Altman Model Verification Using a Multi-Criteria Approach for Slovakian Agricultural Enterprises

146 2021, XXIV, 1 Finance DOI: 10.15240/ ul/001/2021-1-010 ALTMAN MODEL VERIFICATION USING A MULTI-CRITERIA APPROACH FOR SLOVAKIAN AGRICULTURAL ENTERPRISES Roman Va ek1, Pe a Gundo á2, I ana K a čáko á Vozá o á3, Ras isla Ko ulič4 1 Technical Uni e si y o Libe ec, Facul y o Economics, Depa men o In o ma ics, Czech Republic, ORCID: 0000-0002-6047-9434, [email p o ec ed]; 2 Ma ej Bel Uni e si y in Banská Bys ica, Facul y o Economics, Depa men o Co po a e Economics and Managemen , Slo akia, ORCID: 0000-0003-2335-0073, [email p o ec ed]; 3 Uni e si y o P ešo , Facul y o Managemen , Depa men o Economics and Economy, Slo akia, ORCID: 0000-0002-3056-5294, [email p o ec ed]; 4 Uni e si y o P ešo , Facul y o Managemen , Depa men o Economics and Economy, Slo akia, ORCID: 0000-0002-8341-3016, as isla [email p o ec ed]. Abs ac : The Al man model is s ill one o he mos widely used p edic i e models in he 21s cen u y, and i aims o highligh he di e ences be ween bank up and heal hy en e p ises. This model has been modi ied se e al imes; i s mos well-known o ms a e om 1968, 1983 and 1995. Howe e , he use o he Al man Z-sco e o Slo ak en e p ises is mo e han ques ionable. The unsui abili y o he model o he condi ions o Slo ak companies has been con i med by se e al empi ical su eys. The objec i e o his s udy was o e i y he alida ion o hese h ee a ian s o he Al man model, depending on how an unp ospe ous company is iden i ied, using a sample o 996 ag icul u al en e p ises ope a ing in he Slo ak Republic. Fou indica o s we e selec ed o he iden i ica ion o an unp ospe ous en e p ise – economic esul s, o al liquidi y, equi y, and economic alue added – and hey we e moni o ed o e he las yea o , as he case may be, o e he las h ee yea s om 2014 o 2016. Using he Technique o O de o P e e ence by Simila i y o Ideal Solu ion (TOPSIS) and Coe icien o a ia ion (CV) me hods as an objec i e me hod o weigh de e mina ion, a combina ion o he Al man model om 1968 and he nega i e o al liquidi y in he las e e ence yea was de e mined o be he bes . One o ou main indings is ha he way in which an unp ospe ous en e p ise is iden i ied is a signi ican ac o a ec ing he o e all eliabili y o he Al man model. The Al man model om 1968 and 1983 con i med he di e ences esul ing om he na u al condi ions in which he en e p ises ope a e. The economic esul s and economic alue added (EVA) p o ed o be inapp op ia e as indica o s o de ining an unp ospe ous en e p ise in he condi ions o he Slo ak Republic. Keywo ds: Unp ospe ous en e p ise, Al man model, TOPSIS echnique, Coe icien o a ia ion me hod. JEL Classi ica ion: B23, Q14. APA S yle Ci a ion: Va ek, R., Gundo á, P., K a čáko á Vozá o á, I., & Ko ulič, R. (2021). Al man Model Ve i ica ion Using a Mul i-c i e ia App oach o Slo akian Ag icul u al En e p ises. E&M Economics and Managemen , 24(1), 146–164. h ps://doi.o g/10.15240/ ul/001/2021-1-010 In oduc ion In he 21s cen u y, a p e equisi e o a success ul business is good knowledge o pas and cu en ends, o he igh long- e m decisions o be made. Acco ding o B ealey e al. (2011), knowing whe e a company s ands oday is a necessa y p elude o con empla ing whe e he company migh end up in he u u e. One o he op ions o suppo ing sho - e m and long- e m decisions is inancial analysis 147 1, XXIV, 2021 Finance and inancial a ios. Financial a ios ha e adi ionally been indica o s o a co po a e’s o e all pe o mance (Rahman e al., 2017) and may help o quan i y he po en ial impac o in e nal a ings on inancial pe o mance (Belas e al., 2012; Klieš ik e al., 2020). The simples and na owes de ini ion o inancial analysis is based on he ac i i ies ha his e m includes wi hou speci ying hei pu pose, i.e., inancial analysis is an analysis o company da a, which is based on accoun ing (Sů o á & Knai l, 2008). I is ocused on e alua ing he inancial heal h o a company and iden i ying i s weaknesses and s eng hs (M k ička & Kolář, 2006). Acco ding o Bank e al. (2006), his assessmen is dependen on he company’s liquidi y and sho - e m inancial liabili ies, which ep esen i s abili y o egula e sou ces o unding. Acco ding o Bocha o (2007), i is necessa y also o conside inancial analysis in he long e m, i.e., as a sys ema ic p ocess o con olling inancial esou ces. F om a ime pe spec i e, we can di ide inancial analysis in o e ospec i e ex-pos analysis and ex-an e analysis ocusing on p edic ion. The la e can iden i y c i ical ac o s ha could h ea en he su i al o an en e p ise, i.e., an app op ia e esponse o he esul s o ex-an e analysis in he o m o eco e y measu es can signi ican ly a ec a company’s u u e inancial si ua ion o ensu e i s sus ainabili y. Financial dis ess can be de ined in many di e en ways, and simila ly, he e minology e e ing o companies expe iencing such also di e s – bank up , insol en , and in de aul (Čámská & Klecka, 2020; Alaka e al., 2018). Se e al p edic ion me hods based on ex-an e inancial analysis ha e been e i ied o da e (e.g. Mihalo ič, 2018; Ga ú o á e al., 2017; Ko e al., 2017). The aim o his s udy was o e i y he explana o y powe o h ee a ian s o he Al man model ( om 1968, 1983 and 1995) depending on how a non-p ospe ous en e p ise is iden i ied, using a sample o 996 ag icul u al en e p ises ope a ing in Slo akia. To e i y he ou pu s o ex-an e inancial analysis, i is also necessa y o iden i y p ospe ous and unp ospe ous en e p ises ( i s pa ). The e a e usually wo app oaches, depending on he legisla i e and heo e ical de ini ions. The legisla i e de ini ion o an unp ospe ous en e p ise is de e mined by he legisla ion o a gi en coun y; in his case, we encoun e he concep o bank up cy. The heo e ical de ini ion is gi en by he quan i ica ion o a selec ed se o inancial indica o s and hei subsequen scaling (see ex an e me hods). In he ollowing ex , he a ious app oaches aken by bo h o eign and domes ic au ho s o de ining an unp ospe ous en e p ise a e desc ibed, and he equen pene a ion o indi idual c i e ia can be obse ed. The second pa is ocused on a se o ou own indica o s used o iden i y an unp ospe ous en e p ise, i.e., me hodology. The hi d pa desc ibes he esul s o ou own esea ch, i.e., he e i ica ion o he p edic i e abili y o he Al man model applied o a sample o 996 ag icul u al en e p ises in he Slo ak Republic. The use o a mul i-c i e ion app oach in he o m o he CV-TOPSIS echnique as a me hodological ex ension o he discussed app oaches o he e i ica ion o he explana o y powe o p edic ion models can be desc ibed as o iginal and new (see Pa 1). The ou h pa p esen s a discussion o he ob ained esul s in he con ex o o he au ho s’ hough s and esea ch. The las pa p esen s he conclusion and an e alua ion o he ob ained esul s. 1. Di e en App oaches oDe ining an Unp ospe ous En e p ise In he p e ious sec ion, we deal wi h de ining an unp ospe ous en e p ise om di e en pe spec i es, i.e., hose ha can be applied o en e p ises ega dless o he egion in which hey a e loca ed. Fo an app op ia e de ini ion, we conside i necessa y also o ake in o accoun he local condi ions in which a company ope a es. This is hen e lec ed in a mo e p ecise de ini ion o he condi ions unde which he en e p ise can be conside ed unp ospe ous. Bakeš and Valáško á (2018) p oposed c i e ia o iden i ying an unp ospe ous company ha would ake in o accoun cu en ly alid Slo ak legisla ion along wi h economic and inancial aspec s. Such c i e ia include a a io o equi y o liabili ies o <0.08, a o al liquidi y o <1, and nega i e ea nings a e ax. Ďu ica (2018) desc ibed an unp ospe ous en e p ise as an en e p ise in c isis, when he o al amoun o hei cu en liabili ies is highe han he alue o hei cu en asse s, he a io o equi y o liabili ies is less han 0.04, and he company has gene a ed a loss ( alid o analysis in 2016). Klieš ik e al. (2018) and Mendelo á and Bieliko á (2017) iden i ied an 148 2021, XXIV, 1 Finance unp ospe ous en e p ise as one ha mee s he c i e ion ound in applicable legisla ion o he Slo ak Republic, i.e., he alue o i s liabili ies due exceeds he alue o i s asse s, o he en e p ise is in nega i e equi y. Bieliko á e al. (2014) desc ibed a mo e complex iew, exp essed by h ee c i e ia acco ding o which an en e p ise can be conside ed unp ospe ous, namely, i i :  Mee s he legisla i e de ini ion o an en e p ise in he Slo ak Republic, i.e., he en e p ise is obliged o conduc accoun ing p ocedu es acco ding o a special egula ion, i has mo e han one c edi o and he alue o i s liabili ies exceeds he alue o i s asse s, i.e., he en e p ise is in nega i e equi y;  Has made a loss o wo consecu i e yea s;  Has a nega i e e u n on sales (ROS) and o al liquidi y (L3) less han 1. This se o c i e ia was also used by Bieliko á (2016), who addi ionally ake in o accoun he ollowing:  The legisla i e de ini ion o a company in p olonga ion in he Slo ak Republic, i.e., he same c i e ion as in he p e ious case;  The Eu opean Union guidelines 2004/C 244/02 on s a e aid o escuing and es uc u ing i ms in di icul y, which de ine a i m in di icul y as one in which mo e han hal o he basic capi al is co e ed by loss and mo e han a qua e o ha basic capi al was co e ed by loss du ing he p e ious 12 mon hs;  A loss in wo consecu i e pe iods. Boďa and Ú adníček (2016) and K áľ e al. (2016) p esen ed a se o h ee c i e ia o iden i ying an unsuccess ul business. These c i e ia include nega i e equi y and ea nings a e ax (EAT <0), as well as a o al liquidi y (L3) less han 1. Acco ding o Valáško á e al. (2018a, 2018b), an unp ospe ous company is one wi h a a io o equi y o o al deb less han 0.4, a o al liquidi y (L3) less han 1, and nega i e ea nings a e ax (EAT). Acco ding o Ko áčo á and Kubala (2018), i is an en e p ise ha :  Did no achie e posi i e equi y, o in o he wo ds, he di e ence be ween asse s and liabili ies, including he acc ual o liabili ies, was nega i e;  Had a leas wo mone a y liabili ies mo e han 30 days o e due (because we we e unable o iden i y his in o ma ion di ec ly om he inancial s a emen s, i has been eplaced by a de e mina ion o he o e all liquidi y indica o h eshold, i.e., L3 <1);  Exhibi ed he ollowing alues o he sel - inancing coe icien (equi y and liabili ies a io): in 2016, <0.08; in 2015, <0.06; and in 2014, <0.04. Fo an independen g oup o au ho s, he key c i e ion o assessing business p ospe i y is he economic alue added, which ep esen s an agg ega e cha ac e is ic o a company’s inancial pe o mance. This g oup includes Šo anko á e al. (2017), Neumaie o á and Neumaie (2016), Čámská (2016), Maňaso á (2008) and o he s; in all cases, an unp ospe ous en e p ise is one ha has achie ed nega i e economic alue added in a ce ain pe iod. Along wi h Lesáko á e al. (2015), Zalai e al. (2013) and o he s, we conside economic alue added as an impo an c i e ion, as he e has been a ecen shi in hinking away om adi ional indica o s owa ds he ma ke alue o a company. This is con i med by he ac ha he EVA indica o is now inc easingly being used in inancial managemen and decision- making. 2. TheAl manModelasaMe hod o Mul idimensionalDisc imina ion Analysis As a esul o he ecen wo ldwide inancial c isis and economic ecession, he demand o bank up cy-p edic ion models and inancial- isk analysis has gained s ong a en ion. The inabili y o accu a ely p edic bo h bank up cy and c edi isk can ha e de as a ing socio- economic e ec s (An unes e al., 2017). To da e, many models o p edic bank up cy ha e been in oduced, bu esea ch in his ield is e e cons an (Le e al., 2018; Zelenko e al., 2017). Se e al au ho s (Alaka e al., 2018; Ka as & Režnáko á, 2012; Sušický, 2011) a gue ha me hods o mul idimensional disc imina ion analysis a e he wo ld’s mos widely used me hods o p edic ing he inancial heal h o businesses. Among hei suppo e s a e Balcaen and Oooghe (2006) and Sun e al. (2014), who app ecia e hei good classi ica ion capabili y. The ounde and pionee o he use o mul idimensional disc imina ion analysis is Al man (1968). The Al man Z-sco e is cu en ly he mos well-known and widely used p edic ion model (Ga ú o á e al., 2017; Delina & Packo á, 149 1, XXIV, 2021 Finance 2013). I is o en modi ied and e i ied o he needs o na ional economics. In he Slo ak Republic, hese applica ions a e o e ed by Kabá (2011a) and Boďa and Ú adníček (2016, 2019); in he Czech Republic, by Schön eld e al. (2018) and Režňáko á and Ka as (2015); and ab oad, by Li and Fa (2019), Almany e al. (2016), Sulub (2014) and Li schu z and Jacobi (2010). The model was c ea ed using a sample o 33 US companies ha wen bank up be ween 1946 and 1965, while a second g oup included 33 US companies ha p ospe ed in his pe iod (Kočišo á & Mišanko á, 2014). Al man (2002) conside ed he limi ed da a a ailable o be a majo p oblem, causing he g oup o selec ed businesses o be ela i ely he e ogeneous. The au ho ini ially wo ked wi h a se o 22 selec ed inancial indica o s om i e classes – liquidi y, p o i abili y, deb , sol ency and ac i i y (Hosaka, 2019; Kabá , 2011a). Conce ning Al man’s Z-sco e, i ms a e g ouped in o he h ee zones o disc imina ion iden i ied by Al man: he Dis ess Zone, G ey Zone and Sa e Zone (Meggison e al., 2019). Al man’s goal was o selec a small numbe o a io indica o s ha would bes highligh he di e ence be ween a bank up and a heal hy en e p ise. Ra io indica o s we e selec ed based on he g ea es di e ences in he alues be ween he di e en se s o en e p ises (see Tab. 1). In his manne , Al man c ea ed a i e- ac o model. In he o iginal s udy om 1968, he success ully classi ied 94% o bank up US en e p ises and 97% o p ospe ous US p oduc ion en e p ises based on a Z-sco e wi h an annual ad ance. In he 30 yea s ollowing he c ea ion o he model, he es ed i s p edic i e abili y, and ano he 86 p oblema ic en e p ises we e analyzed om 1969 o 1975. F om 1976 o 1995, Al man es ed 110 bank up en e p ises, and in he pe iod om 1997 o 1999, he numbe o en e p ises wen up o 120. The h eshold o p oblema ic businesses was se a 2.67, and businesses we e only es ed wo yea s be o e bank up cy (Maňaso á, 2008). Al man (2006) s a es ha he main eason o he highe e o a e o he model is ha , a ha ime, US businesses we e a highe isk compa ed o when he model was c ea ed. The highe isk le el is e lec ed in he change in he a iables X2 and X4. In 1983, an upda ed Al man Z-sco e model was c ea ed ( o businesses ha did no ha e publicly aded sha es) and included he same a io indica o s as he o iginal model. Howe e , he weigh s o he indi idual a io indica o s we e changed, and hus, he e alua ion c i e ia also changed. The same changes ook place in 1995, when a hi d model was c ea ed o he pu pose o e alua ing non-p oduc i e en e p ises; i was also used in i s own esea ch. E en Al man’s models based on mul iple disc iminan analysis ha e p o ed qui e success ul and s ood up o c i icism (Pe ei a e al., 2016). Acco ding o Klieš ik e al. (2015), he limi a ions o he models a e as ollows. The models ake only a no mal dis ibu ion o independen a iables in o accoun , conside only homogenei y o he a ia ion-co a ia ion ma ix, and assume only a linea ela ionship be ween he independen a iables. Al man’s models a e accoun ing- based, which educes hei abili y o p edic inancial dis ess and bank up cy eliably. Li and Fa (2019) a gue ha unde he going- conce n p inciple, hei applica ion is limi ed, as p edic ions o a i m’s u u e inancial condi ion may be less in o ma i e when hey a e based on he i m’s pas pe o mance. Indica o Heal hy en e p ises Bank up en e p ises Wo king capi al/ o al asse s X10.414 −0.061 Re ained ea nings/ o al asse s X20.355 −0.626 Ea nings be o e in e es and ax/ o al asse s X30.154 −0.318 Ma ke capi alisa ion/ o al liabili ies X42.477 0.401 Sales/ o al asse s X51.900 1.500 Sou ce: Al man (1968) Tab. 1: A e age alues o Al man es indica o s in 1968 150 2021, XXIV, 1 Finance 3. Resea chMe hodology A e a heo e ical o e iew o he di e en iews on wha cons i u es an unp ospe ous en e p ise and a speci ica ion o he basics o he Al man model, i is possible o de ine he aim o he p esen ed manusc ip as a e i ica ion o he Al man model’s p edic i e abili y depending on how an unp ospe ous en e p ise is iden i ied. Based on he heo e ical de ini ion o he di e en iews on wha highligh s an unp ospe ous en e p ise, we p esen a summa y o e iew (Tab. 2), which also se es o de ine i s own iew. Fo he pu poses o he u he analysis o unp ospe ous en e p ises, we will conside ones ha mee :  Sepa a ely, one o he ou condi ions e e ed o abo e in he las e e ence yea , 2016 (Va ian s A1, A2, A3, A4);  Sepa a ely, one o he ou condi ions e e ed o abo e h oughou he pe iod unde e iew, in 2014, 2015 and 2016 (Va ian s B1, B2, B3, B4);  A he same ime, all ou o he abo e condi ions in he las e e ence yea , 2016 (Va ian C). Wi hin he amewo k o he p esen ed esea ch, we discuss he explana o y abili y o h ee a ian s o he Al man model, i.e., he models om 1968, 1983 and 1995 ( o mo e de ails and di e ences, see Ko ulič e al., 2018), which a e calcula ed as ollows: Z1968 = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 0.999X5 (1) Z1968 = 0.717X1 + 0.847X2 + 3.107X3 + + 0.420X4 + 0.998X5 (2) Z1995 = 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4 (3) whe e: X1 = Ne wo king capi al/ o al asse s; X2 = Re ained ea nings/ o al asse s; X3 = Ea nings be o e in e es and ax/ o al asse s; X4 = Ma ke alue o equi y/ o al liabili ies; X5 = Sales/ o al asse s. As can be seen om he cons uc ion o indi idual models o modi ica ions o he Al man model, he s uc u e o he indica o s used emains he same. Views on hei impo ance has changed o e he yea s, exp essed by a change in he weigh s assigned o each indica o . These changes in weigh we e subsequen ly e lec ed in a change in he in e als/bounda ies o he iden i ica ion o an unp ospe ous en e p ise, o a heal hy one, as he case may be (Tab. 3). The explana o y abili y o he Al man model is subsequen ly e i ied by he calcula ion o he ype I e o (α), ype II e o (β) and o e all eliabili y, which a e shown in Tab. 4. Acco ding o Ga ú o á e al. (2017) and Delina and Packo á (2013), he ollowing calcula ion is based on he abo e able o :  Type I e o , i.e., he p opo ion o misclassi ied bank up companies ( he alse-nega i e a e): FNR = FN TP + FN (4) Va ian Condi ion Au ho A1 B1 C 1. P o i < 0 Bakeš & Valáško á, 2018; Ďu ica, 2018; Valáško á e al., 2018a, 2018b; Boďa & Ú adníček, 2016; K áľ e al., 2016; Bieliko á e al., 2014 A2 B2 2. Equi y < 0 Klieš ik e al., 2018; Ko áčo á & Kubala, 2018; Mendelo á & Bieliko á, 2017; Boďa & Ú adníček, 2016; K áľ e al., 2016; Bieliko á e al., 2014 A3 B3 3. L3 < 1 Bakeš & Valáško á, 2018; Ko áčo á & Kubala, 2018; Valáško á e al., 2018a, 2018b; Boďa & Ú adníček, 2016; K áľ e al., 2016; Bieliko á e al., 2014 A4 B4 4. EVA < 0, (EAT − e × E) < 0 Šo ánko á e al., 2017; Čámská, 2016; Neumaie o á & Neumaie , 2013, 2016; Maňaso á, 2008 Sou ce: own Tab. 2: Condi ions o iden i ying an unp ospe ous en e p ise 151 1, XXIV, 2021 Finance  Type II e o , i.e., he p opo ion o misclassi ied p ospe ous companies ( he alse-posi i e a e): FPR = FP FP + TN (5)  O e all eliabili y (o e all accu acy), i.e., he p opo ion o co ec ly classi ied en e p ises: ACC = TP + TN TP + TN + FP + FN (6) The da a o he analyses we e d awn om da a o ag icul u al companies (balance shee s and p o i and loss s a emen s) p o ided by he Minis y o Ag icul u e o he Slo ak Republic in he o m o in o ma ion shee s o anonymous ag icul u al subjec s. The o al ile included 1,867 subjec s o legal and na u al pe sons wi h up o 19 and 20 o mo e employees in he pe iod 2014– 2016. By using he SOFINA_s anda d economic so wa e o inancial planning and manage ial economy, we iden i ied 996 businesses ( om he o al se ) o which he e was a con inuous eco d o inancial da a o he e e ence pe iod and which had ull eco ds o balance shee s and inancial s a emen s (see Fig. 1). Di e ences in he economic esul s o hese en i ies can also be obse ed wi h espec o he na u al condi ions in which hey ope a e. Ko ulič e al. (2017) di ided he e i o y o he Slo ak Republic in o wo g oups (Fig. 2) on his basis. Financial p oblems G ay zone Heal hy en e p ise Z-sco e (1968) – A68 <1.81 <1.81–2.99> >2.99 Z-sco e (1983) – A83 <1.2 <1.2–2.9> >2.9 Z-sco e (1995) – A95 <1.1 <1.1–2.6> >2.6 Sou ce: Boďa and Ú adníček (2016) P edic ion-bank up cy P edic ion-non-bank up cy Fac -bank up cy he co ec esul (TP) e o ype I (FN) Fac -non-bank up cy e o ype II (FP) he co ec esul (TN) Sou ce: Klepáč and Hampel (2017) Tab. 3: E alua ion bounda ies o indi idual Al man models Tab. 4: Type I and II e o Fig. 1: S uc u e o he esea ch sample Sou ce: own 152 2021, XXIV, 1 Finance The me hod o Damoda an (2004, 2014) was used o calcula e he cos o equi y capi al ( e) acco ding o he EVA indica o (Tab. 2); i was also applied by Šo ánko á e al. (2017) and Mařík e al. (2011). The analyses we e pe o med in MS Excel, S a is ica 13.4 and S a g aphics XVIII. 3.1 TOPSISTechniqueasaTool o  Assessing heExplana o yAbili y o  heModel MCDM (Mul i C i e ia Decision Making) me hods we e de eloped o assis decision- making ega ding ei he anking a known se o al e na i es o a p oblem o making a choice om among his se while conside ing he con lic ing c i e ia (Ma dani e al., 2016). Acco ding o Za adskas e al. (2014), he Technique o O de o P e e ence by Simila i y o Ideal Solu ion (TOPSIS) is one o he mos widely used MCDM me hods. The o igin o his me hod can be a ibu ed o Hwang and Yoon (1981) and Yoon (1980), who de eloped i as an al e na i e o he ELECTRE me hod. The esul o he TOPSIS echnique is desc ibed by S eimikine e al. (2012) as a solu ion wi h he sho es dis ance o a posi i e-ideal solu ion (PIS), in e ms o Euclidean dis ance. The TOPSIS me hod o e s a solu ion ha is he closes o he abo emen ioned PIS unde he gi en condi ions and, a he same ime, he a hes om he nega i e-ideal solu ion (NIS) (Za adskas e al., 2016). The TOPSIS echnique was pe o med acco ding o Va ek and Bečica (2020) and Va ek (2019). The indica o s used o he abo e calcula ion a e he esul o an e alua ion o he Al man model’s explana o y abili y:  I1 = Type I e o (FNR) wi h a minimizing cha ac e ;  I2 = Type II e o (FPR) wi h a minimizing cha ac e ;  I3 = O e all eliabili y (ACC) wi h a maximizing cha ac e . Fo each o he MCDM me hods, he i s and essen ial s ep is de e mining he weigh s o he indi idual indica o s. Ke šuliene e al. (2010) di ide he app oaches o weigh ing in o ou g oups: subjec i e, expe , objec i e and in eg a ed (which combines he p e ious app oaches). Subjec i e me hods e lec he decision-make ’s pe sonali y and indi idual p e e ences. Objec i e me hods de e mine weigh s based on a p ede e mined ma hema ical model unique o each me hod, wi h he decision- make ha ing no in luence on he ou come. They include CRITIC (CRi e ia Impo ance Th ough Fig. 2: Spa ial dis ibu ion o analyzed subjec s (LFA – he dis ic s wi h wo se na u al condi ions; NONLFA – he dis ic s wi h be e na u al condi ions) Sou ce: own 153 1, XXIV, 2021 Finance In e c i e ia Co ela ion), MW (mean weigh ), SD (s anda d de ia ion), SVP (S a is ical Va iance P ocedu e) and o he s (see Sude & Kah aman, 2018; Yalcin & Unlu, 2018, and o he s). Fo ou own p ocessing, he Coe icien o a ia ion me hod (CV) was used, which was de ised by Singla e al. (2017) and u he desc ibed in he s udies o Va ek and Cho anco á (2019) and Yalcin and Unlu (2018). The aim o he CV-TOPSIS combina ion is an objec i e assessmen o he Al man model’s explana o y abili y o indi idual a ian s, which would e lec he eliabili y o no only he model (ACC) as a whole bu also speci ic esul s (FNR and FPR), which we belie e should also be conside ed. These esul s a e supplemen ed by addi ional ma hema ical and s a is ical me hods o which we can add he Mann-Whi ney es (W), K uskal-Wallis es (Q), Le ene es (LE) and Kolmogo o -Smi no es (K-S). 4. Resea chResul s The esul s o ou own esea ch can be di ided in o se e al sepa a e pa s. In he i s , he o e all e alua ion esul s a e desc ibed and s a is ically compa ed. In he second, he e alua ion o a se o 996 en i ies is ca ied ou sepa a ely using each condi ion o iden i ying unp ospe ous en e p ises in he las moni o ed yea , 2016 (i.e., Va ian s A1, A2, A3, A4), one o he condi ions o he las h ee yea s is ul illed sepa a ely (i.e., Va ian s B1, B2, B3, B4), and all ou condi ions o iden i ying unp ospe ous en e p ises in he las yea o he pe iod unde e iew a e hen ul illed simul aneously (i.e., Va ian C). The las pa is a mul i-c i e ia e alua ion o he ob ained esul s using he CV-TOPSIS echnique. 4.1 O e allE alua ionResul s wi h heAl manModel The a iabili y o he o e all e alua ion o he esul s using he a ian s o he Al man model ( om he yea s 1968, 1983 and 1995) is shown in Fig. 3, om which di e ences can be iden i ied, especially when compa ing he ange o a ia ion (RA68 = 71.31, RA83 = 50.39, and RA95 = 162.40), which, in combina ion wi h he changing subjec -classi ica ion in e als, unde lines o e all signi ican di e ences (Q = 26.667; p < 0.01; LE = 121.036; p < 0.01). Howe e , he pa icula a ian o he Al man model does no a ec he shape o he o e all esul s o hei dis ibu ion unc ion, because in all h ee cases, we can conside he esul s o be posi i ely skewed and mo e poin ed han a no mal dis ibu ion. Fig. 3: O e all esul s o he e alua ion o subjec s wi h he Al man model (Z-sco e) Sou ce: own 154 2021, XXIV, 1 Finance This s uc u e o he absolu e esul s is e lec ed in a classi ica ion o he inancial heal h o indi idual en e p ises (Fig. 4). The Al man model om 1968 and 1983 ma ked he inancial heal h o he majo i y o he a ed en i ies as nega i e, and signaled inancial p oblems o hese en i ies in he upcoming pe iod (60% o 53%, as he case may be). By con as , he Al man model om 1995 indica ed a sa is ac o y inancial si ua ion o up o 43% o all he en i ies (426). Di e ences in inancial heal h can also be obse ed when compa ing en e p ises ope a ing in be e , o wo se, na u al condi ions (LFA/NONLFA). S a is ically signi ican di e ences we e ound in he case o he Al man model om 1968 and 1983, when no only he median alue bu also he o e all s uc u e o he esul s, i.e., hei dis ibu ion unc ion, was di e en (see Tab. 5). The assessmen o inancial heal h h ough a ian s o he Al man model om 1968 and 1983 is de e mined by he quali y o he soil, i.e., he na u al condi ions. S a is ically signi ican di e ences using hese a ian s we e also demons a ed in he dis ibu ion unc ions, bu we no e hei homoskedas ici y. Howe e , in he case o he 1995 model, he na u al condi ions did no in luence his assessmen . Pa adoxically, subjec s ope a ing in dis ic s wi h be e na u al condi ions (NONLFA) showed a be e a e age a ing. Based on hese esul s, we can say ha e en in he 21s cen u y, he assessmen o inancial heal h using he Al man Fig. 4: Classi ica ion o he inancial heal h o subjec s acco ding o he Al man model Sou ce: own Model Medians check Va iance check DF check A e age LFA NON Al man 68 W = 131417 (<0.01) LE = 0.007 (0.934) K-S = 1.996 (<0.01) 2.37 1.99 Al man 83 W = 136857 (<0.01) LE = 0.0003 (0.985) K-S = 2.483 (<0.01) 1.90 1.51 Al man 95 W = 120612 (0.287) LE = 0.039 (0.843) K-S = 0.910 (0.383) 3.18 3.08 Sou ce: own Tab. 5: Compa ison o esul s o he Al man model (LFA/NONLFA) 161 1, XXIV, 2021 Finance his egion. In addi ion, we plan, in he u u e, o e i y he p edic i e abili ies o he Gu čík index and Ch as ino a model, which we e designed exp essly o he needs o Slo ak a ms. Ano he limi a ion is he e i o y o he Slo ak Republic. In o de o inc ease he objec i i y o he esea ch esul s, we ecommend ex ending he esea ch sample o subjec s wo king he soil in o he V4 coun ies and compa ing hem. Acknowledgmen s: Suppo ed by he g an No. CZ.02.2.69/0.0/0.0/16_027/0008493 “In e - na ional mobili y o TUL esea che s” o he “Minis y o Educa ion, You h and Spo o he Czech Republic”, he g an No. 024PU-4/2020 “Inno a ion o he s uc u e, con en and way o eaching economic subjec s o he s udy p og am Managemen and En i onmen al Managemen a he PhD le el” o he “Cul u al and Educa ional G an Agency o he Minis y o Educa ion, Science, Resea ch and Spo o he Slo ak Republic”, he g an No. 1/0578/18 “Modi ica ion o me hodologies o sus ainable assessmen and managemen ” o he “Scien i ic G an Agency o he Minis y o Educa ion, Science, Resea ch and Spo o he Slo ak Republic and he Slo ak Academy o Sciences”. Re e ences Alaka, H. A., Oyedele, L. M., Owolabi, H. A., Kuma , V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Sys ema ic e iew o bank up cy p edic ion models: Towa ds a amewo k o ool selec ion. 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