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 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 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.

A Review of Business Failure Prediction Models Used on Financial Distressed Companies

2017 ◽  
Vol 23 (11) ◽  
pp. 10497-10502
Author(s):  
Nur Diyana Abdullah ◽  
Sulaiman Abdul Malik
Keyword(s):  
Prediction Models ◽  
Failure Prediction ◽  
Business Failure

scholarly journals Inability of Gearing-Ratio as Predictor for Early Warning Systems

2014 ◽  
Vol 5 (2) ◽  
pp. 23-45 ◽  
Author(s):  
Mario Situm
Keyword(s):  
Early Warning ◽  
Model Building ◽  
Prediction Models ◽  
Failure Prediction ◽  
Financial Statements ◽  
Early Warning Systems ◽  
Business Failure ◽  
Warning Systems ◽  
General Applicability ◽  
Observation Period

AbstractBackground: Research in business failure and insolvency prediction provides numerous potential variables, which are in the position to differentiate between solvent and insolvent firms. Nevertheless, not all of them have the same discriminatory power, and therefore their general applicability as crisis indicators within early warning systems seems questionable. Objectives: The paper aims to demonstrate that gearing-ratio is not an appropriate predictor for firm failures/bankruptcies. Methods/Approach: The first and the second order derivatives for the gearing-ratio formula were computed and mathematically analysed. Based on these results an interpretation was given and the suitability of gearing-ratio as a discriminator within business failure prediction models was discussed. These theoretical findings were then empirically tested using financial figures from financial statements of Austrian companies for the observation period between 2008 and 2010. Results: The theoretical assumptions showed that gearing-ratio is not a suitable predictor for early warning systems. This finding was confirmed with empirical data. Conclusions: The inclusion of gearing-ratio within business failure prediction models is not able to provide early warning signals and should therefore be ignored in future model building attempts.

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