Wha Explans Superor Real Performance? Vshal Gaur, Marshall Fsher, Ananh Raman The Wharon School, Unversy of Pennsylvana vshal@grace.wharon.upenn.edu fsher@wharon.upenn.edu Harvard Busness School araman@hbs.edu Ocober 1999 Workng Paper Deparmen of Operaons and Informaon Managemen, The Wharon School, Unversy of Pennsylvana, Phladelpha, PA 19104-6366.
Absrac We analyze he performance of real frms for he perod 1978-97 usng publc fnancal daa. Our performance measures are long-erm sock reurns and wheher he frm fled for bankrupcy n he perod of sudy. We assume ha over a long me perod of a leas fve years, sock reurns are a reasonable measure of he overall success of a frm. We have found a very wde dspary n performance beween frms. On he one hand, realers lke Wal-Mar, he Gap and Crcu Cy have had phenomenal success (nneeen year compounded sock reurns of 31.2%, 29.5%, and 34.5%, respecvely), whle on he oher, 17% of he publc real frms fled for bankrupcy. We nvesgae how he followng levers managed by he CEO of a real frm affec performance: reurn on asses, sales growh, nvenory urns, gross margn, fnancal leverage, and sellng, general, and admnsrave expenses. The naure of he analyss s conemporaneous, provdng nsghs no manageral acons ha correlae wh success as measured by sock reurns, bu no a predcon of fuure sock reurns. We fnd ha (1) reurn on asses, sales growh, sandard devaon of reurn on asses and fnancal leverage explan more han 50% of he varaon n sock reurns for perods of en years or more; (2) realers n dfferen segmens apparel, deparmen sores, grocery and convenence sores, drugs and pharmaceucals, jewelry, consumer elecroncs, home furnshngs, oys, and varey sores acheve smlar reurn on asses and reurn on equy by followng very dfferen sraeges wh respec o her gross margns and nvenory urns; (3) even whn he same segmen, hgh gross margn correlaes wh low nvenory urns, and wh hgh sellng, general, and admnsrave expenses; (4) rsk of bankrupcy s relaed o he msmach beween how fas a frm aemps o grow versus wha growh rae realzes. We also es for a negave correlaon beween sales growh and reurn on asses, whch s wdely beleved o be rue bu s no borne ou by our daa.
1. Inroducon We analyze he performance of real frms for he perod 1978-97 usng publc fnancal daa. Our performance measures are long-erm sock reurns and wheher he frm fled for bankrupcy n he perod of sudy. We assume ha over a long me perod of a leas fve years, sock reurns are a reasonable measure of he overall success of a frm; he naure of he analyss s conemporaneous, provdng nsghs no manageral acons ha correlae wh success as measured by sock reurns, bu no a predcon of fuure sock reurns. We nvesgae how he followng levers managed by he CEO affec success: reurn on asses, sales growh, nvenory urns, gross margn, fnancal leverage, and sellng, general, and admnsrave expenses. Fgure 1a shows a hsogram of he compounded annualzed sock reurns of 293 publclsed realers from December 1978 o December 1997 (nneeen years). Our daa are drawn from Compusa and CRSP daabases. 1 Noce ha here s a very wde dspary n he sock reurns of companes. There are remendous successes lke Wal-Mar, Gap Inc., and Crcu Cy Sores Inc. and dsmal falures as evnced by he hgh rae of bankrupcy. If you had nvesed $1,000 n Wal-Mar sock a he close of radng on December 31, 1978, would have grown o $173,000 by he end of December 31, 1997. A smlar amoun nvesed n Gap would have grown o $136,000, and n Crcu Cy, o $278,000. On he oher hand, f you nvesed equally n all publc real frms n December 1978, abou 17% of he frms you nvesed n would have fled for bankrupcy before December 1997. Fgure 1b shows he hsogram of average sock reurns of frms n he S&P 500 ndex on December 31, 1978 over he perod 1978 o 1997. Comparng hs wh fgure 1a, we observe ha real frms have a much hgher varaon n her overall success han he S&P 500 frms. Realng s, hus, an excellen laboraory o measure he values of varous manageral sraeges. Our mehodology and many of he resuls can be appled o oher ndusres lke dsrbuon and manufacurng. However, he nsghs obaned may be lmed f accounng saemens do no adequaely capure he key performance varables n some ndusry. Examples 1 Our daa se conans 346 frms, of whch, we ncluded all hose frms ha had sock reurn daa for a leas wo years n hs analyss. All reurns were adjused for dvdends and sock-spls. For frms ha had been publcly raded for less han nneeen years, we compued he reurns based on he daa avalable. For example, for a frm ha had been publc from 1985 o 1994, we compued reurns over hs perod only. 1
nclude labor producvy n auomoble manufacurng and nellecual asses n sofware developmen. The wde dspary n real performance beges several quesons. We movae hem wh an example of four conrasng companes n consumer elecroncs realng. Fgure 2 shows he annual sales, reurn on asses, nvenory urns, gross margn and sock prce apprecaon for Bes Buy Inc., Crcu Cy Sores Inc., Good Guys Inc., and Tandy Corporaon. 2 The graphs show sysemac dfferences beween he realers. Bes Buy conssenly acheved he hghes sales growh, he lowes reurn on asses, and he lowes gross margn. Tandy conssenly had he hghes gross margn and he lowes nvenory urns. Good Guys was comparable n sales volume o Bes Buy n 1978 bu acheved lower sales growh, hgher reurn on asses, and he hghes nvenory urns n almos every perod. Crcu Cy had he mos conssen performance on all dmensons and he second hghes reurn on asses. Gven ha each realer was domnan on some measure of performance, s no obvous from hese comparsons whch frm was more successful or how much mpac sales growh and reurn on asses had on overall success. By analyzng hese and oher quesons n hs paper, we demonsrae he usefulness of he nformaon conaned n publc fnancal daa and provde nsghs no manageral acons ha correlae wh success as measured by sock reurns. Our man fndngs are as follows: 1. Frms wh hgh average reurn on asses, hgh sales growh, and low sandard devaon of reurn on asses acheved hgher long-erm sock reurns. 2. The values of reurn on asses, sales growh, and reurn on equy do no vary sgnfcanly from one real segmen o anoher (e.g., apparel, grocery, deparmen sores, ec.) mplyng ha no one segmen has yelded conssenly hgher reurns over he perod 1978 o 1997. 3. However, he componens of reurn on asses vary consderably beween real segmens. As expeced, grocery frms have hgh nvenory urns and low gross margns, whle jewelry frms have hgh gross margns and low nvenory urns. Gven (2) above, s no surprsng ha hese dfferences are mosly of a compensang naure. 2 Tandy Corporaon owns he Rado Shack chan of sores. 2
4. The componens of reurn on asses exhb sysemac radeoffs even whn he same segmen. Frms wh hgh gross margn have low nvenory urns. Frms wh hgh gross margn also spend more on sellng, general and admnsrave expenses (SGA). However, conrary o our expecaons, frms wh hgh reurn on asses have hgh sales growh raes, hough he exen of assocaon s sronger for long me perods han for one or wo year perods. 5. Frms ha fled for bankrupcy had a much wder gap beween her argeed growh raes and realzed growh raes n he years before bankrupcy han he oher frms. As a resul, her asse producvy declned faser han ha of healhy frms. The paper s organzed as follows. Secon 2 gves a revew of he relevan leraure. Secon 3 provdes defnons of he performance levers obaned from publc fnancal daa and descrbes hypoheses for he followng: for measurng he assocaon beween he managemen levers and frm success n secon 3.1; for sudyng he relaonshps beween gross margn and nvenory urns, gross margn and SG&A expenses, and reurn on asses and sales growh n secon 3.2; and for consrucng a relaonshp beween rsk of bankrupcy and sales growh rae n secon 3.3. Secon 4 descrbes he daa used n he sudy. Lasly, secons 5 and 6 presen our resuls and dscuss her nerpreaon. 2. Leraure Revew 2.1 Operaons Managemen and Hsorcal Perspecve The sgnfcance of hgh nvenory urns was recognzed by real frms as early as n 1870s. The wachword n Marshall Feld s deparmen sore a ha me used o be ha any surplus n Feld s s no sock, s cash. 3 Even oday, realers fx gude rules on accepable gross margn, sell-hrough percenage, promoonal expenses, new sore openngs, ec. based on smlar wachwords. There s lle, f any, research o explan how much mpac superor managemen of hese varables has on he overall success achevng hgh sock reurns wh a low rsk of gong bankrup. 3 Nneeenh Cenury Realng and he Rse of he Deparmen Sore, Harvard Busness School Case No. 9-384- 022, 1983. 3
The growh of a real frm s concepualzed n a framework called he wheel of realng. The wheel represens phases hrough whch some ypes of realers pass. A realer peneraes he marke on he bass of low prce. Over me, rades up o more expensve merchandse, servces, locaons, ec., hereby, openng a nche for new low-prce realers. The wheel of realng and oher heores of real lfecycle, descrbed by Levy and Wez (1995), have been acceped for a long me and bu her mplcaons on realers performance have no been esed emprcally. Levy and Wez (1995) descrbe a model, called he sraegc prof model, o analyze he nerrelaonshps beween managemen levers of a real frm. The model proposes ha dfferen frms may acheve smlar reurn on equy by followng dfferen pahs: he prof pah, or he urnover pah. The prof pah s one wh hgh gross margns and low nvenory urns, whle he urnover pah has hgh nvenory urns and low gross margns. Usng hs classfcaon, he model provdes a framework for he sraegc analyss of real frms. Our paper rgorously ess he nerrelaonshps mpled by hs model and esmaes her radeoff curves, whch can hen be used o compare performance across realers. 2.2 Fnancal accounng: Sudy of assocaon beween sock reurns and accounng earnngs The assocaon beween sock prces and accounng earnngs has been suded n academc leraure n wo conexs: wheher earnngs repors provde mely and useful nformaon o he sock marke, and wheher earnngs levels correlae wh sock reurns over long me perods. Semnal work n he frs conex was done by Ball and Brown (1968) and Beaver (1968a) and a consderable wealh of leraure n fnancal accounng has added o ha. Key ssues n hs research are how o measure he nformaon conen of earnngs repors, whch s called unexpeced earnngs, and wha s he lengh of me perod over whch earnngs nformaon dssemnaes o nvesors. Baruch Lev (1989) provdes an excellen revew of he research n hs area. Some older revews can be found n Foser (1984), Lev (1974), and Was and Zmmerman (1986). In all hese sudes, he correlaon beween unexpeced earnngs and sock prce movemen has been found o be very modes, rarely exceedng 10 per cen. Research n he second conex, nvesgang correlaon beween earnngs and sock reurns over long me perods, s relavely recen and mehodologcally closer o our analyss. Eason, Harrs and Ohlson (1992) showed ha he assocaon beween sock reurns and oal earnngs scaled by marke capalzaon ncreases as he lengh of me perod ncreases. They 4
obaned R 2 values of 15% for 2 year perods, 20-30% for 5 year perods and 63% for a 10 year perod. The managemen levers used n our sudy are derved from accounng saemens. They nclude sales growh and varous componens of reurn on equy: reurn on asses, fnancal leverage, gross margn, nvenory urns, and sellng, general, and admnsrave expenses. Pror research n accounng has no addressed he correlaon beween hese varables and long-erm sock reurns o our knowledge. Our mehodology and economerc models are smlar o hose of Eason, Harrs and Ohlson. 2.3 Research on bankrupcy The oher area of research n accounng relevan o hs paper perans o bankrupcy. There are many models n leraure for he predcon of bankrupcy (also called fnancal dsress or corporae falure). The models can be classfed as unvarae and mulvarae. Unvarae models focus on he selecon of he bes varable for predcng bankrupcy from he leverage, lqudy and profably raos obaned from fnancal saemens, and mulvarae models combne several fnancal varables no a sngle dscrmnan funcon. Poneerng work n unvarae models was done by Beaver (1966). He conduced a comparson of mean fnancal raos of a pared sample of bankrup and non-bankrup frms for 1 o 5 years before he acual occurrence of bankrupcy. Ths analyss s called profle analyss. Some of he raos used were cash flow/deb, ne ncome/oal asses, oal deb/oal asses, workng capal/oal asses, ec. Alman (1968), n a semnal paper on mulvarae models, used mulple dscrmnan analyss o consruc a lnear funcon of fve fnancal raos ha bes dsngushed beween bankrup and non-bankrup frms. Ohlson (1980) used log analyss o compue he probably of a company gong bankrup as a funcon of s fnancal raos. Oher predcve models of bankrupcy are presened n Beaver (1968b), and n Beaver (1968c). Zmjewsk (1983) compares he unvarae and mulvarae models used n he sudes before 1983 usng a common sascal echnque, a common defnon of bankrupcy, and a common sample. Economc heory has played a small role n he developmen of unvarae or mulvarae dsress predcon models. Baruch Lev (1974) noes hus: Aemps o consruc a heory of corporae falure, ha s, o denfy and generalze he major causes of falure, have been meager and generally unsasfacory because of he complexy and dversy of busness operaons, he lack of a well-defned economc heory of he frm under uncerany, and a surprsng relucance 5
by many researchers o ncorporae he falure phenomenon n her models. Two aemps n hs respec are by Wlcox (1971 and 1973) and Vnso (1979). Boh hese papers used gambler s run model o represen a frm and compued s probably of gong bankrup. These models have no been used n emprcal research. Sco (1981) presens a revew of heorecal and emprcal research n bankrupcy wh a vew o reconclng he resuls of hese wo sreams. Our approach o analyzng bankrupcy dffers consderably from exsng research. The daa samples used n all exsng research have spanned several ndusres, whch n our vew, has made dffcul o denfy causes of bankrupcy snce hey may vary from one ndusry o anoher. We focus on one ndusry, realng, and aemp o formulae reasons why real frms go bankrup based on an undersandng of her busness operaons. We fnd evdence ha he rsk of bankrupcy of a real frm s relaed o he msmach beween how fas aemps o grow and how much of ha growh s able o realze. Ths resul s conssen wh he exsng research snce no beng able o realze a argeed growh rae can lead o a lqudy crss n a real company due o declnng asse producvy and lefover nvenory. We are currenly esng a predcve model of bankrupcy usng growh as he explanaory varable. 3. Models and Hypoheses Formulaon Appendx 1 conans he noaon for all he varables used n hs paper. For each varable, he subscrp = 0,,T denoes he year of he fnancal saemen, and denoes he company. For nsance, S represens he sales durng year and Inv represens he nvenory a he end of year. Usng hese ncome saemen and balance shee fgures, we compue he followng performance levers: reurn on asses, reurn on equy, sales growh, fnancal leverage, gross margn, nvenory urns, oal asse urns, gross margn reurn on nvenory nvesmen, and sellng, general and admnsrave expenses as a fracon of sales. Ther mahemacal defnons used n he paper are shown n appendx 2; he subscrp s omed for clary. 3.1 Assocaon beween sock reurns and managemen levers We esmae he mpac of reurn on asses, sandard devaon of reurn on asses, sales growh, gross margn reurn on nvenory nvesmen, and fnancal leverage on frm success usng crossseconal regresson models as descrbed n hs secon. Frm success s measured by long-erm 6
(fve years or more) sock reurns. In shorer perods, movemens n sock prces may occur due o exernal facors, and may no reflec fundamenal changes n he performance of a frm. However, over long me perods, we assume ha sock markes are effcen and reflec he rue value of a frm. The explanaory varables were denfed by sudyng examples of some real frms. We sared wh a larger se of levers ncludng gross margn, nvenory urns, and sellng, general, and admnsrave expenses, besdes he varables lsed above. We observed ha each of hese levers had lmed ably o explan success because frms ha performed well on one lever may no perform well on he ohers. For example, n fgure 2, Tandy has he hghes gross margn bu he lowes nvenory urns. Reurn on asses and gross margn reurn on nvenory are aggregae levers, ncorporang he mpac of more han one componen, and hence, we use hese o correlae wh sock reurns. We analyze sandard devaon of reurn on asses also because we vew as anoher lever conrolled by he managemen. Comparng Crcu Cy and Good Guys n fgure 2, we observe ha hey have very smlar average reurn on asses, bu Crcu Cy shows a more conssen performance han Good Guys. The nerrelaonshps beween dfferen componens of reurn on asses are examned n secon 3.2. The models used for esmaon are as follows. These models are esed wh daa for all real frms ha dd no fle for bankrupcy durng he perod of sudy. Each model s esmaed for dfferen me perods of lengh 5, 10, 15, and 19 years. For each perod, we nclude all frms ha have complee daa avalable durng ha me, and exclude he ohers. R = ß + ß RoA + ß g + ß (s ) + ß DE + e (1) 1 0 2 0 1 1 2 1 1 2 2 2 1 3 2 RoA R = ß + ß RoE + ß g + e (2) R = ß + ß?GMRoII + ß g + ß DE + e (3) 3 0 4 0 3 1 4 1 0 3 2 4 2 3 3 R = ß + ß (GM$ /P ) + ß (SGA$ /P ) + ß (Inv /P ) + e (4) 5 0 5 1 0 5 2 R = ß + ß (GM$ /P ) + ß (SGA$ /P ) + ß (TA /P ) + e (5) Here, R denoes average sock reurns over a gven me perod for frm, RoA, he average reurn on asses, g, he average sales growh rae, DE, he rao of long-erm deb o equy (fnancal leverage), σ RoA, he sandard devaon of reurn on asses, RoE, he average reurn on equy, GMRoII, he sandardzed gross margn reurn on nvenory (explaned below), GM$ 0 0 1 4 3 4 3s 5 3s 1 0 0 4 5 7
he oal gross margn n dollars per share earned by frm over he enre me perod, SGA$ he oal sellng, general and admnsrave expenses n dollars per share ncurred by frm over he enre me perod, Inv he sum of annual closng nvenory n dollars per share for frm over he enre me perod, TA he sum of annual closng oal asses n dollars per share for frm over he enre me perod, and P 0 he share prce for frm a he begnnng of he me perod. Snce we nend o compare success across companes, all fve models are cross-seconal. Tha s, here s one daa pon per frm over he perod of analyss, and he model fs a leas squared error equaon across frms and no over me for he same frm. Equaon (2) uses reurn on equy n place of s componens reurn on asses, fnancal leverage, and GMRoII n order o provde a benchmark for equaons (1) and (3) snce ne earnngs and reurn on equy (rao of ne earnngs o book value of oal asses) have been more commonly suded n academc leraure han her componens. Equaons (4) and (5) use dollar amouns as alernave managemen levers nsead of rao measures. All hese values are scaled by number of shares and share prce o correc for sze dfferences. The coeffcens of nvenory and oal asses represen he cos of nvesmen as perceved by nvesors. The ndex s on he coeffcens ndcaes ha we esmae dfferen coeffcens for each real segmen because nvenory urns and asse urns vary sgnfcanly from one real segmen o anoher as explaned below. Sandardzed GMRoII, GMRoII, s defned as he sandardzed devaon of GMRoII of frm from s real segmen. Mahemacally, s GMROII GMROII GMROII = (6) s s(gmroii ) Here, GMRoII denoes he average GMRoII for frm over some me perod, GMRoII s denoes he average GMRoII for real segmen s over he same me perod and σ(gmroii s ) denoes he sandard error of GMRoII for segmen s. We canno do a regresson of sock reurns drecly wh GMRoII because, unlke he varables n equaons (1) and (2), GMRoII vares sysemacally from one real segmen o anoher. Table 1 shows he resuls of ANOVA ess for he varaon n he values of performance levers beween he followng real segmens: apparel, deparmen sores, grocery and convenence sores, drugs and pharmaceucals, jewelry, consumer elecroncs, home furnshngs, oys, and varey sores. We observe from he F-sascs ha RoA, RoE and sales growh do no have sgnfcan beween-segmen varaon. For fnancal leverage, whle he F-sasc s sgnfcan, par-wse ess for dfferences beween he fnancal 8
leverage of any wo real segmens are no sascally sgnfcan. Thus, we hnk ha fnancal leverage also does no vary sysemacally beween segmens, and he sgnfcance of he F- sasc comes from he large number of degrees of freedom. The resuls n hs able are no unexpeced because: 1. Assumng lmed enry- and ex- barrers n each real segmen, reurn on equy should no vary from one segmen o anoher over long me perods. Segmens wh hgher RoE would arac compeors unl her reurns were equalzed. Smlarly, segmens wh low RoE would see some companes ex he segmen, leavng he remanng companes wh hgher RoE. 2. Fnancal leverage s enrely under he conrol of managers. So we do no expec o vary beween segmens. 3. Snce RoA can be derved from RoE and fnancal leverage, oo should no vary from one segmen o anoher f RoE and fnancal leverage do no vary. 4. Lasly, sales growh s also deermned by he managemen of a company. We expec ha a hgh growh real segmen would see many new enres so ha s growh rae wll slow down over a long me perod, whle a low growh real segmen would see more consoldaon acvy boosng s growh rae. Thus we do no expec growh raes o be hgher for one segmen han for anoher over long me perods. GMRoII, however, has very large dfferences beween real segmens. For example, grocery chans have average GMRoII of 393% whle apparel chans have GMRoII of 240%. To undersand hese dfferences, we break RoA no s componens as follows: RoA = [GM SGA]/Asses = [GM/Invenory] [Invenory/Asses] [GM SGA]/GM = GMRoII Invenory Inensy SGA Inensy (7) We fnd ha he dfferences n he values of he componens of RoA beween segmens are of a compensang naure. Grocery real frms have a hgher GMRoII compared o apparel realers bu her nvenory nensy s much lower a 25% compared o apparel realers, for whch s abou 33%. Thus, dfferen segmens have smlar expeced reurn on asses n spe of very srong sysemac dfferences beween he values of he componens of reurn on asses. The able shows average values n each segmen for four such varables: oal asse urns, nvenory urns, gross margn, and gross margn reurn on nvenory nvesmen. 9
Sock reurns are commonly used by managers, nvesors and analyss alke o compare he performance of frms. However, one concern wh usng sock reurns whou rskadjusmen s ha dfferen frms have dfferen rsk profles so ha a 20% reurn acheved by a low rsk frm may no be comparable o a 20% reurn acheved by a hgh rsk frm. We conrol for hese dfferences by compung rsk-adjused sock reurns and esmang alernave models usng hem as he dependen varable nsead of raw sock reurns. The rsk-adjused sock reurns or unexpeced sock reurns are compued usng he 3 facors model of Fama and French as follows. Le R denoe he monhly sock reurn for frm n monh, R m denoe he value-weghed marke reurn on NYSE, AMEX and NASDAQ, and R f denoe he rsk-free reurn obaned on 1-monh T-blls. All hese values are obaned from CRSP me-seres records. Accordng o Fama and French (1993), he expeced reurn on a sock porfolo n excess of he rsk-free rae [E(R ) - R f ] s explaned by he sensvy of s reurn o hree facors: () he excess reurn on a broad marke porfolo (R m - R f ), () he dfference beween he reurn on a porfolo of small socks and he reurn on a porfolo of large socks (SMB small mnus bg), and () he dfference beween he reurn on a porfolo of hgh-booko-marke socks and he reurn on a porfolo of low-book-o-marke socks (HML hgh mnus low). Tme-seres values of SMB and HML have been compled by Fama and French. Usng monhly closng sock prces (adjused for dvdends and sock spls) for a real frm and monhly me-seres values of he hree facors, we esmae he followng regresson equaon. R - R f = α + β 1 (R m - R f ) + β 2 SMB + β 3 HML + ε (8) If he frm had no abnormal reurn, he nercep, α, n hs regresson should be zero. The unexpeced or rsk-adjused reurn for frm s defned as he value of he nercep α obaned from he regresson. We replace R wh α and re-esmae equaons (1), (2) and (3) o verfy f he manageral levers have an mpac on hs measure of frm success as well. To ensure ha he resuls of he models defned above are no nfluenced by he me perod over whch daa were obaned, we esmaed he model equaons over several me perods of dfferen lenghs and wh dfferen sar and end years. If he resuls of he hypoheses are found o be conssen regardless of me perod hen we shall conclude ha our fndngs are no arfacs of he me perod analyzed. Afer esmang he model equaons, we es he followng hypoheses. 10
HYPOTHESIS 1: Frms wh hgher reurn on asses, hgher sales growh raes, and lower sandard devaon of reurn on asses acheved hgher long-erm sock reurns. HYPOTHESIS 2: Frms wh hgher gross margn reurn on nvenory nvesmen relave o her real ndusry segmen acheved hgher long-erm sock reurns. HYPOTHESIS 3: Frms wh hgher reurn on asses, hgher sales growh raes, and lower sandard devaon of reurn on asses acheved hgher long-erm sock reurns adjused for rsk. 3.2 Inerrelaonshps beween performance levers We analyze how frms acheve superor performance on he aggregae manageral levers (reurn on asses, reurn on equy, sales growh, and gross margn reurn on nvenory nvesmen) by sudyng he followng nerrelaonshps across frms: beween nvenory urns and gross margn, beween gross margn and SG&A expenses, and beween reurn on asses and sales growh. By quanfyng hese nerrelaonshps, we propose o provde ools for comparng performance across frms on hese levers, and o characerze he sraeges followed by successful frms. All frms ha fled for bankrupcy are excluded from he analyss n hs secon. These frms may be expeced o perform poorly on many performance measures (e.g., low reurn on asses and low sales growh, low gross margn and poor nvenory urns, ec.) and may le below he radeoff curves denfed for healhy frms. Gross Margn and Invenory Turns In a compeve marke wh low enry- and ex-barrers, a realer ha has o carry a un of produc longer before beng able o sell (.e., a realer wh slower nvenory urns) would expec o earn subsanally more on s nvenory nvesmen (.e., acheve a hgher gross margn) han a realer ha has o carry he em of nvenory for a shorer perod. Hgh varey fashonable producs lke jewelry and fashon apparel belong o he frs caegory, whle grocery and compuer realng belong o he second. The CEO of CompUSA Inc., a compuer real chan wh gross margn of 5 o 15%, once remarked: We have hgh nvenory urns because we have o. Thus, we expec o see an nverse relaonshp beween gross margn and nvenory urns as shown n fgure 3. In economc erms, we would expec realers whn he same segmen o acheve smlar reurn on nvenory, whch s measured usng GMRoII. Snce 11
GMRoII s a produc of gross margn and nvenory urns, we would expec hese wo varables o be nversely relaed. GM IT = K s, where K s s a consan specfc o each segmen. Equvalenly, usng logarhms, log (IT ) = K s + b log (GM ) + ε (9) I s mporan o noe ha snce he model s cross-seconal and no me-seres, he nverse relaonshp does no mply ha f a frm ncreases s gross margn hrough beer managemen, s nvenory urns wll declne commensuraely. Insead, we propose ha he correlaon beween gross margn and nvenory urns exss due o her muual dependence on he characerscs of a realer s busness. Our man hypohess of neres s as follows: HYPOTHESIS 4: Frms wh hgh gross margn have low nvenory urns and vce versa,.e., b<0 n equaon (9). Gross Margn and SG&A Expenses Anoher mplcaon of he assumpon of compeve markes s ha realers wh hgh gross margns would expec o spend more on adversng and promoon, and hus, have hgh SG&A expenses. In order o es hs hypohess, we correlae average gross margn wh average SGA as a proporon of sales usng a cross-seconal model. GM = a s + b (SGA/Sales) (10) As before, he subscrp s denoes he ndusry segmen. The hypohess wll hold rue f b>0. We esmae he model over perods of dfferen lenghs wh he expecaon ha he relaonshp over longer perods wll be sronger han ha over shorer perods. HYPOTHESIS 5: Frms wh hgher gross margn as a fracon of sales have hgher SG&A expenses as a fracon of sales and vce versa. Noe ha f he compeve marke assumpon holds rue hen also mples a much sronger saemen ha, n expecaon, boh ypes of sraeges, hgh gross margn and hgh SGA versus low gross margn and low SGA, should be equally profable. Ths s formulaed as he nex hypohess: HYPOTHESIS 6: The slope b n equaon (10) has a value of 1. In oher words, a one pon ncrease n gross margn (as a percen of sales) s assocaed wh a one pon ncrease n SG&A 12
expenses (as a percen of sales), so ha frms wh dfferen gross margns acheve smlar reurn on sales on average. Reurn on Asses and Sales Growh I s wdely expeced ha realers wh hgher sales growh should have lower reurn on asses compared o s compeors for he followng reasons: 1. In order o acheve rapd sales growh n a parcular perod, he realer would open new sores durng or before ha perod more aggressvely han s compeors, and new sores beng less profable han old sores, hs would reduce s reurn on asses. 2. The realer mgh reduce s prces and ncrease spendng on adversng and promoon o acheve hgh sales growh. Boh hese acons resul n lower reurn on asses. Amazon.com s a classc example of frms sacrfcng reurn on asses for sales growh. Bes Buy s anoher example because, as we observe from fgure 2, has he lowes reurn on asses and he hghes sales growh raes of he frms consdered. We es hs relaonshp by usng a cross-seconal model o regress average sales growh on average reurn on asses over me perods of lengh 1, 5, 10, 15, and 19 years, smlar o he mehod used for gross margn and SGA above. For annual daa, low reurn on asses n one year may be expeced o correlae wh hgh sales growh n he nex year. Thus, we esmae he one year model boh wh and whou me lag beween reurn on asses and sales growh. However, for longer perods, me lag s dsregarded. The hypohess o be esed s as follows: HYPOTHESIS 7: Frms wh low reurn on asses have hgh sales growh and vce versa. Noe ha he resuls of hs hypohess may also have a bearng on he regresson of sock reurns on reurn on asses and sales growh n secon 3.1 snce would ndcae collneary beween he ndependen varables n ha regresson. 3.3 The relaonshp beween bankrupcy and sales growh As noed earler, real frms show a very hgh ncdence of bankrupcy. Frms ha have fled under chaper 11 nclude no only small realers bu also large chans lke Ames Deparmen Sores, Caldor, Federaed, Jamesway, Today s Man, Flenes Basemen, ec. Frms ha have had a close brush wh bankrupcy nclude he lkes of K-Mar and Bes Buy. Why s bankrupcy so rampan n realng? Undersandably, as researchers n accounng have noed, bankrupcy 13
reflecs a lqudy crunch a a company. The objecve of our research s o undersand why so many realers become llqud by relang he lkelhood of bankrupcy o sales growh. We heorze ha bankrupcy rsk relaes o he dfference beween he growh argeed and he growh acheved by a realer. Realers argeng hgh growh raes have o ncrease asses such as sores, warehouse space, and nvenory n ancpaon of sales growh. Ofen he asse growh has o precede he ancpaed sales growh subsanally. Realers ha aemp o grow faser han her poenal accumulae unproducve asses. When a realer argeng a ceran growh rae fals o acheve, s rsk of bankrupcy ncreases. The sales growh-asse growh msmach scenaro descrbed above has been seen n many real segmens. Mos recenly, he foowear-realng segmen has fallen prey o hs problem. Years of sold growh n boh women s foowear and ahlec foowear nduced he ndusry o projec ha he sales growh would connue. When he sales growh dd no maeralze, realers lke Nne Wes Group Inc. go suck wh unproducve asses, and encounered poor performance. Examples of real frms ha fled for bankrupcy because of growng oo fas (addng oo many sores oo quckly) nclude Merry Go Round Enerprses and All For A Dollar Inc. Bes Buy wen no near bankrupcy n 1995 because of very hgh growh rae n early 1990s. However, we dd no fnd suffcen evdence o relae bankrupcy o fnancal leverage. Conrary o our expecaons, we found ha fnancal leverage of bankrup frms was no sgnfcanly hgher han ha of healhy frms n he years precedng bankrupcy. We found several real frms ha wen bankrup n spe of low long-erm borrowngs. The resuls from hs analyss wll no be repored here for brevy. There are wo alernave scenaros ha can be developed o es our heory. 1. Realers ha wen bankrup argeed hgher growh raes han healhy frms. 2. Realers ha wen bankrup had a greaer msmach beween argeed and realzed growh raes han healhy frms. The frs scenaro s more resrcve han he second. I argues ha realers ha fled for bankrupcy aemped o grow more aggressvely han her healher counerpars, and hus, faced a hgher rsk of bankrupcy. The valdy of hs scenaro can be esed by comparng he fxed asses growh raes of healhy realers wh hose of bankrup realers n he years precedng he even of bankrupcy. If hs scenaro exss hen we can develop a predcve model 14
of bankrupcy whch esmaes he probably of a realer gong bankrup as a funcon of s fxed asses growh rae. The second scenaro mples ha bankrup realers dd no necessarly aemp o grow more aggressvely han healhy realers bu hey could no acheve her argeed growh raes eher because of execuon problems or because hey aemped o grow faser han her poenal. Thus, he bankrup realers show a greaer msmach beween argeed and realzed growh raes han healhy realers. Ths hypohess can be esed by comparng he me rae of change of fxed asses producvy (.e., fxed asse urns) of healhy and bankrup realers n he years precedng bankrupcy. If he hypohess holds hen he fxed asse urns of bankrup realers should declne more rapdly han ha for healhy realers. In hs case, a predcve model can be developed o esmae he probably of a realer gong bankrup as a funcon of he changes n s fxed asses producvy. We es he followng hypoheses o fnd whch of he above wo scenaros holds. Hypoheses 8 and 9 esablsh he grounds for relang bankrupcy o sales growh. Hypohess 8 saes ha sales growh rae of a real frm declnes over me because as flls s marke space, here are fewer and fewer avenues avalable for growh. Hypohess 9 ascerans wheher realers ha fled for bankrupcy had lower realzed growh raes han healhy realers n he years precedng he ncdence of bankrupcy. Hypohess 10 ess wheher bankrup realers aemped o grow more aggressvely han healhy realers n order o deermne he valdy of scenaro 1. Lasly, hypohess 11 deermnes he valdy of scenaro 2. HYPOTHESIS 8: Sales growh and asse producvy show decreasng rends over he lfe of a real frm. HYPOTHESIS 9: Realers ha fled for bankrupcy have lower sales growh raes durng he years precedng bankrupcy han healhy realers. HYPOTHESIS 10: Realers ha fled for bankrupcy have hgher fxed asses growh raes durng he years precedng bankrupcy han healhy realers. HYPOTHESIS 11: Fxed asse urns decrease over me more rapdly for bankrup frms han for healhy frms. We es hypoheses 9 and 10 by comparng annual fxed asses and sales growh raes of realers ha fled for bankrupcy wh he average raes for healhy real frms (n he years precedng bankrupcy). For esng hypoheses 8 and 11, we conduc me-seres regressons of 15
sales growh and fxed asse urns wh respec o me. The regresson equaon for sales growh rae s g = α + β + ε (11) and hypohess 8 mples ha β < 0. The regresson equaon for fxed asse urns s more complex because fxed asse urns vary sgnfcanly across real segmens. We sandardze me seres values of fxed asse urns o mean 0 and sandard devaon 1 by frm o ensure comparably across frms and esmae he followng quadrac model separaely for healhy and bankrup frms. The me-seres for bankrup frms are algned such ha he year of bankrupcy corresponds o he same value of and all daa avalable before he year of bankrupcy are used. A quadrac erm n s added because frms, before flng for bankrupcy, close many sores o ry o de over he oncomng lqudy crss. Ths ncreases her asse urns n he one-wo years before bankrupcy. Here FT denoes sandardzed fxed asse urns for frm n perod. Healhy Frms: FT = α h + β h 1 + β h 2 2 h + ε Bankrup Frms: FT = α b + β b 1 + β b 2 2 b + ε (12) Hypohess 8 mples ha β h 1 < 0 and β b 1 < 0, and hypohess 11 mples ha β b 1 < β h 1. 4. Daa Accounng daa for hs sudy were obaned from S&P s Compusa daabase and he monhly sock prce, sock reurn and marke ndex daa were obaned from CRSP. We obaned daa for all publc lsed brck & morar real companes over he 20 year perod 1978-97 whch comprsed he followng ndusry segmens: Apparel and Accessores, Convenence sores, Deparmen sores, Drug and pharmaceucals, Grocery, Hobby oys and games sores, Home furnshngs, Jewelry, Consumer elecroncs and compuers, and Varey sores. The daabase conans a oal of 346 companes. For each company, we compued performance varables as defned n appendces 1 and 2. From hs se, we chose companes for esng hypoheses 1, 2 and 3 for dfferen me perods of lengh 5, 10, 15 and 19 years. The frms seleced n each case sasfy wo condons: () hey should have me seres daa for he enre me perod under consderaon; () hey should no have gone bankrup, been bough by anoher company or been de-lsed from he sock exchange for oher reasons durng hs perod. The fnal sample sze used for each me 16
perod s shown along wh he resuls. The oal number of dsnc frms numbered 150 across 12 ndusry segmens. Surprsngly, n each case he number of frms s less han one-fourh of he oal sample sze of 346 frms, wh he 19 year perod havng only 32 frms. Ths s n consonance wh our earler observaon ha no many real frms survve for a long perod of me. For he second se of hypoheses, 4 hrough 7, peranng o nerrelaonshps beween performance levers, we used average values for he varables concerned for all healhy frms ha had daa avalable for he relevan me perods. The me perods over whch averages were compued are ndcaed n he respecve ables conanng he resuls. For he hrd se of hypoheses, 8 hrough 11, we consruced wo samples of frms; one a sample of all frms ha had gone bankrup and had a leas 5 years of daa avalable before he year of bankrupcy; and he second, a sample of all frms ha dd no go bankrup durng he 20 year perod 1978-97 and had a leas fve years of daa avalable. 5. Resuls 5.1 Assocaon beween sock reurns and accounng based performance varables The resuls for he regresson of sock reurns on reurn on equy and sales growh, and on reurn on asses, sales growh, sandard devaon of reurn on asses and fnancal leverage are shown n able 2. Before nerpreng hese resuls, we noe ha n he cases where me-perods overlap, he resuls are no ndependen of each oher. The reasons for usng dfferen me wndows are o conrol for he effec of begnnng and endng years on sock reurns, and o es how resuls vary wh he lengh of he perod. We also noe ha here s some collneary beween reurn on asses and sales growh, and lkewse beween reurn on equy and sales growh for longer me perods. Frms wh greaer profably n he long run end o be he frms wh hgher sales growh as well. Ths fndng s dscussed n deal below along wh he resuls for hypohess 7. In mos cases, he collneary was no large enough o affec he parameer esmaes. Dong prncpal componens analyss on he ndependen varables also gave us componens ha were almos he same as he orgnal varables. However, n hree cases, we elmnaed sales growh from he model because of hgh collneary. We make he followng observaons from hese wo ables: 17
1. Hypohess 1 holds. The assocaon beween he ndependen varables and sock reurns s sgnfcan n almos every case. Reurn on asses and sales growh have posve correlaon and sandard devaon of reurn on asses has negave correlaon wh sock reurns. Fnancal leverage, where sgnfcan, has a posve correlaon wh sock reurns, so ha frms wh hgh leverage have hgh sock reurns. 2. Reurn on asses, sandard devaon of reurn on asses, sales growh and fnancal leverage explan beween 35 o 72 per cen of he varaon n sock reurns. These number are as hgh as hose obaned for reurn on equy and sales growh, showng he mporance of managng he componen levers of reurn on equy. 3. Our resuls are conssen wh pror sudes done n fnancal accounng. Specfcally, he values of R-square obaned are comparable o hose obaned by Eason, Harrs and Ohlson (1992) for he assocaon beween ne earnngs and sock reurns. The value of R- square s hgher for longer me perods han for shorer me perods. The sandard error of sock reurn s also found o be lower for longer me perods. One queson ofen asked by realers s whch varable s sock reurn more sensve o sales growh or profably. In order o answer hs queson, we conduced sandardzed regresson of sock reurns on he ndependen varables,.e., we sandardzed all varables o mean 0 and sandard devaon 1 and hen esmaed he regresson equaons. Sandardzaon conrols for dfferences n he uns used o express he ndependen varables, and dfferences n he level of varaon n her values. Table 3 shows he coeffcen esmaes from hese regressons. We see ha he coeffcen of reurn on equy s always larger han he coeffcen of sales growh. Thus, he long-erm sock reurns are more sensve o a un sandard devaon change n reurn on equy han o a un sandard devaon change n sales growh. The pcure as regards reurn on asses versus sales growh s less clear. Sock reurn seems o be equally sensve o a change n eher of hem. We hnk ha he dfferen levels of sensvy o RoE and RoA are found because of fnancal leverage. For example, consder a frm wh RoA of 10% and equy consung 40% of s oal book value. The RoE of hs frm s 25%. Suppose doubles s RoA o 20%, keepng leverage consan. The RoE of he frm also doubles o 50%. If all frms n he cross-seconal model had dencal fnancal leverage (or f fnancal leverage had no mpac on sock reurns) hen he sandardzed coeffcens of RoA and RoE n he above regressons would be he same and would be rrelevan wheher we use RoA or RoE as 18
ndependen varables. However, as we have already seen, leverage has a posve assocaon wh sock reurns. Thus, he sock marke s slghly more sensve o changes n RoE han o changes n RoA. The resuls of he regresson of sock reurn on GMRoII, sales growh and fnancal leverage are shown n able 4. The coeffcen of GMRoII s conssenly posve, and n fve ou of egh cases, s sgnfcan a more han 90% confdence level, confrmng hypohess 2. I s nuvely expeced ha he assocaon beween GMRoII and sock reurn should no be as srong as beween reurn on asses and sock reurn because GMRoII s only one componen of reurn on asses. Good performance by a realer on GMRoII does no necessarly mply good operang profably snce he realer may be ncurrng dsproporonaely hgh sellng expenses or may have oo small an nvesmen n nvenory (low nvenory o asses rao). The conclusons from esmang equaons (4) and (5) are smlar o hose obaned from equaon (1). Table 5 shows he resuls for one of he me perods examned, 1988-97. We fnd ha he coeffcens of gross margn and SG&A expenses are equal and oppose wh more han 99% confdence level. The sock marke values her dfference, operang earnngs, whle gvng equal weghs o gross margn and SG&A expenses. Lasly, we also esmaed he mpac of he above levers on rsk-adjused sock reurns. We used a sngle daa se here conssng of all frms wh a leas 10 years of daa ha dd no fle for bankrupcy a all durng 1978-1997. The resuls obaned are conssen wh hose n ables 2 and 4. Reurn on equy, reurn on asses, and sales growh have sascally sgnfcan posve assocaon wh sock reurns. Sandard devaon of reurn on asses, fnancal leverage and GMRoII also have coeffcens n he expeced drecons, bu her esmaes are no sascally sgnfcan. 5.2 Inerrelaonshps beween performance varables Invenory Turns and Gross Margn Fgure 4 shows he relaonshp beween gross margn and nvenory urns for consumer elecroncs realers usng quarerly me-seres daa for en years, demonsrang he nverse logarhmc relaonshp we expec o see. Table 6 gves he resuls of he regresson for all real segmens usng wo groups of frms: one wh all frms wh a leas en years of daa durng 1978-97 and anoher wh all frms wh a leas fve years of daa durng hs perod. The frs se 19
of frms s smaller and has more me-seres nformaon abou each frm, bu dong he regresson for a larger se as well gves us an ndcaon of he robusness of he resuls. Invenory urns have a very srong relaonshp wh gross margn wh R-square of abou 64% and all parameers beng sgnfcan a 99%. Realers wh hgh nvenory urns have low gross margns, and vce versa. However, he coeffcen of log GM s no -1 bu abou -0.5, ndcang ha GMRoII does no reman consan as GM and IT vary beween real frms. Ths s so because, as GM and IT change, SGA nensy and nvenory nensy also change sysemacally so ha realers n a real segmen need no have smlar gross margn reurn on nvenory even hough hey expec o have smlar reurn on asses. Gross Margn and SG&A Expenses We regressed average gross margns agans average SG&A expenses (as a percen of sales) for me perods of lenghs varyng from en years o one year n he perod 1988 o 1997. Table 7 shows he resuls for he en-year perod 1988-97 and for he year 1997. Real segmen-specfc nerceps were no requred as ndusry effec on he model was found o be neglgble. The R- square for he en-year perod s 82.2% and he regresson model s hghly sgnfcan, provdng evdence for hypohess 5. The R-square for he one-year perod s also surprsngly hgh a 74.3%. Fgure 5 presens a plo of average gross margn versus average SG&A expenses for he en-year perod; can be seen from he plo ha he model fs exremely well and he resduals of he regresson equaon are que low. In boh he me-perods, he slope of SGA s no dfferen from 1 wh a confdence level of 95%. Thus, we also accep hypohess 6. An esmae of 1 for he slope and he surprsngly small resduals ndcae he exsence of an equlbrum n he marke wheren GM and SGA compensae each oher very srongly. Realers wh superor performance dsngush hemselves by locang above he regresson lne, and realers wh poor performance (for example, frms ha fled for bankrupcy) le below he regresson lne. Ths s conssen wh he resuls of esmaon of equaons (4) and (5) gven n able 5. The equal and oppose coeffcens of gross margn and SG&A expenses mply ha her dfference, operang earnngs, correlaes well wh sock reurns. Reurn on Asses and Sales Growh We dd no fnd any evdence o suppor hypohess 7. Que o he conrary, reurn on asses and sales growh have posve correlaon wh each oher for almos all sub-perods durng 1978 o 20
97. The exen of assocaon s sronger for longer me perods han for shorer perods. When we esmaed he nerrelaonshp usng one-year daa wh me lag, we found ha reurn on asses n a year dd no have any sascally sgnfcan assocaon wh sales growh n he nex year. Quarerly daa may be more approprae for esng a me-lagged relaonshp beween reurn on asses and sales growh. 5.3 The relaonshp beween bankrupcy and sales growh Table 8 shows he resuls of regressng me-seres sales growh of each realer on number of years as ndependen varable o fnd f here s a declnng rend n sales growh, as specfed n he generalzed lnear model equaon (11). When he slope parameer s modeled o be dencal across realers, we fnd ha he R-square s 32.2% and he esmae of slope s hghly sgnfcan wh an esmae of 0.997. The negave value of hs esmae confrms he frs par of hypohess 8 ha sales growh raes of realers declne over me. We also esmaed he model usng dfferen slope parameers for dfferen frms. In hs case, he R-square s undersandably hgher a 50.5% and boh he frm effec and he me effec are agan hghly sgnfcan. The medan value of he slope β across realers s 0.941 (whch s close o he esmae obaned usng a sngle slope parameer) and 147 ou of he 185 companes n he sample have negave slopes. Thus, for an average realer, sales growh rae declnes a a rae of abou 1 percenage pon per year. Table 9 shows he esmaed sales and fxed asses growh raes of healhy frms and bankrup frms n he years precedng bankrupcy. The average annual sales growh rae of a healhy real frm s 13.92%. The average sales growh rae of a frm ha fled for bankrupcy decreases from 7.38% four years before bankrupcy o 3.22% hree years before bankrupcy, 0.67% one year before bankrupcy and 12.43% durng he year of bankrupcy. In every year, he sales growh rae of bankrup frms s lower han ha of healhy frms and n hree cases, s sascally sgnfcan a 99% and hypohess 9 s acceped. The fxed asses growh rae of bankrup frms s no hgher han ha of healhy frms n any year. Thus, hypohess 10 s rejeced and scenaro 1 does no appear o hold generally for all bankrup frms. In oher words, frms ha fle for bankrupcy do no necessarly aemp o grow faser han healhy frms n he years precedng he occurrence of bankrupcy. 21
The resuls of esmang equaons (12) are as follows. The fgures n parenheses are sandard errors of parameer esmaes and all values are sgnfcan a 95% or hgher confdence levels. Healhy Frms: FT = 0.60 0.031 0.002 2 + ε (0.062) (0.013) (0.0006) R 2 = 16.12% Bankrup Frms: FT = 11.89 1.298 + 0.034 2 + ε (3.885) (0.477) (0.014) R 2 = 18.57% Snce he coeffcens of n boh equaons are negave, confrms ha fxed asse producvy declnes over me for all realers (hypohess 8). Also, he esmae 1.298 s less han 0.031 a 99% confdence level, confrmng ha bankrup frms exhb a much faser declne n fxed asse producvy n he years precedng bankrupcy han healhy frms. Ths ndcaes ha scenaro 2 provdes a plausble explanaon of he occurrence of bankrupcy n real frms. 6. Concluson We have used fnancal saemen and sock marke daa for publc lsed real frms o sudy varous manageral levers and assess her relaonshp wh frm success measured by long-erm sock reurns and he ncdence of bankrupcy. Some of our resuls confrm our nuon: (1) realers wh superor long-erm sock reurns have hgh reurn on asses, hgh sales growh, and hgh gross margn reurn on nvenory; (2) grocery frms have hgher nvenory urns and lower gross margn han jewelry frms, and oher real segmens le beween he wo as expeced; and (3) realers ha wen bankrup showed more rapd declne n asse producvy han healhy realers. However, we have also uncovered some surprses and some new fndngs: (1) reurn on asses s no negavely correlaed wh sales growh; (2) realers n dfferen segmens have smlar reurn on asses and reurn on equy, alhough her componen measures have very dfferen values; (3) here are srong assocaons beween gross margn and nvenory urns, and beween gross margn and sellng, general and admnsrave expenses, showng ha frms acheve success n many dfferen ways; (4) sock reurns have a negave correlaon wh sandard devaon of reurn on asses; (5) realers ha wen bankrup were no argeng hgher 22
growh raes han healhy realers; and (6) bankrupcy n realng s unrelaed o fnancal leverage. Our research acheves several objecves. I measures he mpac of manageral levers lke sales growh and profably on he overall long-erm frm success. I documens he wdely dfferen sraeges ha realers use o acheve smlar resuls on profably. I provdes rules ha can be used o compare he performance of one company wh anoher by recognzng he radeoffs beween performance varables. Lasly, provdes a mehodology ha can be used o sudy he mpac of manageral levers on success n oher ndusres. References Alman, Edward I. (1968), Fnancal Raos, Dscrmnan Analyss and he Predcon of Corporae Bankrupcy, The Journal of Fnance, 23(4), Sepember, 589-609. Ball, R. and P. Brown (1968), An Emprcal Evaluaon of Accounng Income Numbers, Journal of Accounng Research, Auumn, 159-78. Beaver, W. H. (1966), Fnancal Raos as Predcors of Falure, Emprcal Research n Accounng, Supplemen o Journal of Accounng Research, 71-111. Beaver, W. H. (1968a), The Informaon Conen of Annual Earnngs Announcemens, Journal of Accounng Research, Supplemen, 67-92. Beaver, W. H. (1968b), Alernave Accounng Measures as Predcors of Falure, The Accounng Revew, January, 113-22. Beaver, W. H. (1968c), Marke Prces, Fnancal Raos, and he Predcon of Falure, Journal of Accounng Research, Auumn, 179-92. Eason, Peer D., Trevor S. Harrs and James A. Ohlson (1992), Aggregae Accounng Earnngs can explan mos of Secury Reurns, Journal of Accounng and Economcs, 15, 119-42. Fama, Eugene F. and Kenneh R. French (1993), Common Rsk Facors n he Reurns on Socks and Bonds, Journal of Fnancal Economcs, 33, 3-56. Foser, George (1986), Fnancal Saemen Analyss, Prence-Hall Seres n Accounng, New Jersey. Hcks, J. R. (1939), Value and Capal, Oxford Unversy Press, London. 23
Lev, Baruch (1974), Fnancal Saemen Analyss: A New Approach, Prence-Hall Publshers, New Jersey. Lev, Baruch (1989), On he Usefulness of Earnngs and Earnngs Research: Lessons and Drecons from Two Decades of Emprcal Research, Emprcal Research n Accounng, Supplemen o Journal of Accounng Research, 27, 153-92. Levy, Mchael and Baron Wez (1995), Realng Managemen, Irwn Publshers, Chcago. Nneeenh Cenury Realng and he Rse of he Deparmen Sore, Harvard Busness School Case No. 9-384-022, 1983. Ohlson, James A. (1980), Fnancal Raos and he Probablsc Predcon of Bankrupcy, Journal of Accounng Research, 18(1), Sprng, 109-31. Sco, J. (1981), The Probably of Bankrupcy: A Comparson of Emprcal Predcons and Theorecal Models, Journal of Bankng and Fnance, 5, Sepember, 317-44. Vnso, J. (1979), A Deermnaon of he Rsk of Run, Journal of Fnancal and Quanave Analyss, 14(1), March, 77-100. Was, R. L. and J. L. Zmmerman (1986), Posve Accounng Theory, Prence-Hall, New Jersey. Wlcox, J. W. (1971), A Smple Theory of Fnancal Raos as Predcors of Falure, Journal of Accounng Research, 8, Auumn, 389-395. Wlcox, J. W. (1973), A Predcon of Busness Falure usng Accounng Daa, Emprcal Research n Accounng: Seleced Sudes, Supplemen o Journal of Accounng Research, 163-79. Zmjewsk, M. E. (1983), Predcng Corporae Bankrupcy: An Emprcal Comparson of he Exan Fnancal Dsress Models, Workng Paper, SUNY Buffalo. 24
Appendx 1: Noaon s frm ndex real segmen ndex (e.g., apparel, grocery, consumer elecroncs, ec.) me ndex, represenng eher years or quarers, as he case may be Income Saemen and Balance Shee Iems: S Sales (Ne of markdowns) CGS Cos of Goods Sold (ncludes occupancy and dsrbuon coss) SGA Sellng, General and Admnsrave Expenses EBITDA Operang Prof (Earnngs before neres, ax, deprecaon and amorzaon expenses) FA Fxed Asses Inv Invenory TA Toal Asses OE Owners Equy Performance Measures (defned n appendx 2) GM Gross Margn (%) OM Operang Margn (%) AT Toal Asse Turns IT Invenory Turns FT Fxed Asse Turns GMRoII Gross Margn Reurn on Invenory Invesmen (%) RoA Reurn on Asses (%) RoE Reurn on Equy (%) g Sales Growh Rae (%) h Fxed Asses Growh Rae (%) DE Fnancal Leverage (Rao of long-erm deb o equy) σ-roa Sandard devaon of RoA over some me perod GMRoII GMRoII sandardzed o mean 0 and sandard devaon 1 by ndusry segmen 25
Appendx 2 Defnon of Accounng Saemens based Performance Measures Reurn on Equy Reurn on Asses Componens of Reurn on Asses: Operang Margn Gross Margn Toal Asse Turns Invenory Turns Gross Margn Reurn on Invenory RoE RoA = = PAT ( OE + OE )/ 2 1 EBITDA ( TA + TA )/ 2 1 EBITDA OM = S GM AT Break-up of Reurn on Asses no componen measures: IT = Gross Prof = S = S ( TA + TA )/ 2 1 S = ( Inv + Inv )/ 2 1 S S CGS CGS S GMRoII = = GM 2 1 RoA = OM AT = GMRoII SGA = GM 1 S ( Inv + Inv )/ Inv TA SGA 1 S 1 1 + Inv + TA IT IT ( Invenory o Asses Rao ) 26
Number of Frms 60 Fgure 1a Hsogram of Average Annual Sock Reurns of Real Frms durng 1978-97 (Daa: 293 ou of 346 frms ha had a leas 2 years of sock reurn hsory) 50 40 30 20 10 0-80 -70-60 -50-40 -30-20 -10 0 10 20 30 40 50 60 70 80 90 17% realers wen bankrup Average Annual Sock Reurn (%) Number of Frms 250 Fgure 1b Hsogram of Average Annual Reurns of Frms n he S&P 500 ndex on Dec 31, 1978 durng he perod 1978-97 200 150 100 50 0-80 -60-40 -20 0 20 40 60 80 100 2% frms Average Annual Sock Reurn (%) wen bankrup 27
Fgure 2: Tme Seres of Performance Measures for Four Consumer Elecroncs Realers from Dec 1985 o Dec 1995 4 (a) Annual Sales n $ mllon 8000 7000 6000 5000 4000 3000 2000 1000 0 1985 1987 1989 1991 1993 1995 BEST BUY CO INC 47.3% CIRCUIT CITY STORES INC 24.8% GOOD GUYS INC 28.1% TANDY CORP 7.5% (b) Annual per cen Reurn on Asses 70 60 50 40 30 20 10 0-10 -20 1985 1987 1989 1991 1993 1995 BEST BUY CO INC 11.5% CIRCUIT CITY STORES INC 18.8% GOOD GUYS INC 18.7% TANDY CORP 20.5% 4 Numbers n he legends gve average values of sales growh rae, reurn on asses, nvenory urns, gross margn and annual sock reurns respecvely over hs perod. 28
(c) Average Annual Invenory Turns 6 5 4 3 2 1 0 1985 1987 1989 1991 1993 1995 BEST BUY CO INC 4.0 CIRCUIT CITY STORES INC 3.6 GOOD GUYS INC 5.0 TANDY CORP 1.8 (d) Average Annual per cen Gross Margn 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 1985 1987 1989 1991 1993 1995 BEST BUY CO INC 21% CIRCUIT CITY STORES INC 29% GOOD GUYS INC 29% TANDY CORP 47% 29
(e) Value of $1 nvesmen over me 10 9 8 7 6 5 4 3 2 1 0 Dec-85 Dec-86 Dec-87 Dec-88 Dec-89 Dec-90 Dec-91 Dec-92 Dec-93 Dec-94 Dec-95 BEST BUY CO INC 12.2% CIRCUIT CITY STORES INC 15.9% GOOD GUYS INC 0% TANDY CORP 2.6% 30
Fgure 3: Inverse logarhmc relaonshp beween gross margn and nvenory urns Producs wh more predcable demand Invenory Turns Producs wh less predcable demand Gross Margn Fgure 4: Quarerly Gross Margns and Invenory Turns for all Consumer Elecroncs Realers for 1986-95 Gross Margn (%) 70 60 50 40 30 20 10 0 0 5 10 15 20 25 30 35 Quarerly Invenory Turns 31
Fgure 5: Average Gross Margn Vs Average SGA Expenses for all Real Frms for 1988-97 60 Average Gross Margn (% of Sales) 50 40 30 20 10 0 0 10 20 30 40 50 Average SG&A Expenses (% of Sales) 32
Table 1: Esmaon of effec of real segmen on varous model varables usng ANOVA y s = a s + e s where y s s he average value of a performance varable (IT, GM, RoA, ec.) for frm n real segmen s over some perod of me. The ndependen varables are ndcaor varables for varous segmens. For hs analyss, we used all real frms wh a leas 5 consecuve years of daa avalable n 1978-97. Number of Indusry Segmens for each model varable: 10 Number of frms: 144 y s AT GM GMRoII IT RoA g RoE DE 1 R-square 0.475 0.433 0.325 0.530 0.073 0.073 0.018 0.155 F-sasc for α s 13.45 a 11.37 a 7.18 a 16.77 a 1.17 1.18 0.28 2.74 b Average Values for Real Segmens for Varables wh sgnfcan F-sascs Apparel and accessory sores 2.22 36.13 239.7 6.64 Convenence sores 3.55 21.38 474.3 21.34 Deparmen sores 1.79 32.15 245.1 7.75 Drug & propreary sores 2.99 25.48 171.1 7.00 Grocery sores 4.06 24.48 392.9 16.28 Hobby, oy and game shops Home furnure & equp sore 1.68 31.44 132.8 4.29 1.68 43.41 173.6 4.01 Jewelry sores 1.42 52.44 138.1 2.64 Rado, TV, Cons. Elecroncs sores 2.73 33.12 167.1 5.38 Varey sores 2.65 27.43 152.4 6.13 a sgnfcan a 0.0001 b sgnfcan a 0.01 1 For DE (fnancal leverage), alhough he F-sasc s sgnfcan a 0.01, we dd no fnd sgnfcan dfferences a 95% confdence level beween any par of real segmens usng any of he followng ess, Bonferron, Tukey, and Scheffe. 33
Table 2: Resuls of he Regresson of Sock Reurns on RoE and Sales Growh, and on RoA, Sales Growh, Sandard Devaon of RoA, and Fnancal Leverage Tme Perod Number of Frms Sock Reurn Vs RoE and Sales Growh 1 Sock Reurn Vs RoA, Sales Growh, σ-roa, and DE rao 1 R 2 Inercep RoE Sales Growh R 2 Inercep RoA Sales Growh σ-roa DE rao 19 yrs 1979-97 32 0.602 6.28 (2.24) a 0.67 (0.18) a 0.36 (0.18) b 0.666 10.58 (6.37) 15 yrs 1983-97 35 0.629 7.90 (1.82) a 0.57 (0.13) a 0.40 (0.16) a 0.717-2.17 (5.35) 0.64 0.51-1.52-3.19 (0.27) b (0.16) a 0.45) a (3.36) 1.02 0.48-0.75 3.54 (0.22) a (0.14) a (0.37) b (2.65) 10 yrs 1979-88 42 0.374 12.04 (3.45) a 1.06 (0.22) a - 2 0.384 10.12 (7.52) 1.13-2 (0.33) a -1.82 5.99 (0.69) a (2.85) b 1988-97 59 0.478-2.38 (1.60) 0.57 0.32 0.528-14.51 0.99 0.34-0.006 (0.11) a (0.12) b (4.29) a (0.17) a (0.12) a (0.44) 4.54 (2.28) b 5 yrs 1979-83 81 0.423 10.28 (2.59) a 1.39 (0.18) a - 2 0.352 7.23 (5.19) 0.83 0.57-0.62 (0.28) a (0.19) a (0.57) 3.02 (2.16) 1983-87 49 0.450 18.23 (5.35) a 1.15 (0.41) a 0.77 (0.30) b 0.532-12.75 (11.10) 1.45 1.19 1.33 (0.53) a (0.28) a (1.09) 19.64 (5.74) a 1988-92 69 0.503-9.49 (2.62) a 0.78 (0.16) a 0.74 (0.15) a 0.471-13.99 (5.63) a 0.98 (0.25) a 0.72 (0.17) a -0.44 (0.71) -0.85 (3.46) 1993-97 93 0.250-1.81 (2.21) 0.65 0.25 0.348-16.91 1.41 0.29-0.80 (0.15) a (0.11) b (5.81) a (0.29) a (0.11) a (0.59) 4.79 (2.51) b a sgnfcan a 0.01 b sgnfcan a 0.05 1 F-ess for all models are sgnfcan a 0.01. Fgures n brackes below parameer esmaes gve her sandard errors 2 Sales Growh was no used as an ndependen varable n hese cases because of collneary wh RoE or RoA. 34
Table 3: Sandardzed Coeffcens for he Regresson of Sock Reurn on RoE and Sales Growh, and on RoA, Sales Growh, Sandard Devaon of RoA and Fnancal Leverage 1 Tme Perod Sock Reurn Vs RoE and Sales Growh Sock Reurn Vs RoA, Sales Growh, σ-roa and DE rao RoE Sales Growh RoA Sales Growh σ-roa DE rao 19 yrs 1979-97 0.558 0.298 0.365 0.420-0.433-0.142 15 yrs 1983-97 0.561 0.325 0.613 0.391-0.217 0.173 10 yrs 5 yrs 1979-88 0.611-0.444 - -0.339 0.276 1988-97 0.542 0.279 0.611 0.296-0.004 0.209 1979-83 0.650-0.326 0.323-0.102 0.133 1983-87 0.390 0.354 0.360 0.547 0.134 0.404 1988-92 0.445 0.442 0.392 0.431-0.064 0.027 1993-97 0.402 0.202 0.441 0.235-0.130 0.168 1 R 2 values and sascal sgnfcance of all coeffcens are dencal o hose for correspondng non-sandardzed models n ables 3 and 4. 35
Table 4: Resuls of he Regresson of Sock Reurn on DGMRoII, Sales Growh and DE rao Tme Perod Number of Frms R-square 1 Parameer Esmaes 2 Inercep GMRoII Sales Growh 19 yrs 1979-97 32 0.452 9.38 (3.82) b 0.86 (0.61) DE rao 0.86-1.85 (0.20) a (3.28) 15 yrs 1983-97 35 0.530 6.81 (3.39) b 1.28 (0.47) a 0.97 (0.18) a 0.78 (2.78) 10 yrs 1979-88 42 0.414 12.76 (3.34) a 1.22 (0.80) 0.84 2.58 (0.18) a (3.35) 1988-97 59 0.286-0.25 (2.34) 0.64 0.59 0.78 (0.34) c (0.14) a (2.46) 5 yrs 1979-83 80 0.297 14.55 (2.87) a 0.59 (0.60) 0.97 1.51 (0.17) a (2.29) 1983-87 48 0.517 17.01 (5.82) a 2.58 (0.79) a 1.57 (0.24) a 7.72 (5.59) 1988-92 68 0.378-2.98 (3.36) 1993-97 92 0.153-1.19 (2.80) 1.19 0.97-2.43 (0.59) b (0.17) a (3.62) 0.99 0.38 3.86 (0.46) b (0.12) a (2.77) a sgnfcan a 0.01 b sgnfcan a 0.05 c sgnfcan a 0.10 1 F-ess for all models are sgnfcan a 0.01 2 Fgures n brackes below parameer esmaes gve her sandard errors 36
Table 5: Resuls of he Regresson of Sock Reurns on Gross Margn, SG&A Expenses, Invenory, and Toal Asses for 1988-97 Resuls of equaon (4) 1 Resuls of equaon (5) 1 R 2 0.387 0.530 Number of observaons 59 59 Inercep -4.1 (4.7) -1.38 (4.04) Gross Margn per dollar nvesmen n sock SG&A Expenses per dollar nvesmen n sock Invenory 2 Grocery frms Non-grocery frms Toal Asses 2 Grocery frms Non-grocery frms 15300 (2811) a 20959 (2788) a -16036 (2929) a -21165 (2834) a -2203 (1041) b -1360 (662) b -493 (211) b -1726 (373) a a sgnfcan a 0.01 b sgnfcan a 0.05 1 F-ess for boh models are sgnfcan a 0.01. The fgures n brackes gve sandard errors of esmaes. 2 Because of he small number of daa pons, we dd no esmae separae coeffcens for all ndusry segmens. We ncluded convenence sores and grocery sores n he frs group, and all remanng frms n he second group, snce hese had he wdes separaon beween her nvenory urns and asse urns values. 37
Table 6: Tes for he Inerrelaonshp beween Invenory Turns and Gross Margn Model equaon: log IT = α s + β log GM + ε where log IT s he log of average nvenory urns for frm, log GM s he log of average gross margn for frm, and α s s an ndusry specfc nercep. Resuls for all frms wh a leas 5 years of daa Resuls for all frms wh a leas 10 years of daa Number of frms 157 80 Number of real segmens 14 11 R-square 0.638 0.640 F-es for sgnfcance of ndusry specfc nercep Esmaed coeffcen of log GM (sandard errors n parenheses) 10.51 a 7.01 a -0.530-0.474 (0.126) a (0.207) a a sgnfcan a 0.0001 Table 7: Tes for he Inerrelaonshp beween Gross Margn and SG&A Expenses Model equaon: GM = α + β SGA + ε where GM and SGA are expressed as percen of sales. They denoe average values of gross margn and SGA for frm over me. Segmen specfc nercep was no requred because ndusry effec was neglgble. Resuls for all healhy frms wh 10 years of daa (1988 o 1997) Resuls for all healhy frms for 1997 Number of frms 82 158 R-square 0.822 0.743 Inercep (sandard errors n parenheses) Esmaed coeffcen of SGA (sandard errors n parenheses) 8.10 (1.28) a 9.02 (1.20) a 0.96 (0.050) a 0.94 (0.044) a a sgnfcan a 0.01 38
Table 8: Resuls for he esmaon of declnng rend n sales growh rae over me g = a + b + e Number of frms 185 Number of daa pons 2305 R-square 0.322 F-value for he model 5.44 (sgnfcan a 0.0001) Esmaed coeffcen of me, β -0.997 (sandard error = 0.0996) Table 9: Esmaon of sales growh raes and fxed asses growh raes of healhy frms and frms ha fled for bankrupcy (n he years precedng bankrupcy) Annual average for healhy frms 4 years before bankrupcy Number of frms Average and Sandard Error of Sales Growh Rae (%) 174 13.92 (2.31) 27 7.38 (2.09) Average and Sandard Error of Fxed Asses Growh Rae (%) 17.29 (0.70) 9.39 (3.68) 3 years 29 3.22 10.94 (1.96) b (5.05) 2 years 29 7.72 (4.44) 14.32 (7.20) 1 year 30 0.67 (5.61) b 2.67 (4.41) Durng he year of bankrupcy 33-12.43-15.39 (2.85) a (3.58) a Sales growh rae of a bankrup frm s less han he sales growh rae of a healhy frm wh p 0.01 b Sales growh rae of a bankrup frm s less han he sales growh rae of a healhy frm wh p 0.05 39