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 Assessment applicability of selected models of multiple discriminant analyses to forecast financial situation of Polish wood sector enterprises

2017 ◽  
Vol 59 (1) ◽  
pp. 59-67 ◽  
Author(s):  
Krzysztof Adamowicz ◽  
Tomasz Noga
Keyword(s):  
Prediction Models ◽  
Difficult Problem ◽  
Bankruptcy Prediction ◽  
Net Income ◽  
Multiple Discriminant Analysis ◽  
Financial Situation ◽  
Industrial Enterprises ◽  
Discriminant Analyses ◽  
Wood Sector

Abstract In the last three decades forecasting bankruptcy of enterprises has been an important and difficult problem, used as an impulse for many research projects (Ribeiro et al. 2012). At present many methods of bankruptcy prediction are available. In view of the specific character of economic activity in individual sectors, specialised methods adapted to a given branch of industry are being used increasingly often. For this reason an important scientific problem is related with the indication of an appropriate model or group of models to prepare forecasts for a given branch of industry. Thus research has been conducted to select an appropriate model of Multiple Discriminant Analysis (MDA), best adapted to forecasting changes in the wood industry. This study analyses 10 prediction models popular in Poland. Effectiveness of the model proposed by Jagiełło, developed for all industrial enterprises, may be labelled accidental. That model is not adapted to predict financial changes in wood sector companies in Poland. The generally known Altman model showed the greatest effectiveness in the identification of enterprises at risk of bankruptcy. However, that model was burdened with one of the greatest errors in the classification of healthy enterprises as sick. The best effectiveness in the identification of enterprises not threatened with bankruptcy was found for forecasts prepared using the Prusak 2 model. However, forecasts based on those models were characterised by erroneous classification of sick companies as healthy. The model best fit to predict the financial situation of Polish wood sector companies was the Poznań model Pz = 3.562 · X1 + 1.588 · X2 + 4.288 · X3 + 6.719 · X4 - 2.368 where: X1 - net income / total assets; X2 - (current assets - stock) / current liabilities; X3 - fixed capital / total assets X4 - income from sales / sales revenue).

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 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.

Bankruptcy Prediction Using Quarterly Financial Statement Data

1992 ◽  
Vol 7 (3) ◽  
pp. 269-285 ◽  
Author(s):  
Jane Baldwin ◽  
G. William Glezen
Keyword(s):  
Classification Accuracy ◽  
Prediction Models ◽  
Statistical Significance ◽  
Bankruptcy Prediction ◽  
Securities And Exchange Commission ◽  
Fiscal Year ◽  
Annual Data ◽  
Multiple Discriminant Analysis ◽  
Quarterly Data ◽  
Annual Period

The purposes of this study were to assess the usefulness of quarterly data for predicting bankruptcy and to determine if the earlier prediction by quarterly bankruptcy models can be obtained without the sacrifice of accuracy achieved by annual bankruptcy models. A sample of 40 public firms entering bankruptcy from 1977 to 1983 was matched on the basis of fiscal year, industry, and asset size with 40 nonbankrupt firms. Quarterly financial data were obtained from the firms' 10-Q reports filed with the Securities and Exchange Commission (SEC), whereas annual data were obtained from the 10-K reports. Multiple discriminant analysis was used to derive quarterly bankruptcy prediction models for each of the three quarters before and after the last annual period preceding bankruptcy and for the last annual period preceding bankruptcy. Twenty-four financial ratios that were identified in previous studies as being useful for bankruptcy prediction were selected as the independent variables in the stepwise discriminant process. The classification accuracy, using alternative assumptions regarding prior probability of bankruptcy and cost of misclassification and the statistical significance of the quarterly models for each of the six quarters tested, indicated that quarterly data are useful for predicting bankruptcy. There was no statistical evidence to suggest that the classification accuracy of the annual model was superior to that of the quarterly model. This finding suggests that more timely bankruptcy predictions can be provided to investors, creditors, and auditors by quarterly models without the loss of accuracy provided by annual models.

Corporate bankruptcy prediction models applied to emerging economies

2007 ◽  
Vol 17 (4) ◽  
pp. 295-311 ◽  
Author(s):  
Ariel R. Sandin ◽  
Marcela Porporato
Keyword(s):  
Information Content ◽  
Emerging Economies ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Data Availability ◽  
Business Failure ◽  
Multiple Discriminant Analysis ◽  
Public Companies ◽  
Content Type ◽  
Profitability Ratios

PurposeThe paper's aim is to test the usefulness of ratio analysis to predict bankruptcy in a period of stability of an emerging economy, such as the case of Argentina in the 1990s.Design/methodology/approachFinancial profiles of 22 bankrupt and healthy companies are examined and a model is built using the multiple discriminant analysis technique, thus providing comparability with previous studies.FindingsThe set of models tested in this paper show that the financial data of Argentine companies in the 1990s do have information content, but the model to use depends on the preferences of the decision maker. Comparing models it is observed a common use of solvency ratios in terms of total assets and profitability ratios in terms of sales.Research limitations/implicationsData availability constitutes the primary limitation of this and similar studies, here is reflected in the sample size: 11 healthy and 11 bankrupt.Practical implicationsThe model can be used to assist investors, creditors, and regulators in Argentina and other emerging economies to predict business failure. The Z ′‐score model of Altman can be used for public companies in emerging economies because it pays attention to solvency indicators, but in rapid changing environment, profitability ratios should also be considered.Originality/valueThe incremental information content of profitability and solvency in predicting bankruptcy is examined and a simple and reliable failure prediction model for large Argentinean firms is developed. Also this paper offers a classification method that is publicly available to all investors and creditors interested in Argentinean companies.

scholarly journals Predictors of Financial Distress and Bankruptcy Model Construction

2014 ◽  
Vol 2 (6) ◽  
pp. 34-42 ◽  
Author(s):  
Dana Kubíčková Kubíčková ◽  
Vladimír Nulíček
Keyword(s):  
Discriminant Analysis ◽  
Predictive Models ◽  
Financial Distress ◽  
Model Construction ◽  
Multiple Discriminant Analysis ◽  
Financial Situation ◽  
Transition Economics ◽  
New Model ◽  
Special Regard ◽  
Multivariate Discriminant

The aim of this paper is to prepare the bankruptcy model construction. In the first part, multivariate discriminant analysis and its possibilities in deriving predictive models are characterized. The second part defines the possible indicators/predictors of financial distress of companies, which could be included in the new bankruptcy model. The model itself compares different views of factors that affect the company’s financial situation and contrasts the indicators that were constructed in the model in previous works (with special regard to the models in the transition economics). The result is the collection of 39 indicators to be verified in the next stage of the research project employing the multiple discriminant analysis methods to specify which of them to be included in the new model.

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 The usage of economic profit and other forms of profit as a part of prediction models to forecast the financial stability of business entities in the context of globalization

2021 ◽  
Vol 92 ◽  
pp. 02025
Author(s):  
Dusan Karpac ◽  
Iveta Sedlakova
Keyword(s):  
Financial Stability ◽  
Prediction Models ◽  
Value Added ◽  
Global Market ◽  
Financial Health ◽  
Continuous Change ◽  
Economic Profit ◽  
Business Entities ◽  
Global World ◽  
A Company

Research background: Predicting financial health of a company is in this global world necessary for each business entity, especially for the international ones, as it´s very important to know financial stability. Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. In the current global world of uncertainty and continuous change, it is in each business’s interest to improve its performance. Businesses have to adapt to changing market conditions and keep moving to maintain their, either local or global, market position. In the past, entities preferred to increase primary accounting profit forms. The global modern goal of enterprises, value creation, is achieved through the concept of economic profit. Purpose of the article: The aim of this article was to find out the connection between two very important terms for the global economy, namely prediction models and economic profit. Methods: We focused on the research of both areas and looked for a common connection through how often different forms of profit, and especially the form of economic profit, are used in individual prediction models among the examined sample. Findings & Value added: The output of the whole article is the finding the division of the use of economic and accounting profit in the sample of models and the importance of economic profit for mathematical constructions of prediction models.

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.

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