The Effectiveness of Reputation as a Disciplinary Mechanism in Sell-side Research

Transcription

1 The Effeciveness of Repuaion as a Disciplinary Mechanism in Sell-side Research Lily Fang INSEAD Ayako Yasuda The Wharon School, Universiy of Pennsylvania We hank Franklin Allen, Gary Goron, Pierre Hillion, Soeren Hvidkaer, Brigie Madrian, Massimo Massa, Andrew Merick, Seven Orpur, Michell Peersen, Jay Rier, Cahy Schrand, Mahew Spiegel (he edior), René Sulz, an anonymous referee, and he seminar and conference paricipans a HKUST, INSEAD, LSE, Universiy of Illinois a Chicago, The Wharon School, 9 h Conference of he Swiss Sociey for Financial Marke Research (Zurich), he EFA Annual Meeings (Zurich), he NUS Conference, and he WFA Meeing (Vancouver) for insighful commens and discussions, and I/B/E/S for making heir daa available for academic use. Financial suppor from he Wharon Rodney L. Whie Cener for Financial Research and he INSEAD R&D Commiee is graefully appreciaed. All errors and omissions are our responsibiliy. This paper was previously circulaed under he ile Analys Repuaion, Underwriing Pressure, and Forecas Accuracy. Address correspondence o: Lily Fang, INSEAD, 1 Ayer Raah Avenue, Singapore , el: (65) , fax: (65) , Lily.Fang@insead.edu; and Ayako Yasuda, The Wharon School of Universiy of Pennsylvania, 2300 SH/DH, 3620 Locus Walk, Philadelphia, PA 19104, U.S.A., el: (215) , fax: (215) , yasuda@wharon.upenn.edu. 1

2 Absrac Using U.S. daa, we examine wheher he qualiy differenials in earnings forecass beween repuable and non-repuable analyss vary as he severiy of conflics of ineres varies. We measure personal repuaion using he Insiuional Invesor All-American (AA) awards, and bank repuaion using Carer- Manaser ranks. While boh personal repuaion and bank repuaion are associaed wih higher-qualiy forecass overall, heir effeciveness agains conflics of ineres differs. The severiy of conflics (proxied by he aggregae volume of new equiy issues) has a negaive and significan effec on he performance of non- AAs a op-ier banks relaive o boh AAs a op-ier banks and non-aas a lower-ier banks. In conras, he severiy of conflics has a posiive and significan effec on he performance of AAs a op-ier banks relaive o boh non-aas a op-ier banks and AAs a lower-ier banks. These findings sugges ha personal repuaion is an effecive disciplinary device agains conflics of ineres, while bank repuaion alone is no. (JEL G14, G24, G28, D82, J44) Keywords: Analys research; Earnings forecas; Analys repuaion; Bank repuaion; Conflic of ineres; Invesmen banking. 2

3 Inroducion Around he urn of his cenury, equiy markes were red-ho, as equiy underwriing volumes soared. By 2003, hese ho markes were associaed wih conflic-of-ineres scandals involving sell-side research on Wall Sree, and some of he bes and brighes people and firms were charged wih violaing he public rus by publishing biased research. Individuals, like once-sar analys Jack Grubman, faced muli-million-dollar fines and lifeime bans from he securiies indusry, and en of he larges invesmen banks agreed o pay $1.4 billion in fines hrough he Global Research Analys Selemen. A large number of sudies have documened he presence of conflics of ineres in sell-side Wall Sree research. 1 Bu he fac ha analyss and firms of high repue were involved in hese scandals is paricularly sriking, because i is sharply a odds wih he heory on he role of repuaion. A he personal level, analyss face a rade-off beween a loss in long-erm repuaion and a gain in shor-erm benefis: By publishing biased research, analyss may lose heir sanding wih invesors as valued sources of informaion and opinion and hence forgo long-erm career prospecs, while in he shor run hey may gain subsanial underwriing-relaed compensaion. Because analyss wih a beer repuaion have greaer long-erm benefis o lose, heory predics ha hey are more likely o refrain from opporunism in he shor run. 2 Similarly, banks face a rade-off beween he gains from long-erm repuaion and shor-erm opporunism. Theories on repeaed games imply ha banks have an incenive o build and preserve repuaions, and his incenive should help repuable banks beer supervise he acions of individual analyss. 3 In his paper, we invesigae he apparen disconnec beween heory and realiy by exploring wo quesions: (1) Does he rade-off change enough during new-issue volume peaks, when he gains from shor-run opporunism are much higher han in normal imes, o sysemaically lure repuable analyss and banks ino publishing low-qualiy research? (2) Are personal and bank repuaion equally effecive in miigaing he conflic of ineres problem? By examining hese quesions, his paper bridges wo disinc lieraures on repuaion and on conflics of ineres. Empirically, we examine wheher repuable sell-side analyss produce higher-qualiy earnings forecass relaive o non-repuable analyss, and wheher heir qualiy differenials vary over ime wih he severiy of conflics of ineres. Using U.S. daa, we measure personal repuaion using he Insiuional Invesor All-American (AA) awards, bank repuaion using Carer-Manaser ranks, and he severiy of conflics of ineres using he aggregae 3

4 volume of new equiy issues. By analyzing he ime-varying paerns of analyss research qualiy differenials, we shed ligh on he effec of repuaion as a disciplinary device agains conflics of ineres, he severiy of which rises and falls wih he level of he overall underwriing marke. Prior lieraure has examined he saic, ha is, he ime-averaged, relaion beween repuaion and research qualiy. On personal repuaion, Sickel (1992) shows ha AA analyss produce significanly more accurae forecass han oher analyss. He concludes ha here is a posiive relaion beween repuaion and performance and, hence, pay and performance. Using Ausralian daa, Jackson (2005) finds ha more accurae analyss acquire higher fuure repuaions. Fang and Yasuda (2008) find ha socks recommended by AA analyss earn significanly higher excess reurns han hose recommended by non-aa analyss, paricularly in he ech secor. Regarding bank repuaion, Cowen e al. (2003) find ha analyss working a repuable banks make less opimisic forecass han ohers. Hong and Kubik (2003) repor ha boh accuracy and opimism posiively affec analyss promoions from non-repuable banks o repuable banks. Fang and Yasuda (2008) find ha socks recommended by analyss working a repuable banks earn significanly higher excess reurns han hose recommended by analyss working a non-repuable banks. 4 Beyond documening a posiive correlaion beween repuaion and research qualiy as in he exising lieraure, in his paper we explicily invesigae wheher repuaion miigaes or exacerbaes conflics of ineres. If repuaion simply capures average skill, we do no expec he qualiy differenials beween repuable and nonrepuable analyss o vary over ime wih he severiy of conflics of ineres. If repuaion eiher miigaes or exacerbaes he conflic of ineres problem, however, hen he qualiy differenials would vary wih he severiy of conflics of ineres. Thus, by focusing on he dynamic paerns of analyss research qualiy differenials, we address his unexplored empirical quesion. We furher conribue o he lieraure by examining he effeciveness and limiaions of wo disinc ypes of repuaion personal repuaion and insiuional repuaion. Our findings indicae ha, while boh personal repuaion and bank repuaion are associaed wih higherqualiy forecass overall, heir effeciveness agains conflics of ineres differs. While personal repuaion is effecive in miigaing conflics of ineres, bank repuaion alone is no. We draw hese conclusions from hree main resuls. Firs, we confirm earlier lieraure in finding ha repuaion, boh personal and insiuional, is posiively relaed o research qualiy. Averaged over he 20-year experience in our sample, boh analyss wih AA iles 4

5 (analyss wih personal repuaion) and analyss working a more repuable banks (analyss wih bank repuaion) make significanly more accurae and less posiively biased earnings forecass han oher analyss. 5 Second, we find ha he qualiy of forecass made by non-aas working a repuable banks (i.e., analyss wih only a bank repuaion and no a personal repuaion) significanly worsens relaive o oher groups of analyss when conflics of ineres are severe (i.e., when aggregae underwriing volume in he equiy new-issues marke is high). For example, in a year wih average underwriing volume, non-aas a repuable banks are 6.56% 6 more accurae han non-aas a non-repuable banks, indicaing superior skill in normal imes. However, in he five years when oal new-issues volume reached peaks during our sample period, his differenial in accuracy drops on average by nearly 2/3, o only 2.08%. In 2000 he year wih he highes equiy underwriing volume (in boh nominal and real dollars) he accuracy of non-aas a repuable banks acually lagged behind ha of non-aas a non-repuable banks. Since he payoff from generaing underwriing revenue is larges a he more repuable invesmen banks (large underwriers) and during peak underwriing periods, his negaive ime-series relaion beween he performance of non-aas a repuable banks and underwriing volume srongly suggess ha bank repuaion alone does no effecively miigae analyss opporunisic behavior in he presence of conflics of ineres. In conras, we find ha he relaive qualiy of forecass made by AA analyss working a repuable banks (analyss wih boh personal and bank repuaion) significanly improves in imes of high underwriing volumes in he new issues marke; ha is, op-ier AAs relaive accuracy improves by 11% agains op-ier non-aas and by 7% agains lower-ier AAs during peak underwriing years compared o an average year. This saisically and economically significan resul suggess ha personal repuaion is effecive in miigaing conflics of ineres. The remainder of he paper is organized as follows. Secion 1 oulines our research hypoheses and discusses empirical proxies. Secion 2 discusses our daa and presens descripive saisics. Secion 3 examines he saic relaion beween repuaion and forecas qualiy, and Secion 4 sudies he dynamic relaion beween repuaion and conflics of ineres. Secion 5 concludes. 1. Research Design 1.1. Hypoheses 5

6 Does repuaion miigae he conflic of ineres problem as suggesed by heory, or does i exacerbae he problem as suggesed by some recen evens? We posi hree disinc hypoheses regarding his cenral quesion. Prior research suggess ha conflics of ineres sem from underwriing-relaed compensaion, which rises during booms in he new-issues marke. 7 Thus, he severiy of conflics of ineres also rises and falls wih he underwriing volumes in he new-issues marke. Wheher repuaion serves a disciplinary role depends on how he rising conflic of ineres in booms alers he long-erm/shor-erm rade-off for differen ypes of analyss. If he rise in shor-erm profis in ho markes affecs boh repuable and non-repuable analyss symmerically, hen he differenial in research qualiy beween hem should remain consan over ime. In his case, repuaion has no disciplinary effec beyond perhaps being an indicaor of abiliy or skill, 8 and we call his he repuaion-as-abiliy hypohesis. If, insead, he empaion of rising shor-erm profi is disproporionaely large for non-repuable analyss because non-repuable analyss have less o lose from a ained repuaion han repuable analyss hen non-repuable analyss will ac more opporunisically during marke peaks and heir research qualiy will deeriorae, leading o wider gaps beween repuable and non-repuable analyss. We call his he repuaion-as-discipline hypohesis. Finally, if he rise in shorerm profis in ho markes is disproporionaely large for repuable analyss, hen hese analyss will raionally liquidae heir repuaion capial when i is mos profiable o do so, leading heir research qualiy o fall during peak years. We call his he repuaion-liquidaion hypohesis. 9 Which hypohesis is consisen wih he facs is an empirical quesion. By separaely examining he hree hypoheses for boh personal and bank repuaion, we furher discern wheher hese wo repuaion mechanisms funcion differenly Empirical Proxies To es our hypoheses, we need empirical measures of (i) personal and bank repuaion, (ii) he severiy of conflics of ineres, and (iii) research qualiy. Following prior lieraure, we use he AA ile from he Insiuional Invesor magazine as a measure of personal repuaion and he Carer-Manaser ranking of invesmen banks as a proxy for bank repuaion. We define he en banks wih he highes Carer-Manaser scores as op-ier banks. To measure he severiy of conflics of ineres, we use marke-wide underwriing volume in he equiy newissues marke (boh IPO and SEO). 10 Conflics of ineres are severe when he new-issues marke is srong and large 6

7 amouns of underwriing fees are a sake. We use he marke-wide (insead of bank-specific) underwriing volume so ha all analyss in he cross-secion face he same amoun of exogenous pressure a any given poin in ime. Figure 1 plos he oal real new equiy issuance volume (IPO + SEO) for he overall marke, he echnology secor, and he non-echnology secor. 11 Ineresingly, he peaks of he ech and non-ech secors are differen. 12 While he sock marke bubble a he urn of he cenury is generally associaed wih he ech secor (which wen hrough a dramaic boom-o-bus cycle), he non-ech secor in fac had a more susained level of high aciviy. In our analysis of analys research qualiy during peak and non-peak years below, we define peak years for he overall marke o be 1986, 1993, 1996, 1999, and We use wo empirical measures of analyss research qualiy: accuracy (unsigned errors) and bias (signed errors) of heir earnings forecass. We define analyss forecas accuracy as follows: EPS Forecasi,,, n - Acual EPS Repored, Error,,, = i n, (1.a) Book Value Equiy where i indicaes an analys, indicaes a firm, is he fiscal year, and n orders he forecass made by analys i for firm for fiscal year. Forecas bias, or signed error, is defined analogously:, 1 ( EPS Forecasi,,, n - Acual EPS Repored, ) Bias,,, = i n, (1.b) Book Value Equiy We scale boh measures by he firm s book value of equiy per share a he previous fiscal year-end o address heeroskedasiciy. 14 For a deailed discussion of boh unsigned and signed errors as measures of analys research qualiy, see he Appendix, A.1., 1 2. Daa and Descripive Saisics We consruc a daa se consising of over 800,000 earnings forecass issued by sell-side analyss for U.S. companies for he period Our daa se merges earnings forecas daa wih firm characerisics, sock prices, analys and bank repuaion measures, and economy-wide underwriing volumes. For deailed descripions of daa sources and variable definiions, see he Appendix, A.2. Table 1 conains saisics on he AA elecion process. Panel A shows he frequency disribuion of winning he AA ile. There are 10,696 analyss in he I/B/E/S daabase in our sample period. The 1,121 AA analyss hus 7

8 represen abou 10% of all analyss. This indicaes ha he AA elecion process is quie compeiive. A he same ime, once an analys is eleced, i is highly likely ha he will be eleced again: Among all AA analyss, 80% win he ile muliple imes, and he average AA enure is abou five years. Panel B presens he ransiion marix. Once eleced, AA analyss end o be re-eleced he following year: 77% of he AAs are from las year s AA pool, 22% are from las year s non-aa pool, and less han 1% are compleely new analyss. In an unrepored probi analysis we find ha AA elecion is significanly posiively relaed o pas accuracy, repor frequency, and sock coverage. 15 Combined, hese resuls sugges ha AA elecion is no a resul of pure luck bu raher a reward for perceived skill (including access o he managemen of he covered firms) and/or hard work. A naural quesion is wheher i is easier for analyss a op-ier banks o become AAs. We find ha abou half of all AAs work a he banks we define as op-ier, and his raio is sable across years. Bu, since he en op-ier banks employ only abou 25% of all analyss, he odds of becoming an AA for a op-ier bank analys is hree imes as large as ha for a lower-ier bank analys. Table 2 repors he number of firms, analyss, and forecass in each year in our merged sample. The number of firms covered peaks a 5,475 in 1997 and hen declines. While he number of non-aas more han doubles from 1,939 in 1983 o 4,442 in 2002, he number of AAs increases by only abou 35% from 232 in 1983 o 316 in The oal number of forecass more han doubles from 52,359 in 1983 o 125,215 in These figures imply ha, on average, each covered firm ges beween 20 and 30 repors per year. Ineresingly, he raio of non-aas o AAs for he head coun is always abou wice he raio for he number of repors generaed, 17 indicaing ha AAs end o issue more forecass per year han non-aas. 18 The las observaion is furher suppored by Table 3, which provides saisics on he working paerns of analyss. I shows ha AAs cover more firms and also issue more frequen earnings forecass per firm han non-aas. The differences are significan, and he resuls are consisen wih Sickel (1992). Table 4 compares characerisics of firms covered by AAs and non-aas. AAs cover significanly larger (by marke capializaion) and less risky (by reurn volailiy) firms. AA-covered firms also are more likely o be lised on he NYSE and end o have higher leverage. Since hese sysemaic differences in firm aribues can affec analyss forecas qualiy, we conrol for hem in mulivariae analyses. 8

9 3. The Saic Relaion beween Repuaion and Forecas Qualiy This secion examines he overall relaion beween repuaion, a boh he personal level and he bank level, and analys forecas qualiy. We do so using he cross-secional regression approach developed by Fama and MacBeh (1973). Specifically, we firs esimae an equaion of he form: ForecasQualiy i,, n = α + Repuaion + ln(coverage) + ln( disance) + Leverage + Volailiy + Firm Fixed Effecs + ε 5 6 i 1 i 2 i,, n i,, n + Firm Size 3 4 (2) for each of he 20 fiscal years in our sample and hen es he significance of he coefficiens using he empirical disribuion of he 20 esimaes. In equaion (2), he dependen variable ForecasQu aliy i is eiher he accuracy or,, n bias measure defined in equaion (1). The key variable of ineres Repuaion is analys i s AA saus in our i personal repuaion analysis, and he op-ier saus of he bank employing he analys in our bank repuaion analysis. Since analyss personal and bank saus may change over ime, care has been aken o reflec he correc saus a he ime he forecas is issued. Among he conrol variables, ln(coverage) i is he naural log of he number of socks ha analys i covers; ln(disance) is (he log of) he disance in days beween he forecas dae and he earnings release dae; Firm Size is he naural log of he firm s marke capializaion of equiy a he fiscal year-end in millions of dollars; Leverage is he deb/asse raio a he fiscal year-end; Volailiy is he residual sandard deviaion of he firm s sock reurn agains he marke (CRSP value-weighed index) in he 120-day period prior o he forecas dae. We use he Huber/Whie heeroskedasiciy-consisen covariance esimaors o address heeroskedasic residuals in he crosssecion. 19 To capure any unobserved ime-invarian firm characerisics, firm-fixed effecs are also included in he esimaion Personal Repuaion and Forecas Qualiy Table 5 repors he Fama-MacBeh esimaion resul of equaion (2) for personal repuaion (AA indicaor). Panels A and B use forecas accuracy and bias as he dependen variable (forecas qualiy measure), respecively. In boh panels, all-secor resuls are shown firs, followed by resuls on he ech and non-ech secors. 20 For breviy of presenaion, coefficiens on he conrol variables are shown only for he all-secor resuls; hough no repored, hey are included and are qualiaively and quaniaively similar in all specificaions. 9

10 We find ha AAs are more accurae han non-aas: The average coefficien on he AA variable is negaive and saisically significan for all secors, as well as for he ech and non-ech samples. Since he average forecas error in our sample is 4.51% (unrepored), he coefficien of (for he overall resul) indicaes ha overall AAs are more accurae han non-aas by 4.47% (0.0021/0.0451) afer accouning for various firm, analys, and forecas characerisics. Thus he AA effec is economically significan. The conrol variables have he expeced signs. The posiive coefficien on ln(coverage) (hough no significan) indicaes ha he more socks an analys covers, he less accurae his/her forecass are. 21 The posiive coefficien on ln(disance) indicaes ha he earlier he esimaes are made, he less accurae hey are. 22 The negaive coefficien on Firm Size suggess ha forecas errors are smaller for larger firms, consisen wih more readily available informaion for larger firms. While Volailiy comes in mosly as insignifican, higher leverage is associaed wih larger errors, consisen wih he noion ha leverage reflecs cash-flow risk. Turning o forecas bias (Panel B), we find ha overall AAs are less posiively biased han non-aas. The resul is marginally significan for he whole sample and he ech secor sample, bu insignifican for he non-ech secor sample. In summary, AAs are on average significanly more accurae and somewha less posiively biased han non- AAs, wheher hey cover ech socks or non-ech socks Bank Repuaion and Forecas Qualiy Table 6 repors he esimaion resuls of equaion (2) focusing on bank repuaion. We find ha bank saus is generally associaed wih higher accuracy (Panel A), as indicaed by he negaive and significan coefficien on he bank saus dummy. Specifically, op-ier bank analyss are on average 3.1% (0.0014/0.0451) more accurae han lower-ier analyss for he whole sample. The resuls are qualiaive and quaniaively similar for he ech and nonech secor sub-samples. Top-ier bank analyss are also less posiively biased han lower-ier bank analyss (Panel B). This resul is significan for he all-secor sample and he non-ech secor sub-sample, bu insignifican for he echsecor sample. In summary, resuls in his secion broadly suppor a posiive link beween repuaion and he analyss research qualiy. This is confirmed a boh he personal and bank levels, for boh accuracy and bias, and for he ech 10

11 and non-ech secor socks. However, a posiive correlaion beween repuaion and research qualiy in he saic Fama-MacBeh seing does no prove ha repuaion miigaes conflics of ineres; i only suppors he repuaionas-abiliy hypohesis. The quesion of wheher repuaion helps miigae conflics of ineres mus be examined in a dynamic seing, where he severiy of conflics of ineres varies over ime. We urn o his analysis in he nex secion. 4. The Dynamic Relaion beween Repuaion and Forecas Qualiy 4.1. Does Bank Repuaion Miigae Conflics of Ineres? To invesigae wheher bank repuaion miigaes he conflic of ineres problem in sell-side research, we esimae a pooled regression using all years of daa and include an ineracion erm beween bank repuaion and our measure of conflics of ineres. Specifically, we esimae he following equaion: ForecasQualiy i, i,,, n + ln(coverage) + ln( disance) 3 = α + TopTier + ( TopTier i, i,,, n + Year Dummies + Firm Fixed Effecs + ε 1 i, + Firm Size 4 i,,, n * ln( UWVolume )) +, + Leverage 5, 2 + Volailiy 6, + 7 (3) In equaion (3), he key variable of ineres is he ineracion erm beween he op-ier dummy and he (log of) marke-wide underwriing volume. According o he repuaion-liquidaion hypohesis, analyss a repuable banks liquidae he repuaion capial associaed wih heir employers presige a he peaks of he new-issues marke, when large amouns of underwriing-relaed bonuses are a sake. This hypohesis predics a posiive sign on he ineracion erm, indicaing more inaccurae and biased forecass for op-ier bank analyss during peak years. In conras, he repuaion-as-discipline hypohesis predics a negaive sign, meaning ha he performance of analyss working a op-ier banks improves relaive o hose working a lower-ier banks during peak years. Finally, if bank saus simply capures analys skill, he repuaion-as-abiliy hypohesis predics a zero effec of his ineracion erm over and above he effec of repuaion iself. Regression resuls for equaion (3) are repored in Table Panels A and B use forecas accuracy and bias as he dependen variable (forecas qualiy measure), respecively. In each panel, all-secor resuls are repored firs, followed by he ech and non-ech secor resuls. For each secor, separae regressions are esimaed for all analyss, AAs, and non-aas. For breviy of presenaion, coefficiens on he conrol variables are shown only for he all-secor resuls; hey are qualiaively and quaniaively similar in all specificaions. For he ech and non-ech secor sub- 11

12 samples, we use he secor-specific underwriing volumes raher han he marke-wide underwriing volume because he wo secors have differen boom-and-bus cycles for heir equiy issuance aciviies (see Figure 1). Focusing on accuracy (Panel A), we find ha for he overall sample, he coefficien on he ineracion erm beween bank saus and underwriing volume is insignifican. This, however, masks ineresing paerns in subsamples: The ineracion erm is negaive and significan for he AA sub-sample, and posiive and significan for he non-aa sub-sample. In he non-aa sub-sample, while he negaive and significan coefficien on he op-ier dummy indicaes ha op-ier non-aas are significanly more accurae han lower-ier non-aas in general, he posiive and significan coefficien on he ineracion erm indicaes ha he accuracy of op-ier bank non-aas deerioraes in ho markes relaive o ha of lower-ier bank non-aas. This finding despie having beer skills (as hey are more accurae in general), op-ier non-aas become less accurae in ho markes is consisen wih he noion ha he repuaion conferred upon hese analyss by heir employers presige is being liquidaed a he peaks of he marke. 24 This in urn suggess ha bank repuaion alone is no effecive in curbing conflics of ineres. Wha is he economic significance of his resul? Holding various firm, analys, and forecas characerisics consan, in a year wih average underwriing volume, op-ier non-aas are more accurae han lower-ier non-aas by a margin of (or 6.56% of he average error). 25 Similar calculaions reveal ha during he five peak years, he performance differenial beween op-ier non-aas and lower-ier non-aas narrows o (or 2.29% of he average error). 26 Thus, wo-hirds (4.27% ou of 6.56%) of he superior accuracy achieved by op-ier non-aa analyss vis-àvis heir lower-ier counerpars in normal years disappears in peak years. In 2000, he year wih he larges underwriing volume, op-ier non-aas acually become less accurae han lower-ier non-aas. 27 This ime-varying resul is consisen wih repuaion-liquidaion by non-aa analyss working a op-ier banks during marke peaks. In conras, in he AA sub-sample, he coefficien on he ineracion erm is negaive and significan. This indicaes ha op-ier AA analyss become more accurae in ho markes relaive o lower-ier AAs. The coefficiens imply ha op-ier AAs relaive accuracy over lower-ier AAs improves by abou 11% of he average error (0.0451) beween he average underwriing year and 2000, he year wih he larges equiy underwriing volume; if we consider he five peak years (1986, 1993, 1996, 1999, and 2000) ogeher, he improvemen is abou 7% of he average error

13 Boh ypes of analyss here have personal repuaion (AA iles) and are differeniaed by he repuaion of heir employer banks. Thus, he resul is consisen wih he view ha, when analyss have a personal repuaion, working a repuable banks furher reduces he incenive o ac opporunisically. Thus, he addiional bank repuaion conferred upon analyss who already have personal repuaion seems o miigae conflics of ineres. Secor resuls (Panels A-2 and A-3 for he ech and non-ech secors, respecively) show ha he foregoing conclusion largely sems from he non-ech secor: Here, non-aas a op-ier banks become less accurae during ho markes (relaive o non-aas a lower-ier banks), while AAs a op-ier banks become more accurae during ho markes (relaive o AAs a lower-ier banks). In conras, in he ech secor, he relaive accuracy of op-ier bank analyss over lower-ier banks analyss does no vary wih he flucuaions in he secor underwriing volume. Panel B repors he resuls for bias. Firs, in he all-secor sample (Panel B-1), we find ha op-ier bank analyss become marginally more posiively biased relaive o lower-ier bank analyss during ho markes (significan a 10%). Secor resuls indicae ha his is mainly driven by non-aas in he non-ech secor (Panel B-3). Taking his evidence on bias ogeher wih he resul on accuracy (Panel A-3), we find ha non-aas a op-ier banks covering non-ech socks become boh less accurae (as shown in Panel A-3) and (marginally) more posiively biased compared o non-aas a lower-ier banks during ho markes; ha is, heir relaive performance deerioraes according o boh qualiy measures. AA analyss a op-ier banks, on he oher hand, do no become more posiively biased, while heir relaive accuracy improves relaive o AAs a lower-ier banks (Panel A-3). Summarizing our resuls on bank repuaion as a miigaing device agains conflics of ineres, we find diverging resuls for AAs and non-aas. Top-ier AAs become more accurae relaive o lower-ier AAs during peak underwriing years, while he wo groups forecas bias differenial does no vary wih he marke underwriing volume. In conras, op-ier non-aas become significanly less accurae and (marginally) more posiively biased compared o lower-ier non-aas during peak years. 29 These findings indicae ha bank repuaion alone is no sufficien o miigae conflics of ineres: Analyss whose sole repuaion is conferred on hem by heir employers (op-ier non-aas) ac in a fashion consisen wih he repuaion-liquidaion hypohesis in marke peaks. When personal repuaion is presen, bank repuaion plays a miigaing role Does Personal Repuaion Miigae Conflics of Ineres? 13

14 To invesigae he effec of personal repuaion on he conflic of ineres problem, we esimae a pooled regression analogous o equaion (3): ForecasQualiy + ln(coverage) i, i,,, n = α + AA + ( AA i, + ln( disance) 3 i,,, n + Year Dummies + Firm Fixed Effecs + ε 1 i, + Firm Size 4 * ln( UWVolume )) + i,,, n, + Leverage 5 2, + Volailiy 6, + 7 (4) where he analys s AA saus is ineraced wih he conflic of ineres measure he aggregae underwriing volume. The repuaion-liquidaion hypohesis again predics a posiive sign on he ineracion erm, whereas he repuaionas-discipline hypohesis and he repuaion-as-abiliy hypohesis predic a negaive sign and a zero coefficien, respecively. Table 8 presens he esimaion resuls of equaion (4). Panels A and B use accuracy and bias as he forecas qualiy measure (dependen variable), respecively. Examining forecas accuracy (Panel A), we firs observe ha he sign on he key ineracion erm beween AA saus and he underwriing volume is negaive and significan for he whole sample. This indicaes ha AAs become more accurae relaive o non-aas during marke peaks, which is consisen wih he repuaion-as-discipline hypohesis. Furhermore, sub-sample regressions show ha his overall resul is driven enirely by op-ier-bank analyss. This means ha he performance by AAs a op-ier banks relaive o heir non-aa colleagues significanly improves during peak years. Quaniaively, his performance improves by , or 11% of he average error, beween he average year and he five peak years. 30 This resul and our earlier finding (Table 7), viz., ha he accuracy of non-aa analyss a op-ier banks goes down during peak years relaive o ha of non-aa analyss a lower-ier banks, can be hough of as wo sides of he same coin: Top-ier non-aas research qualiy deerioraes relaive o boh comparison groups op-ier AAs (Table 8) and lower-ier non-aas (Table 7). In oher words, op-ier non-aas consisenly ac according o he repuaionliquidaion hypohesis. In conras, op-ier AA analyss consisenly ac according o he repuaion-as-discipline hypohesis, because heir accuracy improves relaive o boh op-ier non-aa analyss (Table 8) and lower-ier AA analyss (Table 7). Thus, our findings sugges ha while conflics of ineres exis and negaively impac he research qualiy of analyss, i seems o have he greaes impac on non-sar analyss working a op-ier banks. We find ha non-aa analyss working a op-ier banks become significanly less accurae during boom years of he new issues marke, 14

15 when he aracion of large year-end bonuses and he empaion o liquidae one s repuaion for profi is high compared o normal years. In conras, AAs working a op-ier banks acually become more accurae relaive o boh heir non-aa colleagues a op-ier banks and AAs a lower-ier banks a marke peaks. Since AAs are rewarded for heir abiliy o generae business, he empaion for hem o liquidae heir personal repuaion is presumably also sronges during ho markes. Our finding ha he performance of AA analyss significanly improves relaive o oher analyss during marke peaks indicaes ha hey are able o resis pressures from conflics of ineres beer han oher analyss, which is consisen wih he noion ha personal repuaion miigaes conflics of ineres. Turning o sub-secor resuls (Panels A-2 and A-3 for he ech and non-ech secors, respecively), we observe ha he all-secor resul is mosly driven by he non-ech secor. For he ech secor, he ineracion erm beween AA saus and underwriing volume is generally insignifican (only marginally significan for he all-analys sample). Thus, in he ech secor, here is no evidence ha personal repuaion played a srong miigaing role. In conras, he non-ech secor resul shows ha AA analyss a op-ier banks become significanly more accurae during ho markes (relaive o non-aa analyss a op-ier banks), while he relaive accuracy of AAs a lower-ier banks does no change in ho markes (relaive o non-aas a lower-ier banks). Panel B shows he resuls for he bias (signed errors). Overall, AAs eiher become less posiively biased (in he ech secor) or do no change heir level of bias relaive o oher analyss (in he non-ech secor) during boom years. Taken ogeher, he resuls for boh accuracy (Panel A) and bias (Panel B) broadly suppor he view ha personal repuaion eiher improves research qualiy in boom years or is associaed wih higher research qualiy ha is ime-invarian hrough booms and buss. These resuls are consisen wih he repuaion-as-discipline and repuaion-as-abiliy hypoheses and inconsisen wih he repuaion-liquidaion hypohesis. In summary, our resuls in his secion sugges ha, despie some noable and highly publicized excepions, personal repuaion works as an effecive disciplinary device agains conflics of ineres. Wha exacerbaes conflics of ineres is he combinaion of a lack of personal repuaion and he lure of large shor-erm profis. In paricular, his combinaion appears o o undermine he research qualiy of non-aa analyss a op-ier banks. Non-AA analyss working a op-ier banks have lile porable, personal repuaion o lose and much o gain from liquidaing he repuaion conferred upon hem by heir employers in ho markes. In conras, AAs working a he same op-ier 15

16 banks have more o lose namely heir porable, personal repuaion and his concern, on average, helps hem mainain heir research qualiy in he peak years Robusness Checks Composiion Hypohesis Our resuls sugges ha, consisen wih he repuaion-liquidaion hypohesis wih respec o bank repuaion, op-ier non-aa analyss become relaively less accurae during ho markes han eiher AAs a op-ier banks or non-aas a lower-ier banks. Consisen wih he repuaion-as-discipline hypohesis wih respec o personal repuaion, AA analyss a op-ier banks become relaively more accurae during boom markes han eiher non-aas in heir own banks or oher AAs in lower-ier banks. One concern abou his inerpreaion is ha our resuls can be driven by changing composiions of analys pools over he new-issues marke cycle. Changing composiions could explain he drop in relaive accuracy among non-aas a op-ier banks during peak years under he following circumsances: (i) op-ier banks hire disproporionaely more new analyss during peak years o mee addiional demand; (ii) new analyss are on average worse forecasers han exising ones; and (iii) new analyss are more likely o be non-aas han AAs. Likewise, if in peak years op-ier banks hire more new AAs who are more accurae han exising ones, hen he relaive accuracy of he AAs a op-ier banks may increase compared o oher groups. These composiional effecs challenge he inerpreaion of our resuls as due o he miigaing role of repuaion. To invesigae his alernaive hypohesis, we perform wo ess. We firs examine he acual composiion of hree groups of analyss: he op-ier AA pool, he op-ier non-aa pool, and he lower-ier non-aa pool. We hen compare he forecas qualiy of newcomers in each pool relaive o he exising members of ha pool. Figure 2 shows he composiion of each pool. For each analys pool and each year, we divide he pool ino hree subsamples: exising, ransfers, and new hires. An exising analys in a pool is someone who belonged o he same pool in he previous year. The res are newcomers, whom we furher divide ino ransfers and new hires. A ransfer is an analys who did no belong o a paricular pool bu was in he daabase in he previous year. The remaining analyss are compleely new o he daabase and are considered new hires. 16

17 The op-ier non-aa subplo shows ha he fracion of exising analyss hovers around 70%. Exising analyss accoun for abou 75% of he lower-ier non-aa pool and abou 80% of he op-ier AA pool over ime, wih even smaller flucuaions. Imporanly, here is no sysemaic increase or decrease in any of he hree pools during he peak years, indicaed by he verical lines in he figure. These paerns sugges ha changing analys composiion is unlikely o be a main driver of our previous resuls. Nex, wihin each analys pool, we compare he accuracy of exising analyss and newcomers. Tables 9 and 10 presen uni-variae and mulivariae resuls of his es, respecively. Conrary o he predicions of he composiion effec, uni-variae resuls in Table 9 show ha, among op-ier non-aas (Panel A), newcomers (boh ransfers and new hires) are generally more accurae han exising analyss. Among newcomers, ransfers are in urn more accurae han new hires, probably reflecing more work experience. Thus, we find no evidence ha op-ier banks hire disproporionaely more new and inaccurae analyss during peak years. This means ha he relaive fall in accuracy among op-ier non-aas is unlikely o be driven by composiion effecs; insead, i is more likely o be driven by changing incenives among exising analyss. For he lower-ier non-aas (Panel B), newcomers are in general as accurae as exising analyss. 31 Among op-ier AAs (Panel C), here is no discernible difference in accuracy beween newcomers and exising analyss in eiher he whole sample period or in peak years. 32 Thus, neiher pool seems o experience changes in average accuracy in he direcion prediced by composiion effecs. 33 These uni-variae resuls are confirmed in mulivariae analyses repored in Table 10. Specifically, for each analys pool, we esimae he following regression equaions: Error i,,,n Volailiy = α + Type i, 1 + ln( coverage) i, 2 + ln (disance) i,,,n 3 + Firm Size, 4 + Leverage, 5 + (5) + YearDummie s + Firm Fixed Effecs + ε,, 6 i,,,n where Type i, is a dummy variable indicaing wheher an analys is an exising one or a newcomer, and wheher he is a ransfer or a new hire. The se of conrol variables is he same as before. Panels A, B, and C are for he hree analys pools, respecively. Consisen wih he uni-variae findings, Panels A and B show ha, among op-ier non-aas and lower-ier non- AAs, newcomers boh ransfers and new hires are significanly more accurae han exising analyss. Panel C 17

18 indicaes ha among op-ier AAs, exising analyss are more accurae han new hires. These resuls are exacly opposie o hose prediced by he composiion hypohesis. 34 In summary, we find no evidence ha he change in relaive accuracy among various analys pools documened in his paper can be explained by abnormal hiring of new analyss wih differen skill levels from exising analyss. This allows us o rule ou composiion effecs as an alernaive explanaion for our resuls and o conclude ha our resuls are driven insead by he changing incenives among exising analyss Specificaion wih Firm-year Dummies In he main specificaion of he model (equaions (3) and (4)), we conrol for unobserved (and ime-invarian) firm-specific characerisics ha affec earnings forecass by including firm fixed effecs. We separaely conrol for unobserved year-specific facors ha affec all earnings forecass made in a given period by including year dummies. In oher words, he regression is effecively run on ForecasQu aliy* i,,, n = ForecasQualiyi,,, n y y (6) where ForecasQualiy i,,,n denoe he research qualiy variables (accuracy or bias) as defined in equaions (1-a) and (1-b), and y and y denoe is mean for firm and year, respecively. One concern wih his approach is ha AAs may cover socks ha are paricularly easy o forecas (and no covered by oher analyss) in ho marke years, and ha his changing coverage drives our resuls. To check he robusness of our resuls agains his possibiliy, we consider an alernaive approach ha includes firm-year dummies. In oher words, insead of evaluaing relaive performance agains mean accuracy for firm, we now evaluae relaive performance agains mean accuracy for firm in year. The regression is effecively run on: ForecasQualiy * i,,, n = ForecasQualiyi,,, n y, (7) where y, denoes he mean accuracy (or bias) for firm in year. The framework in equaion (7) alleviaes he concern ha changing coverage by analyss could drive our resul because he relaive performance now becomes a funcion of who else covers firm in a given year. However, one implicaion of he new specificaion is ha observaions of firms for which only one forecas was made in year are effecively excluded from he analysis (absorbed by he firm-year dummy). More broadly, firm-year pairs ha are 18

19 covered by analyss from only one repuaion group are also effecively excluded from he analysis insofar as he repuaion variables and heir ineracion erms are concerned. 35 For he repuaion dummies and heir ineracion erms o have explanaory power, we require forecass for firm in year o conain forecass made by boh repuable and non-repuable analyss. To he exen ha many firms draw forecass made by only non-repuable analyss (simply because repuable analyss are fewer in number), we expec he explanaory power of repuaion dummies o go down wih his specificaion. However, he specificaion is useful in checking wheher our resuls are driven by wihin- or beween-firm-year variaions. We re-examine he effec of bank and personal repuaion in he dynamic seing by esimaing: ForecasQu Volailiy, aliy i, i,,, n = α + TopTier ln( coverage ) + ln( disance ) i, i,,, n + Firm Year Dummies ( TopTier 1 + Firm 4,, + ε Size i,, i,,, n * ln( UWVolume + Leverage 5, )) (8) and ForecasQu ln( coverage aliy ) i, i,,, n + ln( disance 3 = α + AA i, ) + ( AA 1 i,,, n 4 i, + Firm * ln( UWVolume Size, + Leverage 5 )) + 2, 6 + (9) Volailiy, 7 + Firm Year Dummies,, + ε i,,, n The resuls for equaions (8) and (9) are repored in Tables 11 and 12, respecively. Panel A of Table 11 shows ha op-ier analyss become significanly more accurae during ho markes for he whole sample. Bu his resul is saisically insignifican for boh sub-samples (AAs and non-aas) and sub-secors (ech and non-ech). Thus he new specificaion reveals weak evidence in suppor of bank repuaion as a miigaing force. In Table 12, we find ha op-ier AAs become significanly more accurae and less posiively biased compared o op-ier non-aas during marke peaks. This resul is primarily driven by he non-ech secor. Overall, AAs performance remains srong, even in ho markes. I is ineresing ha while we sill deec significan deerioraion of relaive performance by op-ier non-aas agains op-ier AAs (in Table 12, peraining o personal repuaion), we no longer deec heir deerioraion agains lower-ier non-aas (in Table 11, peraining o bank repuaion) under his alernaive specificaion. The significan aenuaion of he bank-repuaion resul likely occurs because lower-ier non-aas are more likely han any oher analys groups o cover socks ha are covered only by members of he same group, and, as discussed above, such observaions are effecively excluded in he analysis of he repuaion variables. In unrepored calculaions, we find 19

20 ha abou 1/3 of all firm-year pairs in our sample are covered only by lower-ier non-aas, whereas only 1% of all such pairs are covered only by AAs. Similarly, condiional on being covered by a leas one AA in a given year, he average number of analyss covering he sock during he year is 14.5, whereas condiional on no being covered by any AA analyss, he average number of analyss covering such a sock during ha year is only 3.9 (he difference is saisically significan a 0.01% level). 36 These saisics indicae ha far more firm-year pairs are represened only by lower-ier non-aas han by any oher repuaion group alone. 37 Effecively, we hrow ou more forecass made by lower-ier non-aas han any oher groups wih he new specificaion. Thus i is no surprising ha his specificaion disproporionaely reduces he explanaory power of he bank repuaion dummy in he comparison beween lower-ier non-aas and op-ier non-aas (Table 11). In conras, our main resuls concerning he relaive performance of opier AAs vs. op-ier non-aas remain robus o his specificaion (Table 12), because op-ier AAs end o cover socks ha are covered by many oher analyss (and he wihin-firm-year variaion is sill significan). In summary, he analysis here shows ha AAs cover socks ha are covered by many oher analyss, and heir relaive performance agains oher analyss improves during ho markes. The alernaive specificaion confirms our main finding and shows ha i is no driven by AAs covering hinly covered and easy-o-forecas socks during peak years. 5. Conclusion In he course of he recen scandals in sell-side research, some of he bes names on Wall Sree were singled ou and charged wih abuse of public rus. A relevan economic quesion is wheher repuaion, personal and insiuional, miigaes or exacerbaes he conflic of ineres problem ha is widely believed o be presen in sell-side research. In oher words, do repuable analyss ac less or more opporunisically when general empaion is a is peak? We ackle his quesion by sudying wheher he qualiy differenials in earnings forecass beween repuable and non-repuable analyss vary over ime wih he severiy of conflics of ineres, which rises during peaks of he new-issues marke when large amouns of underwriing-relaed compensaion are a sake. If repuaion only serves as a proxy for research qualiy and has no addiional disciplinary effec, hen he qualiy differenials beween repuable and non-repuable analyss should say consan over ime. If repuaion miigaes or exacerbaes he conflic of 20

21 ineres problem beyond being an indicaor of skill, hen he qualiy differenials would vary wih he severiy of he conflics of ineres. Our findings sugges ha personal repuaion is an effecive disciplinary device agains conflics of ineres, while bank repuaion alone is no. Specifically, he accuracy of non-aa analyss working a op-ier invesmen banks (i.e., analyss wih no personal repuaion bu wih a bank repuaion conferred upon hem) significanly deerioraes relaive o op-ier AA analyss or o lower-ier non-aa analyss during marke peaks compared o an average year. This resul holds wheher we measure research qualiy using forecas accuracy or bias. Since he payoff from generaing underwriing revenue is larges a hese op-ier invesmen banks (large underwriers) and during boom underwriing periods, his ime-varying paern srongly suppors he view ha conflics of ineres disor informaion ransmission by non-aas a op-ier banks during marke peaks. In conras, AAs working a op-ier banks (i.e., analyss wih boh a personal and a bank repuaion) become more accurae and eiher less posiively biased or exhibi no change in heir bias relaive o boh non-aas a op-ier banks and AAs a lower-ier banks during ho markes compared o normal imes. The conrasing resuls on op-ier non-aas and op-ier AAs sugges ha bank repuaion is less effecive han personal repuaion as a disciplinary device agains he conflic of ineres. Bank repuaion serves as an addiional disciplinary device only among AA analyss a op-ier banks, as hese analyss accuracy is found o improve even relaive o he AAs a lower-ier banks. We conecure ha he difference in he effeciveness of personal repuaion and bank repuaion as disciplinary devices may be explained by an agency problem. An individual analys s personal repuaion is human capial over which he has full righs, and i is porable. Thus here is no incenive-alignmen problem in preserving is value. An analys faces a much bigger agency problem in preserving his employer s repuaion, because he does no fully conrol his capial; he can free-ride in good imes or walk away in bad imes. High labor mobiliy may hus make i paricularly difficul for non-aa analyss o raionally care abou preserving his bank s repuaion especially during marke booms. The finding ha bank repuaion heighens AAs (bu no non-aas ) incenives o produce accurae and less biased research may reflec AAs sronger personal idenificaion wih he bank s fuure prosperiy. Anecdoal evidence suggess ha i is no uncommon for AAs o 21

22 become Managing Direcors or parners of he invesmen banks hey work for. We leave more in-deph research on hese quesions o fuure work. Overall, our conclusion ha personal repuaion is an effecive disciplinary device agains he conflics of ineres conradics he highly publicized image of a corrup sar analys. Insead, our findings poin a a differen culpri ha exacerbaes conflics of ineres: he combinaion of a lack of personal repuaion and he lure of large shor-erm profis. This combinaion appears o undermine he research qualiy of non-aa analyss a op-ier banks during marke peaks. As a resul, he posiive link beween performance and insiuional repuaion weakens in ho markes, while he link beween performance and personal repuaion srenghens in such imes. These findings have imporan policy implicaions. In he wake of he sell-side research scandals, some invesmen banks have moved away from using he AA ile as a crierion in evaluaing analyss performance. 38 In ligh of our finding ha no only is he AA ile associaed wih superior research qualiy on average, bu his superioriy also becomes more pronounced in boom years when conflics of ineres are severe, we argue ha his recen move o downweigh he AA ile in performance evaluaion may acually do a disservice o invesors, because analyss have fewer incenives o srive for and mainain research qualiy once hey are deprived of he rewards of being a sar analys. Insead, invesors migh be beer served by mandaory disclosures of boh insiuional and personal repuaion measures (as well as pas research qualiy hisory) so ha invesors can fully incorporae he imevarying link beween research qualiy and he wo ypes of repuaion ino heir evaluaion models. To his end, we suppor he mandaes of he Global Research Analys Selemen ha improve invesors access o hese ypes of informaion on individual analyss. 22