A Returns-Based Representation of Earnings Quality

Transcription

1 A Rturns-Basd Rprsntation of Earnings Quality Frank Eckr (Univrsity of rir and Duk Univrsity) Jnnifr Francis* (Duk Univrsity) Irn Kim (Duk Univrsity) Pr Olsson (Duk Univrsity) Kathrin Schippr (Financial Accounting Standards Board) W xamin th proprtis of a rturns-basd rprsntation of arnings quality, stimatd from firmspcific asst pricing rgrssions augmntd by an arnings quality mimicking factor. h cofficint on th arnings quality factor (th -loading ) capturs th snsitivity of th firm s rturns to arnings quality in a givn yar or quartr, analogous to bta as a masur of th snsitivity of rturns to markt movmnts. Rlativ to othr proxis for arnings quality, -loadings can b calculatd for largr sampls of firms and can b stimatd for shortr intrvals at any point in tim. Along all dimnsions xamind, w find that -loadings prform wll in capturing notions of arnings quality. Novmbr 2005 * Corrsponding author: Fuqua School of Businss, Duk Univrsity, Durham, NC addrss, jfrancis@duk.du. his rsarch was supportd by th Fuqua School of Businss, Duk Univrsity. h viws xprssd in this papr ar thos of th authors, and do not rprsnt positions of th Financial Accounting Standards Board. Positions of th Financial Accounting Standards Board ar arrivd at only aftr xtnsiv du procss and dlibration. Analyst data ar from Zacks Invstmnt Rsarch Databas. W hav bnfitd from hlpful commnts from two anonymous rviwrs, Mosh Barkt, Qi Chn, Dan Dhaliwal, Barry Lwis, Michal Smith, Mohan Vnkatachalam, and Yun Zhang and from workshop participants at Colorado Stat Univrsity, Duk Univrsity, ISCE Businss School - Lisbon, Swdish Institut for Financial Rsarch, 2005 Southast Accounting Rsarch Confrnc, mpl Univrsity and th Univrsity of Rgnsburg.

2 1. Introduction W analyz a rturns-basd masur of arnings quality that can b stimatd for a givn firmyar (in xtnsions, w show th rsults ar gnralizabl to a firm-quartr). Our masur is th slop cofficint from a rgrssion of a firm s daily xcss rturns in yar on a factor mimicking portfolio capturing arnings quality, controlling for othr factors known to affct rturns (markt risk prmium, siz and book-to-markt ratio). W build on th factor mimicking portfolio approach introducd by Fama and Frnch (1993) and applid by Francis, LaFond, Olsson and Schippr (2005) to dmonstrat a risk prmium for firms with poorr arnings quality, as capturd by Dchow and Dichv s (2002) masur of accruals quality (AQ). Spcifically, w follow Francis t al. s (2005) procdurs to crat an AQ factor mimicking portfolio (AQfactor), xcpt that w us daily rturns rathr than monthly rturns to stimat th asst pricing rgrssions. 1 Nxt, for ach firm with at last 100 daily rturns obsrvations in yar = , w stimat 1-factor and 3-factor annual rgrssions that add AQfactor as an indpndnt variabl. Just as th CAPM bta capturs xposur to markt risk, so too will th cofficint on AQfactor in ths rgrssions th -loading captur invstor prcptions of th firm s arnings quality xposur in yar (a largr -loading implis gratr snsitivity to poor arnings quality). Capturing th rturns consquncs of a firm charactristic by applying a factor-mimicking portfolio is not nw, nor ar construct validity tsts of thos rturns consquncs. For xampl, Fama and Frnch (1993) propos and validat th us of factor-mimicking portfolios and associatd loadings as proxis for firm siz and book-to-markt; 2 and Lamont, Polk and Saá-Rqujo (2001) prob th xistnc of a factor capturing th dgr to which firms ar financially constraind. Our papr xtnds this body of work by proposing and validating -loadings as a rturns-basd rprsntation of arnings quality. 1 Using monthly rturns in asst pricing tsts rsults in lss stimation rror than using daily rturns. Howvr, if th tru asst pricing paramtrs vary ovr tim, monthly data will obscur this pattrn, sinc a longr tim priod is ndd to stimat an asst pricing rgrssion with monthly data. Most studis rquir a minimum of 24 to 60 monthly obsrvations to stimat an asst pricing rgrssion; consquntly, th rsulting cofficint stimats ar ffctivly assumd constant ovr this 2-5 yar intrval. 2 h dgr to which factor-mimicking portfolios proxy for undrlying siz and book-to-markt constructs continus to b dbatd. In cross-sctional tsts, Danil and itman (1997) find that additional variation in rturns can b xplaind by firms siz and book-to-markt ratios ovr and abov 3-factor rturns and loadings. Howvr, using tim-sris tsts, Lwlln (1999) documnts no incrmntal information about xpctd rturns in book-tomarkt ratios, byond 3-factor rturns and loadings. 1

3 W viw arnings quality as a masur of information risk, and w dfin arnings quality in trms of prcision, namly, th mapping of currnt accruals into currnt, last yar and nxt yar cash flows. Following Dchow and Dichv (2002), w trm this mapping accruals quality, and dnot it by AQ. hortical support for th viw that information uncrtainty is a nondivrsifiabl (i.., pricd) risk factor is providd by Easly and O Hara (2004) 3 and Luz and Vrrcchia (2005). Empirical support for th viw that arnings quality as masurd by accruals quality is pricd is providd by Francis t al. (2005) who show that th rturns rprsntation of th AQ masur, AQfactor, ntrs 1- and 3-factor asst pricing rgrssions with a rliably positiv avrag cofficint stimat. hus, conditioning on thory that shows information uncrtainty is a pricd risk factor, and on Francis t al. s (2005) mpirical vidnc that th markt prics information risk as capturd by arnings quality masurd by AQ, w viw -loadings as capturing th snsitivity of stock rturns to arnings quality. As discussd in mor dtail in sction 2, our papr contributs to th arnings quality litratur by stablishing an arnings quality mtric that offrs svral advantags rlativ to traditional arnings quality masurs (ithr masurs drivd from accounting data or masurs drivd from both rturns and accounting data). First, -loadings can b masurd for firms that lack th tim sris of accounting data that is typically rquird for stimating accounting-basd masurs of arnings quality. hrfor, - loadings can b masurd for much largr (and mor rprsntativ of th population) sampls of firms, incrasing sampling powr and gnralizability. Scond, -loadings can b rliably masurd ovr intrvals as short as a quartr, so thy can b usd to analyz changs in arnings quality associatd with spcific vnts. In addition, th rturns-basd rprsntation approach is flxibl, in that othr arnings attributs, such as smoothnss and prsistnc, can also b rprsntd by factor loadings. As w discuss 3 In Easly and O Hara s (2004) modl, th risk prmium associatd with information uncrtainty is a function of privat information (which prtains to information asymmtry) and th prcision of public and privat information. Our accruals quality masurs focus on th prcision of currnt accruals with rspct to cash flows; as such, w viw it as capturing th prcision of public information. In xtnsions of our main tsts, w considr th ovrlap btwn -loadings and two trading-basd masurs of information asymmtry: bid-ask sprads (which rflct both public and privat information) and probability-of-informd trading (PIN) scors (which arguably focus mor on privat information). 2

4 in sction 2, ths advantags prmit rsarchrs to xamin arnings quality in sttings and for sampls which ar difficult (or not possibl) to xamin with traditional masurs of arnings quality. Our analysis of th proprtis of -loadings as masurs of arnings quality has four componnts. First, w invstigat whthr -loadings vary cross-sctionally with charactristics xpctd to b rlatd to arnings quality and, sparatly, with othr proxis for arnings quality. Scond, w xamin whthr -loadings ar associatd with prdictabl variation in markt participant bhavior with rspct to arnings. hs tsts focus on whthr invstors attach lowr arnings rspons cofficints to highr - loading firms and whthr thr is gratr disprsion and lss accuracy in analysts arnings forcasts for highr -loading firms. hird, w conduct ovr-tim analyss xamining whthr -loadings xhibit systmatic pattrns as a function of firm ag, whr ag proxis for th amount of information availabl about th firm. W prdict that invstor prcptions of arnings quality ar mor uncrtain (lading to highr -loadings) and lss stabl (lading to lowr autocorrlation in -loadings) for young firms whr lss information is availabl. Fourth, w xamin whthr -loadings ar highr in thr sttings associatd with poor arnings quality: rstatmnts, sharholdr lawsuits, and bankruptcy. In trms of th first analysis, w find that arnings quality dtrminants ar significant in xplaining variation in -loadings, and that -loadings xhibit prdictably positiv corrlations with most othr proxis for arnings quality. Our scond analysis shows that firms with highr -loadings hav lowr arnings rspons cofficints and mor disprsd and lss accurat analysts forcasts, consistnt with markt participants prciving highr -loading firms as having noisir arnings signals than lowr -loading firms. Rsults of our third analysis, of diffrncs in th lvl and stability of -loadings as a function of firm ag, ar also consistnt with prdictions: as th firm maturs, w find both an ovr-tim dclin in th magnitud of its -loading as wll as an ovr-tim incras in th autocorrlation of its - loading. Our fourth analysis rvals that -loadings ar largr in sttings charactrizd by poor arnings quality: -loadings incras prior to, and ar highst during, yars containing rstatmnt announcmnts, lawsuit filings, or bankruptcis. h lvl and chang in -loadings for firms affctd by ths vnts is 3

5 also significantly largr than th lvls and changs obsrvd for sampls of non-vnt firms, matchd in calndar tim. Rsults for all analyss ar significant at th 0.05 lvl or bttr. In summary, w documnt th rliability of -loadings using th following masurs: (i) corrlation btwn -loadings and both th Dchow-Dichv innat dtrminants of arnings quality and svn othr arnings quality proxis, (ii) th rlation btwn -loadings and both arnings rspons cofficints and forcast accuracy and disprsion, (iii) ovr tim changs in -loadings as a function of firm ag, and (iv) th bhavior of -loadings around arnings rstatmnts, class action lawsuits, and bankruptcis. W intrprt th combind rsults as dmonstrating th rliability of -loadings as a rturns-basd rprsntation of arnings quality, controlling for othr factors known to affct rturns. o xamin th robustnss and gnralizability of our findings, w xtnd our tsts in svral ways. First, w vrify that our rsults ar not drivn by th subst of firms with th ncssary data to calculat th accounting masur (AQ) which undrlis AQfactor. Scond, w xamin whthr - loadings basd on othr masurs of arnings quality prform as wll as -loadings basd on accruals quality. Of th svn masurs that w considr (prsistnc, prdictability, smoothnss, valu rlvanc, timlinss, consrvatism, and a masur of abnormal accruals), -loadings basd on mimicking factors for both prsistnc and smoothnss appar to prform about as wll as -loadings basd on accruals quality. Howvr, non of th svn masurs systmatically dominats -loadings basd on accruals quality. hird, w prob th rliability of -loadings calculatd ovr quartrly stimation intrvals, whr w rquir at last 45 daily rturns in quartr Q to calculat th firm-quartr -loading. Quartrly -loadings xhibit th sam pattrns found for yarly -loadings, xcpt that th confidnc intrvals ar widr; howvr, rsults ar significant at th 10% lvl or bttr. Fourth, w xamin th ovrlap btwn - loadings and two trading-basd masurs of information asymmtry bid-ask sprads and probability of informd trading (PIN) scors. h corrlations rang btwn 0.12 and 0.16 and, whil rliably diffrnt from zro, ar rlativly wak in conomic trms. o dtrmin th influnc of sprads and PIN scors on -loadings, w orthogonaliz -loadings with rspct to ths masurs and rpat our tsts. Rsults 4

6 ar similar, suggsting that th ffcts w documnt for -loadings ar not subsumd or drivn by tradingbasd masurs of information asymmtry, such as bid-ask sprads and PIN scors. W bliv that th strngth and consistncy of our rsults, including rsults of xtnsions, allviat concrns that aris bcaus AQfactor itslf is constructd using a sampl of firms with th rquisit tim-sris data to stimat th AQ masur. If this sampl is not sufficintly broad, th rsulting rturns rprsntation of AQ dmonstratd by AQfactor may not rflct th markt s pricing of AQ, impairing our ability to idntify a maningful rturns rprsntation of arnings quality. In particular, if AQfactor is sufficintly noisy, w would not xpct it to load positivly in asst pricing rgrssions. Our findings that AQfactor xhibits significant (at th lvl) avrag loadings for our sampl and that AQfactor itslf is associatd with an conomically substantial and statistically rliabl (at th lvl) avrag rturn indicat that whatvr bias is introducd by using an accounting-basd sampl as th foundation for AQfactor dos not invalidat th approach. akn togthr, w bliv th rsults support th infrnc that -loadings captur th sam undrlying construct as rflctd in othr masurs of arnings quality. hat is, -loadings captur what thy purport to captur and offr svral advantags, rlativ to accounting-basd masurs, in trms of flxibility and adaptability. 4 Whil w bliv that our rsults dmonstrat that an arnings quality masur basd on - loadings offrs significant advantags compard to xisting arnings quality masurs, our rsults do not spak to whthr -loadings ar th bst masur h choic of th bst arnings quality masur will b a function of, among othr things, th natur of th rsarch qustion addrssd, th assumptions ncssary to support th chosn rsarch dsign, and availabl data. For xampl, th -loading xplord in this papr is a masur of invstors prcption of total arnings quality, not discrtionary arnings quality. In a rsarch stting that dmands a masur of th portion of arnings quality that is solly 4 Our vidnc supports Chn, Shvlin and ong s (2004) analysis of ovr-tim changs in th information risk charactristics of firms that chang dividnd policis. hy find that information risk (proxid by th cofficint stimat on monthly AQfactor) is highr in th thr yars following a dividnd dcras rlativ to th thr yars prcding th dcras; thy find mixd rsults for dividnd-incrasing firms. 5

7 attributabl to managmnts discrtionary actions and bhaviors, a diffrnt masur of arnings quality would likly b prfrrd. wo othr cavats about th us of -loadings ar also in ordr. First, unlik masurs that rly xclusivly on accounting data, -loadings captur th markt s prcption of arnings quality. If markt blifs ar not rational, thos prcptions may diffr from th rality of th firm s arnings quality. (Bcaus our tsts focus on broad sampls of firms ovr a 34-yar priod, it is unlikly that our rsults ar attributabl to systmatic mispricing.) Scond, our analyss of th rliability of -loadings focus on th cross-sctional distribution of mpirical masurs of an unobsrvabl construct, not on th corrctnss of an stimatd magnitud. 5 Our rsults hav no implications for th magnituds of th -loadings, and thory dos not prdict valus for ths magnituds. In this rspct, -loadings ar similar to s- and h- loadings (for SMB and HML); only th CAPM bta has a valu that drivs from thory. h rst of th papr is organizd as follows. h nxt sction laborats on th charactristics of rturns-basd masurs of arnings quality and thir advantags rlativ to xisting arnings quality masurs. Sction 3 lays out th construction of th AQfactor mimicking portfolio and th stimation of th -loadings. Sction 4 dscribs th main sampls usd in our tsts, sction 5 dscribs th mpirical analyss, and sction 6 prsnts xtnsions. Sction 7 summarizs th findings and concluds. 2. Rturns-Basd Rprsntations vrsus Accounting-Basd Rprsntations of Earnings Quality. In this sction w dscrib how a rturns-basd rprsntation of arnings quality offrs two distinct but rlatd advantags ovr arnings quality masurs drivd from accounting data. (Whil w focus on accounting-basd masurs, much of th discussion also applis to masurs drivd from a combination of accounting and markt data, for xampl, th xplanatory powr of arnings for rturns.) hos advantags driv from diffrncs in data rquirmnts and priodicity of stimation. W also 5 his aspct of our analysis is similar to som aspcts of Botosan and Pluml s (2005) calibration of mpirical proxis for th cost of quity capital. hy focus primarily on th adquacy of cross-sctional distributions by xamining associations btwn masurs of firm-spcific risk (such as bta) and masurs of th cost of capital. 6

8 dscrib th typs of rsarch sttings whr a rturns-basd masur lik th on w propos and validat might b usd, and whr it would not b usful. Data rquirmnts. Masurs of arnings quality basd on accounting data ar typically stimatd using ithr a firm-spcific tim-sris of annual data or industry cross-sctions. 6 Eithr approach placs significant rstrictions on sampl sizs and, in th cas of firm-spcific tim-sris stimation, biass th sampl toward surviving firms which tnd to b largr and mor profitabl. o illustrat this point, w rfr to abl 1 of Dchow-Dichv (2002), showing th drivation of thir sampl usd to masur accruals quality as th mapping of currnt accruals into this yar s, last yar s and nxt yar s cash from oprations (CFO). A ky dtrminant of th sampl siz and composition is how many firms and of what typ hav data on currnt accruals and CFO. abl 1 in Dchow and Dichv shows that 55,850 firm-yars btwn hav data on CFO, arnings, changs in accounts rcivabl and invntory and total assts (aftr truncation of th most xtrm 1% of obsrvations). Howvr, only 30,317 firm yars hav both lad and lag valus of CFO rducing th sampl siz by narly 46% -- and 15,234 firm yars (1,725 firms) hav ight or mor annual obsrvations ncssary to stimat firmspcific rgrssions a rduction of narly 73% rlativ to th original sampl. h altrnativ approach of stimating masurs in industry cross-sctions also placs svr rstrictions on sampl siz and composition. For xampl, Xi (2001) follows Subramanyam s (1996) cross-sctional modifid Jons modl stimation and rports a sampl siz of 56,692 firm-yars ovr for a rlativly broad industry dfinition: at last six firms in a 2-digit SIC cod (h also xcluds Nasdaq firms prior to 1982). Ovr this priod, thr ar 106,645 firm-yars with non-missing rturns data on CRSP (also xcluding Nasdaq firms prior to 1982), indicating a sampl loss of about 47%. h dcras in sampl siz is mor xtrm if th industry rquirmnts ar tightnd ithr by rquiring mor firms in th industry or by using mor prcis industry dfinitions. For xampl, Dchow-Dichv (2002, tabl 1) rport that, starting with 55,850 firm-yar obsrvations, 27,204 firm-yar obsrvations 6 An altrnativ to th us of annual data is to us quartrly accounting data; this approach introducs possibl sasonal ffcts as wll as sampling rstrictions arising from availability of quartrly data. 7

9 (136 thr-digit industris) rmain a rduction of about 51%--aftr imposing th rquirmnts of thr yars of CFO data and at last 50 obsrvations pr industry. o summariz, w ar not awar of accounting-basd masurs of arnings quality that do not impos data constraints that rsult in substantial dcrass in sampl siz and (in th cas of firm-spcific tim-sris stimation) incrass in survivorship bias. h dcrass in sampl siz that aris in firmspcific tim-sris stimations ar wll known. Industry-stimation can b don using short sris (.g., as short as two or thr yars) but vn this rstriction rducs sampl sizs and th rquirmnt for a minimum numbr of firms pr industry furthr rstricts th sampl. In contrast, a rturns-basd rprsntation rquirs only a sufficint rturns sris. W show that, dspit th additional nois introducd by daily rturns, rliabl stimats of -loadings can b obtaind for priods as short as on quartr. hus, -loadings can b calculatd for largr and mor rprsntativ (of th population) sampls than can accounting-basd masurs of arnings quality. his is a distinct advantag in rsarch sttings whr gnralizability is important or whr positd ffcts ar xpctd to b most likly prsnt in smallr, youngr firms that lack th data ncssary to calculat accounting-basd masurs. Priodicity of masurmnt. In xtnsions of our main findings (rportd in sction 6), w show that -loadings can b rliably calculatd ovr intrvals as short as 45 trading days, or roughly on quartr. Furthr, ths 45 days nd not b alignd with rporting priods. hs two faturs man that -loadings can b usd to xamin shifts in arnings quality ovr short intrvals and around vnts that occur at any tim. In contrast, accounting-basd masurs of arnings quality ar by construction linkd to annual or quartrly rporting priods, cannot b applid to short intrvals, and cannot b spcific to a givn financial statmnt dat bcaus thy will b basd on both currnt and prior data. Rsarch sttings whr rturns-basd rprsntations of arnings quality offr advantags. h two advantags of -loadings (or othr rturns-basd rprsntations of arnings quality) prviously discussd point to th placs whr ths arnings quality masurs can b particularly usful. Bcaus - loadings can b calculatd for sampls comprisd of youngr smallr firms that hav (at last) on or mor quartrs of daily rturns data but lack a tim sris of accounting data, rsarch qustions involving, 8

10 for xampl, arnings quality for firms that hav gon public within th last two yars ar mor radily addrssd using a rturns-basd rprsntation. Whil th sampling advantag mans that using -loadings incrass sampling powr and gnralizability, th priodicity advantag mans that -loadings can b usd whn th rsarch qustion of intrst prtains to an vnt, occurring at any tim, that has th potntial to shift invstor prcptions of arnings quality. Our rliability assssmnts in sction 5.4, for xampl, focus on shifts in invstor prcptions of arnings quality surrounding bankruptcis, rstatmnts and class actions. Othr vnts whr analysis of -loadings could provid usful insights includ adoptions (including arly adoptions) of nw accounting standards, voluntary accounting changs, and managmnt or auditor changs that ar promptd by concrns about financial rporting quality. In som sttings, a shift in arnings quality might appar in conjunction with a shift in fundamntal risks. Exampls includ changs in capital structur, mrgrs/acquisitions and divisiv rstructurings (.g., spin-offs that chang th numbr and natur of sgmnts and thrfor th innat factors that influnc arnings quality). In ths sttings, an analysis of shifts in information risk would hav to control for th ffcts of shifts in fundamntal risks. Finally, th approach usd to dvlop -loadings is flxibl with rgard to othr arnings attributs. hat is, th factor-mimicking approach w us could b applid to attributs calld for by a spcific rsarch qustion, such as smoothnss or prsistnc. hrfor, if th rsarch qustion rquirs, for xampl, a short-intrval assssmnt of possibl shifts in invstor prcptions of arnings prsistnc, it would b possibl to apply th approach w us to dvlop prsistnc-basd -loadings. Rsarch sttings whr rturns-basd rprsntations of arnings attributs ar inapplicabl. hr ar two kinds of sttings whr th approach w dscrib would b ithr inapplicabl or prhaps of low powr. With rgard to th issu of powr, rcall that th -loading masur capturs invstor prcptions of total arnings quality; howvr, in som sttings it is discrtionary arnings quality that is of intrst. Prvious rsarch (Francis t al., 2005) shows that th portion of accruals quality that is du to managmnt s rporting choics is lss pricd than is total or innat accruals quality. his distinction has 9

11 implications for th powr of tsts rquiring a masur of th discrtion in managmnt s short trm dcisions. With rgard to th qustion of applicability, a rturns-basd rprsntation of an arnings attribut cannot captur th sign of th undrlying factor (.g., positiv or ngativ abnormal accruals). A rturnsbasd rprsntation would not, thrfor, b applicabl in sttings which rquir tsts of dirctional prdictions concrning opportunistic arnings managmnt (.g., tsts of whthr arnings ar managd up or down in rspons to som positd incntiv). 3. AQ and AQFactor Mimicking Portfolios Our main tsts us Dchow and Dichv s (2002) accruals quality mtric to captur arnings quality. his choic is basd on Francis t al. s (2004) finding that accruals quality has a strongr association with th cost of quity than othr arnings attributs. As discussd in Francis t al. (2005, sction 2.1) thoris dvlopd by Easly and O Hara (2004) and Luz and Vrrcchia (2005) prdict that information risk is pricd by invstors. Positing that invstors pric scuritis basd on information about cash flows, Francis t al. us accruals quality (which capturs th imprcision of th mapping btwn currnt accruals and cash flows) as a masur of information risk. W oprationaliz accruals quality using McNichols (2002) modification of Dchow and Dichv s modl: CA = φ + φ CFO + φ CFO + φ CFO + φ Rv + φ PPE + υ (1) 0, j 1, j 1 2, j 3, j + 1 4, j 5, j whr CA CA CL Cash SDEB = + = total currnt accruals in yar ; CFO = NIBE A = firm j s cash flow from oprations in yar ; 7 NIBE = firm j s nt incom bfor xtraordinary itms (Compustat 18) in yar ; A = ( ) CA CL Cash + SDEB DEPN = firm j s total accruals in yar ; CA j, = firm j s chang in currnt assts (Compustat 4) btwn yar -1 and yar ; CL j, = firm j s chang in currnt liabilitis (Compustat 5) btwn yar -1 and yar ; Cash j, = firm j s chang in cash 7 W calculat total accruals using information from th balanc sht and incom statmnt rathr than from th statmnt of cash flows (as advocatd by Hribar and Collins, 2002) bcaus statmnt of cash flow data ar not availabl prior to 1988 (th ffctiv yar of SFAS No. 95) and our AQ mtric rquirs svn yarly obsrvations. 10

12 (Compustat 1) btwn yar -1 and yar ; SDEB j, = firm j s chang in dbt in currnt liabilitis (Compustat 34) btwn yar -1 and yar ; DEPN = firm j s dprciation and amortization xpns (Compustat 14) in yar ; Rv j, = firm j s chang in rvnus (Compustat 12) btwn yar -1 and yar ; and PPE j, = firm j s gross valu of proprty, plant and quipmnt (Compustat 7) in yar. All variabls ar scald by avrag assts. W stimat (1) in annual industry cross-sctions for ach of th 48 Fama-Frnch industris with at last 20 firms in that industry-yar. hs stimations produc firm-yar rsiduals, ˆ υ. Our accounting-basd arnings quality mtric for firm j in yar is th standard dviation of firm j s rsiduals ovr th past fiv yars, AQ ˆ j, = σ ( υ j, ), = -5,,-1. 8 Larg (small) valus of AQ j, corrspond to poor (good) accruals quality. W assign firms to AQ dcils using a dynamic portfolio tchniqu that allows for diffrncs in firms fiscal yar nds as wll as ovr-tim changs in accruals quality. Spcifically, w form dcils on th first day of ach month m basd on th firm s most rcnt valu of AQ known prior to m; firms with th smallst (largst) AQ valus ar placd in th first (tnth) dcil. his mans that firm j s AQ signal for fiscal yar, whr fiscal yar nds in month n, will influnc firm j s ranking for months n+4 through n+15. W thn calculat th avrag daily rturn for ach dcil for th priod January 2, 1970 (th first trading day of 1970) to Dcmbr 31, 2003, yilding a tim sris of 8,586 daily rturns for ach dcil (D1,,D10). h AQ factor-mimicking portfolio, AQfactor, quals th diffrnc btwn th daily rturns of th poorst AQ dcils (dcils 7-10) and th bst AQ dcils (dcils 1-4). his procdur (similar to that usd by Carhart (1997) to construct a factor mimicking portfolio for pric momntum) yilds a sris of 8,586 daily AQfactor rturns ( AQfactor t ). 9 8 Calculating th AQ masur in yar using th rsiduals in yars -5 to -1 accounts for th fact that quation (1) contains a lad trm, CFO +1. In total, calculation of AQ rquirs svn yars of data bcaus w rquir fiv rsiduals and th modl contains two lag trms. 9 Carhart s procdur diffrs from Fama and Frnch s (1993) procdurs usd to crat siz and book-to-markt mimicking factors in two rspcts: h uss qually-wighting rathr than valu-wighting and dos not orthogonaliz with rspct to firm siz. As snsitivity tsts, w r-crat an AQfactor that is valu-wightd within ach cll and orthogonalizd with rspct to siz. (Both valu-wighting and orthogonalization srv to rmov any siz-shard variation from th stimation of th -loadings.) Rsults basd on th valu-wightd, sizorthogonalizd AQfactor (not rportd) ar similar in all rspcts to thos documntd. 11

13 For our sampl, th avrag valu of AQfactor t is %, or about 22% on an annualizd basis; th standard rror is %. o put ths figurs in prspctiv, ovr th sam tim priod th avrag daily xcss markt rturn is % (standard rror of %), th avrag valu of th daily SMB is % (standard rror of %), and th avrag valu of th daily HML is % (standard rror of %). AQfactor is rliably diffrnt from zro (t-statistic = 9.17, or th man valu of % dividd by its standard rror of %), and has a highr t-statistic than th othr risk factors (th t-statistics for markt risk, SMB and HML ar 2.12, 0.37 and 4.28, rspctivly). Bcaus AQfactor is tim-spcific, not firm-spcific, w can corrlat AQfactor with th rturns of any firm to dtrmin that firm s xposur to poor arnings quality, much lik w corrlat a firm s rturns with th markt risk prmium to obtain a masur of its xposur to markt risk. h spcific corrlation masur w us is th cofficint stimat on AQfactor obtaind from 1-factor (suprscript 1f) and 3-factor (suprscript 3f) asst pricing rgrssions which includ AQfactor as an indpndnt variabl: 1-factor: 3-factor: R R = α + β ( R R ) + AQfactor + ε (2) 1f 1f 1f 1f t F, t M, t F, t t t R R = α + β ( R R ) + s SMB + h HML + AQfactor + ε (3) 3f 3f 3f 3f 3f 3f t F, t M, t F, t t t t t whr t = indx for th numbr of trading days in yar ; R jt, = firm j s rturn on day t; R Ft, = th risk fr rat on day t; R Mt, = th markt rturn on day t; SMB t = small-minus-big factor on day t; HML t = high-minus-low book-to-markt factor on day t. RM, t, SMBt andhml t ar obtaind from th WRDS databas (whr raw rturns ar from CRSP and SMB and HML factors ar from Kn Frnch). For th 1-factor (3-factor) spcification, ( 3 f, ) is th stimat of firm j s snsitivity to poor arnings 1 f j, j j, j, j, j, quality in yar. h othr slop cofficints, β (or β ), s and h, captur th firm s xposur to rturns rprsntations of markt risk, siz, and book-to-markt, rspctivly, in yar. In summary, w crat an accounting-basd masur of accruals quality AQ j, using a rstrictd sampl of firms with svn yars of accounting data, convrt AQ to a tim-spcific rturns rprsntation ( AQfactor t ), and us this tim-spcific rturns rprsntation in firm-and yar-spcific rgrssions to obtain a firm-yar rturns-basd rprsntation of arnings quality (,, = 1f or3f ). Rlativ to th j 12

14 original accounting-basd masur, -loadings can b calculatd for much largr sampls bcaus thy rquir only nough daily rturns in yar to stimat (2) or (3). An additional bnfit of th rturns approach is that bcaus AQfactor t varis through tim, -loadings ar not constraind to b slow to chang, as is AQ which rquirs svn yars of accounting data and thrfor has a mchanical dpndnc yar ovr yar. 4. Sampl and Dscriptiv Data W bgin by idntifying all firms with th ncssary data to stimat AQ in ach yar = (th AQ Sampl). 10 Using th AQ masurs, w calculat AQfactor using th procdurs dscribd in sction 3. Nxt, w idntify all firms with at last 100 daily rturns in yar (th Rturns Sampl). h rquirmnt of 100 daily rturns in yar to stimat (2) and (3) is ad hoc, and w assss its snsitivity in sction 6. abl 1, panl A shows th numbr and prcntag of sampl firms, by yar, for th two sampls. As xpctd givn th sampl slction critria, th Rturns Sampl is largr than th AQ Sampl, both in numbr of firms (man of 6,408 firms pr yar vrsus 2,147 firms pr yar) and as a prcntag of tradd firms (man of 92.4% pr yar vrsus 30.9%). o furthr contrast th rprsntativnss of th two sampls, panl B rports information about th distributions of siz (markt capitalization, total assts, and total sals) and prformanc (as masurd by rturn on assts and rturn on quity). Comparisons show that th AQ Sampl contains largr firms (man markt capitalization is $1,295 million vrsus $942 million; man assts ar $1,404 million vrsus $1,005 million; man sals ar $1,298 million vrsus $908 million) and mor succssful firms (man ROA is 3.7% vrsus 2.6%; man ROE is 7.3% vrsus 5.5%). hs data dmonstrat that th Rturns Sampl dominats th AQ Sampl on both sampl siz and survivorship bias. Givn that th Rturns Sampl is minimally rstrictd (i.., it rquirs only 100 daily rturns in yar ), it is mor rprsntativ of th population than is th AQ Sampl, which rquirs a firm to hav svn yars of accounting data for inclusion in yar. 10 Bcaus of th data rquirmnts for AQ, as wll as th additional thr-month lag w rquir for accounting data to rach th markt, w us accounting data from as arly as

15 W nxt stimat firm-yar -loadings for th Rturns Sampl, using quations (2) and (3); w discuss but do not tabulat ths rsults. For -loadings basd on th CAPM, th man valu of with a standard dviation of , and th intr-quartil rang is (25 th prcntil) to (75 th prcntil). For -loadings basd on th 3-factor modl, th man valu of 1 f is 3 f is with a standard dviation of , and th intr-quartil rang is (25 th prcntil) to (75 th prcntil). h othr loadings in ths modls (i.., β for th CAPM and β, s and h for th 3-factor modl) xhibit similar variation. h man and mdian xplaind variation of th augmntd 1-factor and 3-factor modls is btwn 3% and 9%, lss than th 16-20% rportd by Francis t al. (2005) for stimations rquiring a minimum of 18 monthly obsrvations pr firm. h lowr xplaind variability is xpctd bcaus w stimat our modls using daily rturns which ar noisir than monthly rturns Analysis of -Loadings Our four analyss of th rliability of -loadings ar complmntary, in that thy us diffrnt rsarch dsigns and distinct indicators of arnings quality. Sction 5.1 uss a cross-sctional dsign to analyz th associations btwn -loadings and both dtrminants of accruals quality and masurs of arnings quality that hav bn usd in othr rsarch sttings. Sction 5.2 also uss a cross-sctional dsign, but th focus of th analysis is on arnings rspons cofficints and proprtis of analyst forcasts. Sction 5.3 shifts to a tim-sris dsign, and considrs th ovr-tim rlation btwn - loadings and firm ag as a summary indicator of availabl firm-spcific information. Finally, sction 5.4 analyzs th rlation btwn -loadings and vnts associatd with poor arnings quality Associations btwn -loadings and dtrminants of and proxis for arnings quality Our first analysis considrs whthr accounting-basd dtrminants of arnings quality xplain cross-sctional variation in -loadings. W focus on th fiv innat dtrminants proposd by Dchow 11 Not that ths R 2 s ar basd on firm-spcific stimations; calndar-tim portfolio rgrssions gnrally show much highr xplanatory powr (oftn abov 40%) bcaus firm-spcific variation is largly liminatd in th portfolio dsign. 14

16 and Dichv: firm siz (log(assts), masurd as th log of total assts; rsults ar not snsitiv to othr masurs of siz, such as rvnus), standard dviation of cash flow from oprations ( σ ( CFO) ), standard dviation of sals rvnus ( σ ( Sals) ), lngth of oprating cycl ( OprCycl, masurd as th log of th sum of days accounts rcivabl and days invntory) and ngativ arnings incidnc ( NgEarn = 1 if yar arnings ar ngativ, 0 othrwis). W masur log(assts), log(oprcycl) and NgEarn as of th nd of yar and w calculat σ ( CFO) and σ ( Sals) using data from yar -6 to. Bcaus of th lattr calculation, th sampl usd for ths tsts is th AQ Sampl not th Rturns Sampl. abl 2, panl A shows th rsults of rgrssing th yar -loadings on th innat dtrminants. W rport th avrag valus of th cofficint stimats obtaind from 34 yarly rgrssions; t-statistics ar basd on th standard rrors of th 34 annual cofficints (Fama and MacBth, 1973). h rsults indicat that -loadings ar highly corrlatd with ach of th innat dtrminants, in th dirctions prdictd by Dchow and Dichv: -loadings ar ngativly corrlatd with firm siz, and positivly corrlatd with th variability of cash flows and sals, th lngth of th oprating cycl, and th incidnc of losss (t-statistics rang in absolut valu from 8.70 to 16.10). Our xamination of th rlation btwn -loadings and othr masurs of arnings quality focuss on th svn arnings attributs considrd by Francis t al. (2004): accruals quality itslf (AQ), arnings prsistnc (Prsistnc, masurd as th ngativ of th AR1 paramtr from firm-spcific rgrssions of currnt arnings pr shar on laggd arnings pr shar), arnings prdictability (Prdictability, masurd as th standard dviation of th rror trm from firm-spcific AR1 modls of arnings), smoothnss (Smoothnss, masurd as th ratio of th standard dviation of arnings to th standard dviation of cash flows), valu rlvanc (Valu Rlvanc, masurd as th ngativ of th xplaind variability of a rgrssion of annual rturns on th lvl and chang in arnings pr shar), timlinss (imlinss, masurd as th ngativ of th xplaind variability from a Basu (1997) rvrs rgrssion of arnings on rturns controlling for th sign of thos rturns), and consrvatism (Consrvatism, masurd as th ngativ of th cofficint on ngativ rturns from th aformntiond 15

17 rvrs rgrssion). W follow Francis t al. s (2004) procdurs and stimat ach attribut ovr rolling 10-yar windows. Associations ar calculatd btwn ths masurs of arnings quality and th - loadings avragd across th sam rolling 10 yar windows. Each masur is ordrd consistntly such that highr (lowr) valus indicat poorr (bttr) arnings quality. abl 2, panl B shows th pairwis corrlations btwn th -loadings and th arnings quality proxis. o th xtnt that th -loadings ar corrlatd with th constructs capturd by ach of ths attributs, w xpct to obsrv positiv corrlations btwn -loadings and ach variabl. For xampl, th corrlation btwn -loadings and AQ provids vidnc on whthr -loadings captur th variabl that undrlis th construction of th AQfactor. W xpct and find that this corrlation is positiv, with magnituds ranging from to , all significant at th lvl. 12 Francis t al. s (2004) rsults suggst that associations btwn th -loadings and th arnings quality attributs should b strong for th thr attributs basd only on accounting numbrs, and wak for th thr attributs that ar basd also on rturns. Our rsults ar for th most part consistnt with thir findings. Both Prsistnc and Smoothnss xhibit significant (at th lvl) positiv corrlations with -loadings ranging in magnitud from to ; for Prdictability, th corrlations ar lowr ( to ; significant at th 0.02 lvl or bttr). Rsults for masurs of arnings quality that also rly on rturns data (Valu Rlvanc, imlinss and Consrvatism) ar gnrally low, with corrlations ranging from (insignificant) to (p<0.0001) Markt participant bhaviors as a function of -loadings In this sction, w xamin whthr -loadings ar associatd with prdictabl variation in invstor and analyst bhaviors. Our first analysis builds on prior rsarch (.g., Imhoff and Lobo, 1992) that posits information uncrtainty as a dtrminant of invstors rspons to arnings as capturd by th cofficint rlating rturns to arnings (arnings rspons cofficint, or ERC). Our tst of whthr firms with highr -loadings hav smallr ERCs is basd on th following rgrssion: 12 his rsult is similar to Fama and Frnch s (1993) vidnc that loadings on th SMB and HML mimicking factors ar positivly corrlatd with thir undrlying variabls, markt capitalization and book-to-markt, rspctivly. 16

18 CAR ( 1,0) t = λ 0 + λ1ue t + λ2ue t + ζ t (4) whr CAR( 1,0) t = firm j s two-day cumulativ abnormal rturn ovr th quartrly arnings announcmnt, whr abnormal rturn is dfind as th raw rturn lss th valu-wightd markt rturn; and UE t = unxpctd arnings convyd by firm j s quartrly arnings announcmnt mad on day t, qual to firm j s rportd arnings for quartr q lss th consnsus analyst forcast, scald by firm j s shar pric twnty days bfor th arnings announcmnt dat. Rsults of stimating quation (4) ar shown in abl 3, Panl A. For ths tsts, unxpctd arnings ar calculatd using analyst forcast data from Zacks, ; on avrag, thr ar 2,885 firms pr quartr. W rport man valus of th cofficints obtaind from th 80 quartrly stimations of (4) ovr th priod ; th t-statistics ar basd on th tim-sris of th standard rrors of th 80 cofficint stimats. 13 h rsults show that arnings nws announcd by highr -loading firms is associatd with a significantly wakr markt rspons than is arnings nws announcd by lowr - loading firms: λ 2 = (t-statistic = -3.96) using 1 f, and λ 2 = (t-statistic = -4.32) using 3 f. W also stimat quartrly rgrssions which includ UE intractd with othr variabls known to affct arnings rspons cofficints: whthr th firm rportd a loss in quartr q (NgEarn = 1 if rportd arnings in quartr q ar ngativ, 0 othrwis), firm siz (ln(siz) = log of firm j s sals rvnus), firm j s markt-to-book ratio (MB), and th ratio of firm j s dbt to quity (Lvrag): CAR( 1,0) = λ + λue + λue + λue NgEarn + λue ln( Siz) t 0 1 t 2 t 3 t q 4 t q + λue MB + λue Lvrag + ξ 5 jt, jq, 6 jt, jq, jt, (5) Rsults of stimating (5), rportd in Panl B, continu to show that firms with largr -loadings hav smallr rsponss to arnings nws (t-statistics for λ 2 ar using 1 f and using 3 f ). Our scond analysis of th rlation btwn -loadings and markt participant bhavior xamins th disprsion and accuracy of analysts arnings forcasts. Basd on prior rsarch, w prdict that firms with highr -loadings hav mor difficult-to-prdict arnings, rsulting in both mor disprsd and lss accurat forcasts rlativ to lowr -loading firms. Mor spcifically, prior rsarch rports positiv 13 An altrnat approach is to stimat (4) as a poold rgrssion, with t-statistics basd on Nwy-Wst (1987) adjustd standard rrors to control for htroscdasticity and autocorrlation. Infrncs (not tabulatd) ar th 1 f sam: in particular, th t-statistic for λ 2 is (-7.61) for ( 3 f ). 17

19 rlations btwn random walk masurs of arnings surpris which w intrprt as indicators of difficult-to-prdict arnings and both forcast disprsion and forcast accuracy. 14 Following prior litratur, w masur forcast disprsion as th standard dviation of analysts EPS forcasts for quartr q, scald by shar pric at th bginning of th quartr. sts of disprsion ar conductd on all firms with at last thr arnings forcasts for quartr q issud in th thr months prcding th announcmnt of quartr q arnings; th thr or mor forcasts produc a standard dviation of th forcasts mad in quartr q for firm Disprsion q. W masur forcast accuracy as th absolut forcast rror (th diffrnc btwn rportd and forcastd EPS for quartr q, scald by shar pric at th bginning of th quartr). sts of forcast accuracy ar conductd on all firms with at last on quartrly arnings forcast for quartr q in th thr-month priod prcding th quartr q arnings announcmnt; our masur of forcast accuracy, FE, is th avrag of th absolut forcast q rrors across all forcasts mad about firm j for quartr q. Largr valus of Disprsion q and FE, indicat mor disprsd and lss accurat forcasts, rspctivly. Similar to abl 3, ths tsts ar conductd using analyst forcast data from Zacks, Our tsts of whthr firms with gratr forcast disprsion and largr absolut forcast rrors hav highr -loadings ar basd on th following rgrssions: j q Disprsion = κ + κ + κ Ag + κ ln(sals) + ς (6) q q 3 q q FE = + + Ag + ln(sals + (7) δ0 δ, 1 j, δ2 jq, δ3 ) jq jq, ψ jq, Equations (6) and (7) control for forcast ag and firm siz, both of which hav bn shown to affct forcast disprsion and accuracy (Bown t al., 2002). Forcast ag ( Ag j, q ) is th avrag numbr of 14 Lang and Lundholm (1996) and Bown, Davis and Matsumoto (2002) masur arnings surpris as th absolut valu of th diffrnc btwn rportd arnings for quartr q and rportd arnings for quartr q-4, scald by SRW SRW shar pric ( UE jq, ). In addition to UE jq,, Hflin, Subramanyam and Zhang (2003) includ two indicator variabls to captur arnings surpris: th first quals on if th sign of th unxpctd arnings nws in th quartr SRW SRW q arnings announcmnt is ngativ ( NgUE, = 1 if UE, < 0 ), th scond quals on if rportd arnings for quartr q ar ngativ, and zro othrwis ( Loss jq, ). jq jq 18

20 days that th forcasts issud in quartr q prcd th arnings announcmnt dat. Firm siz is proxid by th log of sals in quartr q, ln( Sals ) q. W stimat quations (6) and (7) using both poold and quartrly stimations; rsults ar similar for th two approachs, so w tabulat only th quartrly rsults. abl 4 rports th man valus of th cofficints from th 80 quartrly rgrssions, with t-statistics basd on th standard rrors of thos 80 stimats. h rsults for both disprsion (Panl A) and absolut forcast rrors (Panl B) indicat that -loadings ar positivly associatd with both forcast proprtis (tstatistics rang btwn 5.39 and 12.07). W also r-stimat quations (6) and (7) including th thr masurs of arnings surpris considrd in prior rsarch (dscribd in footnot 14). Evn controlling for ths othr surpris masurs, th rsults show that -loadings rmain significant (t-statistics, rportd in abl 4, rang from 2.92 to 8.40) in xplaining proprtis of analysts forcasts Pattrns in -loadings associatd with firm ag his sction xamins pattrns in -loadings associatd with firm ag, which w viw as an invrs masur of invstor prcptions of th amount and stability of firm-spcific information. Our xamination is prdicatd on th viw that th rlativly spars firm-spcific information that is availabl for youngr firms inducs gratr uncrtainty about that firm s businss, including uncrtainty about its financial rporting quality. W conduct two invstigations involving firm ag and -loadings. h first involvs nwly listd firms: for such firms, w xpct information uncrtainty is highst at initial listing, and dclins as mor information bcoms availabl as th firm maturs. his hypothsis follows from Lang s (1991) argumnt (in an ERC stting and in th contxt of IPO firms) that, as mor arnings obsrvations ar rvald, invstors uncrtainty about th prsistnc paramtr undrlying th arnings procss dcrass. 15 Similar to Pastor and Vronsi (2003) and Fama and Frnch (2005), w masur firm ag as th numbr of yars sinc listing (FirmAg), whr listing yar (yar 0) is th first yar for which stock rturns ar availabl on CRSP. W considr ag, rathr than masurs such as firm siz, 15 Rgulation S-X rquirs IPO firms to provid thr yars of incom and cash flows in thir Form S-1 rgistration statmnts. Firms in xistnc lss than thr yars must provid information for th priod thy hav bn in xistnc. hus, for most nwly listd firms, financial data xist which provid a basis for invstors to dvlop viws about th firm s financial rporting quality. 19

21 institutional holdings and analyst covrag (which hav also bn usd to rflct th rlativ uncrtainty or quality of information) so that w can us an ovr-tim dsign to xamin whthr -loadings dclin aftr initial stock markt listing. Figur 1 shows th trnd in man -loadings as a function of FirmAg for firms in th Rturns Sampl that listd in any yar, o b includd in Figur 1, th firm must hav data in vnt yar =0. hrfor, th -loading for yar =+1 is basd on all firms who survivd at last on yar aftr listing. hr is a clar downward trnd in -loadings as a function of FirmAg for both and 1 f 3 f. o tst th significanc of this trnd, w rgrss th avrag -loading of ag τ (τ = 0,1,2 39) on FirmAg. 16 h rsults, also plottd in Figur 1, indicat a significant downward trnd in -loadings as a function of FirmAg (t-statistics, not rportd, ar for and for 1 f 3 f ). o control for ovr-tim changs in th sampl composition, w rpat our tsts rstricting th sampl to firms that survivd at last 10 yars following listing; rsults (not tabulatd) show a significant dclin in -loadings for this sampl. Ovrall, w intrprt dclins in -loadings as a function of firm ag as vidnc that, as firm-spcific information incrass, invstor uncrtainty about rporting quality dcrass. Our scond analysis invstigats th prdiction that -loadings xhibit gratr yar-ovr-yar stability for rlativly matur firms than thy do for rlativ young firms. In xamining stability, w do not suggst that a firm s -loadings must b th sam from on yar to th nxt; indd, a bnfit of - loadings is that thir stimation procdurs do not forc thm to b constant ovr short intrvals. As a practical mattr, howvr, w would not xpct to obsrv significant yar-to-yar changs in a firm s arnings quality (or invstors prcptions of that arnings quality) for firms with rlativly stabl rporting and information nvironmnts bcaus, for such firms, rlativly lss nw information about arnings quality is likly to b rvald ach yar. Our masur of th stability of -loadings is th autocorrlation (AR1) paramtr stimatd for ach cross-sction of firms of FirmAg = 0,,39+ ( ω ): 1 16 W truncat obsrvations at FirmAg =39 bcaus th sampl siz is small for valus of FirmAg > 39; rsults ar not snsitiv to this truncation. 20

22 = ω + ω + ς (8) j, 0 1 j, 1 j, Figur 2 plots th stimats of ω 1 (rprsnting ach FirmAg cross-sction) as a function of FirmAg; w also plot th rsults of rgrssing ω 1 on FirmAg. hr is a pronouncd incras in th stability of 1 f 3 f -loadings: ω and ω incras from 0.22 and 0.08, rspctivly, for FirmAg=0, to 0.46 and 0.40 for 1 1 FirmAg=39+ (t-statistics for ths incrass, not rportd, ar 9.86 and 7.98). In summary, both th lvl of -loadings and thir stability xhibit systmatic pattrns rlatd to firm ag: (1) th lvl of -loadings is highst at initial listing and dclins as th firm maturs; and (2) th yar-ovr-yar autocorrlation in -loadings incrass with firm ag Pattrns of -loadings associatd with rstatmnts, lawsuits and bankruptcis Our final analyss invstigat whthr -loadings ar highr in thr situations charactrizd by objctiv vidnc of poor financial rporting quality: financial statmnt rstatmnts, sharholdr lawsuits, and bankruptcis. hs analyss ar carrid out in vnt tim; so, for xampl, yar 0 for th Rstatmnt Sampl (Lawsuit Sampl) [Bankruptcy Sampl] is th yar th rstatmnt was announcd (th lawsuit was fild) [th firm dlistd du to bankruptcy]. Using an vnt-tim analysis allows us to control for othr (unrlatd) influncs on ovr-tim pattrns in -loadings by comparing -loadings of th vnt firms with -loadings of non-vnt firms matchd on calndar tim. h vnt-tim analysis also allows us to xploit th fact that ach yar thr ar many mor firms who do not rstat, ar not sud, and do not go bankrupt than thr ar firms that xprinc any of ths vnts. Spcifically, for ach of th Evnt Sampls, w slct 100 qual-siz random sampls of non-vnt firms, with a yarly distribution that is idntical to that of th Evnt Sampl. o th xtnt th smallr numbr of Evnt Sampl obsrvations is associatd with gratr masurmnt rror in -loadings than ar th -loadings of th largr st of non-vnt firms, quating th siz of th Evnt and Non-Evnt Sampls minimizs masurmnt rror diffrncs btwn th stimats drivd from th two sampls. Sparatly, quating th yarly distributions controls for calndar tim influncs affcting -loadings. h 100 non-vnt sampls crat an mpirical distribution of non-vnt -loadings. A comparison of vnt -loadings to 21