Does the Market Detect Firms Real Earnings Management? Wei Li. Department of Accounting University of Melbourne

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1 Does he Marke Deec Firms Real Earnings Managemen? Wei Li Deparmen of Accouning Universiy of Melbourne Yunyan Zhang Deparmen of Accouning Universiy of Melbourne This draf: February 12 h 2014

2 1 Inroducion This paper sudies wheher he sock marke can deec real aciviies managemen employed by managers o mee or bea earnings benchmarks. I has been well documened ha firms are rewarded for meeing or beaing earnings expecaions. 1 Thus managers have srong incenives o employ various earnings managemen ools o mee hese arges. Afer he passage of he Sarbames-Oxley Ac (SOX), firms shif from accrual managemen o real earnings managemen (RM) as RM suffers less regulaory scruiny and is harder o be challenged by audiors (Cohen e al., 2008; and Graham e al. 2005). Thus, RM is more pervasive in recen years. Differen from accruals managemen, RM changes he firm s underlying operaions in an effor o boos curren-period earnings. Such a pracice may disurb a firm s normal course of operaion, which impairs fuure firm value. Recen sudies find ha firms engaging in RM incur significan negaive accouning and sock reurns in fuure years. And he decline in firm performance due o RM is more severe han ha due o accrual managemen (Roychowdhury e al. 2012; and Cohen and Zarowin, 2010). Therefore, i poses an ineresing quesion: can he marke deec RM and punish firms for using i o mee or bea earnings benchmarks? Prior lieraure provides evidence on he exisence of RM o achieve various performance objecives. For example, Roychowdhury (2006) develops empirical measures of RM and repors ha managers engage in overproducion and reducing discreionary expenses o avoid reporing losses. Bens e al. (2002) show ha managers cu R&D and capial expendiure o miigae he effec of earnings per share diluion from opion exercise. Along 1 Burgsahler and Dichev, 1997; Degeorge e al., 1999; Barov e al., 2002; Brown and Caylor, 2005, Brown and Pinello, 2011, among ohers. 1

3 his line, Bu e al. (2012) find evidence ha managers engage in RM o avoid deb covenan violaions. Afer he passage of SOX, managers are more inclined o engaging in RM versus using accruals manipulaion as here is a greaer regulaory focus on accruals managemen (Graham e al. 2005; Roychowdhury e al. 2012; and Cohen and Zarowin, 2010). Specifically, he survey resuls of Graham e al. (2005) show ha 80% of inerviewed execuives repor ha hey would decrease spending on R&D, adverising and mainenance o mee an earnings arge. More han half sae ha hey would delay a new invesmen projec even if he delay causes a small sacrifice in firm value. Such a pracice ha depars from normal operaions can be derimenal o a firm s long-erm probabiliy and compeiiveness. Consisen wih his, recen sudies on he consequences of RM show ha RM is associaed wih negaive fuure firm performance. In paricular, Cohen and Zarowin (2010) show ha afer a seasoned equiy offering (SEO), he decline in earnings is more severe due o RM versus accruals managemen. Similarly, Roychowdhury e al. (2012) find ha RM is mos closely associaed wih pos-seo marke under-performance. In sum, managers end o engage in he more opaque channel RM o achieve an earnings arge even hough i is more cosly for firms in he long run. Taken ogeher, i is ineresing o examine wheher invesors can deec RM and price earnings accordingly. To es his, we firs idenify a seing where RM is more likely o happen (when earnings managemen incenives are high). Following prior lieraure (Roychowdhury, 2006; and Gunny, 2010), we choose zero and las year s earnings as earnings benchmarks and define suspec firm-years as hose ha jus barely mee hese benchmarks. Nex, we derive he measures of RM: abnormal discreionary expendiure, abnormal producion coss as well as a comprehensive measure of hese wo proxies. Then, 2

4 we examine wheher firms ha jus mee or bea earnings benchmarks exhibi higher level of RM. Consisen wih prior lieraure (Roychowdhury, 2006; and Gunny, 2010), we find evidence ha managers engage in overproducion and reducing discreionary expendiure o jus mee hese earnings benchmarks. Given he exisence of RM, we nex invesigae wheher he sock marke deecs hese suspec firm-years wih higher level of RM around he earnings announcemen and adjus heir evaluaion accordingly. We measure marke reacion by compuing he abnormal accumulaive sock reurns over a 22-day window (-20, 1) around he earnings announcemen dae. Our resuls indicae ha marke reacs negaively o RM and reacs more negaively o firm-years ha barely mee earnings arge by engaging in RM. For addiional analysis, we also consider he influence of a firm s informaion environmen on he capabiliy of invesors o deec RM. Analyical model suggess ha he exisence of informaion asymmery beween managers and invesors is necessary for he pracice of earnings managemen (Trueman and Timan, 1988; and Dye, 1988). Empirical findings are consisen wih his predicion ha he greaer he informaion asymmery, he more likely he firm is o manage earnings (Richardson 2000; Shivakumar 1996; Rangan, 1998; and Roychowdhury, 2006). Hence, firms are more likely o engage in RM o mee earnings arge when informaion environmen is more opaque. Knowing his, invesors may be more suspicious of RM and penalize firms more heavily ha engage in RM. We use firm size and he number of analys following as proxies for he informaion environmen and pariion firms based on hese measures. The findings suppor our predicion ha marke reacs more negaively in circumsances when invesors suspec ha firms are more likely o manage he earnings hrough RM, such as when firm size is small and when here are fewer analyss. This addiional evidence reinforces our main findings ha marke does reac 3

5 negaively o RM, especially when he capial marke suspecs ha firms are more likely involved in RM. This sudy conribues o he lieraure on earnings managemen in wo ways. Firs, i complemens recen sudies examining he consequences of RM. Roychowdhury e al. (2012) and Cohen and Zarowin (2010) boh focus on he SEO seing and provide evidence ha RM is associaed wih overvaluaion and subsequen negaive reurns. This sudy exends heir research by showing ha invesors can deec he inflaed earnings via RM o some exen a he ime of earnings announcemen and price in accordingly. Second, we show he influence of informaion environmen on firm valuaion: invesors penalize firms engaging in RM more in an opaque informaion environmen versus a ransparen one. The remainder of his paper is srucured as follows. Secion 2 presens research mehodology. Secion 3 repors he main resuls. Secion 4 discusses furher analyses and Secion 5 concludes. 2 Research Mehodology 2.1 Sample selecion We selec firms from he inersecion of COMPUSTA, CRSP and IBES from 1987 o We eliminaed firms in regulaed indusries (SIC code beween 4400 and 5000) and banks and financial insiuions (SIC codes beween 6000 and 6500). We have firmyears in he whole sample 2. The specific sample size for each model is differen based on he availabiliy of variables. To miigae he effec of exreme values, we runcae major variables a he 1% and 99% level. 2 The firm-years ha miss boh abnormal measures abnormal discreionary expendiure and abnormal producion cos, are excluded from he sample. 4

6 2.2 Earnings arges Following prior lieraure (Gunny, 2010), we examine a seing where RM is more likely o happen by focusing on firms jus barely meeing zero or las year s ne income. We selec hese wo earnings arges, bu no analyss forecas for several reasons. Firs, he survey of Graham e al. (2005) indicae ha 85.1% execuives agree ha prior year s earnings is imporan while 73.5% for analyss consensus esimae and 65.2% for reporing a profi. Thus, hese wo earnings arges are imporan benchmarks for managers. Second, analyss frequenly revise heir forecass during he fiscal year even before or on he earnings announcemen dae. Managers should ake acions on RM before he end of fiscal year, bu hey are unlikely o know he analyss forecas prior o he earnings announcemen (Gunny, 2010). Finaaly, Masumoo (2002) shows ha managers end o guide expecaion of analyss downward o mee or bea heir forecass. I is harder o deermine which sraegies dominae earnings guidance or RM when using analyss forecas as benchmark (Gunny, 2010). Therefore, we focus on zero and prior year s ne income as our earnings arges in he sudy. 2.3 Proxies of RM We follow he mehods in Roychowdhury (2006) o derive measures of RM: abnormal discreionary expendiure and abnormal producion coss. Roychowdhury (2006) examines wheher firms reduce heir discreionary expenses or engage in overproducion o avoid reporing losses. These ypes of real earnings managemen would lead o higher earnings bu lower cash flows in he curren period. He derives hese measures from he residuals of he following regressions. PROD A A S A 1 = α 0 + α1( 1 1) + β1( 1) + β2( 1) + β3( 1 1) + ε (1) S A S A 5

7 DISEXP A = 0 + α1( 1 A 1) + β1( S 1 A 1) 1 α + ε (2) A 1 is he beginning period oal asses. 1 S is he ending period oal sales. S is he beginning period oal sales. Discreionary expendiures ( DISEXP ) is he sum of R&D, adverising expenses, and selling, general, and adminisraion expenses. Producion cos ( PROD ) is he sum of he cos of goods sold and he changes in invenory. The models are esimaed by wo-digi SIC code indusry-year. Indusry-years wih fewer han 15 firms are eliminaed from he sample. The residuals from he above models are abnormal measures of abnormal producion coss ( ABPROD ) and discreionary expendiures ( ABDISEXP ). In order o cach he oal effecs of RM, following Zang (2011) and Gunny (2010), we also compue a comprehensive measure of real earnings managemen (Toal_RM) by muliplying ABDISEXP by negaive one and add i o ABPROD. Tha is, Toal _ RM = ABPROD ABDISEXP. The higher he amoun of his aggregae measure, he more likely firms engaged in RM. 2.4 Measure of marke reacion Based on he mehod used in Kinney e al. (2002), he abnormal reurns associaed wih he earnings announcemen are measured as he raw reurns minus he average marke reurn accumulaed over a 22-day window exending 20 days before he earnings announcemen, announcemen dae, and 1 day afer he earnings announcemen dae 3. The 22-day accumulaive reurn is used insead of a shorer window reurn cenered a earnings announcemen dae for wo reasons. One reason is ha Soffer e al. (2000) shows ha he 3 We also conduced ess using cumulaive reurns on a 7-day window exending from 3 days before earnings announcemen, day 0 and 3 days afer he announcemen dae. The resuls are qualiaively similar. 6

8 informaion abou he realized earnings may leak ino he marke and affec he price prior o he dae of earnings announcemen. The oher reason is ha Skinner and Sloan (2002) show ha 22-day window include more of he period which shows a significan reurn effecs in he sudy of quarerly earnings surprise. 2.5 RM o mee earnings arges Firs, we examine wheher firms engage in RM o mee or bea earnings arges (zeros earnings and las year s earnings) using he following model. Y = α + α SUSPECT + α NI + α SIZE + α MTB α MFG + α ANALYST + α BEAT _ NEXT _ E + ε (3) In Equaion (3), SUSPECT is an indicaor variable ha equals 1 if a firm-year barely mees zero ne incomes or las year s ne income (having small posiive earnings or small increase in earnings relaive o las year s figure), and 0 oherwise. Firm-years have small posiive earnings ( SUSPECT =1) if he ne income, scaled by he beginning year oal asses, is greaer han zero and less han 0.01; firm-years have small increase in earnings relaive o las year s figure ( SUSPECT =1) if he change of ne income, scaled by he beginning year oal asses, is greaer han zero and less han We es hese wo earnings arges separaely. Y can be ABDISEXP, ABPROD or Toal_RM. When esing on ABPROD, if suspec firm-years have a higher abnormal producion cos, hen we would expec he 4 We also es he model wih oher range o define he suspec firm years. For he zero earnings benchmark, we have very similar resuls if we define he small posiive earnings as a ne income greaer han 0 and less han For he las year s earnings benchmark, we have quaniaively same resuls if he small posiive earnings is defined as beween 0 and 0.001, and 0.004, respecively. 7

9 coefficien on SUSPECT o be significanly posiive. Similarly, when esing on ABDISEXP Y if suspec firm-years have less abnormal discreionary coss han conrol firm-years do, hen we would expec he coefficien on SUSPECT o be significanly negaive. Regarding Toal_RM, if suspec firm-years have higher level of Toal_RM, hen he coefficien would be significanly posiive. We also include firm size (SIZE), growh opporuniy (MTB), and ne income (NI) o conrol for sysemaic variaion in he abnormal measures. SIZE is he logarihm of he marke value of equiy a he beginning of he year. We conrol for firm size because prior lieraure shows ha size proxies for a firm s risk and informaion environmen (Ohlson, 1980; and Bamber, 1987). MTB is he raio of marke value of equiy o book value of equiy. Growh opporuniy of a firm is correlaed wih is producion, expendiure and cash flows. Hence we need o conrol for variaions in growh. NI is ne income scaled by lagged oal asses. Prior lieraure shows ha managers incenives o manipulae earnings are correlaed wih firm performance (Guay e al., 1996). So we have o consider cross-secional differences in firm performance. MFG is a dummy variable. If a firm belongs o a manufacuring indusry 5, hen MFG is se equal o 1; 0 oherwise. Roychowdhury (2006) finds ha a manufacuring firm is more likely o engage in overproducion han he res. Hence we include his variable in he model. Excep for MFG, all variables are defined as he deviaions from he respecive indusry-year means. We also include ANALYST, he logarihm of he number of analyss who provide earnings forecas. The more analyss follow a firm, he more precise he consensus forecas and he more aenion araced from invesors. Therefore, managers will have sronger 5 Two-digi indusry SIC codes are beween 20 and 39. 8

10 incenives o mee or bea he arges. We also conrol for he ension beween curren and fuure earnings. Li (2010) shows ha firms having high RM in he curren year have low operaion performance (ROA) in he following years. The ension beween meeing curren and fuure earnings come from he negaive fuure performance implicaion of RM. If firms are more concerned abou meeing heir earnings arges in he fuure, hey are likely o engage less in RM in he curren period. Therefore, in his model, we add a variable, BEAT _ NEXT _ E + 1 which equals1 if firms mee earnings arges nex year and 0 oherwise. To conrol for emporal shif in he exernal financing markes, we conrol for year fixed effecs in he regression. Sandard errors are clusered a firm level (Peerson, 2009). 2.6 Marke reacion o RM In his secion, we examine wheher he sock marke reacs o RM by esimaing he following equaion: CAR = α + α SUSPECT + α RM + α RM SUSPECT α ES + α SIZE + α MTB (4) where CAR is he 22-day cumulaive marke-adjused reurn. As noed in he previous lieraure, he presence of RM could be due o wo reasons. One is ha firms modify underlying fundamenal operaions for legiimae sraegic reasons. The oher is ha managers opporunisically manipulae real operaions o mee he earnings benchmarks. I is hard o compleely disenangle hese wo alernaive explanaions. To eliminae/miigae he concerns from he firs alernaive fundamenal facor, we focus on a sample of firms for which earnings managemen incenives are high. Therefore, we look closely a he ineracion erm RM SUSPECT in Eq. (4) as firm-years ha jus barely mee he earnings benchmark 9

11 are more likely o engage in RM. If he marke can deec and discoun RM, hen he coefficien will be significanly negaive. We conrol for firm size and growh opporuniies as in Eq. (4). In addiion, we also conrol for he earnings surprise (ES), which is compued as ne income scaled by he beginning oal asses when we use zero earnings as a benchmark; and compued as he change of ne income scaled by he beginning oal asses when we use he las year s earnings as a benchmark. We conrol for year fixed effecs in he regression. Sandard errors are clusered a firm level. 3 Main Resuls 3.1 Descripive saisics Table 1 presens he descripive saisics for he sample firm-years ha barely mee earnings benchmarks wih zero earnings in Panel A and las year s earnings in Panel B, respecively. In panel A, compared o he conrol firms, firm-years ha barely mee earnings benchmark (SUSPECT=1) have significanly higher abnormal producion ABPROD (a difference in mean of wih p-value <0.01) and higher real managemen RM (a difference in mean of 0.05 wih p-value <0.01). The differences in median of hese variables beween reamen and conrol groups show similar paerns. In panel B, he differences in ABPROD, ABDISEXP and Toal_RM beween firm-years ha barely mee earnings benchmark (SUSPECT=1) and he res firm-years (SUSPECT=0) are similar o hose in panel A. These univariae ess show ha firms do vary a lo in hese abnormal measures in he presence of earnings benchmarks. Suspec firm-years ha jus barely mee earnings benchmarks show sysemaically higher level of RM. 10

12 In addiion, firm-years ha barely mee earnings benchmarks have fewer analyss following, smaller size, and worse performance and less growh opporuniies, which raise he concern of sysemaic differences in he reamen and conrol groups, which we conrol for in our main ess and also conduc addiional analyses. [Inser able 1 abou here] Table 2 displays he correlaion marix for major variables. Abnormal producion ABPROD and abnormal discreionary expenses ABDISEXP are closely correlaed wih Toal_RM (0.819 and respecively wih p-value < 0.01). The indicaor variable for suspec firm-years, SUSPECT, is posiively correlaed wih abnormal producion cos ABPROD and Toal_RM. There is no exreme correlaion among independen variables, which miigaes he issue of mulicollineariy. [Inser able 2 abou here] 3.2 RM o mee earnings arges In Table 3, we show he propensiy of firms engaging in RM o mee earnings arge. Column (1)-(3) demonsraes he effec of earnings incenives on abnormal producion cos ABPROD, abnormal discreionary expendiure ABDISEXP and Toal_RM, respecively, when we use zero earnings as a benchmark. The coefficien of SUSPECT is significanly posiive (0.03 wih p-value <0.01) when he dependen variable is abnormal producion (ABPROD), which shows ha suspec firm-years ha barely mee earnings benchmarks have higher overproducion. To assess he economic significance, compared o firm-years wih SUSPECT=0, firm-years wih SUSPECT=1 have 62.5% more in he abnormal producion 11

13 cos (ABPROD 6 ) relaive o he sample mean. Swiching o discreionary expendiure (ABDISEXP), he coefficien of SUSPECT is significanly negaive (-0.03 wih p-value <0.01), which indicaes ha suspec firm-years have lower abnormal discreionary expendiure. Regarding Toal_RM, he coefficien of SUSPECT is significanly posiive (0.063 wih p-value <0.01), indicaing ha suspec firm-years ha barely mee earnings benchmarks have higher RM. Regarding he economic significance, relaive o firm-years wih SUSPECT=0, firm-years wih SUSPECT=1 conain 37% higher in he abnormal discreionary expendiure (ABDISEXP) and 185% higher in he Toal_RM relaive o he sample mean, respecively. Column (4)-(6) show he resuls when we use las year s earnings as a benchmark o esimae Eq. (3). The change of earnings benchmarks does no change he magniude or significance of he coefficiens. The coefficiens of SUSPECT for ABPROD, ABDISEXP and Toal_RM regressions are 0.031, and 0.052, respecively wih p-value <0.01. The economic significance of he effec of SUSPECT on ABPROD, ABDISEXP and Toal_RM is very similar o he resuls when we use zero earnings as a benchmark. The conrol variables, firm size (SIZE), performance (NI) and growh (MTB) are all significan, which suggess ha hey affec firm s operaion and should be conrolled for. A final observaion on he resuls is ha he number of analys ANALYST and bea nex year earnings BEAT_NEXT_E are significanly correlaed wih RM. When focusing on he oal real aciviies, Toal_RM, he coefficiens on ANALYST are significan and negaive ( and wih p-value <0.01 in he Column (3) and (6), respecively), which shows 6 For ABPROD regression, he economic effec is calculaed by dividing he coefficien of SUSPECT_BOTH (0.03) in Table 3 o he absolue mean of ABPROD (0.048) for SUSPECT =0 firmyears in Table 1. The economic effec on ABDISEXP and Toal_RM are compued by he same oken. 12

14 ha more ransparen informaion environmen as indicaed by larger number of analys, will reduce manager s incenive o engage in RM. Swiching o BEAT_NEXT_E, prior lieraure documens ha managers also consider nex year earnings when hey make a decision, which will consrain heir real earnings managemen in curren year (Li, 2013). When esing wih oal real aciviies Toal_RM, he coefficiens on BEAT_NEXT_E are significanly negaive ( and a p-value<0.01 in he column (3) and (6), respecively), which suggess ha he concern abou nex year s benchmark beaing reduces he curren year s real aciviies managemen. When esing wih specific componens of real aciviies (in he Column (1), (2), (4) and (5)), he coefficiens on ANALYST and BEAT_NEXT_E for abnormal producion cos ABPROD are significanly negaive while hose for abnormal discreionary expendiure ABDISEXP are significanly posiive, which furher convinces ha managers resric he curren year s real aciviies managemen in anicipaion of nex year s arge beaing. [Inser able 3 abou here] 3.3 Marke reacion o RM In able 4, we show he marke reacion o RM. For simpliciy, we describe he resuls saring wih he aggregae measure, Toal_RM. As shown in he column (3), he coefficien on Toal_RM iself is significan negaive ( wih p-value < 0.01), which suggess ha higher RM is reaed negaively by he marke. This is consisen wih prior lieraure examining marke reacion o earnings managemen using abnormal accruals: marke reacs negaively o high abnormal accruals (Defond and Park 2001). Keung e al. (2010) show ha marke has a negaive reacion o firms ha barely mee an earnings benchmark because invesors are scepical abou hese firms earnings. Drawing on his sudy, we expec he firms ha have greaer RM and barely mee earnings benchmarks have a negaive marke 13

15 reacion. The coefficien on Toal_RM SUSPECT is significanly negaive ( wih p- value<0.05), which is consisen wih our expecaion. More ineresingly, he magniude of coefficiens on Toal_RM and Toal_RM SUSPECT differs. The larger coefficien on Toal_RM SUSPECT suggess ha marke reacs more negaively o RM in he circumsances when a firm is more likely o managemen earnings (SUSPECT = 1). Similar resuls hold when esing wih abnormal producion cos ABPROD and abnormal discreionary expendiure ABDISEXP as shown in he column (1) and (2). In paricular, he coefficiens on ABPROD SUSPECT and ABDISEXP SUSPECT are significan negaive ( wih p-value <0.1 and wih p-value < 0.05, respecively) and he magniude are much larger han ha on he ABPROD and ABDISEXP ( and , respecively) alone. The resuls are similar when we use las year s earnings as earnings benchmark as shown in column (4)-(6). In sum, our resuls indicae ha marke reacs negaively o RM and reacs more negaively o firms ha are more likely involved in real earnings managemen, he ineracion of RM and SUSPECT. [Inser able 4 abou here] 4 Addiional Analysis For addiional analysis, we also consider he influence of a firm s informaion environmen on he marke reacions o RM. Trueman and Timan (1988) and Dye (1988) demonsrae ha informaion asymmery is necessary for earnings managemen. When informaion asymmery is high, sakeholders do no have he necessary informaion o see hrough he managed earnings. In addiion, he exisence of firms wih high level of informaion asymmery is evidence of invesors wihou sufficien resources, incenives, or access o relevan informaion o minor manger s acions, which leads o he pracice of 14

16 earnings managemen (Richardson 2000). Thus, in he presence of high informaion asymmery, managers have srong incenives o manipulae accruals and earnings. Consisen wih his analyical predicion, Shivakumar (1996) and Rangan (1998) find evidence ha managers manage earnings upwards around a seasoned equiy offering. Richardson (2000) documens ha he greaer he informaion asymmery, he more likely a firm o manage earnings. Roychowdhury (2006) also finds lower real earnings managemen associaed wih more insiuional monioring. Therefore, if marke predics ha managers have higher incenive o manage earnings in a more opaque informaion environmen, we would expec ha marke will be more suspicious of RM and likely o reac more negaively o RM. We use firm size and he number of analys following as proxies for he informaion environmen. Prior sudies have shown ha he informaion environmen is impaced by firm size larger firms have greaer amouns of informaion han smaller firms (Aiase, 1980; Aiase, 1985; Gran, 1980; Collins e al., 1987; and Slovin e al., 1992). In addiion, previous lieraure shows ha analys coverage improves informaion environmen (Lang e al., 2003; Lang and Lundholm, 1996; and Gebhard e al. 2001). Therefore, following prior lieraure, we choose hese wo measures o idenify bad or good informaion environmen. We pariion he sample ino hree groups based on hese measures: low, medium and high. Table 5 repors he resuls of esimaing Eq. (4), by spliing he sample based on Size_rank and Analys_rank wih zero earnings (Panel A) and las year s earnings (Panel B), respecively. As shown in Panel A column (1), he coefficiens on Toal_RM SUSPECT is significanly negaive ( wih p-value < 0.05) when he firm size is small (Size_rank = Low), indicaing ha marke reacs more negaively o suspec firm-years in a poor informaion environmen. As shown in Panel A column (2) and (3), when he firm size is medium (Size_rank = Medium) and large (Size_rank = High), he coefficiens on 15

17 Toal_RM SUSPECT are insignifican, indicaing marke does no view RM as a red flag of earnings managemen in a good informaion environmen. The coefficien on Toal_RM alone is significanly negaive ( a p-value < 0.01) when he firm size is small as in column (1) and becomes smaller and less or non-significan when firm size increases as in column (2) and (3). These resuls suppor our predicion ha marke reacs more negaively when i suspecs firms are more likely o manage in RM o mee earnings arge, especially when he informaion environmen is more opaque. When we spli he sample based on Analys_rank, The coefficien on Toal_RM SUSPECT is significanly negaive ( wih p-value < 0.01) when here is less analys following as in column (4); and he magniude of coefficiens becomes smaller and he significance disappears wih greaer analyss following as shown in column (5) and (6). These resuls show ha marke reacs more negaively o RM when he informaion environmen is more opaque. The similar resuls hold when we use las year s earnings as benchmark as shown in he Panel B. [Inser able 5 abou here] Taken ogeher, he resuls lead o he conclusion ha marke reacs more negaively o RM in an opaque informaion environmen versus a ransparen one, as firms are more likely o manipulae earnings when invesors are harder o see hrough. This addiional evidence reinforces our main findings ha marke does reac o RM, especially when marke suspecs ha firms are more likely involved in RM. 16

18 5 Conclusion This paper direcly examines wheher he sock marke can deec he inflaed earnings via RM. Prior lieraure provides evidences on he exisence and consequences of RM. We exend he exan lieraure by showing ha invesors will price in he componen of RM o some exens when assessing firm performance using earnings. We focus on a sample of firms when earnings managemen incenives are high. Following prior lieraure, we selec zero and las year s earnings as earnings arges and define suspec firm-years as hose jus barely mee hese benchmarks. We find evidences ha managers engage in RM, in paricular, overproducion and reducing discreionary expendiure o jus mee earnings benchmarks. Nex, we invesigae wheher he sock marke deecs his earnings game when firms announce earnings. Our resuls indicae ha marke reacs negaively o RM and reacs more negaively o firm-years ha barely mee earnings arges by engaging in RM. We also consider he influence of a firm s informaion environmen on he capabiliy of invesors o deec RM. Firms are more likely o engage in RM o mee earnings arges when informaion environmen is more opaque. We use firm size, he number of analys following and analys forecas dispersion as proxies for he informaion. The resuls show ha marke reacs more negaively o RM when he informaion environmen is poor. This addiional evidence reinforces our main findings ha marke does deec RM and discoun firm value accordingly, especially when marke suspecs ha firms are more likely involved in RM. 17

19 References: Ali, A., Chen, T.-Y. & Radhakrishnan, S Corporae disclosures by family firms. Journal of Accouning and Economics, 44, Aiase, R. K Predisclosure informaional asymmeries, firm capializaion, financial repors, and securiy price behavior. Universiy of California, Berkeley. Aiase, R. K Predisclosure informaion, firm capializaion, and securiy price behavior around earnings announcemens. Journal of Accouning Research, 23, Bamber, L. S Unexpeced earnings, firm size, and rading volume around quarerly earnings announcemens. Accouning Review, 62, Bens, D. A., Nagar, V. & Wong, M Real invesmen implicaions of employee sock opion exercises. Journal of Accouning Research, 40, Bu, U. R., Chamberlain, T. & Sarkar, S Accrual Manipulaion and Earnings Managemen Aciviies around Deb Covenan Violaion. Cohen, D. A., Dey, A. & Lys, T. Z Real and Accrual-Based Earnings Managemen in he Pre- and Pos-Sarbanes Oxley Periods. The Accouning Review 83, Cohen, D. A. & Zarowin, P Accrual-based and real earnings managemen aciviies around seasoned equiy offerings. Journal of Accouning and Economics, 50, Collins, D. W., Kohari, S. P. & Rayburn, J. D Firm size and he informaion conen of prices wih respec o earnings. Journal of Accouning and Economics, 9, Defond, M. L. & Park, C. W The reversal of abnormal accruals and he marke valuaion of earnings surprises. The Accouning Review, 76, Dye, R. A Earnings managemen in an overlapping generaions model. Journal of Accouning research, 26, Gebhard, W. R., Lee, C. & Swaminahan, B Toward an implied cos of capial. Journal of accouning research, 39, Graham, J. R., Harvey, C. R. & Rajgopal, S The economic implicaions of corporae financial reporing. Journal of accouning and economics, 40, Gran, E. B Marke implicaions of differenial amouns of inerim informaion. Journal of Accouning Research, 18, Guay, W. R., Kohari, S. & Was, R. L A marke-based evaluaion of discreionary accrual models. Journal of accouning research, 34, Gunny, K. A The Relaion Beween Earnings Managemen Using Real Aciviies Manipulaion and Fuure Performance: Evidence from Meeing Earnings Benchmarks*. Conemporary Accouning Research, 27, Keung, E., Lin, Z. X. & Shih, M Does he sock marke see a zero or small posiive earnings surprise as a red flag? Journal of Accouning Research, 48, Kinney, W., Burgsahler, D. & Marin, R Earnings surprise maerialiy as measured by sock reurns. Journal of Accouning Research, 40,

20 Lang, M. H., Lins, K. V. & Miller, D. P ADRs, Analyss, and Accuracy: Does Cross Lising in he Unied Saes Improve a Firm's Informaion Environmen and Increase Marke Value? Journal of Accouning Research, 41, Lang, M. H. & Lundholm, R. J Corporae disclosure policy and analys behavior. Accouning review, Li, X Real earnings managemen and subsequen sock reurns. Available a SSRN Masumoo, D. A Managemen's incenives o avoid negaive earnings surprises. The Accouning Review, 77, Ohlson, J. A Financial raios and he probabilisic predicion of bankrupcy. Journal of accouning research, 18, Peerson, M.A Esimaing sandard errors in finance panel daa ses: comparing Approaches. Review of Financial Sudies 22, Rangan, S Earnings managemen and he performance of seasoned equiy offerings. Journal of Financial Economics, 50, Richardson, V. J Informaion asymmery and earnings managemen: Some evidence. Review of Quaniaive Finance and Accouning, 15, Roychowdhury, S Earnings managemen hrough real aciviies manipulaion. Journal of Accouning & Economics, 42, Roychowdhury, S., Kohari, S. & Mizik, N Managing for he momen: The role of real aciviy versus accruals earnings managemen in SEO valuaion. Available a SSRN Shivakumar, L Esimaing abnormal accruals for deecion of earnings managemen. Available a SSRN Skinner, D. J. & Sloan, R. G Earnings surprises, growh expecaions, and sock reurns or don' le an earnings orpedo sink your porfolio. Review of accouning sudies, 7, Slovin, M. B., Johnson, S. A. & Glascock, J. L Firm size and he informaion conen of bank loan announcemens. Journal of Banking & Finance, 16, Soffer, L. C., Thiagarajan, S. R. & Walher, B. R Earnings preannouncemen sraegies. Review of Accouning Sudies, 5, Trueman, B. & Timan, S An explanaion for accouning income smoohing. Journal of accouning research, 26, Zang, A. Y Evidence on he rade-off beween real aciviies manipulaion and accrualbased earnings managemen. The Accouning Review, 87,

21 Appendix 1: Variable Definiions Variable Definiion Proxies of real earnings managemen: Equaions (1)-(2) A The beginning period oal asses (Compusa iem a) 1 S The beginning period oal sales (Compusa iem sale) 1 S The ending period oal sales. DISEXP Discreionary expendiures: he sum of R&D (Compusa iem xrd), adverising expenses (Compusa iem xad), and selling, general, and adminisraion expenses (Compusa iem xsga) scaled by he beginning oal asses PROD Producion cos: he sum of he cos of goods sold (Compusa iem cogs)and he changes in invenory (Compusa iem chinv) scaled by he beginning oal asses S The change of oal sales (S S -1) Full model: Equaion (3) Y Dependen variable: ABPROD, ABDISEXP, or Toal_RM. SUSPECT SIZE -1 MTB -1 NI MFG ANALYST An indicaor variable ha equals o 1 if a suspec firm-year barely mees boh earnings and cash flows forecass and 0 if a suspec firm-year barely mees only earnings forecass. Firms barely mee earnings forecass if he difference beween acual earnings per share (IBES iem acual from he able STATSUM_EPSUS) and consensus forecased earnings per share (IBES iem meanes), scaled by he beginning year price (Compua iem prcc_f), is beween 0 and Firms barely mee cash flows surprise if he difference beween acual cash flow (IBES iem acual from he able STATSUM_EPSUS when measure = CPS ) per share and consensus forecased cash flows per share, scaled by he beginning year price, is beween 0 and The logarihm of he marke value of equiy (Compua iem csho*prcc_f),a he beginning of he year The raio of marke value of equiy o book value of equiy (Compua iem ceq), The ne income (Compua iem ib),scaled by lagged oal asses An indicaor: if a firm belongs o a manufacuring indusry (wo-digi SIC code beween 20 and 39), hen MFG is se equal o 1; i is se o 0 oherwise. The logarihm of he number of analyss (IBES iem numes from he able STATSUM_EPSUS)who provide earnings forecass, BEAT _ NEXT _ E + An indicaor ha will be 1 if firms bea he analys earnings forecass 1 nex year and 0 oherwise. Marke reacion: Equaion (4) CAR Dependen variable: 22-day cumulaive marke-adjused reurn RM Real earnings aciviies: ABPROD, ABDISEXP, or Toal_RM. 20

22 ES Earnings surprise compued as ne income scaled by he beginning oal asses when we use zero earnings as a benchmark; and compued as he change of ne income scaled by he beginning oal asses when we use he las year s earnings as a benchmark. 21

23 Table 1: Descripive saisics This able presens he descripive saisics for comparison of firms barely meeing earnings benchmarks. SUSPECT =1 means ha firms barely mee earnings benchmarks and SUSPECT = means all oher firms. Zero earnings (Panel A) and Las year's earnings (Panel B) are used as earnings benchmarks, respecively. See he appendix for variable definiions. ***, ** and * indicaes significance of coefficien a 1 percen, 5 peren, and 10 percen levels, respecively. Panel A benchmark = Zero Earnings SUSPECT=1 SUSPECT=0 Difference Mean Median Mean Median Mean Median ABPROD *** 0.043*** ABDISEXP *** Toal_RM *** 0.008*** NI *** *** SIZE *** *** MTB *** *** ANALYST *** *** # of obs Panel B benchmark = Las year's Earnings SUSPECT=1 SUSPECT=0 Difference Mean Median Mean Median Mean Median ABPROD *** 0.037*** ABDISEXP *** Toal_RM *** *** NI *** *** SIZE MTB *** 0.217*** ANALYST *** *** # of obs

24 Table 2: Pearson correlaion among major variables This able repors he correlaion among major variables wih zero earnings and las year's earnings as a benchmark, respecively. Pearson correlaion coefficien among major variables are repored. Spearman correlaion (no abulaed) has he similar resuls. See he appendix for variable definiions. ***, ** and * indicaes significance of coefficien a 1 percen, 5 peren, and 10 percen benchmark = Zero earnings ABPROD ABDISEXP Toal_RM NI SIZE MTB SUSPECT ABPROD 1 ABDISEXP *** 1 Toal_RM 0.819*** *** 1 NI *** *** 0.128*** 1 SIZE *** *** 0.051*** 0.265*** 1 MTB *** 0.134*** *** 0.056*** 0.175*** 1 SUSPECT 0.054*** *** -0.10*** *** *** 1 benchmark = Las year's earnings ABPROD ABDISEXP Toal_RM NI SIZE MTB SUSPECT ABPROD 1 ABDISEXP *** 1 Toal_RM 0.814*** *** 1 NI *** *** 0.142*** 1 SIZE *** *** 0.049*** 0.268*** 1 MTB *** 0.138*** *** 0.063*** 0.184*** 1 SUSPECT 0.045*** *** *** *** 1 23

25 Table 3: Real earnings managemen o mee earnings benchmarks This able presens he resuls of esing he effec of earnings benchmarks on real aciviies managemen. The model (3) is esimaed in column (1)-(3), using zero earnings as a benchmark, and is esimaed in column (4)-(6), using las year's earnings as a benchmark, during he sample period The dependen variables are measures of real aciviies managemen in erms of abnormal producion ABPROD, abnormal discreionary expendiure ABDISEXP and oal real managemen Toal_RM, (ABPROD-ABDISEXP ), respecively. SUSPECT is 1 if a firm year is barely greaer han zero when zero earnings is he benchmark or han las year's earnings when las year's earnings is he benchmark. Oherwise, SUSPECT is 0. See he appendix for variable definiions. ***, ** and * indicaes significance of coefficien a 1 percen, 5 peren, and 10 percen levels, respecively, based on firm-level clusered sandard errors. benchmark = Zero earnings benchmark = Las year's earnings ABPROD ABDISEXP Toal_RM ABPROD ABDISEXP Toal_RM (1) (2) (3) (4) (5) (6) Inercep *** * 0.055*** 0.038** 0.045** SIZE 0.006*** *** 0.031*** 0.006*** *** 0.029*** MTB *** 0.011*** *** *** 0.012*** *** NI *** *** 0.168*** *** *** 0.187*** SUSPECT 0.030*** *** 0.063*** 0.031*** *** 0.052*** MFG ** ** ANALYST *** 0.019*** *** *** 0.019*** *** BEAT_NEXT_E *** 0.010*** *** *** 0.009*** *** Year-indicaor Included Included Included Included Included Included Sandard errors clusered by firm Yes Yes Yes Yes Yes Yes # of obs R-sq

26 Table 4: Marke reacion o real earnings managemen This able shows marke reacion o real earnings managemen o mee zero earnings (in he column (1)-(3)) or las year's earnings (in he column (4)-(6)), respecively. The sample period is The dependen variable CAR is 22-day cumulaive abnormal reurns around he earnings annoucemen dae 0. SUSPECT is 1 if curren earnings is barely greaer han zero (if benchmark = Zero earnings) or han las year's earnings (if benchmark = Las year's earnings). Oherwise, SUSPECT is 0. To inerpre he abnoral discreionary expendiure in erms of real managemen, we muliply ABDISEXP and he ineracion ABDISEXP SUSPECT by negaive one. The earnings suprise ES is equal o NI (if benchmark = Zero earnings) and change of NI (if benchmark = Las year's earnings), respecively. See he appendix for variable definiions. ***, ** and * indicaes significance of coefficien a 1 percen, 5 peren, and 10 percen levels, respecively, based on firmlevel clusered sandard errors. benchmark = Zero earnings benchmark = Las year's earnings CAR [-20, 1] (1) (2) (3) (4) (5) (6) Inercep 0.019*** 0.020*** 0.018*** 0.019** 0.019** 0.019** ABPROD *** *** ABDISEXP (-1) Toal_RM *** ** SUSPECT *** ** ABPROD SUSPECT * ABDISEXP SUSPECT ** * Toal_RM SUSPECT ** * ES 0.046*** 0.052*** 0.049*** 0.111*** 0.116*** 0.114*** SIZE *** *** *** *** *** *** MTB *** ** ** ** * * Year-indicaor Included Included Included Included Included Included Sandard errors clusered Yes by firm Yes Yes Yes Yes Yes # of obs R-sq

27 Table 5: Effecs of firm size and analys coverage on marke reacions o real earnings managemen This able shows he effec of firm size and analys coverage on marke reacion o real earnings managemen o mee earnings benchmarks. The sample is grouped on he firm size (Size_rank ) and he analys coverage (Analys_rank ), and ordered from he low o high rank (indicaed as Low, Medium and High), respecively. The sample period is The Panel A and Panel B show he resuls by using Zero earnings and Las year's earnings as a benchmark, respecively. The dependen variable CAR is 22-day cumulaive abnormal reurns around he earnings annoucemen dae 0. SUSPECT is 1 if curren earnings is barely greaer han zero (if benchmark = Zero earnings) or han las year's earnings (if benchmark = Las year's earnings). Oherwise, SUSPECT is 0. The earnings suprise ES is equal o NI (if benchmark = Zero earnings) and change of NI (if benchmark = Las year's earnings), respecively. See he appendix for variable definiions. ***, ** and * indicaes significance of coefficien a 1 percen, 5 peren, and 10 percen levels, respecively, based on firm-level clusered sandard errors. Panel A benchmark = Zero earnings Size_rank Analys_rank CAR [-20,1] Low Medium High Low Medium High (1) (2) (3) (4) (5) (6) Inercep 0.043*** *** 0.041*** 0.014* 0.020*** Toal_RM *** *** *** * SUSPECT Toal_RM * SUSPECT ** *** ES 0.052*** 0.036*** 0.052*** 0.071*** 0.029** 0.027* SIZE *** *** *** ** *** MTB ** ** ** * Year-indicaor Included Included Included Included Included Included Sandard errors clusered by firm Yes Yes Yes Yes Yes Yes # of obs R-sq Panel B benchmark = Las year's earnings Size_rank Analys_rank CAR [-20,1] Low Medium High Low Medium High (1) (2) (3) (4) (5) (6) Inercep *** 0.031** *** Toal_RM *** *** SUSPECT *** ** Toal_RM * SUSPECT *** * *** ES 0.154*** 0.073*** 0.066*** 0.144*** 0.090*** 0.055*** SIZE *** *** *** *** MTB ** *** Year-indicaor Included Included Included Included Included Included Sandard errors clusered by firm Yes Yes Yes Yes Yes Yes # of obs R-sq

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