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 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 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 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 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 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.

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 The Application of Altman’s Z-Score Model in Predicting Bankruptcy: Evidence from the Trading Sector in Sri Lanka

2015 ◽  
Vol 10 (12) ◽  
pp. 269 ◽  
Author(s):  
Aloy Niresh J. ◽  
Pratheepan T.
Keyword(s):  
Sri Lanka ◽  
Early Warning ◽  
Financial Distress ◽  
Safe Zone ◽  
Grey Zone ◽  
Z Score ◽  
Financial Reports ◽  
Financial Position ◽  
Score Model

Prediction of bankruptcy is crucial as the early warning may change entire complications and may avoid the high cost that is associated with distress. The main purpose of this study is to examine the likelihood of bankruptcy of the firms belonging to the Trading Sector in Sri Lanka. The research used data from the financial reports of seven trading companies for a period of the last five years from 2010 to 2014. Altman’s original (1968) bankruptcy model has been applied in order to classify the companies in various levels of financial position namely safe, grey and distress. Findings reveal that 71% of the companies belonging to the Trading Sector were in financial distress and the rest of whole 29% were in the grey zone. The fact that none of the companies lie under the safe zone highlights that as a whole the sector is in a menace.

Sign in / Sign up

Export Citation Format

Share Document