scholarly journals Prediction of Default of Small Companies in the Slovak Republic

2018 ◽  
Vol 15 (1) ◽  
pp. 88-95 ◽  
Author(s):  
Lucia Svabova ◽  
Marek Durica ◽  
Ivana Podhorska
Keyword(s):  
Discriminant Analysis ◽  
Prediction Models ◽  
Financial Analysis ◽  
Slovak Republic ◽  
Real Data ◽  
Bankruptcy Prediction ◽  
Small Companies ◽  
Multivariate Statistical ◽  
Financial Difficulties ◽  
The World

Abstract From the time of Altman and the first bankruptcy prediction models, the prediction of default of companies is in the centre of interest of many economists and scientists all over the world. For companies, early detection of the possible threat of imminent financial difficulties or even bankruptcy is a very important part of financial analysis. Over the last few years, many predictive models have been created in the world. However, it has been shown that these models are not very well transferable to the conditions of the economy of another country and their prediction or rating power in another country is lower. Therefore, it is best to create a specific predictive model in the country that takes into account the situation of companies on the basis of real data on their financial situation. This paper is focused on creating a model of failure prediction of small companies in Slovakia using a well-known and widely used method of multivariate discriminant analysis. Discriminant analysis is one of the oldest multivariate statistical methods and sometimes it is difficult to fulfil certain assumptions for data. However, its results are easily interpretable and can be used to classify a company to the group of companies with risk of financial difficulties or, on the contrary, between well-prosperous companies. Prediction model is created based on real data on Slovak enterprises and has a strong classification ability in the specific conditions of the Slovak Republic.

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 Business Failure Prediction for Slovak Small and Medium-Sized Companies

2020 ◽  
Vol 12 (11) ◽  
pp. 4572 ◽  
Author(s):  
Lucia Svabova ◽  
Lucia Michalkova ◽  
Marek Durica ◽  
Elvira Nica
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
National Economy ◽  
Prediction Models ◽  
Failure Prediction ◽  
Real Data ◽  
Business Failure ◽  
Early Prediction ◽  
Combined Model ◽  
Financial Difficulties

Prediction of the financial difficulties of companies has been dealt with over the last years by scientists and economists worldwide. Several prediction models mostly focused on a particular sector of the national economy, have been created also in Slovakia. The main purpose of this paper is to create new prediction models for small and medium-sized companies in Slovakia, based on real data from the Amadeus database from the years 2016–2018. We created prediction models of financial difficulties of companies for 1 year in advance and also a model for 2 years prediction. These models are based on the combination of two methods, discriminant analysis and logistic regression that belong, among others, to the group of the most commonly used methods to derive prediction models of financial difficulties of the companies. The overall prediction powers of the combined model are 90.6%, 93.8% and 90.4%. The results of this analysis can be used for early prediction of the financial difficulties of the company, that could be very useful for all the stakeholders.

scholarly journals Bankruptcy Prediction with the Use of Data Envelopment Analysis: An Empirical Study of Slovak Businesses

2020 ◽  
Vol 13 (9) ◽  
pp. 212 ◽  
Author(s):  
Róbert Štefko ◽  
Jarmila Horváthová ◽  
Martina Mokrišová
Keyword(s):  
Data Envelopment Analysis ◽  
Logit Model ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Estimation Accuracy ◽  
Data Envelopment ◽  
Financial Indicators ◽  
Suitable Alternative ◽  
Financial Difficulties ◽  
Dea Method

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.

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 Real-time neural network based predictor for cov19 virus spread

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243189
Author(s):  
Michał Wieczorek ◽  
Jakub Siłka ◽  
Dawid Połap ◽  
Marcin Woźniak ◽  
Robertas Damaševičius
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Prediction Models ◽  
Numerical Data ◽  
Real Data ◽  
Future Trend ◽  
World Health ◽  
Number Of Patients ◽  
The World ◽  
Health Organization

Since the epidemic outbreak in early months of 2020 the spread of COVID-19 has grown rapidly in most countries and regions across the World. Because of that, SARS-CoV-2 was declared as a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, by The World Health Organization (WHO). That’s why many scientists are working on new methods to reduce further growth of new cases and, by intelligent patients allocation, reduce number of patients per doctor, what can lead to more successful treatments. However to properly manage the COVID-19 spread there is a need for real-time prediction models which can reliably support various decisions both at national and international level. The problem in developing such system is the lack of general knowledge how the virus spreads and what would be the number of cases each day. Therefore prediction model must be able to conclude the situation from past data in the way that results will show a future trend and will possibly closely relate to the real numbers. In our opinion Artificial Intelligence gives a possibility to do it. In this article we present a model which can work as a part of an online system as a real-time predictor to help in estimation of COVID-19 spread. This prediction model is developed using Artificial Neural Networks (ANN) to estimate the future situation by the use of geo-location and numerical data from past 2 weeks. The results of our model are confirmed by comparing them with real data and, during our research the model was correctly predicting the trend and very closely matching the numbers of new cases in each day.

scholarly journals Bankruptcy Prediction And Its Effect On Stock Prices As Impact Of The COVID-19 Pandemic

2021 ◽  
Vol 25 ◽  
pp. 567-582
Author(s):  
Muhammad Ramadhani Kesuma ◽  
Felisitas Defung ◽  
Anisa Kusumawardani
Keyword(s):  
Prediction Model ◽  
Strong Evidence ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Business Sector ◽  
The World ◽  
Hotel Sector

As COVID-19 pandemic hit the world since early 2020, one business sector in many countries that struggling to survive is tourism and its derivatives, such as restaurants and hotels.  This study aims to examine the accuracy of the Springate and Grover models in predicting bankruptcy, as well as the effect on stock prices of tourism, restaurant, and hotel sector in Indonesia. The results show that all sample tourism, restaurant, and hotel companies are bankrupt under the Springate model, whilst according to Grover's model the findings are varied during the study period. Furthermore, the Grover model is implied to be more accurate than the Springate model. The effect of both prediction models on stock price appears dissimilar. Springate's prediction model suggests a positive and significant effect on stock prices, whereas there is no strong evidence about the effect of Grover’s prediction model.

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.

scholarly journals Applications of Distress Prediction Models: What Have We Learned After 50 Years from the Z-Score Models?

2018 ◽  
Vol 6 (3) ◽  
pp. 70 ◽  
Author(s):  
Edward I. Altman
Keyword(s):  
Financial Distress ◽  
Prediction Models ◽  
Empirical Studies ◽  
Warning Signal ◽  
Bankruptcy Prediction ◽  
Z Score ◽  
Multivariate Statistical ◽  
Practical Applications ◽  
Multivariate Statistical Model ◽  
Credit Risk Measurement

Fifty years ago, I published the initial, classic version of the Z-score bankruptcy prediction models. This multivariate statistical model has remained perhaps the most well-known, and more importantly, most used technique for providing an early warning signal of firm financial distress by academics and practitioners on a global basis. It also has been used by scholars as a benchmark of credit risk measurement in countless empirical studies. Practical applications of the Altman Z-score model have also been numerous and can be divided into two main categories: (1) from an external analytical standpoint, and (2) from an internal to the distressed firm viewpoint. This paper discusses a number of applications from the former’s standpoint and in doing so, we hope, also provides a roadmap for extensions beyond those already identified.

scholarly journals Decision tree based model of business failure prediction for Polish companies

2019 ◽  
Vol 10 (3) ◽  
pp. 453-469 ◽  
Author(s):  
Marek Durica ◽  
Jaroslav Frnda ◽  
Lucia Svabova
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Prediction Models ◽  
Value Added ◽  
Real Data ◽  
Economic Research ◽  
Business Failure ◽  
Financial Situation ◽  
Financial Difficulties ◽  
Financial Failure

Research background: The issue of predicting the financial situation of companies is a relatively young field of economic research. Its origin dates back to the 30's of the 20th century, but constant research in this area proves the currentness of this topic even today. The issue of predicting the financial situation of a company is up to date not only for the company itself, but also for all stakeholders. Purpose of the article: The main purpose of this study is to create new prediction models by using the method of decision trees, in achieving sufficient prediction power of the generated model with a large database of real data on Polish companies obtained from the Amadeus database. Methods: As a result of the development of artificial intelligence, new methods for predicting financial failure of the company have been introduced into financial prediction analysis. One of the most widely used data mining techniques in this field is the method of decision trees. In the paper, we applied the CART and CHAID approach to create a model of predicting the financial difficulties of Polish companies. Findings & Value added: For the creation of the prediction model, a total of 37 financial and economic indicators of Polish companies were used. The resulting decision trees based prediction models for Polish companies reach a prediction power of more than 98%. The success of the classification for non-prosperous companies is more than 83%. The created decision tree-based prediction models are useful mainly for predicting the financial difficulties of Polish companies, but can also be used for companies in another country.

scholarly journals Logit and Probit application for the prediction of bankruptcy in Slovak companies

Equilibrium ◽  
2017 ◽  
Vol 12 (4) ◽  
pp. 775-791 ◽  
Author(s):  
Maria Kovacova ◽  
Tomas Kliestik
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Statistical Methods ◽  
Prediction Models ◽  
Slovak Republic ◽  
Relevant Literature ◽  
Value Added ◽  
Bankruptcy Prediction ◽  
Data Set ◽  
Prediction Ability

Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models. Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results. Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.

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