117
4, XIX, 2016
Finance
DOI: 10.15240/ ul/001/2016-4-009
In oduc ion
The economy’s globaliza ion which culmina ed
wi h he global i nancial c isis ha e up ed
in 2007 has a ec ed he economies a ound
he wo ld demons a ing ha he esul s
o esea ch s udies on bank up cy isk
p edic ion a e insu i cien . Fu he mo e, he
s udies conduc ed so a do no p o ide di ec
insigh in o he capi al ma ke o in es o s o
whom he i s s ep in a company’s ailu e is
mani es ed by delis ing he company om
he s ock exchange. Acco ding wi h Al man
(1968) “in he case o lis ed companies, in a 4
days in e al be o e announcing bank up cy,
in es o s lose abou 41% o he capi al in es ed
in bank up companies.” The abili y o p edic
he companies’ bank up cy om he ea ly s age
o delis ing is he main no el y o he p esen
s udy.
In he specialized li e a u e he e a e
ela i ely ew conce ns abou bank up cy’s
isk p edic ion om he in es o ’s pe spec i e.
Mos s udies e alua ing he bank up cy isk
we e s uc u ed o app oach bank up cy
om a “legal” pe spec i e acco ding o which
companies we e g ouped in o ailing companies
( o which he e is a s a emen in his ega d a
he cou ) and he o he g oup consis s o non-
bank up companies. The e is hough, ano he
g oup o s udies which analyse bank up cy
isks om an “economic” pe spec i e acco ding
o which he companies we e g ouped in o
ailing companies ( ep esen ed by companies
wi h low i nancial and economic pe o mance)
and non-bank up companies ( ep esen ed by
companies ha ha e di e en pe o mance
indica o s conside ed o be high). An e en
ewe numbe o s udies a e ocused (in he
economic app oach) on he in es o s’ angle,
acco ding o which companies become
bank up beginning wi h he delis ing phase
(Ch is idis & G ego y, 2010; Tu ada a agool,
2013; Wang & Campbell, 2010).
Ou pape has he ollowing main
objec i es: Fi s o all, we aim o iden i y which
i nancial indica o s ha e a signi i can impac on
he p obabili y o a company o ace bank up cy
isks exp essed om he in es o s’ pe spec i e
by s udying he impac on he p ospec s o
s ocks’ delis ing om he s ock exchange.
Secondly, we would like o de e mine o
wha ex en a e hese indica o s iden i i ed in
o he s udies as signi i can bank up cy signs.
Is he e any di e ence be ween he indica o s
iden i i ed as ha ing a majo impac om he
in es o s’ pe spec i e compa ed o he gene al
one?
The pape is s uc u ed as ollows. In he
nex sec ion (Sec ion 1) we p esen a e iew
o he li e a u e in e ms o bank up cy isk
whe e he “ ailu e” concep is p esen ed, we
p esen he impo ance gi en o i nancial a es
in bank up cy isk s udies highligh ing he main
me hods used in bank up cy’s isk analysis
and i nally we emphasize he ime and space
limi s o ailu e p edic ion models ha exis in
he li e a u e. Sec ion 2 e e s o he gene al
p esen a ion o he elabo a ion me hodology.
Fu he , Sec ion 3 is dedica ed o he desc ip ion
o he ob ained empi ical esul s, hei alignmen
o o he empi ical i ndings and o highligh ing
he changes in bank up cy p edic o s. The las
pa o he pape (Conclusions) is alloca ed o
desc ibing he conclusions and o highligh ing
he added alue o his s udy in o de o co e
some gaps in he niche o de eloping some
bank up cy isk models om he pe spec i e o
he capi al ma ke in es o s.
FAILURE PREDICTION FROM THE
INVESTORS’ VIEW BY USING FINANCIAL
RATIOS. LESSON FROM ROMANIA
Monica Viole a Achim, So in Nicolae Bo lea,
Lucian Vasile Găban
EM_4_2016.indd 117EM_4_2016.indd 117 6.12.2016 10:50:186.12.2016 10:50:18
118 2016, XIX, 4
Finance
1. Li e a u e Re iew
1.1 On he “Failu e” Concep
Fi s ly i is necessa y ha we cla i y he e m o
“ ailu e”. In es iga ing he s udies on bank up cy
isk, we can conclude ha co po a e “ ailu e” is
app oached om wo pe spec i es: a legal one
and an economic one.
Unde a legal aspec , a company’s
bank up cy occu s when he e is a s ong
bank up cy s a emen in a cou . Fo US an
ailing i m migh i le ei he o liquida ing i s
asse s o o business eo ganisa ion (Al man
& Ho chkiss, 2006). Acco ding o Balcaen and
Ooghe (2006) he mos s udies used ‘legal’
in e p e a ions o he ele an e ms because o
he objec i e’s pe spec i e o disc imina e ailed
and non- ailed i ms. Among hese s udies we
can men ion he ollowing: Anghel (2002),
Al man (1968), Bea e , McNichols and Rhie
(2005), Chi and Tang (2006), Pla and Pla
(1990; 1991; 2008), Shumway (2001).
Unde an economic aspec , a ailu e can
be de i ned h ough he company’s i nancial
pe o mance ha can be ep esen ed by:
“Insu i cien e enues o co e cos s and
whe e he a e age e u n on in es men is
below he i m’s cos o capi al” (Al man &
Ho chkiss, 2006);
“nega i e equi y and/o nega i e ea nings”
(Robu-Mi oniuc, 2012; Tu ada a agool,
2013);
“ educ ions in di idends, iola ions o deb
co enan s” (Tu ada a agool, 2013);
“going p i a e o a publicly lis ed company”
which is simila o delis ing he companies’
sha es (Ch is idis & G ego y, 2010;
Ohlson,1980; Tu ada a agool, 2013;
Wang & Campbell, 2010).
On he o he hand, he e a e a ious
s udies which op o “economic” in e p e a ion
when de e mining he wo ypes o companies:
bank up o non-bank up ; as ollows: Bea e
(1966; 2005), Ch is idis and G ego y (2010),
Ohlson (1980), Robu-Mi oniuc (2012),
Tu ada a agool (2013), Wang and Campbell
(2010).
Acco ding o Al man and Ho chkiss
(2006) ou common e ms a e widely used in
bank up cy s udies, namely: ailu e, insol ency,
de aul and bank up cy. We can add he e he
e m o “ i nancial dis ess” which is simila wi h
he economic ailu e (Tu ada a agool, 2013)
which means ha he company has i nancial
p oblems bu is no in bank up cy ye .
As we can see, he e a e a wide ange o
applica ions o he e m “ ailu e”, acco ding o
he speci i c objec i es o each s udy and he
speci i c needs o a ious decision-make s. In
his pape , he concep o co po a e “ ailu e”
is used om he in es o s’ pe spec i e, o
whom he delis ing o he company om he
s ock ma ke is synonymous wi h bank up cy
i sel because hei in es men is comp omised,
a ading pla o m wouldn’ be exis ing anymo e
(Wang & Campbell, 2010). In o he wo ds, in
ou s udy, bo h business “ ailu e” and “delis ing”
a e used in e changeably.
1.2 The Role o Financial Indica o s in
P edic ing he Financial Dis ess
The in e na ional accoun ing egula ion consis s
in In e na ional Financial Repo ing S anda ds.
The i nancial s a emen s’ objec i e is o p o ide
in o ma ion abou he i nancial posi ion,
pe o mance and changes in he en i y’s
i nancial posi ion ha a e use ul o a wide ange
o use s in making economic decisions. I goes
wi hou saying ha he i nancial in o ma ion
om he i nancial and accoun ing s a us is
designed o highligh he company’s i nancial
condi ion.
Mo eo e , se e al s udies conduc ed
by Bea e , Co eia and McNichols (2010)
highligh ha “ he i nancial s a emen s ha e
been used o mo e han 100 yea s o assess
i nancial dis ess’ likelihood”. F om he i nancial
in o ma ion’s ca ego y, i nancial a es a e
he mos commonly used in bank up cy isk
assessmen because i is belie ed ha hei
use compa ed o ha o he indica o s’ absolu e
le els p o ides a gene al deg ee o applicabili y
o he companies. By using i nancial a ios,
he limi s gene a ed by he companies’ size a e
he eby signi i can ly educed.
The p e e ence o using i nancial a es
in bank up cy isk assessmen has been
mani es ed since he s udies o Bea e (1966)
and Al man (1968). In his uni a ia e analysis,
Bea e (1966) iden i i ed he cash l ow indica o
o o al deb a io o be ex emely sensi i e o
a company’s i nancial condi ion. Subsequen ly,
Al man (1968) in oduced he mul i a ia e
analysis and iden i i ed i e ep esen a i e
i nancial a ios o he i nancial condi ion:
wo king capi al/ o al asse s; o al e ained
ea nings/ o al asse s; ea nings be o e in e es
and axes/ o al asse s; ma ke alue o equi y /
book alue o o al deb ; sales/ o al asse s.
EM_4_2016.indd 118EM_4_2016.indd 118 6.12.2016 10:50:196.12.2016 10:50:19
119
4, XIX, 2016
Finance
Ano he pionee o bank up cy isk
assessmen namely Ohslon (1980) in oduced
he Logi model in his analysis and ound ha
a company’s size, p o i abili y and liquidi y
ep esen a nega i e co ela ion wi h he ailu e
p obabili y. Also, he ound ha he company’s
gea ing is posi i ely co ela ed wi h he ailu e
p obabili y.
Also, Chen and Shime da (1981) e iewed
26 a icles ha classi i ed 65 i nancial a ios
inco po a ed in p edic i e s udies be ween 1966
and 1975, and selec ed 41 i nancial a ios ha
we e conside ed o be impo an . Emphasizing
he impo ance o using i nancial a ios in
he analysis, hey ound ha , by using he
i nancial a ios, he accu acy o he p edic ion o
a company’s bank up cy exceeds 90%.
Anyway, we can classi y he business
pe o mance as ollows (Achim & Bo lea, 2014):
he accoun ing-based measu e o i nancial
pe o mance ( ep esen ed by Re u n on asse s,
Re u n on equi y, Le e age a io; Flexibili y
and so on) and he ma ke -based measu e o
i nancial pe o mance ( ep esen ed by Ma ke
capi aliza ion; P ice o book a io; Ma ke o
Book a io, P ice Ea nings a io, Di idend Yield
a io, Tobin’s Q and so on). E en i he e ec
o a iables o he han hose accoun ing-based
measu e on he p obabili y o bank up cy is
al eady p o en (by mac oeconomic a iables,
ma ke -based measu e a iables, co po a e
go e nance a iables, e c.), ecen esea ch
e ealed ha i nancial a iables a e s ill
conside ed signi i can a iables o he
company’s pe o mance, p o iding he majo i y
in l uence on bank up cy isk p obabili y (Achim
& Bo lea, 2012; 2013; Ag a al & Ta l e , 2008;
Bea e e al., 2005; Ka as & Režňáko á, 2014;
Tu ada a agool, 2013). Mo eo e , he e ec s
o non-accoun ing based measu e a iables
ul ima ely s ill e l ec in i nancial pe o mances
(inc ease o sales, inc ease o ne income,
inc ease o weal h and so on). These esul s
a e also suppo ed by he i ndings o Bea e
e al. (2005) which e l ec ha “ma ke -
based a iables a e no a subs i u e o he
accoun ing-based in o ma ion bu a he
a p oxy o he p edic i e powe a ainable by
cap u ing he o al mix o in o ma ion, including
bo h he i nancial s a emen and non- i nancial
s a emen in o ma ion.” The e o e, he ma ke -
based measu e a iables include many o he
in l uences o he han e l ec ing he in e nal
pe o mance and ul ima ely hey also go-
back wi hin he alue o accoun ing based-
measu e. So, based on he li e a u e e iew,
he accoun ing-based measu e a iables i nally
seem o be he bes in e l ec ing he business
pe o mances.
1.3 Re iew o Used Me hodologies
The de elopmen o p edic i e business
ailu e models was he subjec o nume ous
esea che s’ s udies. Since 1968, he p ima y
me hods ha ha e been used o model
de elopmen a e he mul i a ia e disc iminan
analyses (MDA), de eloped by Al man (1968;
1970; 2005; 2006) and Bea e (1966; 1968;
2005; 2010).
Ohlson (1980) c i icizes he MDA, especially
he es ic i e s a is ical equi emen s imposed
by he model and in oduces o he i s
ime he logis ic eg ession me hod mean
o be e p edic he company’s ailu e. The
bene i s o he logis ic eg ession me hod a e
subsequen ly ecognized by many au ho s,
he majo ad an age being ha while he MDA
model calcula es a bank up cy sco e using
a linea unc ion, he Logi model p edic s
ha p obabili y as a ”dicho omous dependen
a iable ha is a unc ion o a ec o o
explana o y a iables” (Aziz & Da , 2006). In
hei la ge su ey, Bello a y e al. (2007) ound
ha he mul iple disc iminan analysis is he
mos common me hod o p edic ing bank up cy
isk, being used in 36% o he in es iga ed
s udies. On he second place (wi h 25%) a e
he Logi and P obi models.
La e , o he me hods suppo ed he
de elopmen o bank up cy isk p edic ion
models as al e na i es o he wo models
men ioned abo e: neu al ne wo ks, haza d
model, dis ance o de aul cox eg ession.
1.4 Limi s o Failu e P edic ion Models
The in e es in de eloping some bank up cy isk
models was mani es ed by esea che s om
he whole wo ld; hey would be applied no only
o de eloped coun ies bu also o he eme ging
coun ies, oo. Fo de eloped coun ies, among
he mos impo an bank up cy isk s udies, we
can men ion: Bea e (1966), Al man (1968;
2005; 2006), Ohlson (1980), Shumway (2001)
( o US), Ch is idis and G ego y (2010) ( o
UK). Fo de eloping coun ies om Cen al and
Eas e n Eu ope, we can men ion: Dominiak
and Mazu kiewicz (2011) (Poland), Ša lija and
Jege (2011) (C oa ia); Dun and B ads ee
EM_4_2016.indd 119EM_4_2016.indd 119 6.12.2016 10:50:196.12.2016 10:50:19
120 2016, XIX, 4
Finance
(2014), Ka as and Režňáko á (2013; 2014),
Pi o a (2011) (Czech Republic); Elenko and
File a (2006) (Bulga ia); Sze e in and László
(2014) (Hunga y); Ugu lu and Aksoy (2006)
(Tu key); Anghel (2002), Siminica (2010), Robu
and Mi oniuc (2012) (Romania). Fo de eloping
coun ies om Asia we no e he s udies o Wang
and Campbell (2010) (China), Sun a uk (2010;
2013) and Tu ada a agool (2013) (Thailand);
Lee W-C (2007) and Lee M-C (2014) (Taiwan).
Despi e he e o s o globaliza ion and
con e gence he economies a ound he wo ld,
he e a e s ill la ge dispa i ies be ween he
wo ld’s coun ies’ na ional economies ha
make impossible he exis ence o a global
model o assessing bank up cy isk. A complex
su ey conduc ed by Pla and Pla (2008) in
h ee global egions namely Asia (including
Aus alia), Eu ope and he US highligh s ha
in e na ional di e ences in accoun ing ules,
lending p ac ices, managemen skills’ le els
and legal equi emen s ha e de e mined he
ejec ion o he null hypo hesis ha assumed
ha a single global model would explain
i nancial dis ess on each egion in a ou o
a ully elaxed model which c ea ed indi idual
i nancial dis ess models o each egion. Some
simila s udies we e conduc ed by Lai inen and
Su as (2013) on a sample o 30 Eu opean
coun ies and hei i ndings highligh ed he
signi i can di e ences in he shape and powe
o p edic ing bank up cy isk models in l uenced
by cha ac e is ics speci i c o each coun y,
like: economic en i onmen , company s a us
classi i ca ion and coding sys ems, legisla ion
and cul u e. On he same le el, Al man and
Ho chkiss (2006) also highligh ed he mos
impo an di e ences be ween eme ging and
de eloping ma ke s, like cu ency ulne abili y,
indus y isk, and compe i i e posi ion and
ake hese ac o s in o accoun o de elop
a bank up cy isk assessmen model speci i cally
o eme ging economies unde he Eme ging
Ma ke sco e (EMS) Model.
A limi ed ca ego y aimed o c ea e he
models om he in es o s’ pe spec i e. Thei
demands a e di e en om hose o o he
decision-make s, he e o e he esul s could no
be used o a gene al pu pose, bu o a speci i c
one. In his ega d, Wang and Campbell (2010)
in hei s udy on Chinese Publicly T aded
Companies analysed which i nancial indica o s
ha e a signi i can impac on a company’s
delis ing s a e om he s ock. They ound ha
nega i e own equi y and a nega i e ne income
o he las wo yea s a e he wo mos in l uen ial
a iables in ailu e p edic ion om he in es o s’
pe spec i e. By using his pe spec i e,
Ch is idis and G ego y (2010), in hei su ey
on he companies lis ed on he London S ock
Exchange, iden i i ed eigh a iables as being
signi i can o he companies’ isk ailu e, such
as: liquidi y indica o s (wo king capi al o e
o al asse s, quick asse s o e cu en asse s),
p o i abili y (change in ne income, a dummy
a iable equal o one i ne income was
nega i e o he las wo yea s, ea nings be o e
in e es and ax o e sha e capi al), cash l ow,
unds- l ow and le e age (measu ed by o al
liabili ies o e o al asse s). Fo he Asian s ock
ma ke , mo e speci i cally o Thailand’s s ock
ma ke , Tu ada a agool (2013) iden i i ed eigh
s a is ically signi i can a ios such as: h ee
a ios (cu en asse s o cu en liabili ies, quick
asse s o cu en liabili ies and wo king capi al
o o al asse s) in he liquidi y g oup; wo a ios
(sales o o al asse s and sales o in en o y) in
he u no e g oup; wo a ios (ea nings be o e
in e es and ax o in e es and o al equi y o o al
liabili ies ) in he le e age g oup and one a io
(ne income o o al asse s) in he p o i abili y
g oup. In es iga ing he abo e esul s, i canno
be es ablished ha he a iables would ha e
a signi i can impac on s ock ma ke delis ing.
Such limi a ion o bank up cy isk models’
applica ion in space and ime is highligh ed
by many au ho s such as Balcaen and Ooghe
(2006) o Cî ciuma u (2011). Fo ins ance,
Cî ciuma u (2011) also ema ked ha e en o
he same economy, he pe iods o economic
ins abili y a ec he ailu e p edic ions’ esul s
and he e o e i is necessa y o egula ly
upda e he models in o de o cap u e he
new economic and i nancial condi ions. We
can add he e ha e en i he bank up cy
p edic ion models a e c ea ed speci i cally om
he in es o s’ pe spec i e, hey signi i can ly
di e om coun y o coun y, om one wo king
me hodology o ano he , om he pe iod in
which hese models we e c ea ed (i hey we e
c ea ed in a pe iod o economic g ow h, he
esul s no longe apply o hose om he pe iod
o economic c isis) e c.
2. Me hodology
2.1 Sample and Da a
In his s udy, he choice o he wo g oups
o companies is made om he in es o s’
EM_4_2016.indd 120EM_4_2016.indd 120 6.12.2016 10:50:196.12.2016 10:50:19
121
4, XIX, 2016
Finance
poin o iew on he s ock ma ke . As long as
a company is delis ed, i becomes wo hless
o he in es o because a ading pla o m
no longe exis s. E en i he company, being
delis ed, will con inue o ope a e o a pe iod
o ime, “ he sha eholde s ha e essen ially los
hei in es men ” (Wang & Campbell, 2010).
Fo ou s udy a i m is iden i i ed as ailed i i
is delis ed om he Bucha es S ock Exchange.
O he wise, i he i m is lis ed, i is iden i i ed as
non- ailed.
The s a is ic popula ion consis s in 88
la ge non- i nancial companies ha a e aded
on Bucha es S ock Exchange a he end o
2013. Banks and o he i nancial ins i u ions
a e excluded om he s udy because hei
i nancial s a emen s a e p epa ed on a di e en
basis om hose o indus ial i ms (B yan ,
1997; Cha i ou, Neophy ou, & Cha alambous,
2004; Flagg, Gi oux, & Wiggins J ., 1991; He &
Kama h, 2006; Ohlson, 1980; Tu ada a agool,
2013). Fu he , he non- i nancial companies
belong o di e en a ea o business such
as: indus y, comme ce, cons uc ion and
accommoda ion.
F om he sample o 88 non- i nancial
companies, we ha e iden i i ed 65 lis ed
companies and 21 delis ed companies. The
companies lis ing on he Bucha es S ock
Exchange is classi i ed on h ee ca ego ies
(Tie 1, Tie 2 and Tie 3) acco ding o hei
pe o mances, as ollows (Bucha es S ock
Exchange, “Issue ’s guide o S ock and bonds”,
2010):
Tie 1 includes he companies which ha e
an equi y alue a leas o 30 million EURO
in he las i nancial yea and ha e ob ained
a ne p o i in he las wo yea s o ac i i y.
We ha e 16 non- i nancial companies, a he
end o 2013.
Tie 2 includes he companies which ha e
an equi y alue a leas o 2 million EURO in
he he las i nancial yea . We ha e 48 non-
i nancial companies, a he end o 2013.
Tie 3 includes he companies which ha e
an equi y alue a leas o 1 million EURO in
he he las i nancial yea . We ha e only one
companies, a he end o 2013.
A special ca ego y o companies aded on
he Bucha es S ock Exchange is ep esen ed
by delis ed companies. Delis ing means he
emo al o a lis ed company om he Bucha es
S ock Exchange, olun a ily o in olun a ily,
because i is no anymo e in compliance wi h
he lis ing equi emen s o he Bucha es S ock
Exchange (no longe quali i ed o lis ing, in none
o he abo e men ioned ca ego ies). A he end
o 2013 he e a e 21 delis ed companies.
Based on economic and s a is ical easons,
we will wi hhold in he “Lis ed” ca ego y
only hose companies ha mee he highes
pe o mance c i e ia. Mo e speci i cally, om he
o al sample o lis ed companies (65 companies)
we e ain only hose lis ed in Tie 1 (namely,
16 companies).
The s a is ical easons on which he decision
o es ablishing he samples o bank up cy
isk’s p edic ion analysis elied, aim he balance
be ween he wo samples. Al hough he e is
a common de aul e s’ sha e o less han 10% in
he en i e da abase, his imbalance can lead o
la ge de aul p edic ion e o s. Ma qués, Ga cía
and Sánchez (2013) men ioned in hei s udy he
p oblem o imbalanced da a and ound ha he
use o esampling me hods could consis en ly
imp o e he pe o mance gi en by he o iginal
imbalanced da a. Many s udies ocused on
hese aspec s and used balanced samples in
hei isk ailu e s udies: Bea e (1966; 1968)
used 79/79, Al man (1968) used 33/33, Deakin
(1972) used 32/32, Bei and Liu (2005) used
31/31, Hossa i (2007) used 247/247, Ugu lu
and Aksoy (2006) used 27/27, He and Kama
(2006) used 20/20, Tu ada a agool (2013)
used 14/14.
The economic easons o choosing he wo
samples a e based on he ac ha he Tie 1
consis s o hose companies ha mee he mos
igo ous pe o mance equi emen s and unde
hese ci cums ance he likelihood ha i nancial
pe o mance a iables o be mo e sensi i e o
he wo non- ailu e/ ailu e s a es, is highe .
Finally, ou s udy will be conduc ed based
on wo samples: a non- ailed sample which
consis s in 16 lis ed companies and a ailed
companies sample which consis s in 21 delis ed
companies. Ou wo samples a e homogeneous,
on he one hand, in e ms o he companies’
size (we conside only la ge companies) and,
on he o he hand, in e ms o he pe o med
ac i i y (we only sample he companies wi h
an economic p o i le and he e o e he i nancial
ins i u ions a e no included).
The da a we e collec ed om he companies’
annual i nancial s a emen s, be ween 2002 and
2012, which a e a ailable on he si e o he
Bucha es S ock Exchange (www.b b. o). Fo
he delis ed i ms, i nancial da a up o wo yea s
EM_4_2016.indd 121EM_4_2016.indd 121 6.12.2016 10:50:196.12.2016 10:50:19

122 2016, XIX, 4
Finance
p io o delis ing we e used in ou analysis. Fo
he lis ed i ms, we used he i nancial da a om
he las 3 yea s, namely be ween 2010 and
2012. The esul was o 90 obse a ions.
2.2 Me hod o Wo k
The dependen a iable Dn is a bina y a iable:
Dn = 1 o he s ock delis ed companies;
Dn = 0 o he s ock lis ed companies. Since he
dependen a iable is bina y, he bina y models
we e used o iden i ying he delis ing p obabili y
(bank up cy isk), which was modelled o depend
on eg esso s. The index unc ion o mula ion
explains an unobse ed con inuous andom
a iable y* (la en a iable), bu all we obse e is
he bina y a iable y, which akes he 1 o 0 alue
acco ding o whe he y* c osses a h eshold o
no . Di e en dis ibu ions o y* lead o di e en
bina y ou come models. Le y* be an unobse ed
a iable. The eg ession model o y* is he index
unc ion model (Came on & T i edy, 2009):
y* = X'β + u (1)
whe e he eg esso ec o X is a K × 1 column
ec o wi h j h en y Xj, he pa ame e ec o β
is a K × 1 column ec o wi h j h en y βj, and
he e o ec o u is a K × 1 column ec o wi h
j h en y uj. Le a ec o o da a deno ed as
Xi = (X1i ,..., Xki) om N obse a ions. Then
X'β = β1X1 + β2X2 + ... + βkXk . The model
(1) canno be es ima ed because y* is no
obse ed. We ha e
y =
{
1 i y* > 0,
0 i y* ≤ 0, (2)
whe e he ze o h eshold is a no maliza ion.
Gi en he la en - a iable models (1) and (2) we
ha e
P (y = 1/X) = F(X'β) (3)
whe e F(X'β) is he cumula i e dis ibu ion
unc ion (c.d. .) o –u. We ob ain he P obi
model i u is s anda d no mally dis ibu ed and
he Logi model i u is logis ically dis ibu ed.
Gi en he model (3), a mo e ele an o m wi h
condi ional p obabili y is gi en by
pi ≡ P [yi = 1/ X] = F(Xi β) (4)
Fo bina y models, he maximum likelihood
es ima o (MLE) is na u al es ima o , because
he densi y is unambiguously he Be noulli. Fo
a sample (yi, Xi), i = 1,...,N, o N independen
obse a ions, ML es ima ion, β
^ , maximizes
he associa ed log-likelihood unc ion. The
β
^
is ob ained by i e a i e me hods and is
asymp o ically no mally dis ibu ed. The Wald
es and he likelihood- a io (LR) es a e used
o p oduce he es s a is ics and p- alue
o a es o he signi i cance o indi idual
coe i cien s, he con i dence in e als o
indi idual coe i cien s, and he es s o o e all
signi i cance. In he s a is ics li e a u e a e y
common in e p e a ion o he coe i cien s is in
e ms o ma ginal e ec s. We a e in e es ing
in de e mining he ma ginal e ec o change in
a eg esso on he condi ional p obabili y ha
y = 1. Fo gene al model (3) and change in he
j h eg esso , assumed o be con inuous, his is
∂P (yi = 1/Xi ) = F ,(Xi β)βj.
∂ xij (5)
The ma ginal e ec s di e wi h he poin o
e alua ion Xi. In measu ing ma ginal e ec s
we calcula e he change in he p obabili y
P (y = 1) when eg esso s change by one uni .
Ma ginal e ec s o logi a iables a e es ima ed.
A measu e o goodness o i in he linea
eg ession model is R2. The gena aliza ions o
nonlinea models a e called pseudo-R2. This
measu e is no always compu able bu i is
o he bina y ou come model. This yields he
R2 measu e o bina y models p oposed by
McFadden. We used a good guide (Came oon
& T i edy, 2009) ha explains how an econome ics
compu e package such as S a a, may pe o m
eg ession analysis o a quali a i e bina y a iable.
Based on in es iga ing he a o esaid
i ndings, a he ini ial s age, we conside ed
12 i nancial a ios. These a iables a e g ouped
as ollows:
A. P o i abili y
1. Re u n on asse s = Ne esul s / To al
asse s (ROA)
2. Re u n on equi y = Ne esul s / Own
Equi y (ROE)
3. Ne p o i ma gin = Ne esul s / Sales
(NPM)
B. Le e age
4. Le e age a io = Deb / Sha eholde ’s
equi y (LEV)
5. S abili y a io = Engaged capi al / (Own
equi y + Liabili ies) (STAB)
EM_4_2016.indd 122EM_4_2016.indd 122 6.12.2016 10:50:196.12.2016 10:50:19
123
4, XIX, 2016
Finance
C. Liquidi y and sol ency
6. Cu en liquidi y a io = Cu en asse s/
cu en liabili ies (CL)
7. Flexibili y a io = Ne wo king capi al/
To al asse s (FLEX)
8. Cash- l ow/Ne esul s (CWR)
9. Gene al sol ency = To al asse s/To al
deb s (SOLV)
D. Ac i i y
10. Asse s u no e = Sales/To al asse s
(ASTU)
11. Cu en asse s u no e = Sales/Cu en
asse s (CASTU)
12. Cu en deb s u no e = Sales/Cu en
deb s (CDTU)
The co ela ion esul s be ween he selec ed
a iables a e p o ided in he ollowing able:
Wi h a ew excep ions, which we will
conside , Table 1 shows no s ong co ela ion
among he en a iables which a e men ioned
abo e. They can be used oge he as po en ially
explana o y a iables in any o de o in any
combina ion. F om he selec ed a iables, in
e ms o he in o ma ion’s impac , h ee models
a e conside ed o be ele an o he p obabili y
o s ock exchange delis ing. I is well known
ha ei he Logi and P obi models can be used
o iden i ying he a iables ha a e signi i can
and ha e p edic i e powe , because hey ha e
simila shapes o cen al alues o F(.) bu
di e in he ails as F(.) app oaches 0 o 1.
3. Empi ical Resul s and Discussions
Empi ical esul s o Model 1, Model 2 and Model
3 a e p o ided in he ollowing. The McFadden’s
R2 ma ginal e ec s and he pe cen age o
co ec ly classi i ed obse a ions p o ide he
explana o y powe o a iables in models.
Model 1
The esul s o he Logi model a e p esen ed in
Table 2. All eg esso s, FLEX, ASTU and CASTU
a e s a is ically di e en om ze o a he 0.05
le el. The null hypo hesis ha he coe i cien s o
FLEX, ASTU and CASTU a e ze o is ejec ed a
he 0.05 le el. This is con i med by he LR es .
The sign o he coe i cien is also he sign o he
ma ginal e ec p esen ed in Table 3.
The a iables FLEX and CASTU ha e
nega i e coe i cien s which means ha
inc easing he l exibili y and also he u no e
in cu en asse s conduc o a dec ease o
he p obabili y o delis ed P (Dn = 1). Bo h
coe i cien s ha e an expec ed signs (nega i es)
meaning ha by assu ing an adequa e alue
o wo king capi al and a high a io o eplacing
he cu en asse s by u no e ac i i y, a well
e i ciency in ope a ing ac i i y is pe o med. To
he con a y, he a iable ASTU has a posi i e
coe i cien , means ha inc easing he u no e
in o al asse s conduc o an inc ease o he
ASTU SOLV FLEX LEV STAB CL ROE NPM CASTU CDTU CWR ROA
ASTU 1.0000
SOLV –0.2375 1.0000
FLEX –0.0834 0.4034 1.0000
LEV 0.0282 –0.1392 –0.0381 1.0000
STAB –0.2812 0.4932 0.8176 0.0171 1.0000
CL –0.1746 0.7191 0.5288 –0.1046 0.3730 1.0000
ROE 0.0636 0.0802 0.1509 –0.8210 0.0110 0.1045 1.0000
NPM 0.2374 0.0376 0.0436 0.0342 0.0454 –0.0726 –0.0291 1.0000
CASTU 0.4965 0.1032 –0.2161 0.0974 –0.0438 –0.3046 –0.1060 0.2146 1.0000
CDTU 0.3071 0.6486 0.4163 0.1043 0.4103 0.5649 0.1111 0.2434 0.3610 1.0000
CWR 0.3433 –0.0825 0.0169 0.0044 –0.0239 –0.0441 –0.0294 0.0280 0.1868 0.0755 1.0000
ROA 0.1310 0.1122 0.4706 –0.0042 0.4088 0.0888 0.1559 0.5755 0.0113 0.3122 –0.0327 1.0000
Sou ce: own calcula ions
Tab. 1: Co ela ion ma ix
EM_4_2016.indd 123EM_4_2016.indd 123 6.12.2016 10:50:196.12.2016 10:50:19
124 2016, XIX, 4
Finance
p obabili y o delis ing. By aking accoun he
abo e commen s, i is means ha he small
alue o he i xed asse s cause p oblem o
disin es men , conduc ing o inc ease o he
p obabili y o delis ing. We will commen in
de ails all hese esul s a e we un all he
conside ed models.
Fo he i ed Logi model he McFadden’s
R2 is 0.3585. Table 2 shows a good model o he
impac o FLEX, ASTU and CASTU on P (Dn = 1).
Table 3 p o ides an es ima e o he
ma ginal e ec a X = X. Among he h ee s ock
exchange delis ing de e minan s, l exibili y a io
(FLEX) shows ha i has he highes nega i e
ma ginal impac on dependen a iable (Dn = 1)
and asse s u no e (ASTU) has a high posi i e
ma ginal impac . An impo an poin o no e is
ha o he ma ginal o all h ee Logi a iables
i.e. FLEX, ASTU and CASTU on Dn, s ock
exchange delis ing is s a is ically signi i can
wi h a signi i can le el o 0.015, 0.00 and
espec i ely 0.003 and hey con ibu e wi h
52.59% o he p obabili y o s ock exchange
delis ing. The ma ginal e ec o he model’s
p edic i e powe on s ock exchange delis ing is
o 0.5259 i.e. 52.59%.
One measu e o goodness o i is he
pe cen age o co ec ly classi i ed obse a ions.
Fo he i ed Logi model, we ob ain Table 4.
Table 4 compa es i ed and ac ual alues.
The pe cen age o co ec ly speci i ed alues
is o 83.33%. In his Table, 5 obse a ions
a e misclassi i ed as 1 (s ock exchange
delis ed) when he co ec classi i ca ion is 0
(s ock exchange lis ed), and 10 alues a e
misclassi i ed as 0 (s ock exchange lis ed) when
he co ec alue is 1 (s ock exchange delis ed).
In he ollowing, we p esen Model 2
and Model 3 compa ed o Model 1, o show
he insigni i can impac o some a iables
conside ed a he ini ial s age.
Model 2
Asse s u no e (ASTU) ep esen s he
de e mining ac o o he p obabili y o s ock
exchange delis ing. Table 1 shows no s ong
co ela ion among ASTU and CDTU. The
esul s o he Logi model wi h ASTU and CDTU
Dn Coe . Robus S d. E . z P>|z| [95% Con . In e al]
FLEX –3.66313 1.508794 –2.43 0.015 –6.62032 –0.70595
ASTU 4.38998 1.232050 3.56 0.000 1.97521 6.80475
CASTU –1.08176 0.369810 –2.93 0.003 –1.80658 –0.35695
_cons –0.89814 0.491670 –1.83 0.068 –1.86181 0.06552
Numbe o obs. = 90
Wald chi2(3) = 12.93
P ob > chi2 = 0.0048
Log pseudolikelihood = –39.891217
Pseudo R2 = 0.3585
Sou ce: own calcula ions
Va iable dy/dx S d. E . z P>|z| [95% Con . In e al] X
FLEX –0.91332 0.36985 –2.47 0.014 –1.63822 –0.18842 0.07215
ASTU 1.09454 0.29883 3.66 0.000 0.50885 1.68024 0.84165
CASTU –0.26971 0.09048 –2.98 0.003 –0.44704 –0.09238 2.24492
y = P (Dn) (p edic )
= 0.525912
Sou ce: own calcula ions
Tab. 2: Logis ic eg ession
Tab. 3: Ma ginal e ec s a e Logi
EM_4_2016.indd 124EM_4_2016.indd 124 6.12.2016 10:50:206.12.2016 10:50:20
125
4, XIX, 2016
Finance
as he eg esso s a e p esen ed in Table 5. The
null hypo hesis ha he coe i cien s o ASTU
and CDTU a e ze o is ejec ed a he 0.05 le el.
The sign o he coe i cien is also he sign o
he ma ginal e ec p esen ed in Table 6. As in
he model 1, he coe i cien o a iable ASTU is
also posi i e (we commen on his). As o he
a iable CDTU, he nega i e coe i cien has an
economic signi i cance, meaning ha a highe
abili y o a company o pay i s deb s, he smalle
is he p obabili y o delis ing. McFadden’ R2 is
0.2960. Table 5 shows a good model o he
impac o eg esso s on he delis ing p obabili y
P (Dn = 1), bu a smalle han ha om Model 1.
The ma ginal e ec s o wo Logi a iables
i.e. ASTU and CDTU on Dn, is s a is ically
signi i can and hey con ibu e wi h a p obabili y
o 51.83 % o s ock exchange delis ing.
The pe cen age o co ec ly speci i ed alues
(p esen ed in Tab. 7) is wi h 80% smalle han
ha om he i s model. The p edic i e powe
o he model on he s ock exchange delis ing
doesn’ change signi i can ly.
Logis ic model o Dn
T ue
Classi i ed D ~D To al
+ 32537
–104353
To al 42 48 90
Co ec ly classi i ed 83.33%
Sou ce: own calcula ions
Dn Coe . Robus S d. E . z P>|z| [95% Con . In e al]
ASTU 3.44703 1.02342 3.37 0.001 1.44115 5.45291
CDTU –0.36271 0.15072 –2.41 0.016 –0.65812 –0.06731
_cons –1.53414 0.47610 –3.22 0.001 –2.46729 –0.60099
Numbe o obs. = 90
Wald chi2(2) = 11.83
P ob > chi2 = 0.0027
Log pseudolikelihood = –43.774824
Pseudo R2 = 0.2960
Sou ce: own calcula ions
Va iable dy/dx S d. E . z P>|z| [95% Con . In e al] X
ASTU 0.86060 0.25191 3.42 0.001 0.366874 1.35433 0.84161
CDTU –0.09055 0.03737 –2.42 0.015 –0.163803 –0.01731 3.56632
y = P (Dn) (p edic )
= 0.518330
Sou ce: own calcula ions
Tab. 4: The pe cen age o co ec ly classi i ed obse a ions
Tab. 5: Logis ic eg ession
Tab. 6: Ma ginal e ec s a e logi
EM_4_2016.indd 125EM_4_2016.indd 125 6.12.2016 10:50:206.12.2016 10:50:20
132 2016, XIX, 4
Finance
Risk Models Based on Financial S a emen
In o ma ion: Compa isons ac oss Eu opean
Coun ies. Jou nal o Finance and Economics,
1(3), 1-26. doi:10.12735/j e. 1i3p01.
Ma qués, A., Ga cía, V., & Sánchez, J.
S. (2013). On he sui abili y o esampling
echniques o he class imbalance p oblem
in c edi sco ing. Jou nal o he Ope a ional
Resea ch Socie y, 64(7), 1060-1070.
doi:10.1057/jo s.2012.120.
Ohlson, J. A. (1980). Financial Ra ios and
he P obabilis ic P edic ion o Bank up cy.
Jou nal o Accoun ing Resea ch, 18(1), 109-
131. doi:10.2307/2490395.
Pla , H. D., & Pla , M. B. (2008). Financial
dis ess compa ison ac oss h ee global egions.
Jou nal o Risk and Financial Managemen ,
1(1), 129-162. doi:10.3390/j m1010129.
Pi o á, K. (2011). Possibili ies o he
Al man Ze a model applica ion o Czech i ms.
E&M Ekonomie a Managemen , 14(3), 66-76.
Robu, M., Mi oniuc, M., & Robu, I. B. (2012).
A p ac ical model o es ing he hypo hesis o
“going-conce n” in he i nancial audi mission
o Romanian lis ed companies. Re is a Audi
Financia , 2, 13-23.
Ša lija, N., & Jege , M. (2011). Compa ing
i nancial dis ess p edic ion models be o e
and du ing ecession. C oa ian Ope a ional
Resea ch Re iew, 2(1), 133-142.
Siminica, M. (2010). Financial diagnosis.
Publishing House SITECH.
Shumway, T. (2001), Fo ecas ing
Bank up cy Mo e Accu a ely: A Simple Haza d
Model. Jou nal o Business, 74(1), 101-124.
doi:10.1086/209665.
Sze e in, E. K., & Laszlo, K. (2014). The
E i ciency o Bank up cy Fo ecas Models
in he Hunga ian SME Sec o . Jou nal o
Compe i i eness, 6(2), 56-73. doi:10.7441/
joc.2014.02.05.
Todea, A., & Laza , D. (2012). Global C isis
and Rela i e E i ciency: empi ical e idence
om Cen al and Eas e n Eu opean S ock
Ma ke s. The Re iew o Finance and Banking,
4(1), 45-52.
Tu ada a agool, S. (2013). The ole
o i nancial a ios in signalling i nancial
dis ess: e idence om Thai lis ed companies
(Doc o al disse a ion). Lismo e: Sou he n
C oss Uni e si y (h p://epubs.scu.edu.au/cgi/
iewcon en .cgi?a icle=1291&con ex = heses).
Ugu lu, M., & Aksoy, H. (2006). P edic ion
o co po a e i nancial dis ess in an eme ging
ma ke : he case o Tu key. C oss Cul u al
Managemen : An In e na ional Jou nal, 13(4),
277-295. doi:10.1108/13527600610713396.
Wang, Y., & Campbell, M. (2010). Financial
a ios and he p edic ion o bank up cy: The
Ohlson model applied o Chinese publicly aded
companies. The Jou nal o O ganiza ional
Leade ship and Business, 17(1), 334-338.
Assoc. P o . Phd. habil. Monica Viole a Achim
Babeş-Bolyai Uni e si y
Facul y o Economics
and Business Adminis a ion
[email p o ec ed]
[email p o ec ed]
Assoc. P o . Phd. So in Nicolae Bo lea Phd.
Wes Vasile Goldis Uni e si y
Facul y o Economics, In o ma ics
and Enginee ing
[email p o ec ed]
Economis Phd. Lucian Vasile Găban
“1 Decemb ie 1918” Uni e si y
Facul y o Economics Alba Iulia
[email p o ec ed]
EM_4_2016.indd 132EM_4_2016.indd 132 6.12.2016 10:50:216.12.2016 10:50:21

133
4, XIX, 2016
Finance
Abs ac
FAILURE PREDICTION FROM THE INVESTORS’ VIEW BY USING FINANCIAL
RATIOS. LESSON FROM ROMANIA
Monica Viole a Achim, So in Nicolae Bo lea, Lucian Vasile Găban
The pu pose o ou s udy is o iden i y which i nancial indica o s ha e a signi i can impac on he
p obabili y o Romanian companies’ bank up cy isk om he in es o s’ poin o iew by s udying he
impac on he p obabili y o sha es delis ing om he s ock exchange. The esea ch is conduc ed
on a sample o 16 ailed and 21 non- ailed non- i nancial companies lis ed on he Bucha es S ock
Exchange be ween 2002 and 2012.
The Logi analysis is used o iden i ying he a iables ha a e signi i can and ha e p edic i e
powe on dis ess likelihood. By using 12 main i nancial a ios, we es ima e h ee al e na i e Logi
models o de e mining hei signs, signi i cance, p edic i e powe , e i ciency o i es s. The i s model
p o ides he highes explana o y powe . Th ee a iables such as Flexibili y a io (FLEX), Asse s
u no e (ASTU) and Cu en asse s u no e (CASTU) a e ound o be signi i can de e minan s o
s ock exchange delis ing. These h ee a iables p o ide 52.59% o co ec p edic ion o bank up cy
isk. The pe cen age o co ec ly classi i ed obse a ions o he i ed Logi model is o 83.33%.
Mo eo e , his esea ch a emp s o e eal he changes ha may appea among bank up cy
p edic o s gi en ha he bank up cy isk model is de eloped om he in es o s’ poin o iew and
no om ha o a simple decision-making pe son. Fo a s ock ma ke in es o , bank up cy al eady
s a s a he s age o delis ing he company because he in es men was s ongly comp omised,
whe he o i con inues i s ac i i y o no . O ien a ion owa ds in es o s when p edic ing bank up cy
isk is he main elemen o o iginali y ha ou esea ch adds o he scien i i c achie emen s in
bank up cy, un il his momen .
Key Wo ds: Delis ing, ailu e p edic ion, i nancial a ios, Logi model.
JEL Classi i ca ion: C25, C52, G33.
DOI: 10.15240/ ul/001/2016-4-009
EM_4_2016.indd 133EM_4_2016.indd 133 6.12.2016 10:50:216.12.2016 10:50:21