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dies across pital market
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2 Accept URL: http
the world t is their abi nies. For thi firms and c iscriminant in Stock Ex redictability spectively. T
also Multip he level logi
it model, Pr
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n Predic
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i Araghi, M nagement, S d University
E-mail: m
ra (Corresp e and Resear
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ted: January p://dx.doi.or
d show tha ility in pred is purpose, comparing t analysis. St xchange of y of Logit, The results ple discrim it model.
robit model,
Multip
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Maryam Science and y, Tehran, Ir m.khaliliarag
onding auth rch Branch,
Zahedan, Ir aramakvand
y 16, 2013 rg/10.5296/a
at one of i dicting the s this study the predicta tatistical po Tehran sinc Probit, and also showe minant analy
, Multiple-d
ple Disc
ankrup
d Research B ran
ghi@gmail.c
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, Islamic Az ran
di@ymail.co
Publish ajfa.v5i1.29
investors an tatus of acti tries to pred ability powe opulation of
ce 2000-20 Multiple-d ed the highe ysis method
discriminant
crimina
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Branch
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nd other a ivity consis dict the ban er of 3 met f the study i
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2013
activists’ stency or nkruptcy thods of included
1. Intro
Predicti 3 decad its cons very im compan (Tonatiu Unrelia that ma in the s patterns assuran Many fi 1. They
2. Con physica 3. A pa which a
Regardi debt. The com causes value de reduces Therefo bankrup commo standard variable stateme
2. Rese
Bankrup of the c bankrup others, predict reduce estimate
oduction
ing bankrup des (Ching– sequences a mportant for ny leads spr uh Pena et a ability from ay discourag same year, s for foreca nce to invest
firms get ban y have to sel
flicts amon al damage an art of compa
are not as im
ing these ca
mpanies lac increase in ecrease enh s the current ore it seem ptcy of co on techniqu d modeling es used in ents (Etemad
earch theor
ptcy always community ptcy. But it Manageme bankruptcy the risk of e the comp
ptcy has bee –Ching Yeh affect the ec r variety of read of crisi
al.2009). survival of ge investme and is out asting firm t in stock m nkrupt annu ll their prop
ng creditors nd inventor any value is mportant as
ases, bankru
cking debt h bankruptcy hances becau
t value of th ms necessar
mpanies. S ues in bank g technique
Building di,2007 ).
ries and bac
s has divert . It is very can be claim ent, investor y, not only t
f your port pany's finan
en a challen h et al.2010 conomy of public and s to other p
f the existin ent in the S of stock. T ms that are market with t ually becaus perties with
s may dela ry depreciati s spent for l
1 and 2.
uptcy cost i
have never b y likelihood use of the c he company ry to find Statistical t kruptcy pre es and has these mod
ckground
ed wide ran difficult to med that it rs, creditors
o prevent th tfolio (Etem ncial bankru
nging issue 0). Bankrupt
the country d commercia parts of the f
ng company Stock Excha Therefore w
going bank the lower ri se such situ low price.
ay liquidati ion increase lawyers’ fee
is high. It j
been bankru d and reduc costs of bank y, enhancing
a model th techniques ediction. Th
focused o dels is the
nge of indiv o provide a will be influ s, competito he risk of bu madi,2007 ) uptcy, becau
for many sc tcy is impo y. In fact, b al organizat financial sy
y in Stock E ange, becau
with the de kruptcy, Ca sk (Rekabd ations:
ing the ass es.
e, court cos
ust occurs f
upt. So, fina ce earnings. kruptcy. Inc g its capital
hat can pre are consid his model n signs bu
informatio
viduals, orga an accurate uenced ban ors, and leg urning their ). Hence, T use in case
cientific stu rtant in fina ankruptcy p tions. Becau stem and ca
Exchange is use perhaps esign and p an be gave ar et al,200
sets. Then
st, and organ
for the com
ancial provis As a resul creasing ban
costs (Briga edict the fi dered as th
has been usiness failu n containe
anizations a definition kruptcy phe gal institutio r capital, bu They are th of bankrup
udies during ancial studi prediction h
use the fail ause system
s one of the bankrupt c propose app e the invest 06 ).
the probab
nizational e
mpanies whi
sions throug lt, the likeli
nkruptcy lik am,2003 ). financial cr he most ba
used from ure. Genera ed in the f
and the gen for interest enomena m ons. Investo ut its use as
he ways tha ptcy, stock
g the last ies since has been lure of a mic crisis
e factors company propriate tors this
bility of
expenses
ich have
gh debts ihood of kelihood
isis and asic and m classic ally, the financial
eral part t groups more than
ors with a tool to at could
compan taken in impact to corp bankrup Theref process Comme For pre viewpo strategi models likeliho Bankrup intellige univaria discrim adjustm Artifici machin techniqu and the Bankrup risk.(Fir Komija model t multiva liabilitie power al(2011 The res model h t-2, resp years t, a resear regressi third re way the did a r compar nies sharply nto conside on lending porate loan ptcy. fore, accura s and the Co
ercial units eventive me int, tools pr c actions an is one of th ood of bankr
ptcy predic ence, and t ate and m minant analy
ment process al neural n es, Sion-ba ues eoretical m
ptcy theor rouziyan et ani et al (20
to predict t ariate probi es,gross pro than comp )compared sults showe have better pectively, %
t-1 and t-2, rch titled" ion to class espectively %
e logit mod research tit red two mod
y decreases eration wide
decisions fo ns demand
ate and tim ontinuance o managers a easures wh redicting fin nd avoided he techniqu ruptcy estim tion models theoretical. multivariate. ysis models, ses. networks, g ased reaso
models is a ry of gam
al,2010) 005) condu the econom t and logit ofit to sale pany econo
Ohlson log d Ohlson lo than perfor %96, %91 , respective Accounting sify the com
%85.1, %87 del to discr tled "Bankr del of Logit
(Rasoulzad ely in Acco for creditors
they shou
mely predict of credit ins also can be
ich prevent nancial ban from bankr ues and tool mate with co
s can be cla Statistical . Multivari , linear prob
enetic algo ning and
also includ mbling, Ca
ucted a rese mic bankrupt t models w s and net p omic bank git analysis ogit analysi rmance. Oh and %89, ly, %85, % g Data and mpanies stu 7.6 and %8 riminate ana ruptcy pred t and Multip
deh,2000). P ounting and s and also th uld be abl
tion of ban stitutions (A informed t t bankruptc nkruptcy pro
ruptcy(Arab ls to predict ompositions assified in th models th iate statist bability, log orithms, rec fuzzy logi ding criterio ash manage earch titled tcy of comp with explana profit to cu kruptcy in s and multip is model th hlson model While the
74and 76% d the Predic
udied. His m 82.6.Hough
alysis techn diction: an ple-discrimi
Predicting f d financial heir profitab
le to predi
nkruptcy ha Arabmazar e timely of th cy (Etemad
ovides this bmazar et a
t the future s a group of hree groups hemselves a
ical model git, probit,th
ursive Afra ic, are com
on analysis ement theo
"The cond panies in Ir atory variab urrent debt Iran. (Ko ple discrim an multiple prediction fulmer mod 6.(Vadiee et ction of Bu model accu hton in their niques.(Hou investigati inant analys financial ba field. Such bility. Befor ict possibil
as a signific et al,2007 ). he risk of b di,2007). Fr possibility al,2007).Ban
state of the f financial ra , statistical are divided l is comp he total cum
az, rough s mposed art
s of sheet ory and t
ditional like ran". The r bles curren have maxim mijani et minant analy e discrimina accuracy is del predicti t al,2011)H usiness Fail uracy was fo
r study conc ughton 1994, on of cash sis models. T
ankruptcy h research h re creditors lity of com
cant impact
bankruptcy rom a mana
to adoption nkruptcy pr e company' atio.
modeling, a d into two posed of M mulative and sets, suppor tificial inte / entropy theories of elihood of results show nt assets to imum of pr
al 2005, )Va ysis fulmer ant analysis s for years t ion accurac Houghton (19
lure" using or the first
cluded that 4)Aziz et a h flow Mo The results
has been has great respond mpany’s
t on the
adopt to agement n timely rediction s in that
artificial groups, Multiple d Partial rt vector elligence theory, f credit optimal wed four current redictive adiee et models. s Fulmer t, t-1and cy is for
better p discrim The res compar overall to 89 p sample importa size, in and Pro (Lennox Thus, th Exchan bankrup 3. Meth
In this probit, busines the rank likeliho followin
Where, paramet
p(z) wil resultin event o include Probit m the valu to Logi than cum
performance minant analy
ults showed re with desi
accuracy be percent (Az
including ant bankrup ndustrial sec obit models x, 1999). his study to nge, compa ptcy such L hodology
study, we and multip ss failures. B
king of eve ood in a def
ng formula:
Xi (i=1,…
ters of the m ll tend to ze ng likelihoo or not, predi a range of model is use ues 0 and 1.
t models. B mulative lo
e of logistic sis.
d better perf igned mode etween 79% ziz et al,198
949 Englis ptcy factors ction, and e
s, compared
o predict the aring the re
ogit, Probit
use to pre le discrimin By allocatin ery sample finite group.
:
, n) shows model. P (z)
ero and if Z od equal to0
icted likelih values betw ed when the . (Such as b But, the form
gistic funct
c regression
formance of el based on 9 to %92 an 88).Lennox( sh compani include pr economic cy
d with disc
e bankruptcy esults of t t, and Multip
dict corpor nant analys ng some w company. . Success or
1 1 e
independen
) likelihood Z move into 0.5.Logit an hood of the ween 0 and
e dependent ankruptcy a mer uses cu
ion.
, a
P
a
n model, th
f designed m n multiple d
nd was accu (1999)exam ies since 1 rofitability,
ycles. The criminant an
y likelihood the applica
ple-discrim
rate bankru sis. Logit m weights to in This ranki r failure lik
1 e
nt variables d is a numb
+∞, p(z) w analysis inst event. Thu
1.
t variable,w and non-ban umulative li
a
=
e model wa
model by th discriminan uracy of dis mined the re 1978-1994. financial le results show nalysis in p
d of manufa tion of 3 minant analy
uptcy from t model has w ndependent
ing is used kelihood in t
1
...
s, and a an er between will tend to tead of pre us dependen
was qualitati nkruptcy)Pr ikelihood fu
a
as designed
he logistic re t analysis. criminant a easons for t Based on everage, cas wed higher predicting c
acturing firm statistical sis.
three statist wide applica variables, t for determ this model i
nd bi (i=1,…
0 and 1.If 1.when Z i edicting wh nt variable c
ve and be a robit models unction whi
d based on m
egression m Logit mode analysis betw
the bankrup Lennox, th ash flows, c
r accuracy o companies'
ms in Tehra models to
tical model ations in pr this model mining mem
is calculate
…, n) are es f Z move in is equal to z hat will be could encom
able to prov s are mostly ich is norma
multiple
model, in els have ween 73 ptcy of a he most company
of Logit failures
an Stock predict
ls, logit, redicting
predicts mbership d by the
stimated nto ∞, zero, the actually mpass to
Multipl into dis among For suc prerequ context identifie contain this me achieve Classifi Multiva point, it the dis Equatio
:T where ‘ compan
x : T
:Coe
u :is th
The firs corpora had mo
3.1 Var
To iden principa large nu several the who highest collecti This ap variable Varianc called t analyzin
le-discrimin stinct group the groups ch predictio uisite for MD
of ratio-b ed: the firs s companie thod, a com ed score and
ication w ariable-discr
t was regard scriminant on 1 gives
The value th ‘ k’ represe nies. ‘ fkm’ The value fo efficients as
he constant st time Altm ate failure.T
deling more
riables
ntify the m al compone umber of in
principal c ole variance amount in ons of varia proach will e with each ce explained the specific
ng principa
nant analysi ps with diff
and predict ons, some DA is that based mode st group in es that are s mpany was d the maxim was done
riminant an ded as a non
function, a the general
hat the disc nts either th
is also know or the financ
sociated wi
term or the man (1968) This study a e than one v
most importa ent analysis nitial variab omponents e. At first Sp n total var ables that ha l apply to se factor is cal d by each fa c value. Th
al compone
s is a multiv ferent quali t the likelih quantitativ a data item els for sign ncludes com still a going attributed to mum similar
based o nalysis mode n-bankrupt c and the fin
specificatio
u u x
criminant fu he group of wn as the sc cial ratio ‘i’ ith each fina
intercept. suggested m lot attentio variable.
ant financia was used. bles into a
are to revie pss, selects c iance. This ave the high elect the thir
lled factor l factor equal he first spec ents, the va
variable me ities. This s hood of a co ve independ m could be c naling corp mpanies tha g concern. o a bankrup rities.
on optimu el. If the sco company.T nancial rati on of the M
u x
unction gen f collapsed core.
’ for compan ancial ratio
multiple dis on because f
al ratios fo In principa small num ewed so tha combination s collection hest contribu rd, fourth an lodding and
to the squa cific value alues over
ethod in whi study aimed ompany’s de dent variab classified in porate colla at have col
The data it pt or non-ba
um short ore of a com
he mathema ios are cal MDA-based m
⋯ u
nerates for c companies
ny ‘ m’ in g ‘x ’.
scriminant a for the first
or selecting al componen mber of new at the new v n of variable n makes fi ution in expl nd subseque d their Value are of the fa of the high 1 were reg
ich the phen d to recogn ependency t bles were u nto two or m
apse, two g lapsed and tem is there
ankrupt gro
cut point mpany was atical equat lled discrim model.
u x
company ‘ or the grou
group ‘ k ’.
analysis me time in ban
main varia nt analysis, w variables variables ha es that their irst factor.
laining the r ent factors. e fluctuates actor loddin hest and is garded and
nomena are nize the dif to a specifi used.Thus, more group groups are d the secon
efore a com oup accordin
t, identifi lower than tion is referr minating va
m’ in grou up of non-c
ethod for pr nkruptcy pr
ables of th convert all that the sa ave a large r correlation Second F remaining v Correlation between -1 ng .This var
greater tha d used as th
e divided fferences c group. a basic s. In the
readily d group mpany.In
ng to its
ed for shortcut red to as ariables.
up ‘ k ’, ollapsed
redicting rediction
e study, lowing a ame first share of ns are the Factor is
variance. n of each and +1. riance is an 1 . In
signific proport or 90 p was ide Indepen -Return -Debt ra -Debt c -Collect -Invento -Debt to -Produc
-Depen 4. Resu
Statistic Exchan 134 no Busines the half bankrup 1. The c 2. Their 3. Finan 4.They 2000- 2
4.1. log
Logit re
cant specifi ional of the percent, to e entified.
ndent variab n on equity (
atio over ratio tion period ory turnove o equity rati ct to workin
dent variab ults
cal populati nge of Tehra on- bankrupt ss Law in Ir f of their c pt companie companies s r fiscal year ncial inform should hav 2010.
git model
egression m
c values.To e change tot express initi
bles of the s (ROE)
er io
ng capital ra
le was the l
ion of the s an since 200 t companies ran, based o capital must
es was base should be m r should end mation of the ve 10 succ
model fitness
o choose t tal percenta ial changes.
study includ
atio
ikelihood o
study includ 00-2010. To s were used on which th t declare ba
d on the fol manufacturin
d in Septem e companie cessive yea
s was tested
the number age cumulat
Thus After
de:
of bankruptc
ded all man o measure m d. Bankrupt he firms wit ankruptcy o llowing con ng.
mber. s should be ars of activ
d based on th
1 1
r of main tive so that r examining
cy or non- b
nufacturing model fitnes tcy measure th minimum or capital lo nditions:
accessible. vity in Stoc
he followin
e e
ε
variables c the this ma g 22 financi
bankruptcy o
companies ss, the data o e in this stud m accumulat
oss. Sample
ck Exchang
ng equation:
can be con ain compone ial ratios, 7
occurrence.
s accepted i of 55 bank dy was Act ted loss, eq e selection
ge of Tehra
nsidered ents, 80 7 factors
in Stock rupt and 141 of qual with
of non-
Table 1
Table 2
0.000
Table 3
RO Inv Co Pro De De De Co
Accord conclud coeffici gives th
48
Table 4
Observed B Percent
Estimat bankrup
.
1 Regressi
Coeffi Nagel 0.273
.
2 Significa
3. Logistic r
OE
ventory turnov llection period oduct of work
bt ratio bt to equity ra bt coverage ra nstant
ing to likeli ded that the
ient it identi he following
7.667 0.0 0
4. To accura
Bankruptcy
tion accurac pt companie
ion statistics
icient of deter kerke
ant regressio
Sig.
regression c
ver d
ing capital rat
atio atio
ihood value e model is ifies %27 o g formula:
024 0. 0.005X ε
ately estima
0 1
cy was %81 es shown in s
Co Co rmination
0.
on test
7
coefficients
Sig. 1 0.000
1 0.09
1 0.761
1 0.927 tio
1 0.005
1 0.004
1 0.691
1 0.054
e in significa statistically f distributio
003 0
ε
ate of bankr
Estimation 0
133 35
1. For non-b n Table4.
oefficient of d ox & Snell .191
df
40.055
Wald df
14.26 1.000
2.821 1.000
0.092 1.000
0.008 1.000
7.983 1.000
8.214 1.000
0.158 1.000
3.723 1.000
ance test (P y significan on. Table 3
.0004X
ruptcy
n of bankrupt 1 1 2
bankrupt co
determination
5
St d Statistic
0.0 64
0.0 0.0 2
0.0 8
0.0 3
0.0 4
0.0 8
25 3
=0.000) sho nt and due
shows logis
0.001X
cy 1 1 20
mpanies, it
-2 Log like 187.898
tandard deviat 006
002 001 007 078 080 012 52.742
own in Tabl to its resu stic regressi
0.220X
Percentag 99.3 36.4 81.0
was %99 a
elihood
C
Coeffic tion
0.024 -0.003 0.0004 0.001 0.220 0.228
-0.005 -487.667
le 1 and 2, i ulted determ
ion coeffici
0.228X
ge of accuracy
and it was %
Chi-square
cient
7
it can be mination ents and
y
4.2 Pro
Probit r
Table 5
0.000
Table 6
Intercept ROE Inventory tu Collection p Product of w Debt ratio Debt to equ Debt covera
Accord conclud coeffici
Table 7
obit model
regression m
5. Significan
6. Probit reg
urnover period working capit
uity ratio age ratio
ing to like ded that th
ients and giv
1 0
7. To accura
Obser
Percen
model was m
nt regressio
Sig. 7
gression coe
al ratio
lihood valu he model i ves the follo
135 0 0.0742X
ately estima
rved Bankrupt
nt
measured ba
1
on test
7
efficients
Sig. 0.279 0.000 0.059 0.924 0.892 0.012 0.009 0.724
ue in signif is statistica owing form
0.0102 0.0024X
ate of bankr
Estim 0 133 0 tcy 1 37
ased on the
df
34.984
Wal df
1.171 1
13.910 1
3.557 1
0.009 1
0.018 1
6.343 1
6.856 1
0.124 1
ficance test ally signific mula:
0.0019 ε
ruptcy
mation of ban 1 1 18
following e
4
ld Statistic 1 0 0 0 0 0 0 0
t (P=0.000) cant. Table
0.0001X
Per nkruptcy
99 33 8
80.
equation:
ε
Standard d 124.7689 0.0027 0.0010 0.0007 0.0040 0.0363 0.0283 0.0069
) shown in e 6 shows
X 0.0005
rcentage of ac
0
deviation 1 -0.
0
-0
-0
-0 -0. 0.
n Table5, it s probit re
5X 0.09
ccuracy
Chi-square
Coefficient 135.00
.0102 0.0019 0.0001 0.0005 0.0915 .0742 .0024
t can be gression
Estimat bankrup
4.3 Mu
Multipl
Table 8
Table 9
Sig. 0.243
Table 1
ROE Inventory t Collection Product to Debt ratio Debt to equ Debt cover (Constant)
Regardi signific 8 and Table formula
Canonical C 0.220
tion accura pt companie ultiple-discr le-discrimin
8. Table eig
9. Table me
10. Table no
turnover period working capit
uity ratio r ratio
ing the lik cance level,
9.
10 shows a:
Correlation
acy was 80 es, shown in riminant mo nant model w
genvalue fun
eaningful tes
df 7
on-standard
tal ratio
kelihood va it can be sa
non-standa
C
%
0 . For no n Table7. odel
was examin
v
nction
st
C 9
dized coeffic
alue in sig aid that the m
ardized coe
Cumulative % 100.0
on-bankrupt
ned based on
b b
Chi-square 9.130
cient Focal
gnificance model is sta
efficient Fo
t companie
n the follow
b xj
functions
test (P=0.2 atistically in
ocal functio
% of Varianc 100.0
s, it was %
wing equatio
Wilks' Lam 0.951
243) and c nsignificant
ons and gi
ce
%99 and %
on:
mbda
Function 1
0.001 -0.001 0.003 0.001 0.121 0.011
-0.015
-55.071
-comparing t, as shown
ives the fo
Eigenvalue 0.051a
33 % for
it with in Table
Table 1
Observed
Percent
Estimat was %8 Table 1 regressi
Multiple
Table. Multipl regressi Multipl
Conclu
In this analysis model w Logit m model f indicati The res corpora compan non-ban bankrup showed model.
55.07 0
11. To accu
Bankruptcy
tion accurac 84 for non-b 12. Ratio t ion.
discriminant
12 show le-discrimin ion is not h le-discrimin
usion
study we c s models to was %81, fo model, Could
from 55 ba ive high pow sults of Pro ate bankrup nies is %9 nkrupt com pt companie d that Probit
7 0.001 0.015X
urately estim
Estim 0 112 0
35 1
cy in the sa bankrupt co test for com
analysis Data e
ws the Ra nant regress
higher. Also nant regressi
compared p o predict b or identify n
d predict fro ankrupt com wer logit mo obit model c ptcy has % 99 and for mpanies wer es has been t model to
0.001
mate of bank
mation of ban
ample is sh ompanies an mparing th
envelopment a
atio test f ion.The res o,Z test res ion and DE
redicting p ankruptcy.T non-bankrup om 134 non mpany, have odel in pred confirm tha
80
% .accurac r bankrupt
re able to p n able to co
identify ban
0.003X
kruptcy
nkruptcy 1 22 20
hown in Ta nd %36 for he models
69.8 72.0 analysis
for compa sults show th sults don’t EA.
ower of Lo The results pt companie n-bankrupt c e been able diction of co at can predi cy of the P
companies predict corr orrectly pred
nkruptcy fir
X 0.001X
able11. Esti r bankrupt c of efficien
Accura 8%
0%
aring the hat the accu confirm a s
ogit, probit showed th es %99 and companies,
to predict b orporate ban ict power th Probit mod s is 33 pe rectly133 dict bankru rms, no sig
X 0.121
Percen 83.6 36.4
69.8
imation acc ompanies. cy and Mu
p- value acy
0.325
models o uracy of Mu significant d
and multiv hat predictin d %36 for ba 134 compa bankruptcy nkruptcy. his model i dels to iden
ercent.This companies uptcy 18 co
nificant dif
X 0.011
ntage of accur
curacy was
ultiple-discr
Z t
0.4
of efficien ultiple-discr
difference b
variate discr ng power o ankrupt com anies correct
20 compan
in the predi ntify non-b model fro and also f ompany.The fference in t
1X
racy
%70. It
riminant
test
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cy and riminant between
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