scholarly journals Bankruptcy Practice in Countries of Visegrad Four

2017 ◽  
Vol 14 (1) ◽  
pp. 108-118
Author(s):  
Maria Misankova ◽  
Katarina Zvarikova ◽  
Jana Kliestikova
Keyword(s):  
Financial Distress ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Major Influence ◽  
Simple Task ◽  
Data Set ◽  
Prediction Ability ◽  
Score Model ◽  
A Company ◽  
Prediction Capability

Abstract Numerous economists and analysts from all over the world have been trying to find an appropriate method to assess company health and to predict its eventual financial distress for many years. No economy is a small isolated subject, and the bankruptcy of a company can cause through its stakeholders′ significant impact on the sustainable economic development. Otherwise, companies are very complicated entities, and it is not a simple task to estimate company’s future development. Together with the best-known Z-Score model of bankruptcy prediction developed by Altman, numerous models worldwide that are based on different methodologies have been developed. We assume that individual state’s economy has major influence on the final form of these models as well as there are several common characteristics between Slovak economy and economy of countries of Visegrad Four. Therefore, we applied chosen bankruptcy prediction models developed in countries of Visegrad Four on the set of Slovak companies and validated their prediction ability in specific condition of the Slovak economy. On the basis of the provided calculations, we compared gained results with the prediction capability of other popular prediction models also applied on the data set of Slovak companies. Through this, we pointed out the importance of the development of unique bankruptcy prediction model, which will be constructed in the specific condition of individual countries, and highlighted the weak forecasting ability of foreign models.

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 THE PORTABILITY OF ALTMAN’S Z-SCORE MODEL TO PREDICTING CORPORATE FINANCIAL DISTRESS OF SLOVAK COMPANIES

2016 ◽  
Vol 22 (4) ◽  
pp. 532-553 ◽  
Author(s):  
Martin BOĎA ◽  
Vladimír ÚRADNÍČEK
Keyword(s):  
Financial Distress ◽  
Economic Environment ◽  
Z Score ◽  
Data Set ◽  
Prediction Ability ◽  
Financial Difficulties ◽  
The Us ◽  
Score Model ◽  
Definition Of ◽  
Corporate Practice

The paper challenges the widespread use of Altman’s bankruptcy formula known as the “Z-score model” in Slovak corporate practice and comes with the goal to verify its usability in the Slovak economic environment. To this end, a definition of financial distress is adopted that summarizes weaknesses of Slovak enterprises stemming particularly from liquidity drain and operating losses. The verification juxtaposes three variants of the Z-score model and assesses their prediction ability using a data set of Slovak enterprises for the period from 2009 until 2013. Both the original 1968 Z-score model and the revised 1983 Z-score devised for the US economic environment are compared with the Z-score model re-estimated to the Slovak data copying the methodological procedure of Altman. The results indicate that Altman’s bankruptcy formula is portable into the Slovak economic conditions and useful for predicting financial difficulties in view of the adopted definition of financial distress. Altman’s original and (especially the) revised formulation of the Z-score model are preferable if overall classification accuracy is the main interest. Finally, it is advisable to re-estimate the coefficients of the Z-score model if financially distressed enterprises are the focus and the goal is to classify distressed enterprises as best as possible.

scholarly journals Financial Distress and Bankruptcy Prediction: an Empirical Analysis of the Manufacturing Industry in Albania

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Empirical Analysis ◽  
Financial Distress ◽  
Manufacturing Industry ◽  
Predictive Accuracy ◽  
Bankruptcy Prediction ◽  
Financial Position ◽  
Reduce Risk ◽  
Score Model ◽  
The Empirical Analysis ◽  
A Company

Bankruptcy is the conclusive affirmation of the inability of a company to support and endure current operations given its current financial position and debt obligations. If bankruptcy could be expected with affordable precision ahead of time, managers and investors of companies may have the possibility to take action to secure their companies, reduce risk and loss of business and perhaps even avoid bankruptcy itself. The aim of this paper is to test the suitability and predictive accuracy of the Altman Z-Score model in the Albanian manufacturing industry. After performing the empirical analysis, the conclusion is that this model clearly fails to effectively predict financial distress and bankruptcy and it isn’t reliable in our case. Lastly, a logistic regression model is proposed, which is more adequate for the Albanian context.

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 Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

2020 ◽  
Vol 13 (5) ◽  
pp. 92
Author(s):  
Katarina Valaskova ◽  
Pavol Durana ◽  
Peter Adamko ◽  
Jaroslav Jaros
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Predictive Ability ◽  
Early Warning Systems ◽  
Emerging Countries ◽  
Bankruptcy Prediction ◽  
Financial Health ◽  
Prediction Ability

The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

scholarly journals MODEL ALTMAN SCORE DAN GROVER SCORE : MENDETEKSI FINANCIAL DISTRESS PADA PERUSAHAAN RITEL DI INDONESIA

AKUNTABILITAS ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 133-154
Author(s):  
Patmawati Patmawati ◽  
Muhammad Hidayat ◽  
Muhammad Farhan
Keyword(s):  
Structural Equation Model ◽  
Financial Distress ◽  
Structural Equation ◽  
Positive Impact ◽  
Poor Performance ◽  
Equation Model ◽  
Analysis Method ◽  
Public Companies ◽  
Score Model ◽  
A Company

This study aims to detect financial distress of listed retail companies at Indonesian Exchange using Altman Score and Grover Score Model. The samples are go public companies in Indonesia, which consist of 20 retail companies. Structural Equation Model (SEM) was employed as the analysis method using PLS software. The result shows that Altman Score Model has positive impact toward financial distress. High score on Altman Score indicates poor performance of a company. Further, Grover Score model has positive impact toward financial distress on retail companies which are go public circa 2015-2018. Higher score of grover score indicates a company has been suffering financial distress. Lastly, we inference that Altman Score Model is more accurate in detecting financial distress compared to Grover Score Model     

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 Pengaruh Reputasi Auditor, Prediksi Kebangkrutan, Disclosure Dan Leverage Terhadap Penerimaan Opini Audit Going Concern

2013 ◽  
Vol 4 (2) ◽  
pp. 139 ◽  
Author(s):  
Eko Budi Santoso ◽  
Ivan Yudhistira Wiyono
Keyword(s):  
Hypothesis Testing ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Observation Data ◽  
Study Objective ◽  
Audit Opinion ◽  
Going Concern ◽  
Score Model ◽  
A Company ◽  
Auditor Reputation

AbstractGoing concern opinion is accepted by a company represents the condition and events which arises auditor’s hesitation of the company’s going concern. Going concern audit opinion used as early warning to the user of financial statements in order to prevent mistakes on decision making. This study objective was to reinvestigate factors that influencing going concern audit opinion. The factors used on this research are auditor reputation, bankruptcy prediction, disclosure and leverage.Samples were collected with purposive sampling method and obtained 229 observation data of listed manufacture companies that meet the criteria from year 2009-2011. Logistic regression was been used for hypothesis testing. The result showed that bankruptcy prediction using Z-score model and leverage affected acceptance going concern audit opinion. The hypothesis testing also showed that auditor reputation and disclosure did not affect acceptance going concern audit opinion.

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 Analisis Penilaian Financial Distress Dengan Menggunakan Model Altman Z-Score Dan Model Springate Pada Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Indonesia Periode Tahun 2015-2019

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Febriana Anindyka ◽  
Makhmud Zulkifli
Keyword(s):  
Data Analysis ◽  
Financial Distress ◽  
Descriptive Statistics ◽  
Z Score ◽  
Financial Condition ◽  
Similarities And Differences ◽  
The Difference ◽  
Score Model ◽  
A Company ◽  
Data Analysis Methods

The aim of this research to analyze financial distress of Manufature Company by using Altman Z-Score and Springate Models. Moreover, this research aimed to know aims to determine the similarities and differences in the results of the analysis of financial distress assessment using the Altman Z-Score model and the Springate Model. This research used Descriptive statistics and data analysis methods used  in this research were Altman Z-Score and Springate Model. To the finding on the research, it showed that (1) ) After evaluating the Altman Z-Score and Springate models, there are fifty companies that fall into different conditions. (2) The similarities and differences in the results of the Atman Z-Score model and the Springate model are the results of the two models that can be seen from having almost the same variable components and the difference is that the results of the financial distress assessment using the Altman Z-score model and the Springate model show that both These models have different criteria in determining the financial condition of a company.

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