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 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 The verification of prediction and classification ability of selected Slovak prediction models and their emplacement in forecasts of financial health of a company in aspect of globalization

2020 ◽  
Vol 74 ◽  
pp. 06010
Author(s):  
Dusan Karpac ◽  
Viera Bartosova
Keyword(s):  
Financial Stability ◽  
Roc Analysis ◽  
Prediction Models ◽  
Predictive Ability ◽  
Business Failure ◽  
Financial Health ◽  
Prediction Ability ◽  
Global World ◽  
A Company ◽  
International View

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. Globalized prediction models compute financial health of companies, but the vast majority of models predicting business failure are constructed solely for the conditions of a particular country or even just for a specific sector of a national economy. Field of financial predictions regarding to international view consists of elementary used models, for example, such as Altman´s Z-score or Beerman´s index, which are globally know and used as basic of many other modificated models. Following article deals with selected Slovak prediction models designed to Slovak conditions, states how these models stand in this global world, what is their international connection to the worldwide economies, and also states verification of their prediction ability in a specific sector. The verification of predictive ability of the models is defined by ROC analysis and through results the paper demonstrates the most suitable prediction models to use in the selected sector.

scholarly journals Overview of Selected Bankruptcy Prediction Models Applied in V4 Countries

2018 ◽  
pp. 70-78 ◽  
Author(s):  
Pavol Kral ◽  
Lucia Svabova ◽  
Marek Durica
Keyword(s):  
Prediction Models ◽  
Slovak Republic ◽  
Predictive Ability ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Financial Health ◽  
Advantages And Disadvantages ◽  
Introductory Part ◽  
Business Entities ◽  
Corporate Practice

Bankruptcy prediction models are often an applied tool for detecting unfavourable development of the financial situation of the company. The prediction of financial health of business entities is the most important information because of dynamic development of the business environment. Many prediction models are known nowadays. They are different by their reliability (predictive ability), the composition of used variables, trade union orientation, the degree of consideration of domestic market conditions etc. It is clear from this that it is not possible to create a universal, unified prediction model that would be able reliably and with sufficient time to indicate unfavourable company financial development leading to bankruptcy applied in all sectors or regions. Introductory part of contribution is devoted to the literature review of issues and the definitions of the concept of bankruptcy based on the so-called non-prosperity indicators (profit, total liquidity and equity/liabilities ratio), that take into account the current legislation of this issue in the Slovak republic. Then the contribution discusses the role and significance of prediction models in corporate practice, compares the advantages and disadvantages of models containing accounting and market indicators. The authors also devoted the space to identifying restrictions on the usability of known foreign bankruptcy models in economic conditions of V4 countries and to define a set of the most frequently applied models taking into account specific economics conditions in these countries.

scholarly journals International comparison of the relevant variables in the chosen bankruptcy models used in the risk management

2017 ◽  
Vol 8 (1) ◽  
pp. 143 ◽  
Author(s):  
Katarina Zvarikova ◽  
Erika Spuchlakova ◽  
Gabriela Sopkova
Keyword(s):  
Comparative Study ◽  
Prediction Models ◽  
National Level ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Financial Health ◽  
Prediction Ability ◽  
International Environment ◽  
The World ◽  
Relevant Variables

Research background: It does not matter if the company is operating in the domestic or in the international environment; its failure has serious impact on its environment. Because of this fact it is not surprising that not only owners of the companies, but also another interested groups are focused on the prediction of the company´s financial health. Purpose of the article: The first studies concerned with this issue are dating back to 1930 but from this time a hundreds of bankruptcy prediction models have been constructed all over the world. Some of them are known world-wide and some of them are known only on the national level. Many researchers share their opinion, that it is not appropriate to use foreign models in the domestic conditions non-critically, because they were constructed in the different conditions. One of the main problems are used variables. Methods: We mention three studies which were focused on the used variables in the bankruptcy prediction models. Our comparative study was concerning with 42 models constructed in the seven chosen transit economics with the aim to realize which variables are relevant and which could be reduce from the bankruptcy prediction models. We focused only on the used variables and abstracted from the used methodology, the date of their construction or the model´s power of relevancy. Findings and Value added: The result of our comparative study is the identification of 20 variables, which were used in three or more prediction models, so we assume that these variables have the best prediction ability in the condition of transit economics and their application should be consider in the construction of new models.

scholarly journals Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Menelaos Pavlou ◽  
Gareth Ambler ◽  
Rumana Z. Omar
Keyword(s):  
Risk Prediction ◽  
Cluster Size ◽  
Linear Models ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Clustered Data ◽  
Predictor Variable ◽  
Simulated Data ◽  
Predictive Ability ◽  
Informative Cluster Size

Abstract Background Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. Methods Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. Results Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. Conclusion Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions.

scholarly journals Family businesses and predictability of financial strength: a Hungarian study

2020 ◽  
Vol 18 (2) ◽  
pp. 476-489 ◽  
Author(s):  
Judit Sági ◽  
Nick Chandler ◽  
Csaba Lentner
Keyword(s):  
Prediction Models ◽  
Cash Flows ◽  
Bankruptcy Prediction ◽  
Family Businesses ◽  
Added Value ◽  
Test Results ◽  
Financial Health ◽  
Financial Strength ◽  
Management Perspective ◽  
Practical Implications

The aim of this study is to examine how bankruptcy prediction models forecast financial strength for family businesses. Three predictive tests are used to study financial strength for three consecutive years (2016, 2017 and 2018) for a sample of 462,200 active Hungarian companies using the Amadeus database and expert data. Complex statistical model tests for credit assessment (bankruptcy predictions) are performed by size and ownership of the companies. It is found that the revised Altman model is impeded by a superfluous high weighting on net working capital; therefore, IN05 Quick Test predicted better chances for businesses in generating cash flows in a small emerging economy. By re-formulating the Bankruptcy Index of Karas and Režňáková and refining its coefficients, the modified Bankruptcy Index is more robust for predicting the financial health of family businesses on a cash flow basis. The test results of this modified Bankruptcy Index confirm the relative advance of family businesses in creating added value for owners. Practical implications arise from a management perspective: family businesses work better with predictability of survival in accordance with the model; therefore, their ability to adapt to financial constraints caused by crises is also more promising.

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 Limitation of Financial Health Prediction in Companies from Post-Communist Countries

2019 ◽  
Vol 12 (1) ◽  
pp. 15 ◽  
Author(s):  
Adriana Csikosova ◽  
Maria Janoskova ◽  
Katarina Culkova
Keyword(s):  
Prediction Models ◽  
Financial Health ◽  
Practical Application ◽  
Economic Interests ◽  
Prediction Ability ◽  
Expert Estimation ◽  
High Prediction ◽  
The Individual ◽  
Communist Countries ◽  
A Company

The financial health of a company can be seen as the ability to maintain a balance against changing conditions in the environment and at the same time in relation to everyone participating in the business. In the evaluation of financial health and prediction of financial problems of the companies, various indexes are used that can serve as input for expert estimation or creation of various models using, for example, multi-dimensional statistical methods. The practical application of the proper method for evaluation of financial health has been analysed in post-communist countries, since they have common historic experiences and economic interests. During the research we followed up the following indexes: Altman model, Taffler model, Springate model, and the index IN, based on multi-dimensional discrimination analysis. From the research results there is obvious a necessity to combine available methods in post-communist countries and at least to eliminate their disadvantages partially. Experiences from prediction models have proved their relatively high prediction ability, but only in perfect conditions, which cannot be affirmed in post-communist countries. The task remains to modify existing indexes to concrete situations and problems of the individual industries in the chosen countries, which have unique conditions for business making.

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.

Does intellectual capital help predict bankruptcy?

2018 ◽  
Vol 19 (2) ◽  
pp. 321-337 ◽  
Author(s):  
Velia Gabriella Cenciarelli ◽  
Giulio Greco ◽  
Marco Allegrini
Keyword(s):  
Performance Measures ◽  
Intellectual Capital ◽  
Financial Stability ◽  
Prediction Models ◽  
Predictive Ability ◽  
Time Frame ◽  
Bankruptcy Prediction ◽  
Superior Performance ◽  
Content Type

Purpose The purpose of this paper is to explore whether intellectual capital affects the probability that a particular firm will default. The authors also test whether including intellectual capital performance in bankruptcy prediction models improves their predictive ability. Design/methodology/approach Using a sample of US public companies from the period stretching from 1985 to 2015, the authors test whether intellectual capital performance reduces the probability of bankruptcy. The authors use the VAIC as an aggregate measure of corporate intellectual capital performance. Findings The findings show that the intellectual capital performance is negatively associated with the probability of default. The findings also indicate that the bankruptcy prediction models that include intellectual capital have a superior predictive ability over the standard models. Research limitations/implications This paper contributes to prior research on intellectual capital and firm performance. To the best of the knowledge, this is the first study to show that the benefits of intellectual capital extend from superior performance to long-term financial stability. The research can also contribute to bankruptcy studies. By using a time frame covering decades, the findings suggest that intellectual capital performance measures can be included in bankruptcy prediction models and can effectively complement traditional performance measures. Originality/value This paper highlights that intellectual capital is associated with long-term financial stability and a lower bankruptcy risk. Firms realising the potential of their intellectual capital can produce a virtuous circle between higher performance and greater financial stability.

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