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 Prediction of Financial Health of Business Entities of Selected Sector Using Balance Analysis II. by Rudolf Doucha and Verification of Its Predictive Ability through ROC

2021 ◽  
Vol 91 ◽  
pp. 01006
Author(s):  
Dusan Karpac ◽  
Viera Bartosova
Keyword(s):  
Financial Management ◽  
Financial Stability ◽  
Prediction Models ◽  
Predictive Ability ◽  
Business Failure ◽  
Financial Health ◽  
Balance Analysis ◽  
Business Entities ◽  
The Given ◽  
A Company

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. A healthy financial management of a business entity is very important for the proper operation of the business, and it is therefore very important to know how to assess financial health and to anticipate possible problems that will be easier to eliminate in advance. 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. Predictive models can indicate whether an entity tends to prosper or bankruptcy, and so we can assess the financial health of the business. This paper provides a description of the balance analysis II. by Rudolf Doucha, discusses its application to a sample of 266 Slovak subjects and points to its prediction in the given field. The verification of the ability to forecast bankruptcy or financial stability has been evaluated through ROC analysis.

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.

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 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.

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.

scholarly journals Predictive Ability of Chosen Bankruptcy Models: A Case Study of Slovak Republic

2018 ◽  
Vol 15 (1) ◽  
pp. 105-114 ◽  
Author(s):  
Anna Siekelova ◽  
Tomas Kliestik ◽  
Peter Adamko
Keyword(s):  
Credit Risk ◽  
Prediction Models ◽  
Slovak Republic ◽  
Predictive Ability ◽  
Financial Health ◽  
Financial Situation ◽  
Financial Condition ◽  
Error Types

Abstract Bankruptcy models are used to assess credit risk and predict financial situation to indicate the probable bankruptcy of the company. Contribution deals with the application of chosen bankruptcy models in analysing and predicting the financial health of selected companies. Most of the models have been developed abroad. In case of Slovak Republic, its application and correctness of the results can be problematic; therefore, we have focused primarily on those that have emerged in countries with a similar economy. We have calculated the selected prediction models in a sample of 500 Slovak enterprises. Predictive ability lower than 64% is considered as unfavourable. As part of the contribution, based on expert literature and relevant legislation, we have defined the criteria that allow to divide businesses into two groups: prosperous and non-prosperous. In the end, we compared the results of the selected models with the inclusion of enterprises in a prosperous and non- prosperous group based on the criteria set by us. We also dealt with examining of error types I (when an enterprise in bad financial condition is included in a non-bankruptcy group) and II (when an enterprise in good financial condition is included in a bankruptcy group). The aim is to analyse the predictive ability of the selected bankruptcy models.

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 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 Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models

2018 ◽  
Author(s):  
Luis Felipe Ventorim Ferrão ◽  
Caillet Dornelles Marinho ◽  
Patricio R. Munoz ◽  
Marcio F. R. Resende
Keyword(s):  
Genomic Selection ◽  
Prediction Models ◽  
Predictive Ability ◽  
Additive Models ◽  
Hybrid Breeding ◽  
Maize Breeding ◽  
Prediction Ability ◽  
Heterotic Groups ◽  
Breeding Programs ◽  
Grain Moisture

AbstractHybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with non-additive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein, we assess modelling approaches where dominance effects and multi-environment statistical are considered for genomic selection in maize hybrid. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three production cycles in different locations. Hybrid genotypes were inferredin silicobased on their parental inbred lines using single-nucleotide polymorphism markers obtained via a 500k SNP chip. We considered the importance to decomposes additive and dominance marker effects into components that are constant across environments and deviations that are group-specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30% in some scenarios. Contrastingly, additive models yielded better results for grain moisture. For multi-environment modelling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype by environment interactions resulted in gains in accuracy and hence could be considered for genomic selection implementation in maize breeding programs.

scholarly journals Logit business failure prediction in V4 countries

2019 ◽  
Vol 11 (4) ◽  
pp. 54-64 ◽  
Author(s):  
Marek Durica ◽  
Katarina Valaskova ◽  
Katarina Janoskova
Keyword(s):  
Logistic Regression ◽  
Failure Prediction ◽  
Predictive Ability ◽  
Binary Logistic Regression ◽  
Business Failure ◽  
Data Set ◽  
Prediction Ability ◽  
Regression Approach ◽  
High Prediction ◽  
One Year

Abstract The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders.

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