Capital Structure Modelling and Analysis of its Impact on Business Performance

The aim of the paper was to investigate the impact of company's capital structure on its performance. To achieve the goal, the data of Slovak businesses were used. An input analysis of the capital structure of the selected sector was carried out in order to generalize and elaborate conclusions aimed at the capital structure of the businesses analysed. Selected indicators of capital structure were calculated to analyse the relationships between these indicators and business performance. The results of the correlation analysis were complemented by examining the impact of selected independent variables on business performance applying regression analysis and Principal Component Analysis. Based on the findings, capital structure model was formulated to quantify the impact of changes in capital structure on business performance. The contribution of the paper is the identification of capital structure indicators that affect business performance as well as the construction of capital structure model. The article as well as the research, which is the basis for paper elaboration, is the result of professional public interest focused on finding whether the capital structure is the determinant of business performance

THE ARCTIC AREA VERSUS THE WORLD IN THE FIELD OF FOSSIL FUELS

The Arctic area could represent one of the largest world powers oriented on the export of oil, natural gas and other minerals. It would provide a significant position on the international energy market at a time when the energy security is becoming the most frequent topic in international negotiations. Countries surrounding the Arctic area are becoming aware of these factors and take an action to get part of the area. Their aim is to acquire the largest area while their attention is focused on seabed of the Arctic Ocean. Presented article deals with the analysis of the Arctic region from the perspective of international law and fossil fuel reserves which are according to recent surveys located in this area. It also provides an overview of climate change taking place in this area

Integration of balanced scorecard and data envelopment analysis to measure and improve business performance

The paper addressed the following research problem: Is the DEA (Data Envelopment Analysis) a suitable method of measuring and improving the performance of a company? Is it possible to calculate the goal values of the key performance indicators based on the BSC (Balanced Scorecard) principles using the DEA results? Is it possible to link the results of the BSC method and the DEA method to increase business performance? The aim of the research was to find out which financial indicators of the company are key performance indicators. We selected the key performance indicators using the BSC principles. These indicators were then applied as inputs/outputs in the DEA model. The partial aim was also to propose goal values for selected key performance indicators. The analysis was carried out on a sample of companies operating in the sphere of heat industry in Slovakia. The sample was created to represent businesses that have problems with their performance as well as those who do not have any problem. When choosing the sample, we made use of Altman's Z-score. We then calculated the values of the selected financial indicators. The correlation matrix and the BSC principles were applied to select key performance indicators. We also made use of the DEA method. This nonparametric method can be applied in the field of business efficiency, financial health, and performance evaluation. The DEA CCR input-oriented model was designed. We see the application of the DEA method in assessing financial health and performance of a company as well as the DEA CCR model itself as the main contributions of this paper. The research also paid attention to the calculation of the goal values of key performance indicators that enter the BSC as goal values. Using these goal values, a company may become more stable and therefore competitive. This research confirmed the possibility of integrating BSC and DEA

Risk of Bankruptcy, Its Determinants and Models

In this paper, the following research problem was addressed: Is DEA (Data Envelopment Analysis) method a suitable alternative to Altman model in predicting the risk of bankruptcy? Based on the above-mentioned research problem, we formulated the aim of the paper: To apply DEA method for predicting the risk of bankruptcy and to compare its results with the results of Altman model. The research problem and the aim of the paper follow the research of authors aimed at the application of methods which are appropriate for measuring business financial health, performance and competitiveness as well as for predicting the risk of bankruptcy. To address the problem, the following methods were applied: financial ratios, Altman model for private non-manufacturing firms and DEA method. When applying DEA method, we formulated input-oriented DEA CCR model. We found that DEA method is an appropriate alternative to Altman model in predicting the risk of possible business bankruptcy. The important conclusion is that DEA allows us to apply not only outputs but also inputs. Since prediction models do not include these indicators, DEA method appears to be the right choice. We recommend, especially for Slovak companies, to apply cost ratio when calculating risk of bankruptcy

Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach

Predicting the risk of corporate bankruptcy is one of the most important challenges for researchers dealing with the issue of financial health evaluation. The risk of corporate bankruptcy is most often assessed with the use of early warning models. The results of these models are significantly influenced by the financial features entering them. The aim of this paper was to select the most suitable financial features for bankruptcy prediction. The research sample consisted of enterprises conducting a business within the Slovak construction industry. The features were selected using the domain knowledge (DK) approach and Least Absolute Shrinkage and Selection Operator (LASSO). The performance of VRS DEA (Variable Returns to Scale Data Envelopment Analysis) models was assessed with the use of accuracy, ROC (Receiver Operating Characteristics) curve, AUC (Area Under the Curve) and Somers’ D. The results show that the DK+DEA model achieved slightly better AUC and Somers’ D compared to the LASSO+DEA model. On the other hand, the LASSO+DEA model shows a smaller deviation in the number of identified businesses on the financial distress frontier. The added value of this research is the finding that the application of DK features achieves significant results in predicting businesses’ bankruptcy. The added value for practice is the selection of predictors of bankruptcy for the analyzed sample of enterprises

Bankruptcy prediction with the use of data envelopment analysis: An empirical study of Slovak businesses

The paper deals with methods of predicting bankruptcy of a business with the aim of choosing a prediction method which will have exact results. Existing bankruptcy prediction models are a suitable tool for predicting the financial difficulties of businesses. However, such tools are based on strictly defined financial indicators. Therefore, the Data Envelopment Analysis (DEA) method has been applied, as it allows for the free choice of financial indicators. The research sample consisted of 343 businesses active in the heating industry in Slovakia. Analysed businesses have a significant relatively stable position in the given industry. The research was based on several studies which also used the DEA method to predict future financial difficulties and bankruptcies of studied businesses. The estimation accuracy of the Additive DEA model (ADD model) was compared with the Logit model to determine the reliability of the DEA method. Also, an optimal cut-off point for the ADD model and Logit model was determined. The main conclusion is that the DEA method is a suitable alternative for predicting the failure of the analysed sample of businesses. In contrast to the Logit model, its results are independent of any assumptions. The paper identified the key indicators of the future success of businesses in the analysed sample. These results can help businesses to improve their financial health and competitiveness

The application of graphic methods and the DEA in predicting the risk of bankruptcy

The paper deals with the issue of analyzing the financial failure of businesses. The aim was to select key performance indicators entering the DEA model. The research was carried out on a sample of 343 Slovak heat management companies. When addressing the research problem, we made use of multidimensional scaling (MDS) and principal component analysis (PCA), which pointed out the areas of financial health of companies that may predict their financial failure. The core of our interest and research was the data envelopment analysis (DEA) method, which represents a more exact approach to the assessment of financial health. The important finding is that the statistical graphical methods - PCA and MDS - are very helpful in identifying outliers and selecting key performance indicators entering the DEA model. The benefit of the paper is the identification of companies that are at risk of bankruptcy using the DEA method. The originality is the selection of key inputs and outputs to the DEA model by the PCA method

Benchmarking: A way of finding risk factors in business performance

The purpose of this study was to emphasize that the Data Envelopment Analysis (DEA) method is an important benchmarking tool which provides necessary information for improving business performance. To fulfil the abovementioned goal, we used a sample of 48 Slovak companies involved in the field of heat supply. As their position in the economic and social environment of the country is essential, considerable attention should be paid to improving their performance. In addition to the DEA method, we applied the Best Value Method (BVM). We found that DEA is a highly important benchmarking tool, as it provides benchmarks for units that have problems with performance and helps us to reveal risk performance factors. The DEA method also allows us to determine target values of indicators. The originality of this paper is in its comparison of the results of the BVM and the DEA methods

The Application of Machine Learning in Diagnosing the Financial Health and Performance of Companies in the Construction Industry

Diagnosing the financial health of companies and their performance is currently one of the basic questions that attracts the attention of researchers and experts in the field of finance and management. In this study, we focused on the proposal of models for measuring the financial health and performance of businesses. These models were built for companies doing business within the Slovak construction industry. Construction companies are identified by their higher liquidity and different capital structure compared to other industries. Therefore, simple classifiers are not able to effectively predict their financial health. In this paper, we investigated whether boosting ensembles are a suitable alternative for performance analysis. The result of the research is the finding that deep learning is a suitable approach aimed at measuring the financial health and performance of the analyzed sample of companies. The developed models achieved perfect classification accuracy when using the AdaBoost and Gradient-boosting algorithms. The application of a decision tree as a base learner also proved to be very appropriate. The result is a decision tree with adequate depth and very good interpretability

THE ARCTIC AREA VERSUS THE WORLD IN THE FIELD OF FOSSIL FUELS

The Arctic area could represent one of the largest world powers oriented on the export of oil, natural gas and other minerals. It would provide a significant position on the international energy market at a time when the energy security is becoming the most frequent topic in international negotiations. Countries surrounding the Arctic area are becoming aware of these factors and take an action to get part of the area. Their aim is to acquire the largest area while their attention is focused on seabed of the Arctic Ocean. Presented article deals with the analysis of the Arctic region from the perspective of international law and fossil fuel reserves which are according to recent surveys located in this area. It also provides an overview of climate change taking place in this area