Do Mutual Funds Profit from Accruals and NOA Anomalies?

Transcription

1 (Preliminary, please do no quoe wihou auhors permission) Do Muual Funds Profi from Accruals and NOA Anomalies? Ashiq Ali*, Xuanjuan Chen**, Tong Yao***, Tong Yu** July 2005 Absrac: In his sudy, we examine hree issues relaed o he accrual and he ne operaing asses (NOA) anomalies. Firs, wheher muual funds rade on hese anomalies. Second wheher hey are able o generae profis, ne of ransacion coss. Third, he reasons why such profis are no compleely exploied. Prior sudies provide evidence on he firs issue by examining he rading behavior of insiuional invesors in he conex of he accruals anomaly. However, here is lile direc evidence on he second and he hird issues. The primary reason, ha prior sudies do no address hese issues is because hey use daa from 13F filings. These daa provide informaion on insiuional holdings bu do no provide informaion on heir fund reurns and fund flows, which are imporan for addressing hese issues. We obain daa on muual fund porfolio holdings from he 13F filings and on muual fund reurns and fund flows from CRSP. We combine hese daa and provide he following resuls. Firs, muual funds acively follow boh he accrual and he NOA anomalies. Second, funds ha follow hese anomalies aggressively exhibi significan profis, ne of acual ransacion coss and fund expenses, suggesing ha for he accrual and NOA anomalies ransacion coss canno compleely explain he magniude and persisence of hese anomalies. Finally, funds ha more aggressively follow he anomalies have higher volailiy in fund reurns and fund flows, suggesing ha residual profis are no compleely arbiraged away because of inelasic supply of arbirage capial induced by arbirage risks (Shleifer and Vishny 1997). Corresponding auhor: Ashiq Ali School of Managemen, SM41 The Universiy of Texas a Dallas Richardson, TX ashiq.ali@udallas.edu Phone: *Deparmen of Accouning and Informaion Sysem, Universiy of Texas, Dallas. **College of Business Adminisraion, Universiy of Rhode Island. ***Deparmen of Finance, Universiy of Arizona. 1

2 Do Muual Funds Profi from Accruals and NOA Anomalies? Absrac Absrac: In his sudy, we examine hree issues relaed o he accrual and he ne operaing asses (NOA) anomalies. Firs, wheher muual funds rade on hese anomalies. Second wheher hey are able o generae profis, ne of ransacion coss. Third, he reasons why such profis are no compleely exploied. Prior sudies provide evidence on he firs issue by examining he rading behavior of insiuional invesors in he conex of he accruals anomaly. However, here is lile direc evidence on he second and he hird issues. The primary reason, ha prior sudies do no address hese issues is because hey use daa from 13F filings. These daa provide informaion on insiuional holdings bu do no provide informaion on heir fund reurns and fund flows, which are imporan for addressing hese issues. We obain daa on muual fund porfolio holdings from he 13F filings and on muual fund reurns and fund flows from CRSP. We combine hese daa and provide he following resuls. Firs, muual funds acively follow boh he accrual and he NOA anomalies. Second, funds ha follow hese anomalies aggressively exhibi significan profis, ne of acual ransacion coss and fund expenses, suggesing ha for he accrual and NOA anomalies ransacion coss canno compleely explain he magniude and persisence of hese anomalies. Finally, funds ha more aggressively follow he anomalies have higher volailiy in fund reurns and fund flows, suggesing ha residual profis are no compleely arbiraged away because of inelasic supply of arbirage capial induced by arbirage risks (Shleifer and Vishny 1997). 2

3 Do Muual Funds Profi from Accruals and NOA Anomalies? I. Inroducion Invesors naïve response o informaion is believed o be a he origin of many accouning-based marke anomalies. Two ypical examples are he accruals anomaly and he Ne Operaing Asses (NOA) anomaly. Sloan (1996) shows ha he accruals anomaly he negaive correlaion beween accouning accruals and subsequen sock reurns -- arises because invesors fixae on repored earnings and fail o appreciae he difference in he persisence of accruals and cashflows. Recenly, Hirshleifer e al. (2004) repor ha firms wih bloaed ne operaing asses end o have lower fuure sock reurns. They aribue his o he limied aenion of invesors who narrowly focus on accouning profiabiliy while neglecing cashflow profiabiliy. 1 Recenly here has been a growing ineres in he rading behavior of insiuional invesors in he presence of marke anomalies. Insiuions are generally considered sophisicaed invesors. Unlike many naïve invesors who misreac o informaion, a leas some sophisicaed insiuional invesors should be aware of he mispricing and rade on i. In his sudy, we examine hree relaed issues. Firs, wheher muual funds rade on accrual and NOA anomalies. Second wheher hey are able o generae profis, ne of ransacion coss. Third, he reasons why such profis are no compleely exploied. Prior sudies provide evidence on he firs issue by examining he rading behavior of insiuional invesors in he conex of he accruals anomaly. However, here is lile 1 Many oher anomalies have also been aribued o invesors mis-reacion o informaion. For example, he pos earnings announcemen drif may be due o invesors failure o incorporae informaion from pas earnings ino heir forecass abou fuure earnings (Bernard and Thomas (1989, 1990), and Freeman and Tse (1989). Lakonishok, Shleifer, and Vishny (1996) argue ha anomalies relaed o earnings-o-price, book-o-marke, and pas sales growh is due o invesors excessive exrapolaion from pas performance in heir expecaion of firms fuure performance. 3

4 direc evidence on he second and he hird issues. 2 The primary reason, ha prior sudies do no address hese issues is because hey use daa from 13F filings. These daa provide informaion on insiuional holdings bu do no provide informaion on heir fund reurns and fund flows, which are imporan for addressing hese issues. We obain daa on muual fund porfolio holdings from 13F filings and on muual fund reurns and fund flows from CRSP. We combine hese daa and provide he following resuls. Firs, muual funds acively follow boh he accrual and he NOA anomalies. Second, funds ha follow hese anomalies aggressively exhibi significan profis, ne of ransacion coss and fund expenses. If ransacion coss do no compleely eliminae he profis from anomaly-based rading, wha have prevened muual funds from pursuing arbirage opporuniies more aggressively? Anoher imporan ype of fricions is shor-sale consrains mos muual funds are self-consrained no o ake shor posiions in socks. Shor-sale consrains may poenially help explain why rading by muual funds does no arbirage away mispricing relaed o accruals and NOAs. However, even wih shor-sale consrains, he long posiions aking advanage of marke anomalies should sill be profiless in heory as long as capial supply is fully elasic. Therefore, here mus be oher reasons why rading on anomalies remains profiable. We conjecure ha his is due o he effec of nonelasic capial supply. A ypical case of nonelasic capial supply is suggesed by Shleifer and Vishny (1997). They argue ha rading on mispricing is no compleely riskless; in fac, i is ofen associaed wih subsanial arbirage risks. Because of asymmeric informaion, invesors may 2 Indirec evidence on he second issue has been provided by using esimaes of ransacion coss. 4

5 wihdraw money from delegaed porfolio mangers when inermediae fund performance suffers due o such arbirage risks, even hough he arbirage opporuniies pursued by fund managers are ulimaely profiable. In his case, muual funds rade on mispricing less aggressively and marke anomalies are no compleely arbiraged away. This conjecure is confirmed by furher empirical analysis. We find ha as a resul of bearing higher arbirage risks in heir porfolios, funds ha more aggressively follow he anomalies have higher oal reurn volailiy and higher idiosyncraic reurn volailiy han oher funds. Higher reurn volailiy, in urn, ranslaes ino more volaile response by invesors he volailiy of flows o he more aggressive funds is also subsanially higher han ha for he oher funds. This paper makes he following conribuions. Firs, exising sudies sugges ha marke fricions such as ransacion coss may have played an imporan role in explaining he magniude and persisence of anomalies as hey may render anomalybased rading unprofiable. Our sudy provides direc evidence ha muual funds generae reurns ne of ransacions coss by rading on he accruals and NOA anomalies. Thus, for hese anomalies ransacion coss canno compleely explain he magniude and persisence of hese anomalies. Second, our finding ha funds ha are more aggressive exhibi greaer volailiy in fund reurns and in fund flows provides suppor o Shleifer and Vishny s (1997) conjecure on why mispricing may no be compleely arbiraged away. Due o asymmeric informaion, invesors may wihdraw money from delegaed porfolio managers when inermediae fund performance suffers due o arbirage risks associaed 5

6 wih rading on anomalies. As a resul muual funds rade on mispricing less aggressively. The remainder of his paper is organized in four secions. Secion II provides a review of relaed lieraure. Secion III describes he daa and mehodology. Secion IV presens empirical resuls. Secion V concludes. II. Relaed Lieraure A. The Accruals and NOA Anomaly The accruals anomaly refers o he negaive relaion beween he curren level of accouning accruals and fuure sock reurns. In a seminal paper, Sloan (1996) documens ha a hedge porfolio of buying socks ranked in he boom accruals decile and selling socks in he op accruals decile generaes over 10 percen abnormal reurn in he following year. The accruals anomaly is confirmed by many follow-up sudies, e.g., Xie (2001), Hribar (2002), Thomas and Zhang (2002), and Chan e al (2004), who analyze he mispricing of differen componens of accruals. More recenly, Hirshleifer, Hou, Teoh, and Zhang (2004) discover a relaed anomaly, namely he ne operaing asses (NOA) anomaly. A firm s NOA, known as he balance shee bloa, is he accumulaion over ime of he difference beween ne operaing income and free cash flow. Hirshleifer e al. (2004) repor ha a hedge porfolio of buying firms in he boom NOA decile and selling firms in he op NOA decile generaes 12.7 percen abnormal reurn in he following year. A generally agreed explanaion for he accruals anomaly is ha, hough he accrual componen of earnings is less persisen han he cash flow componen of 6

7 earnings (Dechow 1994; Dechow e al. 1998; Barh e al. 1999), invesors fail o recognize he difference in persisence of accruals and cash flows. Hirshleifer e al. (2004) provide a similar explanaion for he NOA anomaly ha invesors wih limied aenion focus on accouning profiabiliy, and neglec informaion abou cash profiabiliy. Lev and Nissim (2004) repor ha he accruals anomaly persiss long afer i is well-publicized. The persisence of his anomaly moivaes researchers o examine firm characerisics for exreme accruals socks. Among hem, Desai e al. (2002), Richardson (2004), and Lev and Nissim (2004) repor ha exreme accruals socks ypically are small in size, low in liquidiy, bu high in sysemaic risk. Mashruwala, e al. (2004) finds ha idiosyncraic volailiy of socks in exreme accruals decile is wice as high as hose of firms in he median accruals decile. Similar finding is repored in Rajgopal and Venkaachalam (2005). Richardson (2004) argues ha unfavorable characerisics of high accruals socks, such as small size, low price, and low liquidiy, poenially deer shorsellers o rade on accruals informaion. The relaionship beween insiuional holdings and he accruals anomaly is anoher issue debaed in empirical sudies. Walher (1997), Barov, Radhakrishnan, and Krinsky (2000), and Cohen, Gompers, and Vuoleenaha (2002) provide evidence ha insiuional invesors, on average, have superior abiliy o inerpre financial informaion relaive o individual invesors and miigae marke mispricing hrough informed rading aciviies. However, evidence on he impac of insiuional holdings on he accruals anomaly is mixed. Ali e al. (2000) find ha he accruals anomaly is no miigaed among socks wih large insiuional holdings. In conras, Collins e al. (2003) sugges ha high 7

8 insiuional ownership reduces accruals mispricing. They also find ha ransien insiuional invesors, defined as insiuions ha hold acive sakes and rade frequenly in and ou of socks, are responsive o accruals informaion. Lev and Nissim (2004) also show ha insiuional invesors are responsive o accruals informaion. They sugges ha he coexisence of persisen accruals anomaly and he use of accruals informaion by insiuional invesors is due o he fac ha unaracive firm characerisics of exreme accruals socks limi he use of he accruals sraegy by insiuions. B. Muual Funds Wheher muual funds have sock picking abiliy has been exensively examined in he lieraure. Jensen (1968) was perhaps he firs o address his issue, wih a negaive conclusion. However, follow-up sudies sugges ha funds do have sock-picking abiliy. Grinbla and Timan (1989, 1994), Ferson and Schad (1996), Daniel e al. (1997), Wermers (1997 and 2000), and Cohen, Coval, and Pasor (2004), among ohers, show ha muual funds, paricularly growh-oriened funds, ouperform condiional and uncondiional benchmarks. Grinbla and Timan (1993) inroduce a benchmark-free measure of fund performance and heir resul also indicaes ha fund managers on average have sock-selecion abiliy. Kosowski e al. (2004) apply he boosrap approach o examine if he performance of high-alpha funds comes from luck or genuine sock picking skills. Their analysis rejecs he ``pure-luck" hypohesis for hese sar funds. Kacperczyk, Sialm, and Zheng (2004) argue ha funds holding porfolios concenraed in fewer indusries perform beer afer conrolling for differences in risks and syles. Furher, sudies find muual fund performance persiss (Grinbla and Timan 1992, 8

9 Hendricks, Pael, and Zeckhauser 1993, Brown and Goezmann 1999, and Carhar 1997). Recen empirical evidence generally suggess ha he persisence in fund performance reflecs he cross-secional difference in funds' sock picking abiliy. Despie he subsanial evidence on fund sock picking abiliy, muual funds on average underperform he marke afer ransacion coss and expenses. Gruber (1995) repors underperformance of 0.65 percen per year and Wermers (2000) repors underperformance of 1 percen per year by an average fund. Transacion coss play an imporan role in explaining he underperformance. 3 Grinbla and Timan (1989) show ha ransacion coss accoun for 1.5 percen of fund TNA. Using esimaed ransacion coss based on Keim and Madhavan's (1997) mehod, Wermers (2000) shows ha ransacion cos accouns for 0.9 percen of fund TNA. Furher, Chalmers, Edelen and Kadlac (2001) find ha he difference in fund performance for funds in he highes and lowes ransacion-cos quiniles is -3.2 percen. In summary, he impac of ransacion coss on fund performance is significan. III. Daa and Mehodology A. Daa We obain financial saemen daa from Compusa and sock reurns and prices from CRSP. Informaion abou muual funds is from wo sources. The firs is he CRSP Survivorship Bias Free Muual Fund Daabase (hereafer CRSP daa ), which provides informaion on fund monhly reurns and fund characerisics such as urnover and 3 Besides he academic evidence provided below, subsanial anecdoal evidence shows ha ransacion coss grealy reduce fund performance. Cassidy (2004) documens a negaive relaion beween fund performance and ransacion coss. Clemens (2005) provides examples ha effecively reducing ransacion coss leads o beer performance and makes he fund more compeiive. 9

10 expense raio. The second is he Thomson Financial CDA/Specrum Daabase (hereafer CDA daa ) ha liss he shareholdings of equiy muual funds. The CDA daa has quarerly holdings informaion for mos funds, alhough for some funds he daa is a semi-annual frequency. This daase has been used in a number of previous sudies, such as Daniel, Grinbla, Timan, and Wermers (1997), Grinbla and Timan (1989, 1993), and Wermers (2000, 2003). Combining he CRSP and CDA daabases allows us o obain complee informaion on fund reurns and fund porfolio holdings. In he CDA daa, funds are classified ino nine caegories according o heir selfdeclared invesmen objecives: inernaional, aggressive growh, growh, growh and income, municipal bonds, bond and preferred, balanced, meals, and unclassified. Our focus is on acively-managed U.S. domesic equiy funds wih he following hree invesmen objecives: aggressive growh, growh, and growh and income. Iniially, we obain 3938 such funds from he CDA daa. We hen remove index funds, and foreignbased funds, U.S.-based inernaional funds, fixed-income funds, real esae funds, precious meal funds, and variable annuiies ha misrepor heir invesmen objecive as aggressive growh, growh, and growh and income. The resuling sample has 3067 unique funds. To combine he CRSP daa wih he CDA daa, we firs combine muliple share classes as a single fund. These muliple share classes differ in sales charges and argeed invesor clienele, bu have same porfolio composiions. They are reaed as a single fund in he CDA daa. We hen merge he CRSP daa wih he CDA daa manually by maching fund names and icker symbols in he wo daases. 4 The maching procedure 4 Fund icker symbols are no available in he CDA daa before afer

11 follows Wermers (2000). We exclude fund-quarer observaions if he oal marke value of repored holdings is less han 50 percen or more han 150 percen of he oal ne asses of he fund. The final mached daabase has 2,587 unique funds wih qualified invesmen objecives. 5 The deails of he maching procedure can also be found in Jiang, Yao and Yu (2004). Table 1 repors he summary saisics for our muual fund sample over he period of 1984 and Ou of a oal of 2587 funds, we have 1706 growh funds, 293 aggressive growh funds, and 588 growh and income funds. We calculae he average fund characerisics daa by firs averaging over he ime series daa of each fund and hen averaging across funds. During our sample period from1984 o 2003, he oal ne asses (TNA) of a ypical fund in our sample is million, wih an annual reurn of 9.96%, an annual urnover raio of 0.94, an annualized load of 0.30, and an annual expense raio of 1.31%. The average fund age, calculaed as he ime beween he firs and las monhly reurn observaions in he CRSP daase, is years. The mean number of socks held in a fund is 97.78, while he median is B. Mehodology B.1 Measuring Accruals and Ne Operaing Asses Following Sloan (1996) and Chan e al (2004), we esimae individual sock accruals using he balance shee and income saemen informaion: Accruals = ( CA + CASH) ( CL - STD - TP) DEP 5 Boh he CRSP and CDA daa are free from he survivorship bias. However, he CDA daase is slow in adding new funds. Therefore, here are new funds already in he CRSP daa bu no in he CDA daa ye. In erms of sample size, our merged daa is comparable o hose used in exising sudies. See Jiang, Yao and Yu (2004) for a comparison of fund sample size. 11

12 = ( AR + INV + OCA) ( AP + OCL) DEP (1) where CA is he change in curren asses (Compusa iem 4); Cash is he change in cash/cash equivalens (iem 1); CL is he change in curren liabiliies (iem 5); STD is he change in deb included in curren liabiliies (iem 34); TP is he change in income axes payable (iem 71); and DEP is depreciaion and amorizaion expense (iem 14). AR is he change in accoun receivable (iem 2); INV is he change in invenories (iem 3); OCA is he change in oher curren asses (iem 4); AP is he change in accoun payable (iem 70); OCL is he change in oher curren liabiliies (iem 68). Consisen wih oher sudies, we scale accruals by he average of he beginning and end of quarer oal asses. To alleviae he influence of ouliers, we remove he op and boom one percenile accruals observaions in each year. Following Hirshleifer, Hou, Teoh and Zhang (2004), ne operaing asses are compued as: NOA = (Operaing Asses(OA) Operaing Liabiliies (OL))/Lagged Toal Asses (2) where OA = oal asse (iem 6) cash and shor erm invesmen (iem 1), OL = oal asse STD LTD MI PS CE, STD = Deb included in curren liabiliies (iem 34), LTD = Long Term Deb (iem 9), MI = Minoriy Ineress (iem 38), PS = Preferred Socks (iem 130), and CE = Common Equiy (iem 60). Following Hirshleifer e al (2004), we rea he values of shor-erm deb, axes payable, long-erm deb, minoriy ineres and preferred sock as zero if hey are missing. Similarly we exclude he op and boom one percenile of NOA observaions in each year. 12

13 B.2 Characerisics-Adjused Accruals and Ne Operaing Asses In addiion o using raw accruals and NOA defined above, we consruc characerisics-adjused accruals and NOA. There are several reasons o conrol for sock characerisics in measuring anomaly-based rading by muual funds. Firs, muual funds wih differen invesmen syles ofen inves in differen sub-universes of socks (e.g., large cap growh, small cap value, ec.). Socks in differen sub-universes may have differen levels of accruals and NOA on average. Secondly, Similar o oher insiuional invesors, muual funds may have cerain preferences on he socks, and such preferences may be uncorrelaed wih heir sock selecion abiliy, bu may be relaed o accruals or NOA. Thirdly, i is well-known ha muual funds employ oher invesmen sraegies, such as momenum rading, which may be correlaed wih rading on he accruals and NOA anomalies. To conrol for hese effecs, in each year (referred o as he accrualsmeasuremen year ) we perform he following cross secional regression o esimae characerisics-adjused accruals: Acc = α+β 1 LgSize + β 2 LgBM + β 3 MOM+ β 4 InsHold+ β 5 ( REV/ATA) + β 6 (PPE/ATA) + ε (3) where LgSize is he logarihm of sock marke value, LgBM is he logarihm of book-omarke raio, measured by book value of equiy a he end of he fiscal year ha ends wihin he accruals measuremen year (Compusa iem 60) o marke value of equiy a he end of he accruals measuremen year, and MOM is momenum, measured by he sock s reurn during he las six monhs of he accruals measuremen year. InsHold is he raio of shares held by insiuions divided by oal shares ousanding of a sock a he end of he accruals measuremen year. To compue InsHold, we aggregae he number of 13

14 shares of a sock held by all insiuions as repored in he 13F daa for he end of accruals measuremen year and divide i by he oal shares ousanding of he sock. Noe ha in addiion o he above sock characerisics, we also conrol for nondiscreionary accruals and he indusry effec. ( REV/ATA) is he difference of firms oal revenue (Compusa iem 12) during he fiscal year ha ends in he accruals measuremen year divided by he average oal asses (Compusa iem 6) a he end of he same period; PPE/ATA is he gross book value of propery, plan and equipmen (Compusa iem 7) divided by he average oal asses in he same year. We perform he regression each year wihin firms wih he same wo-digi SIC code. The characerisics-adjused accruals are he esimaed residuals of he cross-secional regression. We consruc he characerisics-adjused NOA in a similar way. We perform following regression in each year for all sample firms and reain he esimaed residuals as he characerisics-adjused NOA: 6 NOA = α+β 1 LgSize + β 2 MOM + β 3 LgBM + β 4 InsHold + ε (4) Table 2 compares he reurn-predicive power of characerisics-adjused accruals and NOA o ha of he raw variables. In April of each year, we form equal-weighed decile porfolios of socks ranked on raw accruals, raw NOA, characerisics-adjused accruals, and characerisics-adjused NOA. The porfolios are held from May of year o April of year +1, before rebalancing. If a sock is delised during he holding period, we assume ha is reurn during he remaining of he holding period is he CRSP delising reurn. Following Shumway (1997), when he delising reurn is missing, i is replaced by 6 Unlike he accruals regression (3), which is run wihin each indusry, he NOA regression (4) is run on all firms. We find ha once conrolling for he indusry effec, he residual NOAs have visibly lower power in predicing sock reurns (Zhang 2005). 14

15 -30% if he delising is performance relaed and zero oherwise. The sample period is from 1980 o From he able, i is clear ha he predicive power of adjused variables on fuure reurns is quie similar o ha of he raw variables. On average, he boom decile sored by characerisics-adjused accruals (NOA) ouperform he op decile by 8.54 (10.98) percen per year. The reurn spreads are of he same magniude as hose repored by Sloan (1996) and Hirshleifer e al. (2004). In Figure 1, we furher plo he ime series of he annual reurn spread beween he op and boom decile porfolios. They are posiive in mos years despie some flucuaions. B.3 Holding-Based Accruals Invesing Measures and NOA Invesing Measures To quanify how aggressively a fund rades on he accruals anomaly, we consruc wo holding-based accruals invesing measures. The firs is he negaive of he weighed sum of porfolios marke-adjused accruals: n MKT MKT AIM H, i, = w i, j, * AdjAcc j, 1 (5) j= 1 where is he porfolio weighs on sock j held by fund i in June of year. 7 w i, j, MKT AdjAcc j, 1 is a sock s accruals in calendar year -1 in excess of he equal-weighed 7 Occasionally, some funds repor heir holdings for a monh ha is no a a quarer-end. For all our analysis, we assume ha he repored holdings are valid a he end of he reporing quarer. In addiion, some funds repor holdings semi-annually raher han quarerly. However, over 90 percen of funds in our sample repor heir holding in June each year. 15

16 average accruals in calendar year -1 of all sample firms. 8 n is he number of socks held by a fund. We include a negaive sign in he expression o make he measure posiive for a fund ha acively employs he accruals sraegy. By consrucion, porfolios wih MKT randomly seleced socks would on average have zero AIM H, i,. Furher, a fund ha holds more low-accruals and less high-accruals socks han a random porfolio would end o MKT have a posiive AIM H, i,. For he reasons discussed in he previous secion, our second measure uses characerisics-adjused accruals insead of he raw accruals: n AIM H, i, = w j, * AdjAcc j, 1 (6) j= 1 where AdjAcc j, 1 is he characerisics-adjused accruals defined as he residual in (3). MKT Again, a posiive AIM H, i, indicaes acive use of he accruals sraegy, afer conrolling for oher facors affecing he level of accruals for socks held by he fund. The higher MKT he AIM H, i,, he more aggressive a fund is in rading on he accruals anomaly. Similarly, we consruc wo holding-based NOA invesing measures o quanify he aggressiveness of he rading of a fund on he NOA anomaly: n MKT MKT NIM _ H i, = w i, j, * AdjNOAj, 1 (7) j= 1 n NIM _ H i, = w i, j, * AdjNOAj, 1 (8) j= 1 8 To ensure ha he accouning variables are known before he holding informaion, we mach porfolio weigh in June of year wih he accruals daa for all fiscal year ends in calendar year -1. Implicily, we are assuming here a 6-monh reporing lag for annual financial saemens. 16

17 MKT where AdjNOAj, 1 is a sock s NOA in calendar year -1 in excess of he equal-weighed average NOA in calendar year -1 of all sample firms. 9 AdjNOA 1 j, is a sock s characerisics-adjused NOA in calendar year -1. Similar o he holding-based AIM MKT measures, a posiive NIM H or NIM H indicaes ha a fund acively uses NOA informaion when selecing socks. B.4 Trading-based Accruals Invesing Measures and NOA Invesing Measures Differen from he holding-based AIM and NIM, he rading-based measures use informaion in a fund s porfolio weigh change. The rading-based, marke-adjused AIM is he negaive of he weighed sum of marke adjused accruals, where he weighs are he porfolio weigh changes during a one-year period: AIM n MKT T, j, = wi, j, wi, j, 1 ) * j= 1 ( AdjAcc (9) where and are porfolio weighs in June of year and -1 and he accruals w j, w j, 1 signal is from calendar year -1. Following Grinbla e al. (1997), o conrol for he effec of passive weigh changes due o changes in sock prices, we calculae he porfolio weighs and using he average of he beginning- and end-of-period share w j, w j, 1 prices. We use he characerisics-adjused accruals o conrol for he impac of oher rading sraegies and firm characerisics, MKT j, 1 9 To ensure ha he accouning variables are known before he holding informaion, we mach porfolio weigh in June of year wih he NOA daa for all fiscal year ends in calendar year

18 AIM n T, j, = wi, j, wi, j, 1 ) * j= 1 ( AdjAcc (10) j, 1 The rading-based measures reflec he change of porfolio holdings in response o accruals/noa informaion, whereas he holding-based measures reflec he value weighed average accruals or NOA of muual funds relaive o heir benchmarks. When fund managers rade owards low-accruals socks and agains high-accruals socks, we expec a posiive rading-based AIM. Similarly, we consruc he following wo rading-based NOA invesing measures: NIM n MKT T, i, = wi, j, wi, j, 1 ) * j= 1 ( AdjNOA (11) MKT j, 1 NIM n T, i, = ( wi, j, wi, j, 1 ) * j= 1 AdjNOA j, 1 (12) B.5 Fund Performance Measures To evaluae fund performance, we apply wo ses of fund performance measures. Following Grinbla and Timan (1989), Daniel, Grinbla, Timan and Wermers (1997) and Wermers (2000), he firs se of performance measure is he fund gross reurn, esimaed on a quarerly basis as he buy-and-hold reurn on beginning-of-quarer porfolio holdings: GR n n i, = wi, j, R j, + 1 wi, j, ) j= 1 j= 1 ( RF (13) where is he porfolio weigh of fund i on sock j a he beginning of quarer, is w i, j, R j, he buy-and-hold reurn of sock j in quarer, and RF is he risk-free reurn in quarer. If a fund repor porfolio holdings semiannually, we esimae heir holding a he 18

19 beginnings of he inerim quarer by assuming ha hey complee half of he porfolio changes in each quarer. We exclude observaions in he analysis if wo reporing daes are more han six monhs apar. The annual fund performance calculaed from equaion (13) is he gross reurn before deducing fund expense raio. To conrol for differences in fund expense raios, we deduc quarerly expense raios from he quarerly gross reurn and obain he aferexpense gross reurn. To conrol for differences in risk exposure, we use he Fama-French 3-facor model and he Carhar 4-facor model o compue risk-adjused alphas. GR i = α + b RMRF + s SMB + h HML + ε, (14) i i i i GR i = α + b RMRF + s SMB + h HML + p UMD + ε, (15) i i i i i where GR i, is he quarerly gross reurn (before or afer fund expenses) of fund i in quarerly in excess of he risk free rae (he yield on Treasury bills wih hree-monh mauriy). RMRF is he quarerly reurn on he NYSE/AMEX/NASDAQ value-weighed index in excess of he risk free rae; SMB, HML, and UMD are he quarerly size, booko-marke, and momenum facors. 10 The inercep of he model, α, is he quarerly riskadjused performance measure. The second se of fund performance measures are based on he CRSP ne fund reurns, which are monhly fund reurns afer expense raios and ransacion coss. We perform similar risk adjusmens using he 3-facor and 4-facor models o obain esimaed alphas. All he performance measures are repored in annual percenage erm. i 10 SMB, HML, and UMD are obained from Ken French's websie: hp://mba.uck.darmouh.edu/pages/faculy/ ken.french/. 19

20 IV. Empirical Resuls A. Anomaly-based Trading by Muual Funds A.1 Holding-based Measures We examine he holdings-based marke-adjused AIM and NIM measures MKT MKT ( AIM H and NIM H ) o see if muual funds hold more low-accruals/noa socks han randomly seleced porfolio. To be specific, we calculae he equal-weighed and valueweighed averages of holding-based measures across all funds in each year, and calculae MKT MKT heir ime-series means. Repored in Table 3, he mean AIM H and NIM H are eiher negaive or indifferen from zero. For example, he equal-weighed (EW) mean of our sample funds is -8.19, significan a he 1 percen level while he EW mean MKT AIM H MKT NIM H is -2.11, insignificanly differen from zero. We obain similar resuls when breaking down funds by heir invesmen objecives. A he firs glance, his seems o sugges ha muual funds on average ac jus like naïve invesors. However, when inerpreing he resuls, we should keep in mind ha as previously discussed, many oher facors may affec muual fund sock selecion and heir effecs are no conrolled for when using marke-adjused accruals or NOA. We herefore focus more on he resuls MKT MKT based on he characerisics-adjused AIM and NIM ( AIM H and NIM H ). Repored in Table 3, he mean AIM H and NIM H are significanly posiive for all funds; hey are also significan for subsamples of aggressive growh funds and growh funds. These resuls sugges ha he muual fund porfolio holdings il oward socks wih lower characerisics-adjused accruals. 20

21 Figure 2 reveals he difference in porfolio holdings beween he op and boom fund deciles ranked by eiher AIM H or NIM H. We firs idenify he op and boom fund deciles by ranking funds in each year according o heir AIM and NIM. D10 is he decile of funds mos inensely using he accruals/noa sraegies, whereas D1 is he decile of funds leas acive (or hose using he sraegies in he opposie direcion). We hen sor socks ino deciles in each year based on heir characerisics-adjused accruals and NOAs. We obain he fracion of socks held in each characerisics-adjused accruals (NOA) decile by he D10 and D1 funds, and compue he ime series means of he fracions. For boh AIM H and NIM H, here are clear differences beween he disribuions for D1 funds and D10 funds. The invesmens of D10 funds il oward low accruals/noa socks while D1 funds inves more in high accruals/noa socks. To sum up, here is subsanial difference in he inensiy of anomaly-based invesing. A.2 Trading-based Measures Table 4 repors he resuls using he rading-based AIM and NIM. The radingbased measures quanify he acive changes in porfolio holdings in response o accruals/noa informaion. For he reasons discussed previously, alhough we repor resuls for boh he marke-adjused measures and he characerisics-adjused measures, we focus on he laer. The mean AIM T is posiive, significan a he one percen level for he full sample and for he subsamples of aggressive growh and growh funds. The mean NIM T is also posiive, bu only significan a he en percen level for he full sample, suggesing ha muual funds rade on he NOA anomaly less aggressively relaive o heir rading on he accruals anomaly. One possible explanaion is ha NOA 21

22 across socks is fairly persisen (Hirshleifer e al. 2004). The sraegy ha akes advanage of he NOA anomaly herefore only requires low rading inensiy. Also noe ha he accruals anomaly has been well published since he work of Sloan (1996), while he NOA anomaly is only recenly documened by academic sudies. Muual fund rading on he laer is perhaps more likely o be coincidenal: hey make invesmen decisions hrough fundamenal analysis, which happens o generae sockpicking signals correlaed wih he NOA variable. We depic he disribuion of socks raded by exreme AIM T ( NIM H ) fund deciles. Because rading involves boh purchases and sales, we employ he following procedure o plo he disribuion. Take he AIM T measure for example. The firs sep is o compue he fracion of purchased value of socks in each accruals decile for he D1 (D10) funds. Second, we calculae he fracion of sold value of socks in each accruals decile for D1 (D10) funds. Third, we compue he ne rade for socks in each accrual decile as he difference beween he purchase and sales raios. Finally, we compue he ime series mean of he ne rading percenages in each accrual decile for D10 (D1) funds. Figure 3 shows ha D10 funds more acively rade on he accruals or NOA informaion han D1 funds. For D10 AIM T funds, he ne posiion in he lowes sock accruals decile is 4.1 percen (i.e., ne buyers) while he ne posiion in he highes sock accruals decile is -3.2 percen (ne sellers). In words, funds mos acively rading on accruals informaion ake a ne long posiion in low-accruals socks and a ne shor posiive in high-accruals socks. By conras, funds leas acively rading on accruals informaion ake a ne shor posiion in low-accruals socks and a ne long posiion in high-accruals socks. The plo of ne rading for he exreme NIM T funds reveals a 22

23 similar paern. Overall, here is significan cross-secional difference in fund rading in response o accruals/noa signals. A.3 Regression-based Analysis Lev and Nissim (2004) employ a cross-secional regression approach o examine insiuional rading on he accruals anomaly a individual sock level. Here we perform similar regressions for our resuls based on AIM and NIM. Take he characerisicsadjused accruals as an example. We perform he following regressions: h α + β AdjAcc + γ ROA + γ 1/ PRICE + γ VOL + ε (16) i, j, = i, i. j, 1 i, j, 1 i, ( ) 1 i, h = α + β AdjAcc + γ ROA + γ 1/ PRICE + γ VOL + ε (17), j, i, i. j, 1 i, j, 1 i, ( ) 1 i, 1 Equaion (16) is o analyze deerminans of fund holdings. The dependen variable,, is he percenage of all ousanding shares of sock j held by muual funds in June of year. Independen variables include characerisics-adjused accruals as well as conrol variables reurn on asses (ROA), he inverse of sock price (1/Price), and he average monhly sock rading volume of he sock divided by is oal shares ousanding (VOL). 11 Equaion (17) is o analyze he deerminans of fund rading, where he dependen variable, h i, j, h i, j,, is he change in he percenage of all ousanding shares of a sock held by muual funds. The independen variables are he same as hose in equaion (16). The regressions are performed in each year across all socks. The ime-series means of he esimaed coefficiens are repored in Table 5. Similar regressions are also performed using characerisics-adjused NOA as an explanaory variable. 11 Lev and Nissim (2004) consider a baery of conrol variables. The conrol variables we use here are a subse of heirs. A few oher variables are implicily conrolled for when we compue he characerisicsadjused accruals. 23

24 Consisen wih Lev and Nissim (2004), we find ha he coefficiens for characerisics-adjused accruals and NOA are significanly negaive. This furher confirms our conclusion ha muual funds on average acively rade on hese anomalies. A.4 Deerminans of Inensiy of Anomaly-based Trading Nex, we invesigae he deerminans of he inensiy of anomaly-based rading. We perform Fama-MacBeh regressions of he accruals based inensiy measures AIM H and AIM T ono several fund characerisics as well as lagged inensiy measures: AIM γ AIM + γ LgAge + γ LgTNA + γ Turn + γ Exp + ε (18) H = 0 H AIM γ AIM + γ LgAge + γ LgTNA + γ Turn + γ Exp + ε (19) T = 0 T where LgAge is he log of fund age in he pas year; LgTNA is he logarihm of TNA in he pas year; Turn is he fund urnover raio in he pas year; Exp is fund expenses raio in he pas year. Similar regressions are performed for he NOA-based measures NIM H and NIM T. The resuls in Table 6 sugges ha funds persisenly use he accruals and NOA sraegies. The average coefficiens on lagged AIM H and AIM T are 0.57 and 0.17, and he average coefficiens on lagged NIM H and NIM T are 0.82 and The evidence on he persisence of hese measures is imporan. I suggess ha he crosssecional difference in anomaly-based rading is no compleely by chance; a leas par of i is due o acive and inenional fund sraegy. 24

25 Some fund characerisics, such as fund size and urnover raio, also have some consisen explanaory power on he rading inensiy. Large funds and high urnover funds seem o rade more inensely on boh he accruals and NOA anomalies. B. Profiabiliy of Anomaly-based Trading B.1 Fund Performance across Holding-based AIM and NIM In his subsecion, we examine muual fund performance associaed wih anomaly-based rading. Table 7 compare he average subsequen-year annual gross reurns across funds in differen AIM H and NIM H deciles, where funds in he D1 decile are hose wih he lowes AIM H or NIM H, and funds in he D10 decile are hose wih he highes AIM H or NIM H. D10 funds significanly ouperform D1 funds in he year subsequen o porfolio formaions. Par of he gross reurn is expense raio. To see if he difference is due o difference in expense raio, we subrac expense raio from he gross reurn and examine he afer-expense gross reurn for funds in differen AIM H and NIM H deciles. The resuls, also in Table 7, show ha expense raio does no explain he performance difference. The performance gap beween D10 and D1 funds is similar o ha before expense adjusmen. In Table 8, we furher compare fund ne reurns across deciles. The spreads in he EW and VW average ne reurns beween he wo exreme AIM H fund deciles are 3.19 percen and 2.74 percen. The differences in 3-facor alphas and 4-facor alphas are a he same magniude. For NIM H AIM H and NIM H, he EV and VW ne 25

26 reurn spreads are 3.08 percen and 2.86 percen. The differences in 3-facor and 4-facor alphas are slighly lower, bu remain saisically significan. The spreads in ne reurns are lower han he spreads in afer-expense gross reurns. A main componen of he difference beween hese wo performance measures is ransacion coss. A number of sudies, including Grinbla and Timan (1989) and Wermers (2000), have used such difference as a measure of ransacion coss. Recenly, Kacperczyk, Sialm, and Zheng (2005) examine various facors ha may poenially affec his measure, including inra-quarer rading and window dressing aciviies. Their evidence confirms ha ransacion cos is a main componen. From his poin of view, ransacion coss reduce he profiabiliy of anomaly-based rading by as high as 1.32 percen (for AIM H ) and 1.51 percen ( NIM H ), based on resuls for equal-weighed fund deciles. The numbers are quie significan; hey also suppor he premise of Lev and Nissim (2004) ha ransacion coss are an imporan impedimen o anomaly-based rading by sophisicaed invesors. B.2 Fund Performance across Trading-based Measures Table 9 shows ha in erms of gross reurns, rading on he accruals anomaly is profiable, whereas he profi from rading on he NOA anomaly appears o be slim. For AIM T, before deducing fund expense raios, D10 funds ouperform D1 funds by 3.19 percen in heir EW gross reurns. The resuls remain similar afer expenses and afer risk adjusmens. In conras, for NIM T, he spread beween D10 and D1 funds is insignificanly posiive. This seems o be consisen wih he fac ha funds rade less inensively on he NOA anomaly on average, as repored in Table 5. 26

27 In Table 10, we furher documens performance differences in erms of ne reurns. The paerns are similar o hose for gross reurns. Accruals-based rading is more profiable han NOA-based rading. Also noe ha he spread in ne reurn is lower han he spread in afer-expense gross reurn, similar o he paerns in holding-based measures. The difference here, however, is smaller. The resuls in Table 7 hrough Table 10 have several imporan implicaions. Firs, ransacion coss significanly affec he profiabiliy of anomaly-based rading. Second, despie significan ransacion coss, rading on anomalies, especially on he accruals anomaly, remains profiable. The imporance of he second implicaion can be beer undersood from he perspecive of a financial marke wih perfecly elasic supply of arbirage capial. In such a financial marke, sophisicaed invesors would aggressively rade on an anomaly unil such rading becomes unprofiable. In he presence of ransacion coss, mispricing would no be compleely eliminaed, bu he profi of anomaly-based rading would be compleely eliminaed due o perfec capial supply. Therefore, ransacion coss alone canno compleely explain why rading on accruals anomaly remains profiable. B.3 The Effec of Arbirage Risk Transacion coss, of course, are no he only ype of marke fricions ha serve as limis o arbirage. Anoher imporan ype of fricion is shor-sale consrains. For various reasons, muual funds are self-consrained no o ake shor posiions in socks (see, e.g., Almazan e al. (2004)). Shor-sale consrains, however, can be viewed as a special ype of ransacion coss and is role should be similar o ransacion coss. In a marke wih 27

28 shor-sale consrains bu perfec supply of arbirage capial, muual funds would ake aggressive long posiions in undervalued socks unil such invesmens become unprofiable. We conjecure ha he co-exisence of coninued profiabiliy of anomaly-based rading and he coninued exisence of anomalies is mainly due o inelasic supply of arbirage capial. A ypical case of inelasic capial supply is suggesed by Shleifer and Vishny (1997). They argue ha rading on mispricing is no compleely riskless; in fac, i is ofen associaed wih subsanial arbirage risks. Because of asymmeric informaion, invesors may wihdraw money from delegaed porfolio mangers when inermediae fund performance suffers due o such arbirage risks, even hough he arbirage opporuniies pursued by fund managers are ulimaely profiable. In his case, muual funds rade on mispricing less aggressively, such rades remain profiable, and marke anomalies are no compleely arbiraged away. To examine he effec of arbirage risks, we perform he following wo ses of analysis. Firs, we es wheher higher arbirage risks in he socks muual funds inves in lead o higher volailiy of fund reurns. 12 Arbirage risks, in he definiion of Shleifer and Vishny (1997), are idiosyncraic risks of mispriced securiies. Such risks may no be fully diversified away in an arbirage porfolio because arbirage opporuniies are limied and scarce. As a resul, higher arbirage risks ranslae ino higher volailiy in fund performance. In each year, we compue boh he average sandard deviaion of ne reurns and he average sandard deviaion of he idiosyncraic componen of ne reurns for funds in 12 Mashruwala, e al. (2004) documen ha socks wih higher accouning accruals have higher arbirage risks as measured by he sandard deviaion of idiosyncraic reurns. 28

29 each decile ranked by characerisics-adjused AIM and NIM. We use boh he holdingbased and rading-based measures. The idiosyncraic componen of fund reurn is he esimaed residual from he 3-facor and 4-facor models. We hen compue he imeseries means of hese volailiy measures. The repored resuls in Table 11 confirm he effec of arbirage risks relaive o funds ranked in he middle deciles, funds ranked in he op decile of rading-based AIM and NIM have higher ne reurn volailiy and idiosyncraic reurn volailiy. In he second se of analysis, we es wheher invesors are sensiive o inerim performance of funds rading on anomalies. In he heory of Shleifer and Vishny (1997), invesors rely on inerim fund performance o infer managers abiliy. If fund performance suffers emporarily, invesors will wihdraw money, hereby undermining fund managers arbirage posiions. This suggess ha flows o funds engaging in arbirage rading are also volaile. We herefore compue he average sandard deviaion of monhly flows o funds in each characerisics-adjused AIM and NIM decile ranking, and repor heir ime series means in Table 12. Boh holding-based and rading-based measures are used. Fund flow is defined as FLOW i, = TNA i, TNA TNA i, 1 * (1 + i, 1 R i, ) where TNA is he oal ne asses repored in each monh and R i is fund monhly reurn. Idiosyncraic fund flow volailiy is he sandard deviaion of he residuals from regressing flow on logarihm of fund size, oal fees, and average monhly fund ne reurn in year -1.The resuls confirm he above hypohesis he volailiy of flows is subsanially higher for funds ranked in he op decile, relaive o ha in he middle 29

30 deciles. Such volaile invesor response limis fund managers aggressiveness in pursuing mispricing opporuniies. V. Conclusion In his paper, we examine wheher muual funds profi from wo accouning-based anomalies: he well known accruals anomaly and he recenly discovered NOA (ne operaing asses) anomaly. We find ha muual funds on average acively rade on hese anomalies and profi from i. The evidence suggess ha ransacion coss have no compleely prevened muual funds from profiing on marke anomalies bu a he same ime muual funds have no raded aggressively enough o exploi he mispricing compleely. Furher analysis suggess ha he paerns are consisen wih inelasic supply of arbirage capial induced by arbirage risks. Funds ha more aggressively follow he anomalies exhibi higher volailiy in fund reurns and in fund flows. Shleifer and Vishny (1997) argue ha because of asymmeric informaion, invesors wihdraw money from he funds when inermediae fund performance suffers due o arbirage risk. 30

31 References Almanzan, A., K. Brown, M. Carlson, and D. Chapman, 2004, Why Consrain Your Muual Fund Manager? Journal of Financial Economics 73, Ali, Ashiq, Lee-Seok Hwang, Mark A. Trombley, 2000, Accruals and Fuure Sock Reurns: Tess of he Naïve Invesor Hypohesis, Journal of Accouning, Audiing, and Finance, Ali, Ashiq, Lee-Seok Hwang, Mark A. Trombley, 2003, Arbirage Risk and he Book-o- Marke Anomaly, Journal of Financial Economics, 69, Barh, M. E., J.R.M. Hand, and W.R. Landsman, 1999, Accruals, Cash flows, and Equiy Values. Review of Accouning Sudies 4, Barov, E., S. Radhakrishnan and I. Krinsky, Invesor sophisicaion and paerns in sock reurns afer earnings announcemens. The Accouning Review 75(1), Bernard, Vicor and Jacob K. Thomas, 1989, Pos-earnings-announcemen Drif: Delayed Price Response or Risk Premium? Journal of Accouning Research Suppl. 27, l- 36. Bernard, Vicor and Jacob K. Thomas, 1990, Evidence ha Sock Prices Do No Fully Reflec he implicaions of Curren Earnings for Fuure Earnings, Journal of Accouning and Economics 13, Brown, Sephen J. and William N. Goezmann, 1995, Performance Persisence, Journal of Finance 50, Bushee, Brian, 1998, The Influence of Insiuional Invesors on Myopic R&D Invesmen Behavior, Accouning Review 73(3): Carhar, Mark M., 1997, On persisence in Muual Fund Performance, Journal of Finance 52, Cassidy, Don, 2004, Muual fund rading and porfolio ransacion coss, Lipper Fund Indusry Insigh Repor. Chalmers, John, Roger M. Edelen, and Gregory B. Kadlee 2001, An analysis of muual fund rading coss, Working paper. Chan, Konan, Louis K.C.Chan, Narasimhan Jegadeesh and Josef Lakonishok, 2003, Earnings Qualiy and Sock Reurns, Working Paper. Cohen, R., J. Coval, and L. P asor, 2004, Judging fund managers by he company hey Keep, Forhcoming, Journal of Finance. 31