Predicting the Bankruptcy of Construction Companies: A CART-Based Model

Karas, Michal; Režňáková, Mária

doi:10.5755/j01.ee.28.2.16353

Cited by 30 publications

(30 citation statements)

References 29 publications

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“…The authors highlight that the increase in number of indicators used in the model probably has no positive impact on its total results. The most effective models are based on three or five indicators (Tomczak and Radosinski 2017;Karas and Reznakova 2017). On the other hand, the worst models cover six to eight indicators.…”

Section: Traditional Statisticalmentioning

confidence: 99%

“…The analyzed scientific research proves contrary opinions regarding neural network models' applicability when identifying companies' bankruptcies. Some scientists (Salehi and Pour 2016;Zieba et al 2016;Belas et al 2017;Karas and Reznakova 2017) claim that company insolvency information retrieved by artificial neural networks is superior to the information obtained when applying traditional statistical models. Meanwhile other authors (Abdipoor et al 2013;Bredart 2014) saw a contrary phenomenon and suggest that the application of neural networks model alone reduces the accuracy of predictions.…”

Section: Traditional Statisticalmentioning

confidence: 99%

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A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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Different economic environments differ in their characteristics; this prevents the usage of the same bankruptcy prediction models under different conditions. Objectively, the abundance of bankruptcy prediction models gives rise to the idea that these models are not in compliance with the changing business conditions in the market and do not meet the increasing complexity of business tasks. The purpose of this study is to assess the suitability of existing bankruptcy prediction models and the possibilities to increase the effectiveness of their application. In order to analyze theoretical aspects of the application of bankruptcy forecasting models and frame the research methodology, a systemic comparative and logical analysis of the scientific literature and statistical data, graphic data representation, induction, deduction and abstraction are employed. Results of the analysis confirm research hypotheses that bankruptcy prediction models based on macroeconomic variables are effective in identifying the number of corporate bankruptcies in a country and that the application of the model created on the grounds of macroeconomic indicators together with the traditional bankruptcy prediction model can improve the reliability of bankruptcy prediction. However, it was identified that models which are not specially adapted to companies in the construction sector are also suitable for forecasting their bankruptcies.

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Section: Traditional Statisticalmentioning

confidence: 99%

Section: Traditional Statisticalmentioning

confidence: 99%

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

et al. 2019

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“…In 2018, Popescu and Dragotă [6] identified the financial distress predictors for five post-communist countries (Bulgaria, Croatia, the Czech Republic, Hungary and Romania) using CHAID decision tree and neural networks. In Slovakia, Gavurova [7] and Karas and Reznakova [8] developed prediction models using decision trees.…”

Section: Literature Reviewmentioning

confidence: 99%

Mda Financial Distress Prediction Model for Hungarian Companies

Ďurica¹,

Švábová²

2019

International Scientific Conference ERAZ - Knowledge Based Sustainable Development

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Company financial distress prediction is one of the most discussed issues of economists around the world in recent decades. From the first attempts in the 1960s to the present, one of the most widely used method to create these models is Multiple Discriminant Analysis. In the paper, we present the prediction model for Hungarian companies created using this method based on real data from the financial statements obtained from database Amadeus. Our database contains data of more than 250,000 companies and 26 financial indicators used as predictors. There is possibility to predict the financial difficulties of companies one year in advance using this model.

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“…In 2014, another bankruptcy model was created by Homolka et al, for which the author indicates the prediction accuracy of 90.96 % (Homolka et al, 2014). The latest bankruptcy model to emerge (Karas & Reznakova, 2017) is focused on construction industry with the accuracy of 92.31 % in bankruptcy prediction and 58.74 % in prosperity prediction. Until then, the Z´´score model (see Altman & Sabato, 2008) was used for the construction industry (e.g.…”

Section: Literature Analysismentioning

confidence: 99%

Prosperity Prediction in Czech Manufacturing Industries

Kuběnka

2018

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There are many ways to determine financial health of a company. Classic tools of financial analysis emphasize data in financial statements. When trying to reach an overall classification of a company, e.g. from the point of view of an investor, indicators of profitability, liquidity, leverage and activity might generate contradictory clues regarding financial health of the company. Such analysis does not only generate ambiguous results, but is also lengthy. Therefore, the IN05 financial model was created for particular specifics of the Czech Republic economic environment to predict prosperity of companies. It measures the prosperity on the base of economic value added (EVA). More than ten years have passed since the launch of the IN05 index. The aim of this study is to quantify contemporary prediction strength of the IN05 index on a sample of almost 500 companies. Moreover, an effort was made to enhance the accuracy of the model for particular branches of manufacturing industries. It was found that contemporary explanatory power of the index IN05 is 48.58 % when applying the original evaluation scale, and 70.79 % when assessing 68.62 % of the sample except the gray zone. The accuracy of the model was enhanced by the author in all categories, i.e. a higher accuracy (correct prediction probability) without considering the gray zone and higher accuracy (conditioned correct prediction probability) using the gray zones in all branches of manufacturing industries. Robustness of these results was verified on another sample of 195 companies.

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Predicting the Bankruptcy of Construction Companies: A CART-Based Model

Cited by 30 publications

References 29 publications

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania

Mda Financial Distress Prediction Model for Hungarian Companies

Prosperity Prediction in Czech Manufacturing Industries

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