanatory variables to sort the variables into clusters, so that the variables within a common cluster are as much similar as possible. The correspondence analysis is used to examine if there is any statistically significant dependence between the monitored factors — bankruptcy prediction models of Visegrad countries and explanatory variables. Findings & Value added: Based on the statistical analysis applied, we confirmed that each country prefers different explanatory variables for developing the bankruptcy prediction model. The choice of an appropriate and specific variable in a specific country may be very helpful for enterprises, researchers and investors in the process of construction and development of bankruptcy prediction models in conditions of an individual country.

scholarly journals Innovation Management of the Bankruptcy: Case Study of Visegrad Group Countries

2019 ◽  
pp. 241-251
Author(s):  
M. Kovacova ◽  
K. Valaskova ◽  
P. Durana ◽  
J. Kliestikova
Keyword(s):  
Statistical Methods ◽  
Prediction Models ◽  
Principal Component ◽  
Analysis Data ◽  
Bankruptcy Prediction ◽  
Financial Indicators ◽  
Probit Regression ◽  
Individual Country ◽  
Explanatory Variables ◽  
Visegrad Group

Since the first bankruptcy prediction models developed in the 60th of 20th century numerous different models have been constructed through the world. These individual models for bankruptcy prediction have been created in different time and space using different methods and variables. During this period various statistical methods have been used starting with the most popular univariate, linear and multivariate discriminant analysis, logistic regression, probit regression, decision trees, neural networks, rough sets, linear programming, principal component analysis, data envelopment analysis, survival analysis and so on. Therefore, we aim to provide deep insight and analyse the bankruptcy prediction models developed in countries of Visegrad four, with the emphasis on methods applied and explanatory variables used in these models, and evaluate them through appropriate statistical methods. Specifically, cluster analysis to explore the differences between basic groups of financial indicators and designed clusters of explanatory variables. Based on the analysis of more than one hundred bankruptcy prediction models we can conclude the most used variables, which serves as a basis for further research and development of prediction models in Visegrad group countries. Three clusters were developed which representing various explanatory variables while these clusters differ from basic groups of financial indicators. According to detected clusters we recommend to choose the most frequently used variables from each created cluster. From the cluster one revenues from sales/total assets ratio; from the cluster two the construction of models should contain current ratio, and from the cluster three we recommend to use ROE. Also if we take into consideration the total frequency together with the constructed clusters we advise to use more variables from clusters two and three. Results of the provided study may be used not only by researchers and enterprises but also by investors during the construction of bankruptcy prediction models in conditions of an individual country. Keywords: bankruptcy, bankruptcy prediction, variables, countries of Visegrad four.

scholarly journals Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis

Equilibrium ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. 569-593 ◽  
Author(s):  
Tomas Kliestik ◽  
Jaromir Vrbka ◽  
Zuzana Rowland
Keyword(s):  
Discriminant Analysis ◽  
Prediction Models ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Net Income ◽  
Multiple Discriminant Analysis ◽  
Financial Situation ◽  
The Future ◽  
A Company ◽  
Visegrad Group

Research background: The problem of bankruptcy prediction models has been a current issue for decades, especially in the era of strong competition in markets and a constantly growing number of crises. If a company wants to prosper and compete successfully in a market environment, it should carry out a regular financial analysis of its activities, evaluate successes and failures, and use the results to make strategic decisions about the future development of the business. Purpose of the article: The main aim of the paper is to develop a model to reveal the un-healthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis. Methods: To conduct the research, we use the Amadeus database providing necessary financial and statistical data of almost 450,000 enterprises, covering the year 2015 and 2016, operating in the countries of the Visegrad group. Realizing the multiple discriminant analysis, the most significant predictor and the best discriminants of the corporate prosperity are identified, as well as the prediction models for both individual V4 countries and complex Visegrad model. Findings & Value added: The results of the research reveal that the prediction models use the combination of same financial ratios to predict the future financial development of a company. However, the most significant predictors are current assets to current liabilities ratio, net income to total assets ratio, ratio of non-current liabilities and current liabilities to total assets, cash and cash equivalents to total assets ratio and return of equity. All developed models have more than 80 % classification ability, which indicates that models are formed in accordance with the economic and financial situation of the V4 countries. The research results are important for companies themselves, but also for their business partners, suppliers and creditors to eliminate financial and other corporate risks related to the un-healthy or unfavorable financial situation of the company.

scholarly journals Family Businesses in Global Economy and Evaluating their Financial Health Using Bankruptcy Models

2021 ◽  
Vol 129 ◽  
pp. 03031
Author(s):  
Maria Truchlikova
Keyword(s):  
Financial Distress ◽  
Global Economy ◽  
Agricultural Sector ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Value Added ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Family Businesses ◽  
Financial Health

Research background: Predicting and assessing financial health should be one of the most important activities for each business especially in context of turbulent business environment and global economy. The financial sustainability of family businesses has a direct and significant influence on the development and growth of the economy because they still represent the backbone of the economy and play an important role in national economies worldwide accounting. Purpose of the article: We used in this article the financial distress and bankruptcy prediction models for assessing financial status of family businesses in agricultural sector. The aim of the paper is to compare models developed by using three different methods to identify a model with the highest predictive accuracy of financial distress and assess financial health. Methods: The data was obtained from Finstat database. For assessing the financial health of selected family businesses bankruptcy models were used: Chrastinova’s CH-Index, Gurcik’s G-Index (defined for Slovak agricultural enterprises) and Altman Z-score. Findings & Value added: This article summarizes existing models and compares results of assessing financial health of family businesses using three different models.

scholarly journals Eliciting Weights of Significance of Criteria for a Monitoring Model of Performance of SMEs for Successful Insolvency Administrator’s Intervention

2019 ◽  
Vol 11 (20) ◽  
pp. 5667 ◽  
Author(s):  
Podviezko ◽  
Kurschus ◽  
Lapinskiene
Keyword(s):  
Major Part ◽  
Prediction Models ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Multiple Criteria ◽  
Satisfactory Level ◽  
Monitoring Model ◽  
Multiple Criteria Evaluation ◽  
Evaluation And Monitoring ◽  
The Eu

Small and medium-sized enterprises (SMEs) are accounted for as a major part of the economy of the EU in terms of part of the population employed, turnover, value-added, etc. Causes of insolvency of SMEs can be different; they are categorized in the paper. A considerable shift from resolving cases of bankruptcy with the sole aim to satisfy creditors’ rights to augmenting and enhancing liquidation and reorganization procedures evolved interest of the authors in creating efficient bankruptcy prediction models and, in particular, methodologies for evaluation and monitoring of the performance of SMEs. In the paper, we reviewed several initiatives and instruments created by the EU for supporting SMEs. The paper laid a foundation for creating a more comprehensive methodology for evaluation of the state of a firm undergoing the process of reorganization. A hierarchy structure of criteria for the evaluation of SMEs was used in the paper; methodologies for eliciting weights of importance of criteria from experts and gauging the level of concordance of opinions of experts were applied. Resulting weights of criteria of performance of an insolvent SME were obtained; the importance of the managerial category of criteria was revealed. Prominent features of hierarchy structures and methodology of using the structure for calculating ultimate weights were described and demonstrated. Gauging concordance of opinions of experts revealed a satisfactory level of concordance of opinions of experts; this allowed to prepare the ultimate weights of criteria for multiple criteria evaluation of SMEs for further research.

scholarly journals Financial Distress and Bankruptcy Prediction in Conditions of Slovak Republic

2021 ◽  
Vol 92 ◽  
pp. 08017
Author(s):  
Filip Rebetak ◽  
Viera Bartosova
Keyword(s):  
Financial Distress ◽  
Prediction Models ◽  
Financial Analysis ◽  
Slovak Republic ◽  
Value Added ◽  
Theoretical Background ◽  
Bankruptcy Prediction ◽  
Important Place ◽  
Non Profit ◽  
Analysis And Synthesis

Research background: Prediction of bankruptcy has an important place in financial analysis of an organization in the globalized economy. Ever since the first publication of a paper on bankruptcy prediction in 1932, the field of bankruptcy prediction was attracting researchers and scholars internationally. Over the years, there have been a great many models conceived in many different countries, such as Altman’s Z score or Ohlson’s model for use for managers and investors to assess the financial position of a company. Globalization in last few decades has made it even more important for all stakeholders involved to know the financial shape of the company and predict the possibility of bankruptcy. Purpose of the article: We aim in this article to examine the financial distress and bankruptcy prediction models used or developed for Slovakia to provide an overview of possibilities adjusted to specific conditions of the Slovak Republic in context of globalization. We will also look at the possibility of use of these prediction models for assessing financial status of non-profit organizations in the Slovak Republic. Methods: We will use analysis and synthesis of current research and theoretical background to compare existing models and their use. Findings & Value added: We hope to contribute with this paper to the theoretical knowledge in this field by summarizing and comparing existing models used.

scholarly journals Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1886
Author(s):  
Michal Pavlicko ◽  
Marek Durica ◽  
Jaroslav Mazanec
Keyword(s):  
Prediction Model ◽  
Central Europe ◽  
Financial Distress ◽  
Prediction Models ◽  
Value Added ◽  
Ensemble Model ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction ◽  
Visegrad Group

The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.

scholarly journals Liquidation and merger conditions in the banking industry: the Itaú-Unibanco case

2020 ◽  
Vol 31 (82) ◽  
pp. 99-115
Author(s):  
Monique de Abreu Azevedo ◽  
Ivan Ricardo Gartner
Keyword(s):  
Agency Theory ◽  
Financial Stability ◽  
Prediction Models ◽  
Banking Industry ◽  
Classical Model ◽  
Asset Price ◽  
Bankruptcy Prediction ◽  
Interbank Market ◽  
Explanatory Variables ◽  
Tax Benefits

ABSTRACT This study’s main objective is to present the circumstances that signal an imminent commercial bank liquidation and the conditions in which mergers are advantageous for a potential acquirer. In addition, it applies the method in an empirical investigation within the context of the domestic banking industry. The research reveals new explanatory factors for liquidations and mergers between robust and insolvent banking institutions, such as bankruptcy costs and tax credits derived from a corporate union. The framework stands out for highlighting the role of creditor financial institutions participating in the open and interbank markets, which in the search to maximize their utility together with that of the shareholders have a decisive influence over the continuity or closure of the bank in crisis. The soundness of the financial system is an essential public good for society. Systemic financial crises cause significant costs for economic agents, such as a fall in production, increased unemployment, a rise in the fiscal deficit, and asset price instability. Efforts to achieve stability involve the regular functioning of banks. In this context, it is important to understand the circumstances under which banking institution distress can be solved by alternatives that are less costly for the treasury. Often, the research indicates the causes of disruptions to corporate activities; however, the explanatory variables and the tools used by bankruptcy prediction models are constantly being evaluated. Theories that elucidate the phenomenon are even scarcer. The paper’s result suggests the effectiveness of the method developed from the paradigmatic perspective of the field of economics and management, corroborating agency theory. The explanatory variables of bankruptcy and bank merger highlighted in this research can contribute to the elaboration of robust models to predict financial distress. The mathematical model of liquidation and merger was constructed from the viewpoint of an imperfect world where informational asymmetry and conflict of interests among shareholders, open and interbank market creditors, and bondholders (which includes depositors and holders of bonds issued by the bank) prevail. Bankruptcy maximizes shareholder and creditor utility if liquidation costs plus the value payable to the bondholders after liquidation are lower than the value they receive in the event of continuity. A merger is feasible for an acquirer if expected return plus tax benefits minus bondholder expenses is greater than the value payable to interbank market creditors. The method is applied to the merger between Itaú and Unibanco, considered a milestone in the process of consolidating the banking market in Brazil. This paper suggests the use of an algebraic model, based on agency theory, as an indicator of conditions for liquidations and bank mergers. The proposed approach was adequate for explaining the union between Unibanco and Itaú, which culminated in the largest private financial conglomerate in the Southern Hemisphere. Unibanco experienced the bankruptcy circumstances and there was evidence that Itaú’s tax benefits encouraged the merger. This article contributes to academic epistemology because it revisits the classical model, characterized by mathematical and theoretical robustness, and adjusts it to the specificities of banks. In addition to this methodological novelty, it applies it to an emblematic case, making it a useful tool for corporate decision-making and bank supervision, especially with regards to actions focused on financial stability.

scholarly journals Logit and Probit application for the prediction of bankruptcy in Slovak companies

Equilibrium ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. 775-791 ◽  
Author(s):  
Maria Kovacova ◽  
Tomas Kliestik
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Statistical Methods ◽  
Prediction Models ◽  
Slovak Republic ◽  
Relevant Literature ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Data Set ◽  
Prediction Ability

Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models. Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results. Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.

scholarly journals Impact of earnings management on bankruptcy prediction

2021 ◽  
Vol 129 ◽  
pp. 03016
Author(s):  
Maria Kovacova ◽  
Martin Lacny ◽  
Jaroslav Gonos
Keyword(s):  
Literature Review ◽  
Earnings Management ◽  
Prediction Models ◽  
Value Added ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Direct Impact ◽  
Prediction Ability ◽  
Positive View ◽  
The Impact

Research background: Managers of the companies intentionally manipulate business earnings to achieve the required status of the company. Earnings management is a legal and widely preferred phenomenon of business finance that financial managers use to maintain and improve the company´s competitiveness. The consequence of these activities is to provide a positive view for the owners, encourage the profitability for the creditor and the investors as well as demonstrate economic strengths to competitors. Consequently, these activities lead to the modification of financial statements of the companies, which have a direct impact on the prediction ability of bankruptcy models. Purpose of the article: The main goal of the paper is to point out the impact of earnings management in the companies on the possibility and ability of bankruptcy prediction. There is a correlation between application of earnings management in companies followed by changes in financial statements of the companies. Therefore, the ability of bankruptcy prediction models to predict possible financial problems of the company is questionable. Methods: The paper presents the connection of earnings management and its impact on bankruptcy prediction based on the bibliometric overview and deep literature review. Findings & Value added: The paper presents results, connections and impact of earnings management on bankruptcy prediction.

An Analysis of the Usefulness of Debt Defaults and Going Concern Opinions in Bankruptcy Risk Assessment

1998 ◽  
Vol 13 (3) ◽  
pp. 351-371 ◽  
Author(s):  
Benjamin P. Foster ◽  
Terry J. Ward ◽  
Jon Woodroof
Keyword(s):  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Loan Default ◽  
Bankruptcy Risk ◽  
Explanatory Variables ◽  
Going Concern ◽  
Going Concern Opinion ◽  
Loan Defaults ◽  
The Impact ◽  
Covenant Violations

This study extends the research of Hopwood et al. (1994) and Mutchler et al. (1997) by empirically investigating the relationships between loan defaults, violation of loan covenants, going-concern opinions, and bankruptcy in bankruptcy prediction models. One objective of this study is to empirically test the ability of loan defaults/accommodations and loan covenant violations to assess the risk of bankruptcy. Another objective of this study is to investigate the impact of failing to control for these two distress events on results from tests of the usefulness of going-concern opinions in assessing bankruptcy risk. Results suggest that loan default/accommodation and loan covenant violation are both significant explanatory variables of bankruptcy at the time of the last annual report before the event. While a going-concern opinion variable appears to significantly explain bankruptcy, it is not significant when included in a model with loan default/accommodation and covenant violation variables. Consequently, our results suggest that researchers should include both loan default/accommodation and covenant violation as control variables when using bankruptcy to test the usefulness of going-concern opinions.

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