Financial compass for Slovak enterprises: Modeling economic stability of agricultural entities

Valaskova, Katarina; Durana, Pavol; Adamko, Peter; Jaros, Jaroslav Article Financial compass for Slovak enterprises: Modeling economic stability of agricultural entities Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Valaskova, Katarina; Durana, Pavol; Adamko, Peter; Jaros, Jaroslav (2020) : Financial compass for Slovak enterprises: Modeling economic stability of agricultural entities, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 13, Iss. 5, pp. 1-16, https://doi.org/10.3390/jrfm13050092 This Version is available at: https://hdl.handle.net/10419/239180 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Risk and Financial Management Article Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities Katarina Valaskova 1,* , Pavol Durana 1, Peter Adamko 1and Jaroslav Jaros 2 1 Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia; [email protected] (P.D.); peter[email protected] (P.A.) 2Center for Technology Transfer, University of Zilina, University Science Park, Univerzitna 1, 010 26 Zilina, Slovakia; jaroslav.jar[email protected] *Correspondence: [email protected] Received: 10 April 2020; Accepted: 6 May 2020; Published: 7 May 2020   Abstract: The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed. Keywords: modeling in economic; economic sustainability; prediction models; financial distress; market risk; agriculture 1. Introduction Financial stability and a healthy financial situation are very important aspects of enterprise assessment by potential creditors and business partners. Analyzing the past results is not the only important measure, each enterprise should also look at its future direction, which led to the development of prediction models. The main importance of these methods is to estimate the future potential of an enterprise in order to correctly set its real financial objectives (Rahman et al. 2017). The question of corporate financial distress and bankruptcy prediction has been the most discussed problems of economists around the world in recent decades (Delina and Packova 2013). According to Barbuta-Misu and Madaleno (2020), bankruptcy prediction is a very real issue worldwide both in academic research and practice. Durica et al. (2019) claim that the problem of bankruptcy forecasting is a topical issue, especially in times of strong market competition and business crises. They affirm that if an enterprise wants to thrive and successfully operate in a market environment, it should conduct a regular financial analysis of its activities, evaluate its successes and failures, and use the results to make J. Risk Financial Manag. 2020,13, 92; doi:10.3390/jrfm13050092 www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2020,13, 92 2 of 16 strategic decisions about future business development. In order to predict the financial reliability of enterprises, there is a need to follow the development of crucial financial ratios. As investigated in the study of Kliestik et al. (2020) and Kovacova et al. (2019), recognition of key financial indicators enables modeling of the probability of failure and prediction of financial difficulties at a specific accuracy level. Virag (2004) proves that the choice of ratios in the process of prediction modeling is restricted strongly by the fantasy of the analyst and the number of items in the financial statement. Despite the large number of existing bankruptcy prediction models that have been developed worldwide using different statistical methods to achieve the best prediction accuracy, the prediction of bankruptcy risk is still challenging. The reason is that the prediction models have the most significant predictive ability provided that they are formed in specific national environment and exact sector of the economy (Kovacova and Kliestik 2017;Krastev et al. 2019). Thus, the purpose of the paper is to analyze the bankruptcy prediction models, which were developed in the environment of the Slovak economy in the agricultural sector. The choice of the sector is underlined by the fact that after the transformation of the Slovak economy, the first bankruptcy models were developed in this sector (and until now they are the most reputable bankruptcy prediction models of Slovakia). The assessment of the potential risks and the threat of bankruptcy in the agricultural sector plays an important role from economic, social, ethno-cultural, and environmental perspectives. Dadayan et al. (2020) affirm that activities in agriculture are risky, and the growth of production and financial stability depend on how subjects overcome economic risks. As declared by Sedlacek (2010), business entities in the agricultural sector are influenced not only by financial problems but also by many force majeure circumstances, confirming that is more difficult for these companies to transform their activities compared to subjects in other economic sectors. As stated by Dinterman et al. (2018), enterprises in the agricultural sector experience financial stress if they are able to generate cash flow to meet their debt services payments. Patrick et al. (2016) stress that the recent development of the agricultural sector causes lower net income, stagnation of land values and an increase in the volume of debts causing huge problems in the agricultural sector worldwide. The main aim of the paper is to portray the bankruptcy models which were developed in conditions of the Slovak republic, especially in the sector of agriculture, verify their predictive ability using divergent statistical methods, and explore the importance of financial ratios in the prediction of financial stability. Its originality lies in the fact that it is the pioneering study analyzing all models for agricultural enterprises developed in the same economic environment. The first models formed in the specific Slovak environment, were the multiple discriminant analysis (MDA) models of Chrastinova (1998) and Gurcik (2002) focused on the assessment of enterprises in the agricultural sector, which used to be the key industry of the country. However, since that time, the researchers and academicians have been mostly focused on the development of predictions models for the Slovak market, without specific orientation on the industry. Kliestik et al. (2018a) published a detailed analysis of prediction models developed and used in transition economies and they formed several models for Slovak environment, which are also included in the sample of analyzed models. The research problem includes the application of several statistical methods, contingency tables, confusion matrix, and receiver operating characteristic curve (ROC) to compare the accuracy of the prediction models. Comparison of several models being developed in the same economic conditions helps judge not only the correctness of individual models, but also the effect of financial ratios used for prediction. The research confirms that those models which are proposed for the agricultural sector highly outperform other models considering their prediction accuracy and refuses the use of models which were not developed in similar economic conditions. The paper is divided into four main parts. The literature review highlights the most important and the most relevant studies and researches being published in Slovak conditions and worldwide in the field of bankruptcy modeling. Materials and Method addresses a description of prediction models entering the analysis, portrays statistical methods and tests used, introduces the database of enterprises, and delineates the methodological steps of the research. Results and Discussion summarizes the J. Risk Financial Manag. 2020,13, 92 3 of 16 important findings, presents the results of the comparison of prediction models, and compares them with other relevant studies. 2. Literature Review The origins and literary sources of predictive bankruptcy models date back to 1930, starting with studies on using ratio analysis to predict future bankruptcy. Until the mid-1960s, research focused only on one-factor analysis. The one-factor study by Beaver (1966) is generally the most widespread and most recognized. In 1968, Altman published the first multi-factor study, which is still very popular today. From Beaver (1966) and Altman (1968), hundreds of models have been developed both in specific national conditions and different economic groupings. As portrayed in the study of Kliestik et al. (2018b), it is extremely important to predict bankruptcy risk as corporations have become more global and more complex. Jiang et al. (2018) stress that the ability to predict the occurrence of extreme changes is significant also in financial risk management. Moreover, company management needs to ensure that the conditions of financial distress do not develop into a bankruptcy by identifying the financial ratios which have a major impact on the financial conditions of enterprises (Mahtani and Garg 2018). Managing insolvency and failure help strengthen the market competitiveness and growth (Eklund et al. 2020;Krastev et al. 2019), form more efficient strategies when modeling financial stability (Popescu and Victor 2018;Ludbrook et al. 2019), and improve the recovery rate of stakeholders (Sautner and Vladimirov 2018). The importance of financial health modeling is confirmed in the research of Prusak (2019) who assesses and reviews the level of advancement of bankruptcy prediction research in the eastern European countries. There are several different statistical methods which can be used when developing bankruptcy prediction models, which are distinguished by the level of prediction accuracy achieved. Ben Jabeur and Fahmi (2018) and Garcia et al. (2019) compare the most frequently used statistical methods of financial distress modeling (discriminant analysis, logistic regression, and random forest) to verify their prediction accuracy. Their results unveil that the random forest method allows better accuracy and minimizes type I and type II errors which correspondents with the finding of Durica et al. (2019). Despite the fact, that the researches of many authors claim, e.g., Naidu and Govinda (2018); Le et al. (2018)orHosaka (2019), that the machine learning models are much better when predicting the corporate bankruptcy, there are also several studies, e.g., Barboza et al. (2017), which affirm that machine learning models achieve only 10% better accuracy compared to traditional methods (multiple discriminant analysis and logistic regression). The scientific importance of the MDA method is intensified by the fact that also the first models, and still the most important, are based on this method. Considering the fact that this method takes into account the whole set of common characteristics of the analyzed enterprises as well as their mutual interactions (Siekelova Anna and Adamko 2018), makes the MDA analysis the most preferred method of bankruptcy prediction. Hajek (2018) affirms that systems for predicting corporate rating have attracted significant interest in soft computing research, as these models achieve good results in both accuracy and interpretability. Nonetheless, not only the statistical methods do play an important role, but there are also perpetual discussions if accounting of market-based information should be used when predicting corporate default (Farooq and Qamar 2019). The research of Li and Faff(2019) reports that relevance of accounting-based information is crucial for enterprises with high information asymmetry while the market-based should be preferred during the financial recession periods (Stefko et al. 2019; Krastev et al. 2020 ). The empirical findings of Binh et al. (2018) prove that both accounting and market factor, accompanied by changes in the macroeconomic factors, affect the financial distress of enterprises. They underline the importance of the development of prediction models, which includes accounting and macroeconomic indicators (rather than market-based with macroeconomic factors), to achieve high predictive accuracy. The important role of financial ratios in predicting financial distress is explored in the study of Waqas and Md-Rus (2018), whose inclusion of profitability, liquidity, leverage, and cash flow ratios improved the accuracy rate of financial distress modeling using a logit model. J. Risk Financial Manag. 2020,13, 92 4 of 16 Inam et al. (2019) confirm that profitability and leverage indicators are the best financial discriminants to predict the financial distress which was proved analyzing the Pakistani firms of the non-financial sector. Another important aspect is presented in the study of Korol (2019) who investigated the effectiveness of bankruptcy prediction models in shortand long-term horizons. Developing the models for bankruptcy prediction is Slovakia, authors use various statistical methods. Alaka et al. (2018) contribute to a clear understanding of the feature of the tools used to develop bankruptcy prediction models. A closer view of the frequently used statistical methods, analyzing their advantages and disadvantages in term of data requirements are discussed by Svabova and Durica (2019) . The first models introduced by Chrastinova (1998) and Gurcik (2002) are based on the principles of the MDA method in the agriculture sector. However, many other models are formed using logistic regression due to more accurate results (Hurtosova 2009; Gulka 2016 ;Delina and Packova 2013;Harumova and Janisova 2014). Mihalovic (2016) in his study points out that the models based on a logit function outperform the classification ability of the discriminant models. The successful use of logit and probit models is highlighted in the paper of Kovacova and Kliestik (2017) , who use these methods to estimate the financial health of Slovak enterprises. The outstanding classification accuracy of the logistic regression model was the main reason why this model was selected for the comparison with learning algorithms in the Slovak conditions. Horvathova and Mokrisova (2018) applied Altman ´ s model and DEA method to search the most appropriate method to measure the financial health, performance, and competitiveness of enterprises operating in tourism. The latest research of Zoricak et al. (2020) provides a complex analysis of the financial predictors and identifies those methods which are the most appropriate to bankruptcy prediction. They reveal that the highest prediction performance is achieved using the geometric mean score validating the model on the database of manufacturing and construction industries—the significant industries of any developed economy. 3. Materials and Methods The verification of the prediction ability of selected Slovak models and the identification of the crucial financial ratios in a specific industry is based on a dataset of Slovak enterprises operating in the agricultural sector (NACE A—Agriculture, forestry, and fishing). Stehel et al. (2019) point out, that one of the basic principles of sustainable agriculture is forecasting its future development. The information about the financial performance of the enterprises was acquired from the Amadeus database provided by the Bureau van Dijk. The final dataset is formed of 3329 enterprises from the NACE A sector, with the financial reports of the last three years—2018, 2017, and 2016. The three-year-long period was chosen to analyze the enterprises as a response to a newly adopted legislation (establishing a status of a company in crisis) which came into force in 2016 and determined limit values to judge the corporate financial stability. According to the new Act No. 7/2005 Coll. on bankruptcy and restructuring and Act No. 513/1991 Coll. of the Commercial Code, an enterprise is in a crisis when it is in decline or in a threat of bankruptcy. Companies are at risk of bankruptcy if their equity-to-liability ratio is less than 8 per 100. Under the transitional provisions, the ratio of 8 to 100 is only used in 2018 and after. In 2016, a critical value is assumed to be 4 to 100 and in 2017—6 to 100. The enterprise is non-prosperous if together with this restriction of the value of the equity-to-liability ratio, their profit after taxes is negative and the ratio of current-assets-to-current-liabilities is less than 1. Moreover, the enterprises which value of assets is lower the value liabilities (negative equity), are also considered non-prosperous. 3.1. Database Description The enterprises may be then categorized into two main groups of prosperous (financially healthy enterprises with no threat of bankruptcy) and non-prosperous (with the risk of bankruptcy). Table 1 portrays the number of prosperous—financially stable and non-prosperous—financially unstable enterprises in each analyzed year. J. Risk Financial Manag. 2020,13, 92 5 of 16 Table 1. Number of prosperous and non-prosperous enterprises in each year. Year Prosperous Non-Prosperous 2016 2861 468 2017 2858 471 2018 2872 457 Source: processed by authors. The models applied to assess the financial performance of agricultural enterprises and reveal the risk of bankruptcy use different financial ratios as predictors. The models use together 23 financial-economic ratios, their descriptive statistics—mean, median, standard deviation and coefficient of variance (CV)—are presented in Table 2. The ratios cover all important perspectives of financial determinants (Lukason and Camacho-Minano 2019)—including ratios of liquidity, profitability, and leverage. Table 2. Descriptive statistics of financial-economic ratios. Financial—Economic Ratios Mean Median Std. dev. CV X2Current Assets/Current Liabilities 9.94 1.14 299.85 30.16 X3Gross Profit/Total Assets 0.13 0.09 0.12 1.29 X4Net Income/equity −0.95 0.07 47.71 −50.06 X5EBITDA/Sales 0.37 0.2 24.41 65.92 X7Net Income/Total Assets −0.02 0.02 0.33 −14.68 X8Working Capital/Total Assets 0.08 0.09 0.14 1.78 X9Operating profit/Total assets 0.01 0.03 0.33 33.07 X10 (Non-Current +Current Liabilities)/Total Assets 0.77 0.63 0.93 1.22 X11 Current assets/Total assets 0.59 0.56 0.1 0.17 X12 Cash and Cash Equivalents/Total Assets 0.24 0.1 0.1 0.42 X13 Cash flow/Total Assets 0.09 0.09 0.12 1.28 X14 Cash flow/(Non-current +Current Liabilities) 0.68 0.17 17.26 25.47 X15 Current Liabilities/Total Assets 0.64 0.46 0.91 1.42 X16 Current Assets/Sales 14.05 0.81 183.96 13.1 X18 Inventories/Sales 1.03 0.11 20.99 20.48 X20 Net income/Sales −31.71 0.02 937.75 −29.58 X21 Non-Current Liabilities/Total Assets 0.12 0.02 0.12 1.03 X22 Cash and Cash Equivalents/Current Liabilities 7.66 0.22 305.79 39.95 X24 Working Capital/Sales −320.73 0.12 10246.75 −31.95 X26 (Current Assets—Inventory)/Current Liabilities 2.95 0.8 3.56 1.21 X27 Return on Assets (Profit Before Tax/Total Assets) 0.04 0.02 0.1 4.24 X28 Return on Equity (Profit Before Tax/Equity) 0.1 0.09 0.19 1.93 X30 Equity/(Non-Current +Current Liabilities) × 100 0.36 0.28 0.11 0.31 X35 Profit before Tax/Operating Income 0.06 0.04 0.12 2.06 Source: processed by authors. To simplify the calculations, the authors unify the nomenclature of the financial predictors in all analyzed models. 3.2. Description of the Analyzed Models The individual prediction models, which are applied to verify the prediction ability of the models are the models based on the multiple discriminant analysis. The purpose of the MDA method is to J. Risk Financial Manag. 2020,13, 92 6 of 16 formulate one dependent variable as a linear combination of other independent variables. The essence of the MDA analysis lies in the best classification of the assessed company (industry) into the group of financially stable or unstable enterprises (Vrbka et al. 2019). As mentioned in the literature review, there are several prediction models being developed in Slovakia, but this study is focused on the verification of eight models which were developed either in the same economic conditions of Slovakia or in the same sector—agriculture. For all these models, the Z score values were calculated for each enterprise in the dataset and for the analyzed years 2016–2018. The first bankruptcy prediction models developed in Slovakia, are the models of Chrastinova (1998) and Gurcik (2002), which are focused exclusively on agricultural enterprises. Ch-index of Chrastinova used the sample of 1,123 enterprises to form the model and the results claim the practicability of the model to assess the financial health of enterprises. The Ch-index is calculated as (all the variables are explained in Table 2) CH =0.37X7+0.25X20 +0.21X14 –0.1X4–0.07X10 (1) The calculated value of the Ch-index can then be classified into three groups. If the resulting value is higher than 2.5 an enterprise belongs to the group of prosperous enterprises, so it has a healthy financial situation. A value lower than − 5 indicates a non-prosperous business with an unhealthy financial situation, and if the resulting value is in the range of − 5 to 2.5, an enterprise is placed in the neutral zone. However, as the enterprises need to be grouped only to prosperous and non-prosperous the limit value (mean value) −1.25 was used to categorize the enterprises. G-index of Gurcik, primarily based on 60 Slovak enterprises and 35 financial ratios, uses five financial ratios as predictors when predicting the future financial development of enterprises G=3.412X9+2.226X3+3.277X5+3.149X13 –2.063X18 (2) The calculated G-index value can be divided into three groups. If the value is higher than 1.8 the company belongs to the group of prosperous, if the value is lower than − 0.6 the enterprise is non-prosperous. An enterprise belongs to the neutral zone if the G-index is in the range of 1.8 to − 0.6. Due to the same reason, the need for two-stage classification, the limit value (mean value) to categorize enterprises to prosperous and non-prosperous is set at the level 0.6. To identify other prediction models in the agricultural sector, the models presented by Kliestik et al. (2018a) were adopted. The models may be distinguished according to several criteria, although, for the purpose of the analysis the authors selected the model for small enterprises in the agricultural sector, model for medium-sized enterprises and large enterprises in the agricultural sector, the general model for Slovak agricultural enterprises, and the general Slovak model (calculated for all sectors). The last model was chosen to compare the differences in prediction accuracy. Moreover, to confirm the predictive ability, the revised Altman’s model (Altman et al. 2014) was also included. The prediction model for small enterprises in the agricultural sector was calculated based on the sample of 88,252 small enterprises (66,070 prosperous and 22,182 non-prosperous). The enterprises are classified into two groups (prosperous and non-prosperous) based on the value of the calculated Z score—a positive value represents a non-prosperous enterprise and a negative value an enterprise that is prosperous. The Z-score is calculated as ZSNACE A =−1.412 −1, 512X4+2.504X10 −0.06X24 +1.256X28 (3) To form the prediction model for medium-sized enterprises in the agricultural sector, the financial statements of 15,275 medium-sized enterprises (13,160 prosperous and 2115 non-prosperous) were used. The limit value to classify the enterprises into prosperous and non-prosperous was 0 (positive value for non-prosperous, negative for the prosperous ones). Z-score for medium-sized agricultural enterprises is ZMNACE A =1.082 +5.105X4−1.742X10 −4.063X28 (4) J. Risk Financial Manag. 2020,13, 92 7 of 16 The model for the bankruptcy prediction of large enterprises was developed on a sample of 2181 Slovak enterprises (2062 prosperous and 119 non-prosperous). The formula to calculate Z-score is ZLNACE A =−0.863 −3.72X4+8.503X9+1.417X15 +0.617X16 −1.418X24 (5) As in the previous two models, a positive value of Z score represents a non-prosperous enterprise and a negative value an enterprise that is prosperous. To develop the general prediction model for Slovak agricultural enterprises, the data of 105,708 enterprises (81,292 prosperous and 24,416 non-prosperous) was used. The model has the following formula ZSRNACE A =−1.179 +0.376regionBA +0.86small −0.095X2−0.003X4+1.348X8 +0.397X12 +0.9X15 −0.008X16 +1, 569X21 −0.028X24 +0.074X26 −1.815X27 −0.249X28 −0.805X35 (6) This model consists of financial ratios discussed in Table 1, however, there are also two dummy variables included in the calculation of the bankruptcy prediction. A zero to one scheme has to be applied to define a certain variation or a category of a dummy (qualitative) variable—region where an enterprise operates and its size. Zero is used when the given variation or category does not occur and one demonstrates the opposite situation, i.e., the occurrence of a given variation or category. The positive value of Z score determines a non-prosperous business entity, the negative value is a sign of corporate prosperity. The last introduced model is the general model for Slovak enterprises (with no orientation for a specific sector of the economy). The classification for prosperous and non-prosperous enterprises follow the same principle. The value of Z score is calculated based on the equation ZSR =−2.255 +0.548NACEI+0.1NACEN−0.285NACEQ+0.139regionBA +0.097regionKE +0.980small +0.326medium +0.016X2 −0.385X4−6.716X7+2.244X10 −0.358X11 +0.761X15 +0.011X22 +5.431X27 +0.1X28 (7) 3.3. Description of the Statistical Methods Assessing the Predictive Ability of Selected Models Calculating the Z score in the analyzed period 2016–2018 for all 3329 agricultural enterprises using the determined models enables the further comparison of the models to verify their prediction accuracy. To compare the contingency tables of individual models, confusion matrix, and receiver operating characteristic curve (ROC) were used. A confusion matrix (Table 3) is a technique for summarizing the performance of a classification algorithm. It is a predictive analytics measure, which reports the number of false positive (FP, type I error), false negative (FN, type II error), true positive (TP, sensitivity), and true negative (TN, specificity) cases (Klepac and Hampel 2016). Table 3. Confusion matrix. Actual Values Predicted values Prosperous Non-prosperous Prosperous True positive (TP) False positive (FP) Non-prosperous False negative (FN) True negative (TN) Source: Klepac and Hampel (2016). Sensitivity measures the proportion of truly positive cases to the total number of positive cases; it is the percentage of properly classified prosperous enterprises. Specificity is defined as the ratio of correctly identified negative cases to all negative cases in the dataset; it is the percentage of correctly J. Risk Financial Manag. 2020,13, 92 8 of 16 classified non-prosperous enterprises. Type II error defines the number of true negative cases which were identified as positive, and vice versa, type I error quantifies the number of true positive cases classified as false negative. The total accuracy of the prediction model is calculated using the equation total accuracy =TP +TN TP +FP +FN +TN (8) Based on the calculated values of specificity and sensitivity, the ROC curve is portrayed. It is a plot of the true positive rate against the false positive rate for the different possible cutpoints of a diagnostic test. The ROC curve reveals a trade-offbetween sensitivity and specificity—increasing sensitivity implies decreasing specificity and vice versa. The biggest benefit of using the ROC curve is that it is independent of the change in the proportion of outcomes. The predictive accuracy is measured by the area under the curve (AUC). An area of 1 represents a perfect test; an area of 0.5 represents a worthless test. Five different levels of accuracy are distinguished: (i) excellent when the AUC value is in the range h0.9; 1i ; (ii) good 0.8; 0.9) ; (iii) fair 0.7; 0.8) ; (iv) poor 0.6; 0.7) . If the value of the AUC is less than 0.6, the model is not appropriate to be used for the bankruptcy prediction. Determination of the most accurate prediction models is then used to explore the importance of financial predictors. 4. Results and Discussion The whole dataset of 3329 enterprises was used when calculating the Z score of Ch-index, G-index, Altman model, the general prediction model for Slovak agricultural enterprises and the general model for Slovak enterprises. To model the economic stability of small, medium-sized, and large enterprises, the whole sample was divided according to the size of companies, Table 4. The prosperity of enterprises is determined based on the legislation principles as depicted earlier in the paper. Table 4. Number of prosperous and non-prosperous enterprises in each year. Year Categorization of Enterprises Small Medium-Sized Large Prosperous Non-Prosperous Prosperous Non-Prosperous Prosperous Non-Prosperous 2016 1888 391 941 66 43 0 2017 1867 412 948 59 43 0 2018 1866 413 953 54 42 1 Source: processed by authors. The results of the empirical research and their statistical processing are summarized in Table 5, which portrays the results of the confusion matrix. Firstly, to judge the prediction accuracy of Ch-index and G-index, their high predictive strength would have been expected. Both models are based on five-factor discriminant analysis, but their predictors differ significantly. However, quantifying their total accuracy, Ch-index achieves much better results, an average value of 86.97%. The ability of the model to classify correctly prosperous enterprises is excellent, more than 90% in each analyzed year, compared to the low level of identification of non-prosperous enterprises, which is only slightly over 10%. It means that the type II error is dominant, allowing the model to identify true negative cases as positive ones. The three-year average of the total accuracy of G-index is 53.11%. Despite the fact, that this prediction ability is not significant, the model achieves more than 70% (in each analyzed year) accuracy when identifying the non-prosperous enterprises. Modeling economic stability of small agricultural enterprises, the results are not very acceptable, as the total accuracy of the model only slightly exceeds 50%. However, the type II error is low and the model succeeds in the identification of the non-prosperous agricultural enterprises. The prediction strength of the model for medium-sized agricultural enterprises is weak, achieving only a level of 40% of total accuracy, sensitivity, and specificity on average. Nonetheless, the best accuracy is modeled for J. Risk Financial Manag. 2020,13, 92 15 of 16 Hajek, Petr. 2018. Prediciting corporate investment/non-investment grade by using interval-valued fuzzy rule-based systems—A cross-region analysis. Applied Soft Computing 62: 73–85. [CrossRef] Harrower, Kathryn. 2019. Algorithmic decision-making in organizations: Network data mining, measuring and monitoring work performance, and managerial control. Psychological issues in Human Resource Management 7: 7–12. 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