Bankruptcy prediction models based on value measures

Jaki, Andrzej; Ćwięk, Wojciech Article Bankruptcy prediction models based on value measures Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Jaki, Andrzej; Ćwięk, Wojciech (2021) : Bankruptcy prediction models based on value measures, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 1, pp. 1-14, https://doi.org/10.3390/jrfm14010006 This Version is available at: https://hdl.handle.net/10419/239423 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Bankruptcy Prediction Models Based on Value Measures. Journal of Risk and Financial Management 14: 6. https://doi.org/ 10.3390/jrfm14010006 Received: 26 November 2020 Accepted: 18 December 2020 Published: 24 December 2020 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2020 by the authors. LicenseeMDPI, Basel, Switzerland. This articleisanopenaccessarticledistributed under the terms and conditions of the CreativeCommonsAttribution(CCBY) license(https://creativecommons.org/ licenses/by/4.0/). College of Management and Quality Sciences, Cracow University of Economics, Rakowicka 27, 31-510 Kraków, Poland; [email protected] *Correspondence: [email protected].pl Abstract: In the existing studies devoted to predicting bankruptcy, the authors of such models only used book measures. Considering the fact that the evolution of corporate measure efficiency (in addition to book measures) brought into existence and exposed the importance of cash measures, market measures, and measures based on the economic profit concept, it is justified to carry out research into the possibility of using these measures as variables within the discriminant function. The studied dataset was divided into a training set and a testing set based on two variants of the sample division. The assessment of the statistical significance of the built discriminant functions as well as the diagnostic variables was conducted using the STATISTICA package. The research was conducted separately for each variant. In the first step, a total of 30 discriminant models were created. This enabled us to select 20 diagnostic variables that were considered within the two models that were characterised by the highest predictive abilities—one for each variant. The discriminant function that was estimated for the first variant was based on the use of eight diagnostic variables, and 13 diagnostic variables were used in the function that was estimated for the second variant. The conducted analysis has proven that shareholder value measures are a useful tool that can be applied for the needs of corporate risk management in the area of the assessment of a firm’s bankruptcy risk. Using two variants of the division of the research sample into the training and testing sets, it turned out that the division affects the predictive efficiency of the discriminant functions. At the same time, the obtained findings tend to claim that the presence of the value measures from all four of the studied groups in the output set of the diagnostic variables is necessary for possibly building the most efficient tool for the early warning signs of bankruptcy risk. Keywords: bankruptcy risk; risk management; discrimination analysis; value measures 1. Introduction Risk is one of key attributes of an enterprise as a market entity and an economic organisation. This arises from managers’ incomplete knowledge with regard to future changes in the conditions of management and their impact on the firm, competitors’ behaviours, and the formation and evolution of customers’ expectations. The aforementioned conditions are objective and permanent.; thus, risk is considered to be the factor that promotes some enterprises that derive additional benefits from it on the one hand and eliminates inefficient entities from the market on the other (for the latter, it becomes only a negative factor). In this context, risk should be perceived both as an opportunity to obtain extra benefits and a threat for the continuation of the activity of a business (Horváthováand Morikrišová2018). The first of the mentioned perspectives of perceiving risk is related to the risk management process; during the course of this, risk is treated like an incurred cost with regard to the effort of implementing the goals of an enterprise, including the basic financial goal (namely, the maximisation of its market value, where risk is one of the key factors that determine the efficiency of value creation and a component of the so-called pro-value management triad). The other perspective of risk perception refers to the problem of the risk of bankruptcy and its prediction. The needs that are connected with risk management and the assessment of bankruptcy risk have brought about the necessity to search for J. Risk Financial Manag. 2021,14, 6. https://doi.org/10.3390/jrfm14010006 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2021,14, 6 2 of 14 tools for the quantification of risk, necessary both in the process of risk management (risk measurement as one of its stages) and during the course of building models for predicting a firm’s bankruptcy. The pioneer of research into the prediction of bankruptcy based on the application of a multidimensional discriminant analysis was E.I. Altman (Altman 1968), who used a model based on five variables for this purpose. The model then became a contribution to the creation in the following years of different discriminant analysis models by various economists. In the existing theoretical, methodological, and empirical studies devoted to predicting bankruptcy, the authors of such models only used book measures based on the information coming from a company’s financial statement as the variables that describe the company’s financial standing at which the corporate profitability measure were different forms of its profit—namely, book surplus calculated on an accrual basis ( Gavurova et al. 2017 ;Prusak 2018;Wieczorek-Kosmala et al. 2018). Considering the fact that the evolution of corporate measure efficiency (in addition to book measures) brought into existence and exposed the importance of cash measures (based on cash flows and cash basis), market measures, and measures based on the economic profit concept, it is justified to carry out research into the possibility of using these measures as variables within the discriminant function. In this context, the authors see a research gap that establishes the basic objective of this article, which is building a discriminant function based on the use of measures that belong to all groups. In particular, it is about shareholder value measures, which are generally defined as value measures. At the same time, the identified research gap became a premise for formulating the following research questions focused on the effort to implement the aforementioned goal: • How do we choose value measures that are intended to play the function of estimators of the bankruptcy risk of those construction companies that were listed on the Warsaw Stock Exchange during the years of 2010–2015? • How do we build a discriminant function with the highest predictive efficiency for the aforementioned companies? • How does the division into training and testing sets influence the predictive efficiency of the discriminant function that has been built? We attempt to fill the identified research gap by answering the formulated questions as a key achievement of this article, giving its features of originality. The choice of the time horizon and the objects of the analysis is not accidental. This was a period for the accumulation of construction investments concerning the erection and extension of sports, road, and touristic and recreational infrastructures that took place in Poland during the years 2010–2012 due to the organisation of the Euro 2012 UEFA European Football Championship by Poland and Ukraine and that also contributed to a rapid increase in the number of the bankruptcies of construction companies on the Polish market over the following years (2013–2015). The following research methods were used in the article: • Methods for analysing and evaluating the literature that shows the current scientific achievements in the field of essence as well as using a discriminant analysis in order to predict the bankruptcy of enterprises—with a particular emphasis on the types of enterprise effectiveness measures used for this purpose. • Statistical methods for verifying and supplementing the missing values of the variables, estimating the discriminant power of the variables, examining the information capacity of the variables, and examining the normality of the empirical distributions of the value measures. • Statistical methods for assessing the statistical significance of the built discriminant functions as well as the diagnostic variables. The article is structured as follows. First, we present the theoretical background according to a discriminant analysis based on a literature review, followed by describing J. Risk Financial Manag. 2021,14, 6 3 of 14 the used materials and methods. The next section presents the research results as well as a discussion. Finally, we present the conclusions that resulted from the conducted research. 2. Discriminant Analysis Models—Theoretical Background Discriminant analysis is an empirical and inductive method that aims to assign objects being compared (enterprises, among others) to a group of objects that are most similar to each other due to the properties describing them (Shiker 2012). The analysis base is the linear discriminant function, whose primary form was formulated by R.A. Fisher and which has been used in natural studies (Fisher 1936). In the most universal approach, the function is described by Equation (1): Z=a0+a1·X1+a2·X2+. . . +an·Xn, (1) where: Z—dependent variable; a0—constant; a1, a2, . . . , an—discriminant coefficients; X1,X2, . . . , Xn—exogenous (diagnostic) variables. The starting point to indicate the possibility of using this function for the prediction of corporate bankruptcy risk was the research by W.H. Beaver, who in the 1960s verified the prognostic ability of financial ratios from the point of view of their discriminant power; thus, the usability for the needs of building a discriminant function that would serve to predict corporate bankruptcy risk (Gupta 2017). Among others, the research finding was used by E.I. Altman, who built the first discriminant analysis model and initiated the use of multidimensional discriminant analysis in economic studies in 1968 (Altman 1968). The further development of this analysis is connected with Altman’s cooperation with a group of eminent statisticians, the effect of which was the ZETA model developed in the mid-1970s (Altman et al. 1977). The aforementioned models were developed for different sectors of the American economy. The varied conditions that accompany the functioning of firms in different regions or countries are the reason why the problem of predicting bankruptcies cannot be generalised or requires an individualised research approach based on the use of empirical data that refers to a specific economy or group of economies with similar conditions of functioning (Bărbu¸tă-Mi¸su and Madaleno 2020). In this context, the following discriminant analysis models were created: • G.L.V. Springate’s model that was developed for the Canadian economy (Talebnia et al. 2016); •models for the Japanese economy (Takahashi et al. 1984); •models for Asia’s emerging economies (Ashraf et al. 2019); •H. Koh and L. Killough’s model for the American economy (Rahimipoor 2018). The problem of the individualisation of the model from the point of view of the specificity of the businesses in which it is to be used also refers to their sizes, the effect of which was J. Falmer’s model that was used to assess the condition of American small- and medium-sized enterprises (Rahimipoor 2018). The processes of the economic and political transformation of the economies of Central and Eastern Europe countries (initiated in Poland in 1989) contributed to the initiation and development of research into predicting the bankruptcy of enterprises functioning in these countries. The results were the discriminant analysis models developed at various stages of the transformation processes of the aforementioned economies (Korol 2019;Kristóf and Virág 2020). An important factor that stimulated the development and improvement of discriminant analysis models for the prediction of bankruptcy was the development of mathematical, statistical, and analytical tools as well as the soft computing techniques initiated in the 1990s (Gavurova et al. 2017; Prusak 2018). The development and evolution of these discriminant analysis models were also connected with the number of exogenous (diagnostic) variables that were used in these J. Risk Financial Manag. 2021,14, 6 4 of 14 models over the decades. Within this scope, J.L. Bellovary, D.E. Giacomino, and M.D. Akers carried out research into the number and character of the variables used in the models, among other things (Bellovary et al. 2007). Based on the research findings, Figure 1presents the number of diagnostic variables used in the bankruptcy prediction models. J. Risk Financial Manag. 2021, 14, x 4 of 14 The development and evolution of these discriminant analysis models were also connected with the number of exogenous (diagnostic) variables that were used in these models over the decades. Within this scope, J.L. Bellovary, D.E. Giacomino, and M.D. Akers carried out research into the number and character of the variables used in the models, among other things (Bellovary et al. 2007). Based on the research findings, Figure 1 presents the number of diagnostic variables used in the bankruptcy prediction models. Figure 1. Number of diagnostic variables in bankruptcy prediction models. Source: own study based on (Bellovary et al. 2007). Designations: MIN—minimal number of diagnostic variables in the model; AVG—average number of diagnostic variables in the model; MAX—maximum number of diagnostic variables in the model. The most frequently used variables included the following financial analysis ratios:  Debt and debt coverage ratios;  Turnover ratios;  Profitability ratios;  Liquidity ratios;  Asset, equity, and debt structure ratios. This confirms the thesis formulated in the introduction that the bankruptcy prediction models applied so far have been exclusively based on the use of book measures. At the same time, the authors formulate the postulate of the application of these measures for the needs of creating new bankruptcy prediction models by considering the development of the paradigm of the measurement of economic values that have contributed to the emergence and dissemination of the applications of cash measures, market measures, and measures based on the concept of economic profit. In particular, this is about those measures that enable the measurement and assessment of shareholder value. Thus, they are called “shareholder value measures” (or, in short, “value measures”). In this way, we indicate a new potential area of their use that is above and beyond the existing areas that are related to the value-based management concept (Young and O’Byrne 2001; Ehrbar 1998), business valuation (Fernandes 2019), value creating analysis and value controlling (Schierenbeck and Lister 2002), value-focused restructuring (Jaki 2012), pro-value motivation systems, and performance management systems (Mancini and Piscitelli 2018; Škare and Hasić 2016). 5 15 30 2 8 18 1 9 47 2 11 57 5813 0 10 20 30 40 50 60 MIN AVG MAX MIN AVG MAX MIN AVG MAX MIN AVG MAX MIN AVG MAX 1960s 1970s 1980s 1990s 2000s Number of variables in models Years Figure 1. Number of diagnostic variables in bankruptcy prediction models. Source: own study based on (Bellovary et al. 2007). Designations: MIN—minimal number of diagnostic variables in the model; AVG—average number of diagnostic variables in the model; MAX—maximum number of diagnostic variables in the model. The most frequently used variables included the following financial analysis ratios: •Debt and debt coverage ratios; •Turnover ratios; •Profitability ratios; •Liquidity ratios; •Asset, equity, and debt structure ratios. This confirms the thesis formulated in the introduction that the bankruptcy prediction models applied so far have been exclusively based on the use of book measures. At the same time, the authors formulate the postulate of the application of these measures for the needs of creating new bankruptcy prediction models by considering the development of the paradigm of the measurement of economic values that have contributed to the emergence and dissemination of the applications of cash measures, market measures, and measures based on the concept of economic profit. In particular, this is about those measures that enable the measurement and assessment of shareholder value. Thus, they are called “shareholder value measures” (or, in short, “value measures”). In this way, we indicate a new potential area of their use that is above and beyond the existing areas that are related to the value-based management concept (Young and O’Byrne 2001;Ehrbar 1998), business valuation (Fernandez 2019), value creating analysis and value controlling (Schierenbeck and Lister 2002), value-focused restructuring (Jaki 2012), pro-value motivation systems, and performance management systems (Mancini and Piscitelli 2018;Škare and Hasi´c 2016). 3. Materials and Methods The time span of our analysis includes the years of 2010–2015. This was the period when Poland and Ukraine prepared for and implemented the UEFA European Championship, Euro 2012. During the years of 2010–2012, there was an accumulation of various construction investments that contributed to the rapid growth of the bankruptcies of construction companies on the Polish market over the years of 2013–2015 (Jaki 2018). Therefore, the study included those companies that were listed on the Warsaw Stock Exchange that belonged to the construction sector. The classification was made based on the Interna- J. Risk Financial Manag. 2021,14, 6 5 of 14 tional Industrial Standard Classification (ISIC)—Section F (Constructing). In the group of “bankrupts”, only those companies were included that filed bankruptcy petitions (both liquidations and arrangement bankruptcies) during the analysed period. On the other hand, those entities that were distinguished by good economic and financial standings and continued their activities during the analysed period were classified to the group of “non-bankrupts”. Forty-four construction companies were analysed, and the structure of the studied population was as follows: 33 bankrupts (75%) and 11 non-bankrupts (25%). The preliminary analysis showed that the share of the missing values of the diagnostic variables for the group of “bankrupts” that were estimated for the year that preceded the year of bankruptcy was as high as 14.42%; for the two years preceding the bankruptcy year, this share was 6.35%. Due to the estimated values of the shares of the missing values of the diagnostic variables, a decision was made to build a discriminatory model for the data from the two years previous to the declaration of bankruptcy by those companies that represented the group of “bankrupts”. It should also be emphasised that, in the article, the learning and test samples were unbalanced, which means that the number of “bankrupt” and “non-bankrupt” enterprises was not the same. This was dictated by the aforementioned small number of “bankrupt” companies, which made it impossible to create a balanced sample that was in compliance with the recommendations of the statistical analysis. It was also taken into account that the models that had been built that were based on the unbalanced sample at that time were not characterised by a bad predictive quality and were inferior to the built models that were based on the balanced sample ( Pawełek et al. 2020 ). The source of the necessary financial data was the EMIS Intelligence—Polska database (EMIS 2020). For the needs of our calculations, the STATISTICA package was used. In order to prepare the database and identify the value measures that were to be used as diagnostic variables in the discriminant function being built, the following actions were taken (Ramachandran and Tsokos 2009): • adoption of the output set of book measures (BM), cash measures (CM), market measures (MM), and measures based on economic profit (EPM) as potential estimators of the bankruptcies of the analysed companies; • verification and supplementation of the missing values of the variables with the use of the median as the well as verification of the variables from the point of view of outliers (using a two-way Tukey’s criterion with α= 0.05); • estimation of the discriminant power of the variables with the use of the classical coefficient of variation; • examination of the information capacity of the variables on the basis of Pearson’s linear correlation level; • examination of the normality of the empirical distributions of the value measures using the following tests: Kolmogorov–Smirnov, Lilliefors, and Shapiro–Wilk, with the assumption of αat a level of 0.05. In order to find the most efficient discriminant function, the studied dataset was divided into a training set and a testing set. The training set serves to build a discriminant model, whereas the testing set verifies the efficiency of the model. The article considers two variants of the sample division into the training and testing sets. The number of companies that formed the training and testing sets depending on the variant of the division is presented in Table 1. The assessment of the statistical significance of the built discriminant functions as well as the diagnostic variables was conducted based on the following statistical measures (Ramachandran and Tsokos 2009): • Wilks’ lambda serves to determine the statistical significance of the discriminatory ability of the whole model and is calculated as the proportion of the discriminant of the variance matrix and total covariance. This takes its value from a [0; 1] range, where “0” represents perfect discriminatory power and “1” represents no discriminatory power. J. Risk Financial Manag. 2021,14, 6 6 of 14 • Partial Wilks’ lambda—determines the contribution of the individual variables for the discrimination of the groups. The value of this measure is determined as the quotient of Wilks’ lambda value after entering a given variable in the model and Wilks’ lambda value before adding this variable. The partial Wilks’ lambda also takes values from a [0; 1] range, where “0” represents the perfect discriminatory power of a given variable and “1” represents no discriminatory ability of a given variable. • Standardised discriminant coefficients—these indicate the contribution of the individual variables in the discrimination of enterprises to the populations of “bankrupts” and “non-bankrupts”. A higher absolute value of the standardised discriminant coefficient means the higher contribution of a given variable with which the coefficient occurs in the division of enterprises into both of the discussed groups, as determined by the estimated model. • Canonical correlation—provides information on how efficiently a discriminant function divides the studied businesses into groups. This takes the value from a [0; 1] range; the higher the value, the stronger the relationship is between the groups and the discriminant function. • Tolerance of variable—this is defined as 1 minus the squared multiple correlation coefficient of that variable with all other independent variables in the regression equation. This means that the lower the tolerance of the variable, the more excessive its contribution to the regression equation is (that is, it is indispensable in light of the contribution of the remaining variables—it is redundant). • Multiple regression (R 2 )—this is an indicator of the quality of the model fit to the data (an R 2 close to 1.0 indicates that almost all of the variability of the dependent variable can be explained through the independent variables included in the model). Table 1. Sample division into training and testing sets on 8:3 and 9:2 bases. Source: own study. Variants Division Proportions Division into Groups Training Set Testing Set Total 1. 8:3 (72.737%) 104 companies, incl.: •8 “bankrupts” •96 “non-bankrupts” 39 companies, incl.: •3 “bankrupts” •36 “non-bankrupts” 143 companies, incl.: •11 “bankrupts” •132 “nonbankrupts” 2. 9:2 (81.818%) 117 companies, incl.: •9 “bankrupts” •108 “non-bankrupts” 26 companies, incl.: •2 “bankrupts” •24 “non-bankrupts” 4. Results and Discussion With the use of the described methodology and the research procedure, we attempted to identify the most efficient discriminant function based on applying the book, cash, market measures, and measures as diagnostic variables based on the economic profit concept. The research was conducted separately for each variant of the sample division in the training and testing sets. In the first step, a total of 30 discriminant models were created (without using any limitation as for the maximum number of variables to be included in a given model). This enabled us to select 20 diagnostic variables that were considered within the two models that were characterised by the highest predictive abilities—one for each variant. The variables that are based on the use of the different value measures are presented and characterised in Table 2. J. Risk Financial Manag. 2021,14, 6 7 of 14 Table 2. Value measures used as diagnostic variables in most effective discriminant models—Variants 1 and 2. Source: own study based on Ehrbar 1998 and Young and O’Byrne 2001. Measures Calculation Formulas Designations book Measures NOPATPS NOPATt Nt=EBITt·(1−T) Nt NOPATPS—net operating profit after taxes per share. NOPATt—net operating profit after taxes in period t. Nt—number of shares at end of period t. EBITt—earnings before interests and taxes in period t. T—income tax ratio. ROS NPt St ·100% ROS—return on sales. NPt—net profit in period t. St—net sales in period t. Cash measures FCFEPS FCFEt Nt= NP+DEP−Inv−∆WC+CL−RCL Nt FCFEPS—free cash flow to equity per share. FCFEt—free cash flow to equity in period t. DEP—accumulated value of depreciations. Inv—investments. ∆WC—changes in net working capital. CL—taken credits and loans. RCL—repaid credits and loans. other designations—as previously. CFROS CFOt St ·100% CFROS—cash flow return on sale. CFOt—operating cash flow in period t. Other designations—as previously. Measures based on economic profit concept REVA NOPATt−WACCt·ICMV,t REVA—relative market value added. ICMV,t—market value of invested capital at end of period t. WACCt—weighted average cost of capital in period t. CEC EVA WACC·IC ·100% CEC—cost efficiency of invested capital. EVA—economic value added. IC—invested capital. Other designations—as previously. Market measures P/BV PS,t BVt Nt P/BV—price to book value. PS,t—market price of share at end of period t. BVt—book value of equity at end of period t. other designations—as previously. P/BV—Gr. PS,t CAt−Lt Nt P/BV—Gr.—price to book value by B. Graham. CAt—current assets at end of period t. Lt—liabilities at end of period t. Other designations—as previously. P/E PS,t NPt Nt P/E—price to earnings. Other designations—as previously. P/EBIT PS,t EBITt Nt P/EBIT—price to earnings before interest and taxes. Other designations—as previously. P/EBITDA PS,t EBITDAt Nt P/EBITDA—price to earnings before interests, taxes, depreciation, and amortization. EBITDAt—earnings before interests, taxes, depreciation, and amortization in period t. Other designations—as previously. P/CE PS,t NPt+DEPt Nt P/CE—price to cash earnings. Other designations—as previously. P/FCFF PS,t FCFFt Nt P/FCFF—price to free cash flow to firm. FCFFt—free cash flow to firm in period t. Other designations—as previously. J. Risk Financial Manag. 2021,14, 6 8 of 14 Table 2. Cont. Measures Calculation Formulas Designations Market measures EV/S EVt St EV/S—enterprise value to sale. EVt—enterprise value at end of period t. Other designations—as previously. EV/CFO EVt CFOt EV/CFO—enterprise value to cash flows from operating activities. Other designations—as previously. EV/FCFF EVt FCFFt EV/FCFF—enterprise value to free cash flow to firm. Other designations—as previously. EY EPSt PS,t ·100% EY—earnings yield. EPSt—earnings per share in period t. Other designations—as previously. DY DPSt PS,t ·100% DY—dividend yield. DPSt—dividend per share in period t. Other designations—as previously. DPR DPSt EPSt ·100% DPR—dividend payout ratio. Other designations—as previously. RMVA MVAt ICt−1 ·100% RMVA—relative MVA, MVA index. MVAt—market value added at end of period t. Other designations—as previously. MVAE,t BVt−1 ·100% As arises from Table 2, there were 2 book measures, 2 cash measures, 2 measures based on the economic profit concept, and 14 market measures of the variables that best estimated the risk of bankruptcy of the studied companies. In Table 3, descriptive statistics of these variables are selected. However, Tables 4and 5present the discriminant power of diagnostic variables and an examination of the normality of the empirical distributions of diagnostic variables for the analysed companies. Table 6presents the discriminant functions that were estimated for each variant with the values of the statistical parameters. Table 3. Selected statistics of diagnostic variables for the analysed companies. Source: own calculations based on data provided by the EMIS Intelligence database (EMIS 2020). Measures—Diagnostic Variables “Bankrupts” “Non-Bankrupts” All Companies Min Me Max Min Me Max Min Me Max NOPATPS −1.564 0.064 4.866 −5.619 0.096 5.702 −5.619 0.096 5.702 ROS −0.711 0.012 0.134 −0.218 0.031 0.289 −0.711 0.030 0.289 FCFEPS −41.064 0.291 51.366 −8.204 0.168 8.877 −41.064 0.168 51.366 CFROS −0.087 −0.016 0.152 −0.472 0.052 0.547 −0.472 0.049 0.547 CEC −3.665 −0.849 −0.251 −6.011 −0.880 1.102 −6.011 −0.880 1.102 REVA −0.542 −0.144 0.298 −0.569 −0.079 0.046 −0.569 −0.084 0.298 RMVA −1.842 0.353 8.988 −0.778 −0.102 4.359 −1.842 −0.102 8.988 P/BV 0.162 1.010 13.082 0.283 0.889 5.070 0.162 0.889 13.082 P/BV-Gr. −9.889 −0.153 121.803 −33.101 1.930 37.123 −33.101 1.930 121.803 P/E −8.286 23.248 508.620 −51.384 10.515 74.720 −51.384 10.515 508.620 P/EBIT −43.262 6.002 78.995 −33.523 7.643 50.573 −43.262 7.643 78.995 P/EBITDA −58.570 4.672 79.349 −19.485 5.572 30.672 −58.570 5.499 79.349 P/CE −11.687 8.607 482.909 −24.217 7.094 38.242 −24.217 7.094 482.909 P/FCFF − 855.401 0.334 665.674 − 121.864 −2.301 123.833 − 855.401 −2.301 665.674 EY −0.241 0.012 0.161 −0.383 0.065 0.182 −0.383 0.063 0.182 DY 0.000 0.000 0.000 0.000 0.000 0.187 0.000 0.000 0.187 DPR 0.000 0.000 0.000 −2.359 0.000 2.831 −2.359 0.000 2.831 EV/S 0.320 0.991 9.870 −0.019 0.651 3.949 −0.019 0.651 9.870 EV/CFO −63.911 −2.226 214.365 −64.101 8.137 83.642 −64.101 8.137 214.365 EV/FCFF − 860.017 2.975 1032.467 − 154.770 −2.991 158.711 − 860.017 −2.955 1032.467 Designations: Min—minimum value; Me—median; Max—maximum value.