Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

Industry 4.0 and related automation and digitization have a significant impact on competition between companies. They have to deal with the lack of financial resources to apply digital solutions in their businesses. In Slovakia, Industry 4.0 plays an important role, especially in the mechanical engineering industry (MEI). This paper aims to identify the groups of financial ratios that can be used to measure the financial performance of the companies operating in the Slovak MEI. From the whole MEI, we selected the 236 largest non-financial corporations whose ranking we obtained according to the amount of generated revenues in 2017. Using factor analysis, from eleven traditional financial ratios, we extracted four independent factors that measure liquidity (equity to liabilities ratio, quick ratio, debt ratio, net working capital to assets ratio, current ratio), profitability (return on sales, return on investments), indebtedness (financial leverage, debt to equity ratio), and activity (assets turnover, current assets turnover) of the company. Our analysis is an essential prerequisite for developing a realistic financial plan for companies operating in the MEI, especially when considering investments in new technologies related to Industry 4.0.

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“…513/1991 Coll. Commercial Code [49] of the Slovak Republic; therefore, it occurs mainly in Slovak studies, e.g., [78][79][80][81][82][83][84].…”

Section: Discussionmentioning

confidence: 99%

Management and Economic Sustainability of the Slovak Industrial Companies with Medium Energy Intensity

et al. 2021

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“…Shetty et al (2012) applied the DEA model to determine the assumption of bankruptcy of the analyzed sample of businesses. The result of their study was the proposal of indicators that should be applied as predictors of bankruptcy (in Horváthová and Mokrišová 2020). It is also possible to mention the research of Ouenniche and Tone (2017), who proposed an out-of-sample evaluation framework for DEA and confirmed that DEA as a classifier is a very suitable contender for discriminant analysis.…”

Section: Introductionmentioning

confidence: 98%

The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy

Štefko

Horváthová

Mokrišová

2021

JRFM

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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.

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Multi‐class financial distress prediction based on stacking ensemble method

Chen,

Wu,

Zhang

et al. 2024

Int. J Fin Econ

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The motivation of this article is to help financial soundness companies understand their specific financial status so that they can take timely measures to avoid financial distress. Existing multi‐class financial distress prediction (FDP) studies have mainly segmented financial crisis status, with less attention paid to financial soundness companies. To fill this gap, we propose a new multi‐class definition of FDP from the perspective of financial soundness enterprises. The financial states are defined as financial soundness, moderate financial soundness, mild financial soundness and financial distress. We propose a stacking ensemble model for multi‐class FDP. First, deep neural network, multinomial logit regression (MNLogit) and multivariate discriminant analysis models are used as basic classifiers to obtain preliminary prediction results. Second, MNLogit is used to integrate the results from the previous step. To increase the effective information, stock information is then added into the model. The proposed model was trained using data from 2007 to 2019 for Chinese listed companies and tested using data from 2020. The results show that the MacroR‐Pre, MacroR‐Rec, MacroR‐F1 and MacroR‐AUC of the proposed model are better compared with the benchmark model, including individuals and ensembles, with 87.05%, 90.68%, 88.70% and 88.20%.The addition of stock information and non‐financial indicators can improve the accuracy of the multi‐class FDP model by about 8%. The innovativeness of this paper is twofold. First, it proposes a new multi‐class definition of enterprise financial status. Second, a multi‐class FDP based on stacking is constructed, which provides a new method for solving the multi‐class FDP problem. The study shows that the proposed multi‐class definition and stacking model are suitable for analysing financial soundness enterprises, which can help managers effectively grasp the specific financial status and have strong practical significance.

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Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

Cited by 11 publications

References 74 publications

Management and Economic Sustainability of the Slovak Industrial Companies with Medium Energy Intensity

Management and Economic Sustainability of the Slovak Industrial Companies with Medium Energy Intensity

The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy

Multi‐class financial distress prediction based on stacking ensemble method

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