Search

Industry 4.0 uses artificial intelligence and machine learning algorithms to optimize processes. The main goal of the manuscript is to analyze the possibilities of applying automated machine learning to predict company bankruptcy. The data sample consists of financial data of 9,771 manufacturing companies for the years 2020 and 2021 collected from the Finstat database. Two methods of automated machine learning, AutoML and H2O, were tested. The results were compared with five other methods - linear discriminant analysis, logistic regression, naive Bayes classifier, CatBoost and XGBoost. The resulting model was cross-validated through the 10-fold approach. The best results were achieved by H2O automated machine learning algorithm with an AUC of 90.13%, followed by the gradient boosting methods CatBoost with AUC of 90.05% and XGBoost (AUC of 88.61%) and another automated learning algorithm AutoML with AUC of 81.17%. The findings of this paper indicate possibilities to apply automated machine learning methods in predicting bankruptcy. However, it is necessary to distinguish between individual automated machine learning algorithms since they provide a different range of results. [ABSTRACT FROM AUTHOR]

This paper addresses a underlying assumption of financial statements: going concern assumption. The going concern assumption constitutes a foundational premise presuming that the entity will conduct its operations in the forthcoming period (at least 12 months) without significant risk of business interruption. The primary objective of financial reporting is to provide information regarding the entity's financial position and performance to diverse users. Management is obligated to apprise users, and auditors are tasked with scrutinizing the assertion that the entity will continue its operations for a period exceeding 12 months. This paper meticulously examines the regulatory framework grounded in International Financial Reporting Standards and International Standards on Auditing. It particularly scrutinizes the role and significance of auditors in assessing the going concern assumption, encompassing an analysis of factors influencing the auditor's opinion on the going concern assumption and addressing criticisms directed at auditors. Furthermore, the paper explores past experiences in developing models for evaluating going concern assumptions, potentially aiding forensic accountants in uncovering irregularities in financial statements, given the correlation between a heightened bankruptcy risk and fraudulent activities. [ABSTRACT FROM AUTHOR]

The paper examines the effect of gender composition of executive body on financial performance and financial health of the firms. The data of more than thousand Czech travel agencies and tour operators for the period 2008–2015 was employed in the paper. To test the relationship between firm performance and gender diversity in leadership, the regression model was applied. After using alternative measurement for gender diversity of executive body and controlling for firm size, firm age, executive body size, leverage ratio and industry, the results have shown that the gender composition of executive body has no statistically significant effect on both firm performance and financial health of firms. The study deepens empirical knowledge about the relationship between gender diversity of executive body and corporate performance and financial health and brings new insights into the link between these phenomena in the Czech Republic. First published online 17 November 2020

Copyright of Listy Cukrovarnicke a Reparske is the property of VUC Praha a.s. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The development of bankruptcies in the Czech Republic is closely related to the impact of the global financial economic crisis, which, among other things, has also affected the competitiveness of Czech companies to a great extent. The future state of overall company financial health can be determined through prediction models. This paper discusses the history of financial analysis and the most widely used models, with the main purpose of the paper to compare the accuracy of various prediction models and to decide which model has the highest prediction success rate. The sample consisted of the total of 90 Czech companies, out of which 1/2 were companies in bankruptcy and 1/2 were non-bankrupt companies. Ratio indicators of given models were calculated from balance sheets as well as profit and loss statements for a five-year period. The reliability of the accurate classification of accounting units is verified by a confusion matrix. The highest total success rate of classification was achieved by Zmijevski model, which had the highest predictive value. Another partial objective of the paper is to determine whether the accuracy rate of the bankruptcy models changes with branches within which the companies operate. The hypothesis about differences between the branches is confirmed. The most statistically significant differences were shown between Wholesale and Retail and the Processing Industry, with the results of models varying among different branches. The results show that taking into account the branches the company is operating in is advisable for selecting prediction models.

Measuring competitiveness in central Asian post-socialist countries is problematic as many well-known metrics systems fail to warn about bankruptcy risks sufficiently early or at all. This article aims to present options for analyzing bankruptcy and creditworthiness models developed and used frequently in the Czech Republic. Bankruptcy likelihood is frequently measured by two famous models, the Alt-man z-score model, and Taffler z-score model. But there are other models which can be considered as more useful for companies in Kazakhstan such as IN99, IN01, IN05, and a creditworthiness model. The IN models were developed in an environment of Czech economy developing from socialistic to market oriented during the 1990s. During this period the IN models were developed so they are newer than the other two more famous models mentioned. Since the Czech Republic uses IFRS accounting standard which is also frequently used in Kazakhstan that is another reason why the IN models should be considered for wider use by companies in Kazakhstan.

The paper examines the effect of gender composition of executive body on financial performance and financial health of the firms. The data of more than thousand Czech travel agencies and tour operators for the period 2008-2015 was employed in the paper. To test the relationship between firm performance and gender diversity in leadership, the regression model was applied. After using alternative measurement for gender diversity of executive body and controlling for firm size, firm age, executive body size, leverage ratio and industry, the results have shown that the gender composition of executive body has no statistically significant effect on both firm performance and financial health of firms. The study deepens empirical knowledge about the relationship between gender diversity of executive body and corporate performance and financial health and brings new insights into the link between these phenomena in the Czech Republic. [ABSTRACT FROM AUTHOR]

Financial distress and bankruptcy modeling represents large and important field of economic research topics. This paper concerns with general structure of score oriented models in form of linear or affine forms built upon data available from standard accounting reports. For illustration purposes, we present well-known models, i.e. Altman, Neumaier, Ohlson, and Zmijewski one. However, the paper is focused mainly to probabilistic framework, which is getting more actual attention in practice for company financial health quantitative analysis, now. At first, the logit and probit transformations used for Ohlson, and Zmijewski model, respectively, are presented, as well as one model for calculation of probability of company default based upon Z-score values. Our contribution to this field is presented in the central part of the paper. We assume the score value to be an outcome of a random variable, which is introduced by its cumulative distribution function. This function is constructed systematically as a piecewise smooth function collected from three branches. Two of them, there are tail functions, belong to exponencial family. Whereas the central branch is specified as a linear function. At each joint, which is related to model grey zone bound, point smoothness of two adjacent branches is maintained by specific boundary conditions applied. Several cumulative distribution functions are presented,together with their probability density functions. Next, we select a SME ranked company, and apply the proposed technique to company survival analysis, as well as an analysis of company default probabilities. All calculations were performed by sw Mathematica.

Background and Purpose: In Slovenia, few management buyout (MBO) studies have been carried out. The focus was mostly on the motives for acquisition of companies and the success rate of the acquisitions. This paper aims to analyse the indicators which suggest an impending bankruptcy or financial restructuring of companies and explore how these indicators are different for successful and unsuccessful MBOs.

The development of bankruptcies in the Czech Republic is closely related to the impact of the global financial economic crisis, which, among other things, has also affected the competitiveness of Czech companies to a great extent. The future state of overall company financial health can be determined through prediction models. This paper discusses the history of financial analysis and the most widely used models, with the main purpose of the paper to compare the accuracy of various prediction models and to decide which model has the highest prediction success rate. The sample consisted of the total of 90 Czech companies, out of which 1/2 were companies in bankruptcy and 1/2 were non-bankrupt companies. Ratio indicators of given models were calculated from balance sheets as well as profit and loss statements for a five-year period. The reliability of the accurate classification of accounting units is verified by a confusion matrix. The highest total success rate of classification was achieved by Zmijevski model, which had the highest predictive value. Another partial objective of the paper is to determine whether the accuracy rate of the bankruptcy models changes with branches within which the companies operate. The hypothesis about differences between the branches is confirmed. The most statistically significant differences were shown between Wholesale and Retail and the Processing Industry, with the results of models varying among different branches. The results show that taking into account the branches the company is operating in is advisable for selecting prediction models. [ABSTRACT FROM AUTHOR]

Today the bankruptcy institution develops dynamically under the influence of various factors. This is required the formation of the universal goals of the bankruptcy institution with considering of features of the current socio-economic development. On the other hand, the transformation purposes of the bankruptcy institution towards rehabilitation focus is not always accompanied by an increase of efficiency of bankruptcy procedures, that confirms by the statistics data in the Russia. Thereby author investigated the issues of determining the universal target installation of bankruptcy institution, as well as an analysis of the bankruptcy models, which confirmed the effectiveness of the bankruptcy models in which decisions on rehabilitation or liquidation of the debtor is based on value criteria. In order to enhance the rehabilitation capacity of the bankruptcy institution in Russia on the basis of a systematic approach author proposed changes in the concept of the impact of the bankruptcy procedures, measures to improve the efficiency of bankruptcy procedures, and explore the essence of the process of restoring the debtor's solvency. It is proved that the purpose of rehabilitation of bankruptcy procedures should be the preservation of the debtor's business with the new owner of more efficient, taking into account interests of society as a whole.

The textile and clothing industry in Europe can be considered as a not profitable sector. The goal of the contribution is an evaluation of selected indicators of financial analysis, credit score and bankruptcy models as well as strategic analysis in selected companies of the textile and clothing industry in Slovakia. The next goal is an outline of development possibilities of the sector in the future. During the research we used data from the five most important companies doing business in the textile and clothing industry in Slovakia. The data obtained were processed by the bonity and Altman index, providing the possibility to determine possible future development in the industry. The results show a decrease in the number of textile and clothing companies in Slovakia. Such results can be used for the setting of scenarios of development, which show that the Slovakian textile, clothing and leather industry should multiply its effort to maintain its position on the international markets. [ABSTRACT FROM AUTHOR]

Objectives: Banks' failure is a significant concern to the economy as it creates high cost and heavy losses to the individual banks and society. To avoid the risk of bank failure, banks need to find reliable ways to predict bankruptcy. Certain bankruptcy models are not working in the current economic environment. Therefore, the objective of the present paper is to recalibrate and apply Springate, Zmijewski and Grover models to assess the Indian banks' bankruptcy. Method the current study tries to recalibrate the said models by considering the fact of models criticism in past studies with respect to their predictive power, selection of variables, time factor, accuracy rate, change in the economic environment, etc. Models are recalibrated by changing coefficients of the original models using current data through multiple regression technique. Further, this study applies the recalibrated bankruptcy models such as Springate, Zmijewski and Grover to assess the banks' bankruptcy. Results the result shows that the recalibrated Grover model outperforms the original model; however in case of Springate and Zmijewski model, original model performs better than the recalibrated model. Conclusion the implications of the study direct the Reserve Bank of India to make a policy of using advanced modes such as Multiple Discriminant Analysis technique, logit, Probit models along with the CAMEL model for the financial health assessment of banks. [ABSTRACT FROM AUTHOR]

Each model for measuring performance and prediction is different as it uses different mathematical approaches and works with different indicators. In the era of rapidly changing economic environment, the standard methods for measuring financial performance and assessing financial health are less adequate. Most authors focus on enhancing the predictive ability of original models by responding appropriately to the existing changed economic environment as the identification, increasing and managing enterprise performance and efficiency represent a key tool of today's competitive struggle. The objective of the paper is to create and then apply in practice a new innovative 3D model (PPE model) evaluating the current financial position of Slovak spa companies (P - positon), their future development prognosis (P - prognosis) as well as their efficiency (E - efficiency). The aim of the paper is to identify and implement traditional key indicators, predictive models and efficiency indicators within each of the model dimensions while respecting sectoral characteristics and financial particularities of Slovak spa enterprises. Creating a PPE model will help to better identify the current financial position of the sampled enterprises and, in this way, it will be able to reveal the causes hindering the development of their financial performance to a more accurate extent. [ABSTRACT FROM AUTHOR]

Measuring competitiveness in Central Asian post-socialist countries is problematic as many well-known metrics systems fail to warn about bankruptcy risks sufficiently early or at all. With 2013-2017 data, we have compared data on the selected poultry sector companies operating in Kazakhstan to analyze their competitiveness, while financial analysis metrics provided us with the financial performance data. In particular, we have analyzed two publicly traded companies -- Alatau-Kus JSC and Ust-Kamenogorskaya pticefabrika JSC, and two non-publicly traded companies -- Alel-Agro JSC, and Sever Ptica JSC. These are compared with the largest Czech poultry meat producer Vodnanska drubez, JSC. Firstly, we measured competitiveness through bankruptcy and creditworthiness models and compared competitiveness as compared to financial situation of the main competitors at that market. To measure competitiveness, we used such indicators as IN99, IN01, IN05. We show the IN models can be beneficial in the case of the post-socialist countries. By using INFA, we have aimed to confirm that only financially healthy companies, producing economic value added, can be competitive in the long run. For this analysis, we have additionally compared data with those of Czech food industry. We show that Czech INFA metrics system can serve as a very useful indicator for competitiveness analysis of Kazakhstan companies. [ABSTRACT FROM AUTHOR]

Under the influence of the change in the overall economic environment, the problem of measuring the performance of a company and its financial health is also changing. At present, every agricultural company is exposed to a number of internal as well as external risks, the failure of which can lead to potential bankruptcy. It is a known fact that the risks in agriculture are significantly greater than those in other sectors of the national economy. Proper diagnosis of critical aspects and measuring the development of individual financial indicators of agricultural holdings are basic prerequisites for eliminating these risks and maintaining, as well as increasing, their competitiveness. Among the key tools for measuring the financial situation of a company are bankruptcy models, three of which have been used (Altman model, Taffler model, and Bonity index). The aim of this study was to identify the comprehensive financial health of 469 agricultural enterprises in the Slovak Republic using the three above-mentioned bankruptcy models in 2016. The obtained results were verified using the Kruskal–Wallis test, Levene test, or Moran index. Altman’s model indicated potential future problems of businesses and agricultural cooperatives. Using the Bonity index, the neutral situation was assessed (the financial health could be regarded as neither insufficient nor optimal). Taffler model offered contradicted results and does not expect the occurrence of problems soon. In the study, we also verified the assumption of the dependence of financial health of companies on the legal form and territorial division, which was confirmed to be insignificant.

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.

La importancia de las micro-entities como generadoras de empleo y propulsoras de la actividad económica conlleva, unida a sus mayores tasas de quiebra y a su dificultad para acceder a las fuentes de financiación, la necesidad de diseñar métodos apropiados que anticipen sus quiebras. Con este fin, en este trabajo se desarrolla un modelo híbrido mediante la combinación de enfoques paramétricos y no paramétricos para la detección de sus quiebras. Para ello, se seleccionan las variables con mayor poder predictivo para detectar la quiebra mediante un modelo híbrido de regresión logística (LR) y árboles de regresión y clasificación (CART). Nuestros resultados muestran que este modelo híbrido obtiene una mejor performance que aquellos modelos implementados de forma aislada, además de tener una más fácil interpretación y una convergencia más rápida. Por otra parte, se constata la conveniencia de la introducción de variables no financieras y macroeconómicas que complementen a la información proporcionada por los ratios financieros para la predicción de la quiebra de las micro-entities, lo cual está en línea con las características propias e idiosincrasia de este tamaño empresarial recientemente definido por la Comisión Europea. ------------------------------------ The importance of micro-entities due to their generation of employment and propelling economic activity, together with the fact of their particularities, implies the need to design appropriate methods that anticipate their bankruptcies. For that purpose, a hybrid model by combining parametric and nonparametric approaches is developed in this paper. First, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, a non-parametric method, namely regression trees and classification (CART), is then applied to companies classified as "bankruptcy" or "non-bankruptcy". Our results show that this model provides a better result than when it is implemented in isolation, which joins its easier interpretation and faster convergence. Moreover, we demonstrate that the introduction of non-financial and macroeconomic variables complement the financial ratios for bankruptcy prediction. Findings are based on a data set of micro-entities (MEs), as recently defined by the European Union.

Credit risk management is a key element in bank management. For credit risk management, statistical models are used, the so‑called scoring and rating models. For enterprise risk assessment, rating models are used. Rating models consist of quantitative models (based on financial ratios) and qualitative models (based on a questionnaire). For estimation of quantitative models, econometric and statistical models are used, mainly logistic regression models. In this paper, statistical models for quantitative assessment are presented, including an empirical example based on the sample of data for SMEs made available by one of Polish banks. A logistic regression model with a nominal variable – the region of activity, including territorial differences, was used. The construction of rating model was presented, including the sector and region of activity.

In this paper are presented the results of a study examining the ability of Ohlson’s Logit model assessing and predicting the financial condition development of SMEs in comparison with the other models outcomes. Ohlson´s model was created using logit regression, which allows in the evaluation of the financial situation involve qualitative and discrete variables. The aim of the study is to determine whether the method used to derive the model influences the final assessment of the financial condition and indication of bankruptcy. The solution is based on the comparison of the resulting assessment of these four models, value of which were calculated on the same sample of Czech firms. As compared models were selected Z-score model, derived in the terms of US enterprises, IN05 model, which was derived in the conditions of Czech companies and Taffer´s model, derived in the conditions of UK firms. The sample consisted of 1996 small and medium firms in the manufacturing industry in Czech Republic. Data were obtained from the database of Albertina for the period of the years 2012 and 2013. It was found that the assessment of the firm´s financial situation matches in case of the results of Ohlson´s model and Taffler´s model, greater differences were found between the resulting values of Ohlson´s and Taffler´s model on one side and IN05 and Altman's model on the other side. Ohlson´s model and the Taffler´s model confirmed a good financial situation of companies in about 90 per cent of firms, Altman´s model and IN05 model in about 40 per cent of firms. The influence of the method used to derive the model on the assessment of the financial condition of companies was not proven.

The importance of micro-entities due to their generation of employment and propelling economic activity, together with the fact of their particularities, implies the need to design appropriate methods that anticipate their bankruptcies. For that purpose, a hybrid model by combining parametric and nonparametric approaches is developed in this paper. First, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, a non-parametric method, namely regression trees and classification (CART), is then applied to companies classified as "bankruptcy" or "non-bankruptcy". Our results show that this model provides a better result than when it is implemented in isolation, which joins its easier interpretation and faster convergence. Moreover, we demonstrate that the introduction of non-financial and macroeconomic variables complement the financial ratios for bankruptcy prediction. Findings are based on a data set of micro-entities (MEs), as recently defined by the European Union. [ABSTRACT FROM AUTHOR]

Copyright of Ekonomika a Management is the property of Ekonomika a Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Research Papers of the Wroclaw University of Economics / Prace Naukowe Uniwersytetu Ekonomicznego we Wroclawiu is the property of Uniwersytet Ekonomiczny we Wroclawiu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper deals with payment discipline in the building industry in the Czech Republic. The aim of this paper is to identify, compare and evaluate the financial situation of building companies with different payment practices in the Czech Republic in the period 2010 - 2014. The paper uses enterprise and statistical methods. The payment discipline of companies is expressed by a payment index, which is constructed by Bisnode. The results are based on analysis of 1374 companies which are operated in the building industry. The analysis shows that payment habits increase with higher index IN05, Taffler's model, index IN99 and with larger size of the firms. Payment habits of building companies are very good. However, about 2 % of solvent companies have poor payments habits. [ABSTRACT FROM AUTHOR]

Financial ratios are key constituents of bankruptcy models. The bankruptcy prediction accuracy substantially improves when a limited number of ratios are used. From the literature, it has been found that human expertise has been applied to select financial ratios for bankruptcy models. Different experts tend to have different opinions and hence the bankruptcy prediction results depend upon their competency levels in that domain. Accordingly, there is a need for finding a systematic method or a tool to identify the influencing ratios. This paper develops a genetic bankrupt ratio analysis tool using a genetic algorithm to identify influencing ratios from different bankruptcy models and their influences in a quantitative form. The accuracy of values of influencing ratios is validated by comparing original threshold value of the bankruptcy models. The performance of influencing ratios has been compared with other feature selection techniques and the Altman model is considered to explain the effect of the influencing ratios. [ABSTRACT FROM PUBLISHER]

The construction industry is a key sector of the national economy. In many areas of construction industry does not reach pre-crisis levels before 2008. Its development reflects a range of factors, estimates of future are different. To analyze the current situation and for prediction of the future was used bankruptcy model Z" Score. The analysis was based on the confirmation or rejection of the three hypotheses. To verify the hypothesis 1 was analyzed data from the statistics of the Ministry of Industry and Trade, which lists the cumulative values of the balance sheet and profit and loss account in the construction industry in the years 2009-2011. Hypothesis 1 was confirmed on the basis of the growing Z" Scores trend. It means that the situation in the sector is improving. To verify the hypothesis 2 was used a sample of balance sheets and profit and loss accounts (year 2010) with 473 companies. This data were obtained from the Commercial Register. Based on the calculation of Z" Score was found the number of enterprises in the area of bankruptcy. Subsequently was applied Z - test on one sample proportion. It statistically confirmed that 20 % of businesses have symptoms of bankruptcy. To test the hypothesis 3 was used global statistics CR. There have been extracted data for the calculation of Z" Score for the total economy (or selected industries in total) in the years 2009-2011. The resulting trend was compared with the values of Z" Score for the construction sector in the years 2009 to 2011. The results showed that the construction sector achieves higher levels of indicators Z" Score in all years. Thus, we can say that the situation in the construction industry better than the national economy as a whole. [ABSTRACT FROM AUTHOR]