scholarly journals Reliability of the Slovak Legislative Categorization in Comparison with the Selected Prediction Models - Application in Companies from the Creative Industry – Architecture in Slovakia

2021 ◽  
Vol 115 ◽  
pp. 02011
Author(s):  
Miroslav Uhliar ◽  
Andrej Kovalev
Keyword(s):  
Prediction Models ◽  
Statistical Testing ◽  
Creative Industry ◽  
Grey Zone ◽  
Financial Health ◽  
Z Score ◽  
Quick Test ◽  
Industrial Companies ◽  
Commercial Code ◽  
Current Design

One of the important changes in the world economies is the increasing significance of the human capital and high emphasis on the Creative Industry. Many companies have been founded within this sector, and like the classic industrial companies, these also need to use models to analyze their financial health and predict their future development. Thus, the Authors decided to verify whether the categorization of the financial situation of the companies based on the Slovak legislative corelate with renowned models like the Altman Z-Score, the IN05 and Creditworthiness Indexes, the Quick Test and the Taffler model. Within the Creative Industry, the Authors targeted the sector of Slovak Architecture and focused on the legislative categorization based on the Acts No. 513/1991 Coll. and 7/2005 of the Commercial Code, as amended. According to the statistical testing results, it was the Quick Test which showed the highest rate of correlation to the legislative Acts. However, the current design of the Quick Test is not sufficient enough to assess the companies in architecture which, based on their results, fall into the so-called grey zone. Therefore, the Authors declare that it might be necessary to form a new predictive model for the assessment of a company’s financial health for the Creative Industry.

scholarly journals Validating the Reliability of Predictive Models for the Companies in the Slovak Creative Industry

Auspicia ◽  
2021 ◽  
Vol XVIII (1) ◽  
pp. 22-42
Keyword(s):  
Predictive Model ◽  
Business Environment ◽  
Creative Industry ◽  
Grey Zone ◽  
Financial Health ◽  
Quick Test ◽  
Commercial Code ◽  
Definition Of ◽  
A Company ◽  
Level Of Agreement

Globalization of business environment has brought new challenges and trends. The importance of human capital has been growing especially since the 1990s when the Creative Industry was recognized as a full-fledged component of the national economy. There are a number of models available to analyse the financial health of the companies operating in the Creative Industry. The authors first focused on Acts No. 513/1991 Coll. and 7/2005 of the Commercial Code, as amended, which define the financial health of a company. After applying Altman Z-Score, IN05 and Creditworthiness Index, Quick Test and Binkert and Taffler models, it follows from the results that Quick Test shows the highest level of agreement with the legislative definition of a non-prosperous company. Therefore, the authors decided to analyse this predictive model further. The results of their study show that, while very reliable for the category of non-prosperous companies, the model does not achieve sufficient reliability for other categories, the average/grey zone in particular. Based on the results of the study, the authors conclude that a new predictive model to assess a company’s financial health is necessary.

scholarly journals A STUDY ON FINANCIAL HEALTH OF SELECTED PUBLIC SECTOR MANUFACTURING COMPANIES OF INDIA : WITH REFERENCE TO Z-SCORE ANALYSIS

2021 ◽  
Vol 9 (08) ◽  
pp. 857-865
Author(s):  
Nidhi Sharma ◽  
◽  
Shivani Peppal ◽  
Keyword(s):  
Public Sector ◽  
Grey Zone ◽  
Manufacturing Companies ◽  
Financial Health ◽  
Z Score ◽  
Common Condition ◽  
Score Analysis ◽  
Score Model

Financial distressed from a decade has become a common condition for manufacturing companies of India. Many public sector manufacturing companies have also witnessing poor financial health. This study has examined the financial health of eighteen selected public sector manufacturing companies which are further divided into four sectors as Metal, Sugar, Paper and Textile. The examination of financial health of selected companies has been performed by calculating Altman Z-score model for four year prior to become distressed. And it has been found by the analysis that most of the company was in either distressed zone or in grey zone. The study also finds that Altman Z-Score Model is a perfect tool to examine the health of public sector manufacturing companies.

Diagnosing the Financial Distress in Oil Drilling and Exploration Sector of India through Discriminant Analysis

2019 ◽  
Vol 23 (4) ◽  
pp. 364-373
Author(s):  
Anita Nandi ◽  
Partha Pratim Sengupta ◽  
Abhijit Dutta
Keyword(s):  
Discriminant Analysis ◽  
Manufacturing Industry ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Total Asset ◽  
Financial Health ◽  
Z Score ◽  
Working Capital ◽  
Oil Drilling ◽  
Retained Earnings

The present study is mainly devoted to the bankruptcy prediction models and their ability to assess a bankruptcy probability for oil drilling and exploration sector of Indian. The study puts an effort to determine the financial health of 12 selected companies from this sector of India for a period of 5 years. These companies serve the backbone of many other industries such as transport industry, manufacturing industry, automobile industry and so on of the Indian economy. The study has taken the reference of Altman’s Z-score model, where ratios such as working capital to total asset, retained earnings to total asset, earnings before interest and tax to total assets, market value of equity to book value of debt and sales to total assets have been taken. The discriminant analysis is conducted to validate the outcomes of Altman’s model to predict group membership and to forecast the overall industry condition. The study reveals that 75 per cent of the companies are in financially healthy zone. The results indicate that working capital/total assets can very well explain the Z-score. The research on financial health using Altman’s score is very limited in Indian context. Therefore, this study will add value to the existing body of literature for financial risk.

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 ANALISIS FINANCIAL DISTRESS DENGAN MENGGUNAKAN BANKRUPTCY PREDICTION MODELS PADA EMITEN ROKOK PERIODE 2014-2018

2021 ◽  
Vol 4 (1) ◽  
pp. 16-27
Author(s):  
Ani Wahyuningsih ◽  
Hartono Hartono ◽  
Rini Armin
Keyword(s):  
Early Warning ◽  
Financial Distress ◽  
Prediction Models ◽  
Stock Exchange ◽  
Financial Statements ◽  
Bankruptcy Prediction ◽  
Correct Prediction ◽  
Financial Health ◽  
Z Score ◽  
Financial Difficulties

ABSTRACT Financial Distress is a condition of financial difficulties where if this happens to the company foa along period of time, the company is in the initial stages before bankruptcy. Bankruptcy is a state of being or a situation in which company failed to or not able to meet obligations because firm experienced lack of. If the company goes bankrupt there will be many parties who are harmed. Therefore it is necessary to conduct financial distress analysis for early warning. The research aims to determine the financial health of the cigarette sub-sector companies by analyzing financial distress using three bankruptcy prediction models with Altman Z-Score, Springate, Grover and to determine which of these three models has the highest level of accuracy. The data used in this research is the company’s financial statements published on the Indonesia Stock Exchange website. The population in this research is the cigarette sub-sector companies listed on the Indonesia Stock Exchange in the 2014-2018 period. Based on the result of research shows that in the calculation Altman and Springate models, PT. Bentoel International Investama in the category of the company experiencing symptoms of bankruptcy. While in the Grover model calculation, all companies fall into category healthy companies. Of the three models that have the highest level of accuracy are Altman and Springate models by one hundred percent. This shows that Altman and Springate models have the correct prediction of the company correctly.

scholarly journals Analysis Of Financial Health Level On Financial Performance In Cement Companies Listed On The Indonesian Stock Exchange (Idx)

2020 ◽  
Vol 18 (3) ◽  
pp. 125
Author(s):  
Dhea Zatira ◽  
Ria Puspitasari
Keyword(s):  
Financial Performance ◽  
Stock Exchange ◽  
Market Value ◽  
Financial Health ◽  
Z Score ◽  
Working Capital ◽  
Book Value ◽  
Panel Data Regression ◽  
Data Regression ◽  
Retained Earnings

This study aims to analyze the Level of Financial Soundness on Financial Performance in Cement Companies that are Go Public Listed on the Indonesia Stock Exchange (BEI). Analysis of the level of financial health using the Altman Z-Score with several ratios, namely the ratio of Working Capital to Total Assets (X1), the ratio of retained earnings to total assets (X2), the ratio of EBIT to Total Assets (X3), the ratio of stock market value to book value ofabilities (X4), the ratio of Sales to Total Assets (X5) to the dependent variable on Financial Performance (Return on Assets). The data analysis technique used in this research is the Altman Z-Score with the criteria for bankruptcy and to find its effect with the panel data regression model assisted by E-Views software. The results of the calculation and analysis of the Z-Score criteria in cement companies in Indonesia, it is known that there is no cement company whose company finances are stated in a healthy condition. One company is prone to bankruptcy (gray zone) while the rest according to the Z-Score criteria are bankrupt. Furthermore, based on the panel data regression examiner simultaneously the five independent variables on financial performance (Y), while partially the working capital ratio to total assets (X1) affects financial performance (Y), the retained earnings ratio to total assets (X2) has no effect on Financial performance (Y), EBIT ratio to total assets (X3) affects financial performance (Y), stock market value ratio to book value of liabilities (X4) has no effect on financial performance (Y), Sales to Total Assets ratio (X5) affect financial performance.

scholarly journals Evaluating Financial Performance of Saudi Listed Firms: Using Statistical Failure Prediction Models

2019 ◽  
Vol 2 (1) ◽  
pp. 1-18
Author(s):  
Deena Saleh Merza Radhi ◽  
Adel Sarea
Keyword(s):  
Financial Performance ◽  
Prediction Models ◽  
Stock Exchange ◽  
Failure Prediction ◽  
Financial Decisions ◽  
Listed Firms ◽  
Industrial Companies ◽  
Statistical Relationships ◽  
The Given ◽  
Altman Model

The study aims to compare the classification power of three statistical failure prediction models for evaluating financial performance of Saudi Listed Firms. The study sample consisted of 122 listed industrial companies in the Saudi Stock Exchange for the period from 2014 to 2016. Altman model 1968, Kida model and Zmijewski are used as examples of statistical failure prediction models to evaluate the classification power of the given models to assess the financial performance of firms listed on Saudi Stock Exchange. The results showed that Zmijewski model was more powerful in predicting the financial performance of Saudi listed firms than Altman model (1986) and Kida model. The results showed that there are a statistical relationships between some ratios included in the three models and the financal performance of industrial companies, which was measured by EPS. The study recommended users of financial statements of Saudi listed companies to use Zmijewski ?model, which performs well in evaluating their finacial position to be used when making the ?financial decisions.

scholarly journals PREDICTION OF FINANCIAL DISTRESS: CASE OF MINING ENTERPRISES IN CZECH REPUBLIC

2020 ◽  
Vol 15 (1) ◽  
pp. 1-14
Author(s):  
Zuzana Rowland ◽  
Alla Kasych ◽  
Petr Suler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Financial Stability ◽  
Grey Zone ◽  
Financial Health ◽  
Classification Problems ◽  
Mining Companies ◽  
Artificial Neural

The ability to predict a company's financial health is a challenge for many researchers and scientists. It is also a distracting topic, as many other new approaches to financial health predictions have emerged in recent years. In this paper, we focused on identifying the financial health of mining companies in the Czech Republic. We chose the neural network method because, based on various instances of related research, neural networks represent a more reliable financial forecast than mathematical-statistical methods such as discriminant analysis and logistic regression. The concept of a neural network emerged with the first artificial neural networks, inspired by biological systems. The existence of prediction and classification problems directly predetermines artificial neural networks with respect to a given issue. We used the Amadeus database for processing, including financial indicators, SPSS, and Visual Gene Developer software. In total, we analyzed sixty-four mining companies. Complete data on financial stability were available for fifty-three companies, which we explored, and based on these results, identified financial situations for the other thirteen. Based on the available information, we processed a neural network and regression analysis. We managed to classify thirteen companies as solvent, insolvent, and in the grey zone, with the help of prediction.

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.

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