scholarly journals Does The Development Context Affect Bankruptcy Prediction Models’ General Accuracy? A Comparative Analysis Of Four Multivariate Discriminant Models In The Italian Context

2016 ◽  
Vol 12 (10) ◽  
pp. 445 ◽  
Author(s):  
Salvatore Madonna ◽  
Greta Cestari ◽  
Francesca Callegari
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Bankruptcy Prediction ◽  
Development Context ◽  
Wide Scale ◽  
Discriminant Models ◽  
Score Model ◽  
The One ◽  
Italian Context ◽  
Multivariate Discriminant

This research starts from the work by Madonna and Cestari (2015) that aimed at assessing the usability of three bankruptcy prediction models applied in contexts other than the ones of their elaboration, in order to evaluate their generalizability and the possibility to apply them in wide-scale investigations. We took the cue from that study to assess the usability of four bankruptcy prediction models, when applied to a sample with characteristics other than the ones related to their elaboration. We aimed at verifying the predictive accuracy and the discriminant capacity of the four models, basing on the assumption that the performances displayed by bankruptcy prediction models are usually better when they are applied in contexts similar to the one of their elaboration. Given this premise, we hypothesized that Italian models should perform better than the American one. In order to verify this hypothesis, we tested the four multivariate discriminant models twice: the predictive accuracy was tested applying the models on a sample of firms gone bankrupt within 2012 and 2014; the discriminant capacity on a sample equally composed by bankrupt and operating firms. Both samples were composed by firms located in Italy and operating in recent years. Hence the sample provided and the context of application were different from the ones of the models‘ elaboration. The results show that even if the Italian models were elaborated basing on contexts more similar to the one of the present application, the best performance is reached by the American Altman’s Z‘-Score model.

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 Bankruptcy prediction model for private limited companies of Lithuania

Ekonomika ◽  
2016 ◽  
Vol 95 (1) ◽  
pp. 134-152 ◽  
Author(s):  
Gediminas Šlefendorfas
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Common Type ◽  
Primary Input ◽  
Primary Sample ◽  
Limited Company ◽  
Stepwise Method ◽  
Bankruptcy Probability ◽  
Multivariate Discriminant

The paper is mainly devoted to the bankruptcy prediction models and their ability to assess a bankruptcy probability for Lithuanian companies. The study showed that the most common type of companies in Lithuania is a private limited company, therefore, the main objective was to analyse such companies’ financial information and by using these results, create a new bankruptcy prediction model, which would allow to predict the bankruptcy probability as accurately as possible. 145 companies (73 already bankrupt and 72 still operating) were chosen as a primary sample and by using multivariate discriminant analysis stepwise method a linear function ZGS has been created. To achieve that, 156 different financial ratios were selected as a primary input data by using correlation calculation between bankruptcy and still operating companies and Mann – Whitney U test techniques. The results showed that 89% of companies were classified correctly, which states that the model is strong enough to predict bankruptcy probability for private limited companies operating in Lithuania in a sufficient accuracy.

scholarly journals Comparative Analysis of Bankruption Prediction Models in Property and Real Estate Sector Companies Listed on the IDX 2017-2019

2021 ◽  
Vol 6 (1) ◽  
pp. 170-173
Author(s):  
Fika Andriani ◽  
Pardomuan Sihombing
Keyword(s):  
Comparative Analysis ◽  
Real Estate ◽  
Prediction Models ◽  
Stock Exchange ◽  
Sample Selection ◽  
Sampling Technique ◽  
Bankruptcy Prediction ◽  
Real Estate Sector ◽  
Score Model ◽  
The Right

This study takes the topic of comparative analysis of bankruptcy prediction models in the Property and Real Estate Sector companies listed on the IDX in 2017-2019. In this case, the test was conducted to compare the accuracy between the Altman Z-Score, Springate S-Score and Zmijewski X-Score models in predicting the bankruptcy of property and real estate companies. The sample consisted of 20 companies after the sample selection was carried out using purposive sampling technique. To obtain the right level of accuracy, a comparison of the distress or non-distress score categories of each bankruptcy model with the earnings per share (EPS) value of each company was carried out through a dummy variable. Through this test, it is known that the Zmijewski X-Score model is the most accurate model in predicting bankruptcy in the property and real estate sectors listed on the Indonesia Stock Exchange (IDX) by 90%.

scholarly journals Bankruptcy prediction models for large agribusiness companies in AP Vojvodina

2021 ◽  
Vol 68 (3) ◽  
pp. 805-822
Author(s):  
Dragan Milić ◽  
Dragana Tekić ◽  
Vladislav Zekić ◽  
Tihomir Novaković ◽  
Milana Popov ◽  
...  
Keyword(s):  
Financial Stability ◽  
Agricultural Sector ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Food Sector ◽  
Bankruptcy Risk ◽  
Quick Test ◽  
Score Model

The aim of this paper is to present application of different methods used for predicting bankruptcy of large agricultural and food companies in AP Vojvodina, as well as to determine which model is the most suitable for analyzing the companies from the observed sectors. The following three models were applied in the paper: Altman's Z'-score model, Kralicek DF indicators and Kralicek Quick test. The analysis included five companies from the agricultural sector and five companies from the food sector operating on the territory of AP Vojvodina in the period from 2015 to 2019. The results of the research based on the applied models showed that different conclusions can be made about the financial stability of the observed companies. Altman's Z'-score model provided the most rigorous forecast in terms of the bankruptcy risk, while the results of Kralicek DF indicators and Quick test are relatively similar.

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.

Prediksi Kebangkrutan Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia

2021 ◽  
Vol 5 (2) ◽  
pp. 126-141
Author(s):  
Melati Eka Putri ◽  
Auliffi Ermian Challen
Keyword(s):  
Prediction Models ◽  
Stock Exchange ◽  
Secondary Data ◽  
Sampling Technique ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Analytical Models ◽  
Ratio Analysis ◽  
Debt Capital ◽  
Score Model

This study aims to examine the potential for bankruptcy of companies with three analytical models, namely Altman Z-Score, Springate S-Score, and Zmijewski X-Score, and assess the level of accuracy of the three models. Each model uses ratio analysis with the elements of assets, debt, capital, and company profits. This study uses a sample of coal mining companies listed on the Indonesia Stock Exchange (IDX) during the 2014-2018 period. The sampling technique in this study used purposive sampling and obtained 24 sample companies. This study uses secondary data, namely the company's financial statements obtained from IDX's official website. This study calculates financial ratios, compares the scores of the three bankruptcy prediction models, and tests the model's accuracy. The results of this study show that of the three models, the Springate S-Score model is the most accurate in predicting bankruptcy, with an accuracy rate of 83.33%, as evidenced by two companies that were delisted from the IDX. This study can be used as a reference and as material for consideration in making investment decisions for companies and investors.

scholarly journals Bankruptcy Prediction using Multivariate Discriminant Analysis - Empirical Evidence from Cases Referred to NCLT

2019 ◽  
Vol 8 (9) ◽  
pp. 13-17
Keyword(s):  
Bankruptcy Prediction ◽  
Company Law ◽  
Annual Reports ◽  
Empirical Literature ◽  
Z Score ◽  
The Subject ◽  
Bankruptcy Code ◽  
Score Model ◽  
Insolvency Proceedings ◽  
Multivariate Discriminant

The study is about bankruptcy prediction using multivariate model of analysis for the case of twelve large accounts which were referred to National Company Law Tribunal for insolvency proceedings. Corporate failures affect all stakeholders. It’s also a fact that companies are never shielded from bankruptcy. With the mounting of India’s non-performing assets, the pronouncement of Insolvency and Bankruptcy Code is a strong suit for maximising value of lenders as well as borrowers. The first part of the paper throws light on the code, the progress made and challenges faced. On the empirical literature side, the paper applies the famous Altman’s Z-Score model on the first twelve companies on which insolvency proceedings are on. Suitability of the model is supported by literature review on the subject. Financial data gathered from Annual reports are analysed to arrive at Z-Score results. The strength of the results are statistically examined through hypotheses tested using regression and feasibility of the model is tested using ANOVA. The paper presents the limitation of the study, a discussion of the suitability of the model and further scope of research in the area

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.

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