___An in-Depth Analysis of the Altman's Filure Prediction Model on Corporate Financial Distress Un Uchumi Supermarket

8/20/2019 ___An in-Depth Analysis of the Altman's Filure Prediction Model on Corporate Financial Distress Un Uchumi Superm…

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European Journal of Business and Management www.iiste.org

ISSN 2222-1905 !aper" ISSN 2222-2#$9 %nline"

&ol.'( No.2$( 201)

2*

An In-Depth Analysis of the Altman’s Failure Prediction Model

on Corporate Financial Distress in Uchumi Supermarket

in Kenya

+dam S,isia1 illiam Sang

1( 2Sera, aitindi

1( 2( $ r alter Bi/,anga %io

1( 2( $( )

1. S/,ool of Business and E/onomi/s( Mount en3a 4niersit3( !% Bo6$)2 /ode 01000 7,ia( en3a8 E-Mail /orresponding aut,or adams,isia:gmail./om

2. S/,ool of Business and E/onomi/s( aara 4niersit3( !riate Bag ; 2015* Nauru( en3a8 E-Mailsanipwil:gmail./om

$. S/,ool of Business and E/onomi/s( Mount en3a 4niersit3( !% Bo6$)2 /ode 01000 7,ia( en3a8 E-Mail waitsara,:3a,oo./o.u

). S/,ool of Business and E/onomi/s( Mount en3a 4niersit3( !.% Bo6$)2 /ode 01000 7,ia( en3a8 E-Mail walter.oio:3a,oo./om

Astract

Man3 firms in deeloping and transitional e/onomies are in finan/ial distress situation( due to low leel of det

seri/e /oerage. 7,e stud3 of finan/ial distress ,as e/ome a signifi/ant gloal issue after t,e gloal finan/ial/risis of 200#. 7,e soaring gloal finan/ial /risis w,i/, ,as resulted to in/reased /ases of usiness failuresresulting from t,e effe/t of anrupt/3 as well as insolen/3. 7,is stud3 t,erefore was /ondu/ted wit, t,eoation=s re/ords su/, as in-,ouse maga>ines(


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Harrett 200#" noted t,at w,en t,e firm is in a finan/ial distress( it fa/es one of two possile /onfli/tsAeit,er /as, s,ortage on t,e assets side of t,e alan/e s,eet( or as a det oer,ang in liailities. Bot, sets of/ir/umstan/es ,oweer draw similar results( namel3 t,at /as, flow is insuffi/ient to /oer /urrent oligations.7,is for/es firms into negotiations wit, /reditors aout t,e /onditions of deferment on t,eir det repa3mentduring t,e ensuing period of distressed restru/turing. ,en t,e firms enter finan/ial distress( t,e3 are ?ui/l3

/onfronted wit, t,e dilemma of raising /apital to fund t,eir restru/turing %ute/,ea( 200*". Hien t,at( few areliale to trust t,is ris3 inestment( espe/iall3 w,en taing into /onsideration t,at a finan/ial oost is not aguarantee to proide a lasting solution to t,e prolems at ,and.

7raditional iews of t,e /auses of finan/ial distress( w,i/, ,ae oer time een partiall3 /onfirmed 3empiri/al results +ndrade and aplan( 199#A +s?uit, et al.( 199)A 7,eodossiou et al.( 199' and ,itaer 1999"(

proide some eiden/e t,at finan/ial distress arises in man3 /ases from endogenous ris fa/tors( su/, asmismanagement( ,ig, leerage( and a non-effi/ient operating stru/ture in pla/e.

#$#$# Financial Distress

%pler and 7itman 199)" define finan/ial distress more roadl3 as a /ostl3 eent t,at affe/ts t,e relations,ip todet ,olders and non-finan/ial stae,olders. +s a /onse?uen/e( a /ompan3 gains an impaired a//ess to new/apital and ears t,e in/reasing /osts of maintaining t,is stri/en relations,ip. +s a rule( t,e term Dfinan/ialdistress is used in a negatie /onnotation in order to des/rie t,e finan/ial situation of a /ompan3 /onfrontedwit, a temporar3 la/ of li?uidit3 and wit, t,e diffi/ulties t,at ensue in fulfilling finan/ial oligations on

s/,edule and to t,e full e6tent Hordon( 19*1".Hordon 19*1" argued on ,is arti/le t,at t,e deelopment of t,e t,eor3 of finan/ial distress as a

pro/ess ,aing spe/ifi/ d3nami/s. Hordon ,ig,lig,ts t,at finan/ial distress is onl3 one state of t,e pro/ess(followed 3 failure and restru/turing( and s,ould e defined in terms of finan/ial stru/ture and se/urit3 aluation.7,e /orporation enters t,is state w,en its power to generate earnings is e/oming wea and t,e amount of dete6/eeds t,e alue of t,e /ompan3=s total assets. ,itaer 1999" interpreted finan/ial distress as a /ru/ial eentw,ose o//urren/e separates t,e time of a /ompan3=s finan/ial ,ealt, from t,e period of finan/ial illness andre?uires undertaing /orre/tie a/tions in order to oer/ome t,e trouled situation.

+ndrade and aplan 199#" identif3 two forms of finan/ial distress t,e first one is default on a det pa3ment( and t,e se/ond one is an attempt to restru/ture t,e det in order to preent t,e default situation.inan/ial distress o//urs w,en a /ompan3 does not ,ae /apa/it3 to fulfill its liailities to t,e t,ird parties+ndrade and aplan( 199#". In/reasing non-performing loan of /ommer/ial ans and delisted of puli//ompanies in Indonesia is a t3pi/al p,enomenon of /orporate finan/ial distress. Hestel et al. 200'" /,ara/teri>efinan/ial distress and failure as t,e result of /,roni/ losses w,i/, /ause a disproportionate in/rease in liailitiesa//ompanied 3 s,rinage in t,e asset alue.

7urets3 and Ma/Ewen 2001" define finan/ial distress as a series of suse?uent stages /,ara/teri>ed 3 a spe/ial set of aderse finan/ial eents. Ea/, stage of finan/ial distress ,as a distress point and /ontinuesuntil t,e ne6t distress point is rea/,ed. 7e/,ni/all3( ea/, stage of finan/ial distress is defined as an interal

etween two distress points. 7,e onset of finan/ial distress egins wit, a olatile de/rease from positie tonegatie /as, flow. 7,e following diidend redu/tion signali>es t,e /,ange to t,e ne6t stage leading to default.7e/,ni/al default on det pre/edes trouled det restru/turing w,i/, usuall3 tends to redu/e t,e ris of potential

anrupt/3. 7,us( for t,e first time( resear/,ers su//eeded in des/riing finan/ial distress as a /ontinuous pro/ess wit, a /lear stru/ture and a /ategori>ation of t,e distress eents.

#$#$% Altman’s Failure Prediction Model+ltman deeloped seeral dis/riminant fun/tionsA t,e first one /alled @-s/ore was deeloped in 19'# using

puli/ firms stratified 3 industr3 and si>e. 7,is model ,as ,ig, predi/tie power two 3ears prior to anrupt/3.+dditionall3( two adaptation of t,e 19'#=s @-s/ore model are presented t,e @=-s/ore +ltman 199$" w,i/, issimilar to t,e preious one e6/ept t,e dis/rimination >ones and t,e @-s/ore +ltman 200'" w,i/, differs fromt,e preious M+ models in t,at it uses four finan/ial ratios and ,as lower dis/rimination >ones /ompared tot,e preious ones.

+ finan/ial ratio is a relatie magnitude of two sele/ted numeri/al alues taen from an enterprisesfinan/ial statements !ande3( 2010". %ften used in a//ounting( t,ere are man3 standard ratios used in ealuatingt,e oerall finan/ial /ondition of a /orporation. In finan/ial anal3sis( a ratio is used as a en/,mar forealuating t,e finan/ial position and performan/e of a firm Edmister( 19*2". inan/ial ratios ma3 e used 3managers wit,in a firm( 3 /urrent and potential s,are,olders owners" of a firm( and 3 a firms /reditors.inan/ial anal3sts use finan/ial ratios to /ompare t,e strengt,s and weanesses in arious /ompanies.

7,e /ore ingredient of multiariate dis/riminant anal3sis is finan/ial ratios. 7,is /onfirms t,atfinan/ial ratios and ratio anal3sis are aluale tools for tra/ing finan/ial ,ealt, of an enterprise. %lson 19#0"

used eig,t traditional finan/ial ratios in ,is model and /on/luded t,at total liailit3 diided 3 total assets(/urrent liailit3 diided 3 /urrent assets( and si>e are t,e most important predi/tors. !redi/tie power offinan/ial ratio depends on its ailit3 to dis/riminate etween anrupt and non-anrupt. inan/ial ratios


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appli/ations in/lude determination of internal li?uidit3( finan/ial riss( operating performan/e and growt,.inan/ial ratios are interrelated and t,erefore are anal3>ed in relation to ea/, ot,er. ,anges in finan/ial ratiosand /as, flow trend oertime or /ompared wit, similar firms in t,e industr3 ma3 indi/ate potential prolems ors3mptoms in spe/ifi/ area +ltman( 200'". or e6ample in/reasing or ,ig, /urrent ratio indi/ates poor effi/ien/3of woring /apital and related s3mptoms /ould e ,ig, /as, /onersion /3/les( low re/eiales turnoer or low

return on assets. Benation. 7,e first t,ree ran/,es wereopened in 19*'. 4/,umi e/ame a trendsetter in low pri/ing to t,e adantage of all /onsumers( w,ile at t,e sametime maintaining ,ig, standards in ?ualit3 of goods and seri/es.

In t,e 1990s 4/,umi spear,eaded t,e ,3permaret /on/ept in en3a. 7,e introdu/tion of t,e,3permaret /on/ept and spe/ialt3 s,ops ,as een a runawa3 su//ess. It was /redited for ,aing reolutioni>edt,e retail food se/tor 3 giing /ustomers a ariet3 of produ/ts to /,oose from and introdu/ing t,e /on/ept ofself-seri/e. It ,as also een a ma


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%$#$% 0ntropy .heory

+//ording to t,e Entrop3 7,eor3 Balan/e S,eet e/omposition Measure 7,eor3"( one wa3 of identif3ing firms=finan/ial distress /ould e a /areful loo at t,e /,anges o//urring in t,eir alan/e s,eets +>i> G ar( 200'".7,is t,eor3 emplo3s t,e 4niariate +nal3sis and Multiple is/riminant +nal3sis M+" in e6amining /,angesin t,e stru/ture of alan/e s,eets. 4niariate +nal3sis is t,e use of a//ounting ased ratios or maret indi/ators

for t,e distress ris assessment Natalia( 200*". 7,e finan/ial ratios of ea/, /ompan3( t,erefore( are /omparedon/e at a time and t,e distin/tion of t,ose /ompanies t,roug, a single ratio wit, a /ut ; off alue is used to/lassif3 a /ompan3 as eit,er distressed or non- distressed Monti G Moriano( 2010".

M+ Multiariate Statisti/ or Multiariate anal3sis" is a statisti/al anal3sis in w,i/, more t,an oneariale are anal3>ed at t,e same time Slotemaer( 200#". 7,e aim of M+ is to eliminate t,e weaness ofuniariate anal3sis. irst( single ratios /al/ulated 3 uniariate anal3sis do not /apture time ariation of finan/ialratios. 7,is means t,at a//ounting ratios ,ae t,eir predi/tie ailit3 one at a time( and it is impossile toanal3>e( for instan/e( rates of /,ange in ratios oer time. Se/ond( single ratios ma3 gie in/onsistent results ifdifferent ratio /lassifi/ations are applied for t,e same firm. 7,ird( man3 a//ounting ariales are ,ig,l3/orrelated( so t,at t,e interpretation of a single ratio in isolation ma3 e in/orre/t. 7,e single ratio is not ale to/apture multidimensional interrelations,ips wit,in t,e firm. inall3( sin/e t,e proailit3 of failure for a sampleis not t,e same as for t,e population( spe/ifi/ alues of t,e /utoff points otained for t,e sample will not e alidfor t,e population Natalia( 200*". 7,erefore( if a firm=s finan/ial statements refle/t signifi/ant /,anges in t,e

/omposition of assets and liailities on its alan/e-s,eet it is more liel3 t,at it is in/apale of maintaining t,ee?uilirium state. If t,ese /,anges are liel3 to e/ome un/ontrollale in future( one /an foresee finan/ialdistress in t,ese firms +>i> G ar( 200'".

%$#$& Credit /isk .heory

redit is t,e proision of goods and seri/es to a person or entit3 on agreed terms and /onditions w,ere t,e pa3ments are to e made later wit, or wit,out interest. uring t,e /ontra/t period( not all detors will repa3 t,eirdues as and w,en t,e3 fall due. ,en t,e detor does not pa3 t,eir dues on t,e due date( t,e lender is e6posed to/redit riss w,i/, ma3 in turn lead to default. redit ris is t,erefore t,e inestor=s ris of loss( finan/ial orot,erwise( arising from a orrower w,o does no pa3 ,is or ,er dues as agreed in t,e /ontra/tual terms Natalia(200*". redit ris t,eories( /losel3 related to Basel I and Basel II a//ordsA mostl3 refer to t,e finan/ial firm. 7,e

proposed Basel II framewor /onsists of t,ree pillars 1" minimum /apital re?uirements( /urrentl3 set e?ual to#( a//ording to a purposel3-defined /apital ratio( 2" superisor3 reiew of an institution=s internal assessment

pro/ess and /apital ade?ua/3( $" effe/tie use of puli/ dis/losure to strengt,en maret dis/ipline as a/omplement to superisor3 efforts. 7,e /urrent Basel II +//ord utili>es /on/ept of a /apital ratio t,at is/al/ulated diiding an=s /apital amount 3 a measure of ris fa/ed 3 it referred to ris-weig,ted assets"

+s noted 3 estgaard and ie in large amounts promptl3 !ande3( 2005". +n imalan/e etween /as, inflows and outflows wouldmean failure of /as, management fun/tion of t,e firm. !ersisten/e of su/, an imalan/e ma3 /ause finan/ialdistress to t,e firm and( ,en/e( usiness failure +>i> G ar( 200'".

%$#$1 2amler’s /uin .heory

Hamler Cuin t,eor3 was deeloped 3 eller( in 19'# w,o ased it on t,e proailit3 t,eor3 w,ere agamler wins or loses mone3 3 /,an/e. 7,e gamler starts out wit, a positie( aritrar3( amount of mone3w,ere t,e gamler wins a dollar wit, proailit3 p and loses a dollar wit, a proailit3 1-p" in ea/, period. 7,e

game /ontinues until t,e gamler runs out of mone3 Espen( 1999". 7,e firm /an e t,oug,t of as a gamler pla3ing repeatedl3 wit, some proailit3 of loss( /ontinuing to operate until its net wort, goes to >eroanrupt/3". In /onte6t of t,e firm=s finan/ial distress( firm would tae t,e pla/e of a gamler. irm would


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/ontinue to operate until its net wort, goes to >ero( point w,ere it would go anrupt. 7,e t,eor3 assumes t,atfirm ,as got some gien amount of /apital in /as,( w,i/, would eep entering or e6iting t,e firm on random

asis depending on firm=s operations. In an3 gien period( t,e firm would e6perien/e eit,er positie or negatie/as, flow. %er a run of periods( t,ere is one possile /omposite proailit3 t,at /as, flow will e alwa3snegatie. Su/, a situation would lead t,e firm to de/lare anrupt/3( as it ,as gone out of /as,. Fen/e( under

t,is approa/,( t,e firm remains solent as long as its net wort, is greater t,an >ero. 7,is net wort, is /al/ulatedfrom t,e li?uidation alue of sto/,olders= e?uit3.it, an assumed initial amount of /as,( in an3 gien period(t,ere is a net positie t,at a firm=s /as, flows will e /onsistentl3 negatie oer a run of periods( ultimatel3leading to anrupt/3 +>i> G ar( 200'". 7,e mae t,einternal signals of failure and lame e6ternal /,anges for t,eir usiness de/line. Fot/,iss 1995" e6amined t,erelations,ip etween management /,anges and post-anrupt/3 performan/e. %er )0 out of 19* puli//ompanies t,at emerged from etween 19*9 and 19## /ontinued to e6perien/e operating losses in t,ree 3earsfollowing anrupt/3( $2 re-enter anrupt/3 or priatel3 restru/ture t,eir det. Fot/,iss 1995" suggestedt,at t,e /ontinued inolement of pre-anrupt/3 management in t,e restru/turing pro/ess is strongl3 asso/iatedwit, poor post-anrupt/3 performan/e. Fer results s,ow t,at retaining pre-anrupt/3 management is strongl3related to worse post-anrupt/3 performan/e.

,ere ot,er /ompanies ,ae undertaen management su//ession planning for e3 roles and identified,ig, potential in t,eir /ompan3=s emplo3ee=s( usuall3 firms in finan/ial distress do not prepare at all for topmanagement su//ession Hallowa3 G Jones( 200'". 7,is /ould lead to re/ruiting unalan/ed management teamw,i/, la/ essential sills to steer t,e /ompan3 a,ead. +n3 wrong inestment de/ision made ma3 plunge t,e/ompanies to finan/ial distress sin/e some of t,e de/ision s inoles ,uge /as, outla3 are irreersile.

7,e importan/e of innoation to a firms= future ,as een do/umented e6tensiel3( t,oug, t,e leel ofris asso/iated wit, innoation ,as een e6amined to a small degree ,ao( Kipson G Koutsina( 2012". 7,e

proailit3 t,at innoation will drie a firm to finan/ial distress is ,ig, espe/iall3 w,ere t,e /ompetitorsintrodu/es innoatie and /ompetitie produ/ts w,i/, redu/es t,e attra/tieness of t,e /ompan3=s produ/ts andseri/es Ja,ur G Puadir( 2012". 7,erefore( innoation /an eit,er gie a firm a /ompetitie edge to its rials orwill see its demise e?uall3.

,ile most /ompanies rel3 on t,eir finan/ial performan/es as t,e e3 arometer of finan/ial ,ealt,( itis important not to ignore managerial and operational signals @waig G !i/ett( 2012". Man3 profitale

usinesses ,ae found t,emseles in troule due to rapid e6pansion lie 4/,umi Supermarets or t,eintrodu/tion of a formidale /ompetitor @waig G !i/ett( 2012". In ea/, of t,ese instan/es( t,e /ompanies weresu//essful efore an operational eent or un,eeded signal led to finan/ial prolem and in some /ases t,esuse?uent failure of t,e /ompan3. In ot,er /ountries( t,e usiness t,at were ale to re/ogni>e earlier warningsigns su/, as @ellers( anadians 7ire and 7,e Ba3 ,ae suried 3 diffentiating t,emseles or /,anging andimproing t,eir usiness model @waig G !i/ett( 2012"

%$& .he Costs of Corporate Financial Distress

In t,eor3( finan/ial distress and anrupt/3 matter if t,e3 impose dead-weig,t /osts on t,e firm t,at are orne 3t,e s,are,olders t,roug, an e6 ante /ompensation to t,e /reditors for t,e possiilit3 of in/urring t,ese /osts e6

post. In addition( finan/ial distress and anrupt/3 ma3 impose /osts on stae,olders ot,er t,an t,e firm=s/apital /ontriutors. 7o t,e e6tent t,at finan/ial distress and anrupt/3 are /ostl3( and if t,ese /osts areineitale( t,en irtuall3 all /orporate finan/ial de/isions will e affe/ted 3 su/, /osts. 7,us( t,e magnitude oft,e finan/ial distress and anrupt/3 /osts is an important empiri/al ?uestion.

%$&$# Direct Costsire/t anrupt/3 /osts are t,e legal( administratie and adisor3 fees t,at t,e firm ears as a result of enteringt,e formal anrupt/3 pro/ess. arner 19**" estimates t,e dire/t /ost to e around ) of t,e firm=s pre-


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anrupt/3 alue( using a sample of railroad anrupt/ies during 19$$ and 1955. eiss 1990" uses a sample of$* anrupt firms in t,e period 19#0-19#'( and estimates t,e dire/t /osts to e around $ of t,e pre-anrupt/3firm alue. +ltman and Fot/,iss 200'" proide a ni/e summar3 of t,e estimates of t,e dire/t anrupt/3 /ostsin t,e literature. 7,e findings in all t,ese studies suggest t,at dire/t anrupt/3 /osts are unliel3 to represent asignifi/ant determinant of t,e firm=s /apital stru/ture de/ision.

In more re/ent 3ears we ,ae witnessed seeral mega anrupt/3 filings 3 /ompanies( su/, asKe,man Brot,ers( Enron( and orldom. 7,e total dire/t anrupt/3 /osts for Enron were estimated to top one

illion dollars. Een t,oug, t,is onl3 represents aout 1.' of t,e firm=s pre-anrupt/3 alue( t,is staggeringnumer still implies t,at a lot of resour/es were used up in t,e anrupt/3 pro/ess of t,e former energ3 giant.it, t,e pre-anrupt/3 assets alue of Q'$9 illion( Ke,man Brot,ers= anrupt/3 is 3 far t,e largest/orporate anrupt/3 in t,e 4S ,istor3. It is also liel3 to e t,e most e6pensie /orporate failure. +s of

Noemer 2011( t,e legal /osts asso/iated wit, Ke,man Brot,ers= anrupt/3 ,ae totaled aout Q1.5 illion.

%$&$% Indirect Costs

!otentiall3 more signifi/ant and sustantial are t,e indire/t /osts of finan/ial distress and anrupt/3. 7,ese/osts /an e iewed as opportunit3 /osts( in t,at t,e3 /olle/tiel3 represent t,e out/ome of su-optimal a/tions

3 /orporate stae,olders w,en t,e firm e/omes finan/iall3 distressed +ltman and Fot/,iss( 200'". 7,us(/osts t,at arise e/ause of inter- or intra-group /onfli/ts of interest( as3mmetri/ information( ,oldout prolems(lost sales and /ompetitie positions( ,ig,er operating /osts( and ineffe/tie use of management=s time all

potentiall3 represent t,e indire/t /osts of anrupt/3.Seeral studies /laim t,e indire/t /osts of finan/ial distress to e signifi/ant and positie. or e6ample(

+ltman 19#)" measures t,e indire/t /osts of anrupt/3 as t,e de/line in t,e sales of anrupt firms relatie toot,ers in t,e same industr3 and as t,e differen/e etween t,e reali>ed earnings and t,e fore/asted earnings. %nt,at asis( t,e aut,or argues t,at indire/t anrupt/3 /osts on aerage range from 11 to 1* of firm alue upto t,ree 3ears prior to anrupt/3. Foweer( t,is stud3 does not /learl3 distinguis, /osts attriutale to finan/ialdistress from t,ose attriutale to e/onomi/ distress.

%$( Financial Distress Corporate .urnaround Strate'ies

,itaer 1999" /ategori>es finan/ial distress into /ategories. istress due to poor management firm spe/ifi/distress" and distress as a result of e/onomi/ de/line /ommon fa/tors". 7,ere are arious finan/ial distress/orporate turnaround strategies( t,ese in/ludeA

%$($# Mana'erial /estructurin'

,anges in top management are argued to e one of t,e main /onditions for su//essful turnarounds as t,e3 are atangile signal to /reditors t,at a/tion is eing taen 3 t,e distressed firm Fofer( 19#0". In/ompetent managersma3 ,ae een t,e /ause of finan/ial distress t,roug, poor planning or ineffi/ient de/ision maing. ,itaer1999" refers t,is as firm-spe/ifi/ distress. 7,ese managers need to e repla/ed wit, management teams w,o /ana//uratel3 assess t,e sour/e of distress and implement strategies ne/essar3 for su//essful turnaround Ko,re(Be,eian( G !almer( 200)". !ear/e G Coins 199$" also stress t,e importan/e of management in turningdistressed firms around. 7,e3 argue t,at a management team la/ing in t,e sills needed to respond effi/ientl3and in a timel3 manner will result in /ontinued de/line and t,e eentual failure of t,e /ompan3.

Sudarsanam and Kai 2001" suggest t,at /reditors will onl3 proide /ontinued finan/ial support if t,e3are reassured t,at management will e ale to /ope wit, distress. enis G ruse 2000" find t,at $' of t,esample firms t,e3 stud3 e6perien/e managerial turnoer in top e6e/uties following performan/e de/lines.Managerial restru/turing in/ludes repla/ement of senior management andor t,e ,ief E6e/utie %ffi/er.%erall( managerial restru/turing ma3 e a /ru/ial fa/tor in t,e turnaround pro/ess of a distressed firm.2.).2%perational Cestru/turing

%perational restru/turing refers to t,e effi/ien/3operating turnaround stage. 7,is stage aims to restore profitailit3 3 /ontrolling /osts and redu/ing oer,eads t,roug, t,e sale of surplus fi6ed resour/es su/, as land(e?uipment( and offi/es. B3 de/reasing input and ma6imi>ing output firms /an generate /as, flow at least in t,es,ort term" and en,an/e effi/ien/3. ,en firms re/ogni>e distress( operational restru/turing is usuall3 t,e firststrateg3 implemented. Foweer( alt,oug, ne/essar3( operational restru/turing is primaril3 a s,ort term fi6 usedto generate /as, flow ?ui/l3. Sudarsanam G Kai 2001" argue t,at if used as a stand-alone strateg3( it ma3 not

e enoug, for re/oer3 from distress. !ast literature suggests t,at operational restru/turing in t,e form of pur/,ases are less liel3 t,an sales. Neert,eless( if produ/tiit3 /an e signifi/antl3 improed( distressed firmsma3 uild new plants or inest in more adan/ed te/,nolog3 and e?uipment.

%$($& Asset /estructurin'

,en a distressed firm sells off lines of usinesses w,i/, are unprofitale or not at t,e /ore operations of t,e/ompan3( it is /onsidered to e engaging in asset restru/turing. 7,e aim of t,is form of restru/turing is to realign

t,e fo/us of t,e firm 3 redu/ing unrelated diersifi/ation and refo/using t,e usiness portfolio around /ore/ompeten/ies arner( 199*". ,ang 199'" finds t,at poorl3 performing firms will e motiated to diest linesof usiness w,i/, do not generate /ompetitie adantages. +sset restru/turing allows t,e firm to re-ealuate its


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operations and reorgani>e usiness units into more effi/ient groups. 7,is form of restru/turing is espe/iall3ne/essar3 if agen/3 /osts ,ae resulted in oer diersifi/ation 3 management.

+lt,oug, /ontra/tion poli/ies ,ae een found to e t,e dominant form of restru/turing Jo,n( Kang(G Netter 1992". +sset restru/turing /ould also refer to a/tions w,i/, in/rease t,e si>e of t,e firm su/, asinestments( strategi/ allian/es(


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some e/onometri/ prolems wit, t,e single period logit model were dis/ussed 3 Fillegeist 200)". irst( is t,esample sele/tion ias t,at arises from using onl3 one( no randoml3 sele/ted oseration for ea/, anrupt/ompan3( and se/ond( t,e model fails to in/lude time ar3ing /,anges to refle/t t,e underl3ing ris of

anrupt/3. Being ased on a di/,otomous /lassifi/ation( t,e traditional stati/ model is not suited to ,andle t,etemporal /on/ept.

S,umwa3 2001" demonstrated t,at t,ese prolems /ould result in iased( ineffi/ient( and in/onsistent/oeffi/ient estimates. 7o oer/ome t,ese e/onometri/ prolems ,e proposed t,e ,a>ard model for predi/ting

anrupt/3 and found t,at it was superior to t,e logit and t,e M+ models. 7,is parti/ular model is a/tuall3 amulti-period logit model e/ause t,e lieli,ood fun/tions of t,e two models are identi/al. or t,is reason( t,edis/rete-time ,a>ard model wit, time-ar3ing /oariates /an e estimated 3 using t,e e6isting /omputer

pa/ages for t,e anal3sis of inar3 dependent ariales. 7,e main parti/ularities of t,e ,a>ard model /onsist int,e fa/ts t,at firm spe/ifi/ /oariates must e allowed to ar3 wit, time for t,e estimator to e more effi/ient anda aseline ,a>ard fun/tion is also re?uired( ut w,i/, /an e estimated dire/tl3 wit, ma/roe/onomi/ ariales torefle/t t,e radi/al /,anges in t,e enironment.

urt,er on( Nam et al 200#" e6tended t,e wor of S,umwa3 2001" and deeloped a duration modelwit, time ar3ing /oariates and a aseline ,a>ard fun/tion in/orporating ma/roe/onomi/ ariales( su/, ase6/,ange rate olatilit3 and interest rate. 4sing t,e proposed model( t,e3 inestigated ,ow t,e ,a>ard rates oflisted /ompanies in t,e orea Sto/ E6/,ange are affe/ted 3 /,anges in t,e ma/roe/onomi/ enironment and

3 time ar3ing /oariate e/tors t,at s,ow uni?ue finan/ial /,ara/teristi/s of ea/, /ompan3. B3 inestigatingt,e out-of-sample fore/asting performan/es of t,eir model /ompared to t,e results of ot, a traditionaldi/,otomous stati/ model and also a logit model wit, time-ar3ing /oariates ut no aseline ,a>ard fun/tion(t,e3 demonstrated t,e improements produ/ed w,en allowing temporal and ma/roe/onomi/ dependen/ies.

In anot,er stud3( +dulla, et al 200#" /ompared t,ree met,odologies of identif3ing finan/iall3distressed /ompanies in Mala3sia t,is are multiple dis/riminant anal3sis M+"( logisti/ regression and ,a>ardmodel. In a sample of 52 distressed and non-distressed /ompanies wit, a ,oldout sample of 20 /ompanies( t,e

predi/tions of ,a>ard model were a//urate in 9)(9 of t,e /ases e6amined. 7,is was a ,ig,er a//ura/3 rate t,angenerated 3 t,e ot,er two met,odologies. Foweer( w,en t,e ,oldout sample was in/luded in t,e sampleanal3>ed( M+ ,ad t,e ,ig,est a//ura/3 rate of #5. +mong t,e ten determinants of /orporate performan/ee6amined( t,e Catio of et to 7otal +ssets was a signifi/ant predi/tor of /orporate distress regardless of t,emet,odolog3 used. In addition( Net In/ome Hrowt, was anot,er signifi/ant predi/tor in M+( w,ereas t,eCeturn on +ssets was an important predi/tor w,en t,e logisti/ regression and ,a>ard model met,odologies wereused.

In re/ent 3ears man3 t3pes of ,euristi/ algorit,ms su/, as neural networs and de/ision trees ,ae also een applied to t,e anrupt/3 predi/tion prolem and seeral improements in t,e finan/ial distress predi/tionwere noti/ed. or e6ample t,e studies made 3 7am and iang 1992" and Sal/,energer et al. 1992" proidedeiden/e to suggest t,at neural networs outperform /onentional statisti/al models su/, as dis/riminantanal3sis( logit models in finan/ial appli/ations inoling /lassifi/ation and predi/tion. Soon after t,at( ,3rid+rtifi/ial Neural Networ met,ods were proposed in some finan/ial distress predi/tion studies. or e6ample(Rim and Mit/,ell 2005" tested t,e ailit3 of a new te/,ni?ue( ,3rid +NN=s to predi/t /orporate distress inBra>il. 7,e models used in t,eir stud3 were /ompared wit, t,e traditional statisti/al te/,ni?ues and /onentional+NN models. 7,e results indi/ated t,at t,e most releant finan/ial ratios for predi/ting Bra>ilian firm failure areCeturn on apital Emplo3ed( Ceturn on 7otal +ssets( Net +ssets 7urnoer( Solen/3 and Hearing.

&arious aspe/ts of /orporate finan/ial distress ,ae een reiewed in t,e en3an /onte6t. ogi 200$"did a stud3 to deelop a dis/riminant model in/orporating finan/ial ratio stailit3 t,at /ould e used to predi/t/orporate failure. Fe soug,t to identif3 /riti/al finan/ial ratios wit, signifi/ant predi/tie ailit3. Fis findings,owed t,at it was possile to predi/t /orporate failure wit, up to *0 a//ura/3 t,ree 3ears efore a/tualo//urren/e using stailit3 dis/riminant model. iege 1991" ,ad earlier formulated a model to predi/t usinessfailure among en3an /ompanies w,i/, a/,ieed a predi/tion a//ura/3 of 90 two 3ears efore a/tual failure.

Ng=ang=a 200'" soug,t to e6plore and e6pose possile indi/ators of impending failures among man3firms in deeloping /ountries and deeloped a predi/tion model for insuran/e /ompanies in en3a. Fe deried afailure predi/tion model using /as,-flow information and multiple dis/riminant anal3sis te/,ni?ues. 7,e model3ielded an oerall /orre/t /lassifi/ation a//ordan/e of #5 a 3ear prior to failure /onfirming t,at /as,-flows /an

e used to gie /lear and pre/ise information aout an entit3=s finan/ial ,ealt,.7aliani 2010" /arried out a stud3 on predi/ting finan/ial distress in /ommer/ial ans in en3a. Fis

stud3 reealed t,at none of a/tiit3 and turn-oer ratios was found to e /riti/al in predi/ting finan/ial distress in/ommer/ial ans in en3a. 7,e model attained *0 and 100 /orre/t /lassifi/ation in 3ear 1 and 3ear $

respe/tiel3. 7,e findings are /onsistent wit, t,e studies 3 iragu 1991"( iege 1991" and Nganga 200'".Bwisa 200*" in ,is stud3 noted t,at +ltman finan/ial distress predi/tion model was appli/ale lo/all3. Fe foundout t,at model is appli/ale in t,e sense t,at ' out of 10 failed firms t,at were anal3>ed indi/ated *0 alidit3 of


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$5

t,e model.

&$,$ /esearch Methodolo'y

7,is stud3 applied multiariate dis/riminant anal3sis model in predi/ting finan/ial distress in an organi>ation.7,e resear/, design adopted in t,is resear/, was a des/riptie stud3. +//ording to ooper and S/,indler 2001"(

a des/riptie stud3 or a formal stud3 is a stud3 t,at is t3pi/all3 stru/tured wit, /learl3 stated inestigatieoed in a manner to fa/ilitate anal3sis. ataanal3sis inoled preparation of t,e /olle/ted data( /oding( editing and /leaning of data in readiness for

pro/essing using S!SS pa/age ersion 20. S!SS was preferred e/ause it is s3stemati/ and /oers a wide rangeof t,e most /ommon statisti/al and grap,i/al data anal3sis.

or t,e purpose of t,is stud3( Multiariate is/riminant +nal3sis M+" statisti/al te/,ni?ue as used 3 +ltman 200'" was adopted. +ltman 200'" is of t,e opinion t,at ratios measuring profitailit3( li?uidit3 andsolen/3 are t,e most signifi/ant ratios. +ltman /omined a numer of ratios and deeloped on insolen/3

predi/tion model.$.2.1 +nal3ti/al ModelCeised @-s/ore model( +ltman 200'" was used. @-s/ore is a linear /omination of four /ommon usinessfinan/ial ratios( weig,ted 3 /oeffi/ients. +nal3sis of t,e four measures were oones34 ; %$9 DSafe >one

#$#< 34 < %$ 9 DHre3 >one Z” < 1.1 “Distress” zone

4.0. Findings4.1 The Z- Model calculated from 2001 to 2005


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&ol.'( No.2$( 201)

$'

Table 4.1 The Calculated values of Z in 2001Independent +ariales =alues Discriminant Independent +ariales> Discriminat 3 +alue

oring /apital7otal assets 0.$'$9)) '.5' 2.$#*)*$9#)

$.90'259Cetained earningstotal asset 0.0)*'55 $.2' 0.155$55595

Earnings efore interest ta6estotal assets 0.0*90$* '.*2 0.5$112*2##

Boo alues of e?uit3total asset 0.*92''9 1.05 0.#$2$0209*Source Cesear/, findings7,e findings in tale ).1 indi/ates t,at t,e /al/ulated @-s/ore model alue in t,e 3ears 2001 was $.90'259.

Table 4.2 Calculated values of Z in the year 2002Independent ariales &alues is/riminant Independent ariales8 is/riminat @ alue

oring /apital7otal assets 0.$5#$# '.5' 2.$509*$)1#

$.#*)5'9Cetained earningstotal asset 0.0)#50# $.2' 0.15#1$*2)#

Earnings efore interest ta6estotal assets 0.0#05'5 '.*2 0.5)1$9)##5

Boo alues of e?uit3total asset 0.*#)#2$ 1.05 0.#2)0'$'99

Source Cesear/, findings7,e findings in tale ).2 indi/ates t,at t,e /al/ulated @-s/ore model alue in t,e 3ears 2002 was $.#*)5'9

Table 4. Calculated Z value in 200Independent +ariale =alues Discriminant Independent +ariales> Discriminat 3 +alue

oring /apital7otal assets 0.$'#*1) '.5' 2.)1#*'5$15

$.#$1'**Cetained earningstotal assest 0.0)9125 $.2' 0.1'01)'91

Earnings efore interest ta6estotal assets 0.0'#09 '.*2 0.)5*5'5*1'

Boo alues of e?uit3total asset 0.*5*$$2 1.05 0.*9519##''

Source Cesear/, findings7,e findings in tale ).$ indi/ates t,at t,e /al/ulated @-s/ore model alue in t,e 3ears 200$ was $.#$1'**.

Table 4.4 Calculated value in 2004Independent +ariale =alues Discriminant Independent +ariales> Discriminat 3 +alue

oring /apital7otal assets 0.$$*'01 '.5' 2.21)''0*)'

$.5#*9##Cetained earningstotal assest 0.0)9$)9 $.2' 0.1'0#*#95'

Earnings efore interest ta6estotal assets 0.0*15## '.*2 0.)#10*$'1'

Boo alues of e?uit3total asset 0.'9'5)* 1.05 0.*$1$*)22*

Source Cesear/, findings7,e findings in tale ).) indi/ates t,at t,e /al/ulated @-s/ore model alue in t,e 3ears 200) was $.5#*9##.

Table 4.! Calculated value of Z in 200!Independent +ariale =alues Discriminant Independent +ariales> Discriminat 3 +alue

oring /apital7otal assets 0.0$#9#9 '.5' 0.255*''1#9

1.950*)#Cetained earningstotal assest 0 $.2' 0

Earnings efore interest ta6estotal assets 0.2001'5 '.*2 1.$)510*15$

Boo alues of e?uit3total asset 0.$$$21) 1.05 0.$)9#*)2'*

Source Cesear/, findings7,e findings in tale ).5 indi/ates t,at t,e /al/ulated @-s/ore model alue in t,e 3ears 2005 was 1.950*)#

4.2. Inferential tatistical anal!sisTable 4." Descri#tive statistics of the variables

Mean Std. eiation N

@ alues $.)$02)#2 .#$'50$#9 5oring /apital7otal asset .29$525' .1)2*#)'' 5Cetained earnings7otal asset .0$#92*5 .021**105 5Earnings efore interest ta6es7otal asset .099###9 .05'29291 5Boo alues of e?uit37otal liailities .'*291'# .19$'1)$$ 5

Source Cesear/, findings

7,e findings in tale ).' indi/ate t,e des/riptie statisti/s of t,e ariales in w,i/, t,e mean of t,e @-s/ore alueis $.)$0 wit, standard deiation of 0.#$'5. 7,e mean of woring /apital 7otal assets is 0.29$5 wit, standarddeiation of 0.1)2#. 7,e findings also indi/ate t,at t,e means of Cetained earnings7otal assets( Earnings efore

interest ta6estotal asset and oo aluestotal liailities are 0.0$#9( 0.0999( and 0.'*29 wit, standard deiations0.021*( 0.05'$ and 0.19$' respe/tiel3.


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&ol.'( No.2$( 201)

$*

Table 4.$ The correlation

@alues

oring/apital7otalasset

Cetainedearnings7otalasset

Earnings eforeinterestta6es7otal asset

Boo alues ofe?uit37otalliailities

!earsonorrelation

@ alues 1.000 .99' .9#5 -.9** .999

oring/apital7otal asset

.99' 1.000 .995 -.992 .991

Cetainedearnings7otal asset

.9#5 .995 1.000 -.99* .9*'

Earnings eforeinterest ta6es7otalasset

-.9** -.992 -.99* 1.000 -.9'5


.999 .991 .9*' -.9'5 1.000

Sig. 1-tailed"

@ alues . .000 .001 .002 .000oring/apital7otal asset

.000 . .000 .000 .001

Cetainedearnings7otal asset .001 .000 . .000 .002

Earnings efore

interest ta6es7otalasset

.002 .000 .000 . .00)


.000 .001 .002 .00) .

N 5 5 5 5 5


7,e findings in tale ).* indi/ates t,at t,ere is a strong positie /orrelation etween @ alues and oring/apital7otal asset r0.99'". 7,e findings indi/ate t,at t,e /orrelation is signifi/ant at 5 signifi/an/e leelgien t,at p-alue 0.000" is less t,an alp,a 0.05" t,e findings in tale ).* indi/ate t,at t,ere is a strong negatie

/orrelation etween @ alues and Earnings efore interest ta6es7otal asset r-0.9**". 7,e findings indi/ate t,att,e /orrelation is signifi/ant at 0.05 leel of signifi/an/e sin/e t,e p-alue 0.002" is less t,an alp,a 0.05".

Table 4.% &e'ression coefficient

Model 4nstandardi>ed oeffi/ients Standardi>edoeffi/ients

t Sig.

B Std. Error Beta

1

onstant" 1.*$5 .210 #.2'9 .00*

Cetained earnings7otal asset -'.$2$ $.)') -.1'5 -1.#25 .$19

Earnings efore interestta6es7otal asset

-).9)# 1.12' -.$$$ -).$9* .1)2

Boo alues of e?uit37otalliailities

$.'19 .10' .#$# $).21' .019

a. ependent &ariale @ alues


7,e findings in tale ).# indi/ate t,e regression model generated 3 t,e independent and t,e dependent ariale.7,e model generated is gien as @1.*$5 ; '.$2$ Cetained earnings7otal assets ; ).9)# Earnings efore interestta6es7otal asset O $.'19 Boo alues of e?uit3 7otal liailities. 7,e findings indi/ate t,at t,e /oeffi/ient ofBoo alues of e?uit3 7otal liailities is positie and signifi/ant at 0.05 leel of signifi/an/e gien t,at t,e p-alue 0.019" is less t,an alp,a 0.05". 7,e findings indi/ate t,at t,e /onstant 1.*$5" is signifi/ant at 0.05 leelof signifi/an/e gien t,at p-alue 0.00*" is less t,an alp,a 0.05" indi/ating t,at @ depends on t,e independentariales.


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&ol.'( No.2$( 201)

$#

Table 4.( The variables e)cluded

Model Beta In t Sig. !artialorrelation

ollinearit3 Statisti/s

7oleran/e

1 oring /apital7otal asset 1.120 . . 1.000 1.*1*E-005

a. ependent &ariale @ alues . !redi/tors in t,e Model onstant"( Boo alues of e?uit37otal liailities( Earnings efore interest ta6es7otal asset(Cetained earnings7otal asset


7,e findings in tale ).9 indi/ates t,at woring /apital was e6/luded in t,e regression model sin/e it isinsignifi/ant as indi/ated 3 t,e null p-alue and null t alue.

Table 4.10 *i'nificance of the re'ression +odelA?*=A

Model Sum of S?uares df Mean S?uare Sig.

1

Cegression 2.*99 $ .9$$ 15)#5.)50 .00'

Cesidual .000 1 .000

7otal 2.*99 )a. ependent &ariale @ alues . !redi/tors onstant"( Boo alues of e?uit37otal liailities( Earnings efore interest ta6es7otal asset( Cetainedearnings7otal asset


7,e findings in tale ).10 indi/ates t,at t,e regression model generated 3 t,e ariales @ as t,e dependentariale( and Boo alues of e?uit37otal liailities( Earnings efore interest ta6es7otal asset( Cetainedearnings7otal asset as t,e independent ariales" is signifi/ant at 0.05 leel of signifi/an/e gien t,at t,e p-alue 0.00'" is less t,an alp,a 0.05" as indi/ated 3 t,e +N%&+ tale.

Table 4.11 *u++ary statistics

Model C C S?uare +d


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&ol.'( No.2$( 201)

$9

7,e C s?uared 1.000" indi/ates t,at 100 of t,e ariation in @ is a//ounted for 3 t,e independentariales Boo alues of e?uit37otal liailities( Earnings efore interest ta6es7otal asset( Cetainedearnings7otal asset". 7,e ade andinest in distressed det. Fooen( NJ ile3.


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&ol.'( No.2$( 201)

)0

+le6ander( +. 2001". Japan onfronts orporate Cestru/turing. Japan E/onomi/ Institute. orld Ban Instituteeelopment Studies( as,ington( ( 4S+

+>i>( M. G ar( F. 200'". !redi/ting orporate inan/ial istress ,it,er o e StandT orporategoernan/e( '1"( 1#-$$.

+ndrade( H G aplan( S 199#". Fow ostl3 is inan/ial Not E/onomi/" istressT UEiden/e from Fig,l3

Keeraged 7ransa/tions t,at Be/ame istressed 7,e Journal of inan/e( 5$5"( 1))$-1)9$.+s?uit,( !( Hertner( C G S,arfstein( . 199)". +natom3 of inan/ial istress= +n E6planation of Jun Bond

Issuers 7,e Puarterl3 Journal of E/onomi/s( 109( '25-'**.Beaer( . 19''". inan/ial Catios as !redi/tors of ailureA Empiri/al Cesear/, in +//ounting( Sele/ted

Studies( Journal of +//ounting Cesear/,( 5" *1-111.Bwisa( +.+ 2010". Ealuation of appli/ailit3 of +ltmans reised model in predi/tion of finan/ial distress.

4npulis,ed MB+ proerland.

!ande3( I.M. 2005". inan/ial Management 10t, Ed". New elp,i( India( &ias !ulis,ingS,umwa3( 7. 2001". ore/asting anrupt/3 more a//uratel3 + simple ,a>ard model Journal of Business( *)

1"( 101-12).Slotemaer( C. 200#". !redi/tion of orporate Banrupt/3 of !riate irms in t,e Net,erlands + Master=s

7,esis( Erasmus 4niersit3( Cotterdam( Net,erlands"Sudarsanam( S G Kai( J 2001". orporate inan/ial istress and 7urnaround Strategies +n Empiri/al +nal3sis

Britis, Journal of Management( 12( 1#$-199.7aliani( I. J. 2010". !redi/ting finan/ial distress in /ommer/ial ans in en3a. 4npulis,ed MB+ pro


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)1

7am( .R. and M.R. iang 1992"( VManagerial appli/ations of neural networs 7,e /ase of an failure predi/tions( Management S/ien/e $#92' ; 9)*.

7,eodossiou( !( a,3a( E( Saidi( C G !,ilippatos( H 199'". inan/ial distress and /orporate a/?uisitionsfurt,er empiri/al eiden/e( Journal of Business inan/e and +//ounting( 2$2". '99;*19.

7urets3( F G M/Ewen( C 2001". +n Empiri/al inestigation of irm Kongeit3. + Model of t,e E6 +nte

!redi/tors of inan/ial istress. In Ceiew of Puantitatie inan/e and +//ounting( 1'( $2$-$)$.arner( Jerold B.( 19**". Banrupt/3 /osts Some eiden/e. Journal of inan/e $2( $$*;$)*.,itaer( C 1999". 7,e Earl3 Stages of inan/ial istress Journal of E/onomi/s and inan/e. 2$2"( 12$-1$$.ru/( 1990". inan/ial istress( Ceorgani>ation( and %rgani>ational Effi/ien/3 Journal of inan/ial

E/onomi/s( 2*( )19-))).Rim J. and F. Mit/,ell 2005". + /omparison of /orporate distress predi/tion models in Bra>il ,3rid neural

networs( logit models and dis/riminant anal3sis( Noa E/onomia Belo Fori>onte 15( *$-9$@mi


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