Disclosure Standards and Market Efficiency: Evidence from Analysts' Forecasts

Transcription

1 Dsclosure Standards and Market Effcency: Evdence from Analysts' Forecasts Hu Tong * November 6, 004 Abstract Snce the Mexcan and Asan crses, there has been a prolferaton of nternatonal ntatves, ncludng an ambtous standard-settng agenda, to encourage banks, frms and governments to dsclose more nformaton about ther fnancal affars. Ths paper studes whether and how such transparency standards affect nformaton accuracy and dsperson. I show that the mpact of transparency ntatves may be more lmted than often thought to the extent that publc dsclosure crowds out prvate nvestments n nformaton. I frst develop a theoretcal model of the ncentve to nvest n nformaton and the mpact of publc dsclosure. I then analyze a panel data set of stock market analysts forecasts for sxty countres for the perod I fnd that dsclosure standards enhance forecast accuracy drectly but at the same tme reduce the number of analysts per stock (the varable that serves as my proxy for prvate nvestments n nformaton). The net effect of dsclosure standards on forecast accuracy and dsperson thus ranges from weak to nonexstent. The mplcaton s that studes that fal to analyze ths crowdng out effect may exaggerate the mpact of dsclosure standards on market outcomes. * Bank of England, Threadneedle Street, London, ECR 8AH, UK. Emal: hu.tong@bankofengland.co.uk. I am partcularly grateful to Barry Echengreen for gudance, encouragement and patence. I would also lke to thank Perre-Olver Gournchas, Shachar Karv, Rchard Lyons, Danel McFadden, Maurce Obstfeld, Terry Odean, Andrew Rose, Emmanuel Saez, and partcpants n the semnars at UC Berkeley and the Bank of England for helpful comments. All remanng errors are mne. Ths paper represents the vews of the author and should not be thought to represent those of the Bank of England. The author gratefully acknowledges the contrbuton of Thomson Fnancal for provdng earnngs per share forecast data, avalable through the Insttutonal Brokers Estmate System. Ths data has been provded as part of a broad academc program to encourage earnngs expectatons research.

2 . Introducton The 990s saw a wave of ntatves, ncludng an ambtous nternatonal standardsettng agenda, desgned to encourage banks, frms and governments to dsclose more nformaton about ther fnancal affars. Ths movement ganed tracton followng the 997 Asan crss, whch many analysts blamed at least partly on the opacty of fnancal, corporate and government fnances n the regon (see for example Goldsten 998). The concluson drawn by organzatons lke the Internatonal Monetary Fund, the World Bank, and the Bank for Internatonal Settlements and governments lke those of the Unted States and other G-7 members was that greater transparency was a key to reconclng nternatonal captal moblty wth fnancal stablty. A range of nternatonal ntatves followed. The IMF adopted the General Data Dssemnaton System and the Specal Data Dssemnaton Standard for countres actve on nternatonal fnancal markets. The Fund and Bank establshed and undertook perodc Revews of Standards and Codes (ROSCs) to assess the adequacy of ther members complance wth the growng prolferaton of nternatonal transparency standards. The Fnancal Stablty Forum was created to further promote the promulgaton of standards and codes. These organzatons all argued that the mplementaton of nternatonally accepted economc, fnancal, and statstcal standards would help to strengthen domestc fnancal systems by encouragng sound regulaton and supervson, greater market dscplne, and more effcent and robust nsttutons, markets, and nfrastructure. They further asserted that standards would promote nternatonal fnancal stablty by facltatng better-nformed lendng and nvestment decsons, mprovng market ntegrty, and reducng the rsks of fnancal dstress and contagon. The logc for these ntatves has not gone undsputed. There s stll actve debate over what caused the Asan crss and specfcally about whether nadequate transparency was really to blame. Furman and Stgltz (998) and Morrs and Shn (00), among others, have rased questons about the conventonal wsdom and suggested that there are The Forum was frst convened n Aprl 999 at the ntatve of the G7 group. FSF s Compendum of Standards lsts the varous economc and fnancal standards that are mportant for sound, stable, and well-functonng fnancal systems, and hghlghts key standards whch t beleves to be deservng of prorty mplementaton.

3 crcumstances where greater transparency may be destablzng rather than stablzng. One possble reason for ths s that transparency may result n the provson of too much nformaton, actually ncreasng volatlty. Another s that when publc nformaton serves as a focal pont for the belefs of a group, nosy publc nformaton can precptate destablzng reactons. The generalty of these objectons, whch tend to be based on specal cases, remans an open queston. In addton, prevous authors treated prvate nvestments n nformaton as exogenous or predetermned, whereas n the real world agents decde how much to nvest n acqurng and processng nformaton. In ths paper I attempt to address both lmtatons of the extant lterature. I treat prvate nvestments n nformaton as an endogenous varable. And I conduct systemc tests desgned to determne the generalty of the argument that nternatonal ntatves ntended to promote the publc provson of nformaton may be counterproductve. For present purposes, I defne transparency as the precson of publc nformaton. Accordng to ths defnton, more transparency means that publc nformaton s more precse. I examne the effect of transparency by focusng on the nteracton between publc nformaton avalablty and prvate nformaton acquston. The analytcal framework s provded by a par of theoretcal models. The frst model analyzes the actons of a representatve agent who receves publc nformaton for free, collects prvate nformaton for cost, and mnmzes the gap between hs belef and the true underlyng value. It shows that greater avalablty of publc nformaton dmnshes out the agent s ncentve to assemble prvate nformaton. Dependng on the nformaton cost functon form, the net effect of transparency on nformaton accuracy can be zero (wth a lnear cost functon), postve (wth a convex cost functon) or negatve (wth a concave cost functon). Moreover, the degree of crowdng out can be very senstve to the form of the cost functon. Crowdng out between publc and prvate nformaton can be one to one (wth a lnear cost functon), less than one to one (wth a convex cost functon), or more than one to one (wth a concave cost functon). The second theoretcal model extends the analyss to a contnuum of strategcally nteractng agents. In ths model, when the publc nformaton s not precse, the crowdng out effect wll reduce the nfluence of publc nformaton on nformaton dsperson. 3

4 Owng to herdng among agents, the net nfluence of transparency on nformaton accuracy can be negatve even when the nformaton cost functon s lnear. Ths analyss thus shows that there s no presumpton that addtonal publc nformaton must be effcency enhancng. I then address the questons emprcally. The tests consder how nternatonal standards affect analysts forecasts of lsted companes earnngs, where the accuracy (dsperson) of these forecasts s used as a measure of nformaton accuracy (dsperson). Ths s n contrast to prevous emprcal work on transparency, whch has used soveregn bond spreads and country credt ratngs as measures of nformaton effcency. I argue that analysts forecastng effcency s a better measure of nformaton effcency, snce forecast accuracy drectly measures the gap between the antcpated and actual outcomes, whereas prevous measures do not. Moreover, the varable beng forecast - frms earnngs - s consequental; for equty prcng, frms' earnngs represent the core of fundamental value. Those dong the forecastng have a stake n dong t well: ther jobs are on the lne. Ths contrasts wth surveys of expectatons of macroeconomc varables n whch the people fllng out the surveys are typcally not beng judged personally for the adequacy of ther answers. 3 A varety of dfferent dsclosure standards and codes mght be analyzed. In ths study I focus on the Specal Data Dssemnaton Standard (SDDS) and Internatonal Accountng Standards (IAS). The Internatonal Monetary Fund establshed the SDDS n 996 wth the am of enhancng the operaton of nternatonal fnancal markets through the broader publc dssemnaton of economc and fnancal data. The SDDS consders four dmensons of data dssemnaton: the comprehensveness of the data (coverage, perodcty, and tmelness), publc access to ths nformaton, the ntegrty of the nformaton provded, and data qualty. The SDSS covers data for four sectors: the real sector (such as natonal accounts and forward-lookng ndcators), the publc sector (such as government revenue, spendng and debt), the fnancal sector (such as money supply, 3 Examples of surveys of expectatons on macroeconomc ndcators nclude the ASA-NBER survey n Keane and Runkle (990), Currency Forecasters Dgest n Chnn and Frankel (994), and Blue Chp Economc Indcators n Laster, Bennett and Geoum (999). 4

5 domestc credt, and nterest rates), and the external sector (such as nternatonal reserves and external debt). 4 The SDDS s ncluded n ths study because t s desgned to promote the tmely and accurate dsclosure of macroeconomc varables that affect frms profts and analysts forecasts. As stated n Relly and Brown (003) whch s wrtten as a practcal gude for stock analysts the forecastng of aggregate macroeconomc condtons s necessarly the frst step n forecastng frms earnngs. 5 Moreover, prevous emprcal studes, such as Chan and Hameed (00), fnd that stock analysts predomnantly process market-wde nformaton (rather than frm-specfc nformaton) n developng countres. 6 One nterpretaton s that t s more dffcult to collect frm-specfc nformaton n less developed countres, so that stock analysts rely more on economy-wde aggregates, whch consequently domnate ther forecasts. A related nterpretaton, due to Morck, Yeung and Yu (000), s that weak property rghts dscourage nformaton arbtrage n developng countres and thereby lmt the ncorporaton of frm-specfc nformaton nto the stock prces. 7 Consstent wth such conjectures, my fndngs n ths study confrm that SDDS mplementaton has sgnfcant drect mpact on analyst forecast accuracy and dsperson. Internatonal accountng standards (IAS) are ncluded n ths study because they are central to the process of encouragng frm-level dsclosure -- frms fnancal reports are publc nformaton and thus drectly affect analysts forecasts. The Internatonal Accountng Standards Board has been developng IAS snce the 980s. IAS cover a varety of dfferent areas of fnancal reportng, such as segment reportng and related party dsclosure. 8 A motvaton for the development and adopton of IAS s the need for relable and transparent accountng and fnancal reportng to support sound decson- 4 For more detals, see 5 Ths pont s dscussed on page 45 of Relly and Brown (003). 6 Chan and Hameed (00) arrve at ths fndng by examnng the relatonshp between the stock prce synchroncty and analyst actvty n the emergng markets. 7 Morck, Yeung and Yu (000) also fnd that stock prces move together more n developng countres than n developed countres, whch suggests that less frm-specfc nformaton s produced n emergng markets. 8 The Appendx II lsts varous rules n IAS. 5

6 makng by nvestors, lenders, and regulatory authortes. Snce the Asan crss, a sgnfcant number of addtonal IAS rules have been developed and promulgated. 9 A goal of ths study s to dscover how the SDDS and IAS affect stock analysts behavor and specfcally how they nfluence forecast accuracy and the number of analysts per stock. To ths end, I analyze forecasts for some 8,000 stocks ssued and traded n 60 countres n the perod The key fndngs are as follows. Frst, transparency standards have lmted benefts nsofar as standards dmnsh the ncentve for market partcpants to nvest n prvate nformaton. Instead of complementng prvate nformaton, publc nformaton resultng from transparency crowds out prvate nformaton. As publc nformaton becomes more accurate, the need for costly nvestment n the acquston of prvate nformaton declnes. Ths crowdng out reduces the effcacy of the publc standards. In the emprcal component of the paper, the specfc mechansm through whch ths crowdng out takes place s the ext of analysts. I show that the number of analysts per stock declnes when nternatonal standards are adopted, and that ths works to dmnsh the accuracy of average forecasts. Second, emprcal assessments of the mpact of transparency standards on market outcomes wll be based to the extent that they do not control for the mpact on the ncentves for market partcpants to nvest n prvate nformaton. When the number of analysts per stock s excluded from the explanatory varable set, t appears that adoptng IAS and meetng SDDS specfcatons have no sgnfcant effect on forecast accuracy and dsperson. However, when the number of analysts s ncluded, the drect effect of both IAS and the SDDS s to reduce forecast error and dsperson. Thrd, crowdng out of prvate nformaton by publc nformaton s more severe n developng than developed countres. An nterpretaton s that publc nformaton s n relatvely short supply n developng countres, and the degree of crowdng out declnes as publc nformaton becomes more precse. The contrbutons of ths paper may be summarzed as follows: In terms of theory, t consders not only the crowdng out effect between publc and prvate nformaton but 9 In 998, G7 Fnance Mnsters and Central Bank Governors commtted themselves to ensurng that prvate sector nsttutons n ther countres comply wth nternatonally agreed upon prncples, standards, and codes of best practce. They called on all countres that partcpate n global captal markets to smlarly commt to complance wth these codes and standards. 6

7 also how crowdng out affects the mpact of transparency on nformaton effcency. Whle prevous work has examned whether publc and prvate nformaton are substtutes or complements, authors usually focus on the mpact on market partcpants utlty or tradng prce, rather than on nformaton effcency, whch s the focus here. 0 In addton, ths paper seeks to determne not just whether publc and prvate nformaton are substtutes, but also the degree of substtuton. Fnally, I extend the model of Morrs and Shn (00) by endogenzng prvate nformaton producton and by studyng how the nteracton between publc and prvate nformaton affects nformaton dsperson n the equlbrum. On the emprcal sde, the contrbuton of the paper s to suggest a new approach to analyzng the effect of transparency. To my knowledge, ths s the frst paper to attempt to separate emprcally the drect and ndrect effects of transparency and thus to estmate the degree of crowdng out. In addton, the paper addresses an mportant polcy queston by examnng how and whether the adopton of transparency standards works n developng countres. The remander of the paper proceeds as follows. Secton II frst revews the lterature on transparency standards. Secton III then develops to theoretcal analyss. Secton IV descrbes the data, whle Secton V dscusses the emprcal models and fndngs. Secton VI summarzes the conclusons and polcy mplcatons.. Lterature Revew The nternatonal communty has been actvely engaged n promotng the desgn and adopton of transparency standards snce the early 990s, efforts that accelerated n the wake of the Asan Crss. The IMF has encouraged ts members to subscrbe to the Specal Data Dssemnaton Standard (SDDS), whch was ntated n early Aprl 996. Subscrpton carres a commtment to provde tmely nformaton to the IMF n 8 data categores coverng four sectors of the economy. Two years later the Fund publshed ts 0 Bushman (99) studes how the structure of the prvate nformaton market affects the demand for publc dsclosure. Instead of lookng at the effect on nformaton effcency, Bushman (99) looks at traders utlty functon, whch depends on the tradng prce, traders rsk tolerance, and fnal wealth after tradng. Lundholm (99), and Alles and Lundholm (993) take a smlar approach. Verrecha s (98) s one of the frst papers to study the crowdng out effect. Verrecha (98) focuses on the convex nformaton cost functon form, but does not study the degree of crowdng out and does not explctly analyze how nformaton effcency changes after the crowdng out. 7

8 Code of Good Practces n Fscal Transparency, whch sets out standards for the collecton and dssemnaton of fscal data and nformaton. The objectve of the code s to encourage a well-nformed publc debate about the desgn and results of fscal polcy, thereby makng governments more accountable. Smlarly, the IMF's Code of Good Practces on Transparency n Monetary and Fnancal Polces dentfes desrable data transparency practces for central banks and other fnancal agences. 3 The desgn of these codes rest on the prncple that monetary and fnancal polces can be made more effectve f the publc knows and understands the goals and nstruments of polcy and f central banks and fnancal agences make a credble commtment to meetng them. Other nternatonal organzatons have promulgated related standards. The Basel Commttee on Bankng Supervson publshed ts Core Prncples for Effectve Bankng Supervson; the Internatonal Organzaton of Securtes Commssons (IOSCO) outlned Objectves and Prncples for Securtes Regulaton; the Internatonal Accountng Standards Board (IASC) constructed Internatonal Accountng Standards (IAS); the Internatonal Federaton of Accountants proposed Internatonal Standards on Audtng; and the World Bank released the standards on nsolvency and credtor rghts. The IMF and the World Bank also examne a country's observance of the nternatonally recognzed standards and codes lsted above n ther Reports on the Observance of Standards and Codes (ROSCs). As of the end of October 003, more than half of the IMF's 84 member countres had completed one or more ROSC modules. Although the effort to encourage transparency ganed momentum n both the offcal and prvate sectors followng the Asan Crss, opnon s stll dvded on whether nadequate transparency was actually to blame for the crss. Stgltz (998) nssts that clams about transparency are just a form of blame shftng. There s no systematc evdence lnkng lack of transparency to economc crses. The last major bankng-cumcurrency crses were n Scandnava models of transparency. Even f there were, there s no evdence that corrupton or transparency were sgnfcant problems n all of the East The IMF adopted ths Code at ts ffteth meetng n Washngton, D.C., on Aprl 6, 998. For more detals see 3 The code was adopted by the IMF's Interm Commttee n September 999. The Code covers four categores for transparency: () clarty of roles, responsbltes and objectves; () open process for formulatng and reportng polcy decsons; () publc avalablty of nformaton of polces; and (v) accountablty and assurances of ntegrty. See for detals. 8

9 Asan countres affected by the crss. On the other sde, Fsher (998) clams that []n weak fnancal systems, excessve unhedged foregn borrowng by the domestc prvate sector, and a lack of transparency about the tes between government, busness, and banks have both contrbuted to the crss and complcated efforts to defuse t. Ferguson (998), a governor of the Federal Reserve System, echoes ths vew, argung that n Asan countres, Standards for the transparency and dsclosure of prvate fnancal nformaton were extremely lax. Once problems arose, t was dffcult for credtors to dstngush good rsks from bad, and ths caused them to wthdraw credt from all borrowers ndscrmnately. The debate has also found reflecton n academc research. Furman and Stgltz (998) argue that n a ratonal expectatons model where prce equals the expected value of the underlyng varable, more nformaton wll only consttute a mean-preservng spread and result n greater prce volatlty. Moreover, greater transparency wll flatten the dstrbuton of agents prors, reduce the dsperson n expectatons across ndvduals, and cause any nformaton that they receve to have a larger effect on ther belefs and hence on market condtons -- even when the nformaton n queston s just unfounded nose. In addton, followng Radner and Stgltz (984) t s easy to construct general equlbrum models wth mperfect markets n whch more nformaton and hence greater prce volatlty leads to lower economc welfare. Morrs and Shn (00) examne the mpact of transparency by focusng on the coordnaton motve arsng from strategc complementartes n agents actons. They show that when ndvduals have prvate nformaton, the welfare effect of ncreased publc dsclosure s ambguous. Specfcally, the greater the precson of prvate nformaton, the more lkely t s that ncreased provson of publc nformaton lowers socal welfare. As they argue, the detrmental effect of publc nformaton arses from the fact that the coordnaton motve entals people placng too much weght on the publc sgnal relatve to the weghts that would be used by the socal planner. Furman and Stgltz (998) and Morrs and Shn 9

10 thus (00) reach the same concluson: greater transparency may be counterproductve nsofar as the mpact of publc nformaton s too large. 4 Emprcal work to date has, however, provded lttle support to these objectons. Glennerster and Shn (003) look at spreads on the soveregn bonds of emergng markets and conclude that SDDS fulfllment reduces spreads, other thngs equal, especally for countres wth low ntal transparency. Chrstofdes, Mulder, and Tffn (003) also study whether nternatonal standards are relevant to country bond spreads and soveregn ratngs. They fnd that standards are ndeed relevant, especally when these cover accountng practces, property rghts, and data dstrbuton (SDDS subscrpton). Gelos and We (00) examne how transparency standards affect nternatonal portfolo nvestment; they fnd that emergng market equty funds hold fewer assets n less transparent countres, and that herdng among funds s somewhat less prevalent n more transparent countres. In Glennerster and Shn (003), the bond spread s defned as a country s EMBI portfolo yeld over the theoretcal US zero coupon curve, where the soveregn yeld s set to equate the total net present value of the soveregn rsk cash flows to zero. They contend that transparency may reduce nvestors perceved rsk and thus the bond spread. For example, f nvestors can observe the true level of nternatonal reserves, they may be able to ext before a potental devaluaton by montorng the level of reserves. In practce, however, the bond spread may also depend on many other factors that are not necessarly related to transparency, such as a country s nterest rate, stock market returns, nflaton rates, and exchange rate movement. Suppose a country s followng a predetermned devaluaton path. In ths stuaton, ts bond spread wll stll ncrease even though the devaluaton s pre-announced. Smlarly, f the U.S. zero-coupon curve shfts upward, an emergng market s bond spread can drop even though that emergng market has not mplemented any reform to mprove ts transparency. Therefore, transparency s not drectly related to bond spreads. Fund holdngs and soveregn ratngs all have a smlar 4 Note that these theoretcal models ether look only at publc nformaton wthout consderng prvate nformaton or treat prvate nformaton as exogenous/predetermned. In the next secton, we wll derve theoretcal models for the equlbrum demand and supply of prvate nformaton when publc nformaton s present. 0

11 problem: They can be greatly affected by macroeconomc varables that are not closely related to transparency. Ths study argues that transparency s related to the gap between the expectaton of an economc varable and ts true value. If the exchange rate s deprecatng and the nternatonal communty has enough knowledge of the deprecaton path, then the exchange rate polcy s transparent. However, f the nternatonal communty cannot predct the deprecaton path, then the exchange rate polcy s not transparent. If we can construct a varable that records the gap between nvestors perceved bond default rate and the actual bond default rate, ths varable wll better measure transparency s effect than the bond spread proposed by Glennerster and Shn (003). Expectatons tend to be unobservable and dffcult to measure. Therefore, ths paper looks at some economc varables related to market outcomes, such as frms earnngs per share (EPS). An advantage of focusng on ths varable s that analyst expectatons (as well as actual outcomes) are avalable. Moreover, these varables are closely related to fscal and monetary polces, nternatonal standards, and global captal flows. Snce frms revenues, costs, and profts are affected by macroeconomc polces, more transparent polces can help stock analysts and nvestors forecast frms profts more accurately. If frms follow Internatonal Accountng Standards, then nvestors can glean more nformaton from frms balance sheet, ncome, and cash flow statements. In ths way, nvestors gan access to resources that allow them to accurately forecast frms futures. Economc varables such as EPS can also play a crucal role n nternatonal nvestors decsons on whether to purchase a frm s stock or set up a jont venture. Gven ths, n ths paper I ask whether transparency standards help nvestors mprove ther forecasts. The accuracy and dsperson of forecasts wll offer drect evdence on the nfluence of such standards. A few artcles n the subfeld of accountng have prevously nvestgated how transparency affects forecasts. Lang and Lundholm (996), for example, examne the relatons between analysts' forecasts and the dsclosure practces of frms. Focusng on U.S. frms, they fnd evdence that forecasts are more accurate and less dspersed for frms wth more nformatve dsclosure polces. Hope (003) fnds the same results by analyzng twenty-two countres. However, these studes focus on developed countres and

12 accountng rules, rather than on emergng markets and other transparency ndcators such as SDDS mplementaton. Furthermore, ther data sets cover the years before 996, whereas the nternatonal communty began to place heavy emphass on transparency after the 997 crss. The accountng lterature has also examned the mpact of the number of analysts per stock on forecast accuracy. The model mplctly nformng these studes s one n whch the equlbrum number of analysts s determned by supply and demand. Lang and Lundholm (996) argue that f t s less costly to receve nformaton from a frm than from other sources, then addtonal transparency may shft the supply curve for analysts to the rght. However, the effect of addtonal transparency on the demand for analysts s more complcated and reles on the role of analysts. If analysts are effectve nformaton ntermedares nformaton flows frst from the frm to the analysts, who then process t and transmt t to the captal market then more frm-provded nformaton gves analysts more resources to dstrbute valuable reports and s essental to the performance of ther task. In ths stuaton, the demand for analysts wll rse. However, f analysts are nformaton provders who need to compete wth frm-provded dsclosures made drectly to nvestors, then more frm-provded nformaton wll substtute for analysts reports. In ths case, the demand for analysts wll declne. Lang and Lundholm (996) and Hope (003) fnd that more frm dsclosure wll ncrease analyst followng. Hope (003) further fnds that as analyst followng rses, forecast accuracy ncreases. But nether Lang and Lundholm (996) nor Hope (003) provdes explct theoretcal models of the mpact of frm dsclosure on analyst followng. Ths paper wll provde theoretcal models to explan how greater access to publc nformaton (through mechansms such as hgher levels of frm dsclosure) affects market partcpants ncentves for prvate nformaton (such as the demand of analyst followng). 3. Theoretcal Models Ths secton presents theoretcal models to explan the crowdng out effect between publc and prvate nformaton, and to nvestgate how the crowdng out affects nformaton accuracy and dsperson.

13 3.. Basc Model In ths subsecton, a model for the optmal producton of prvate nformaton s derved. The frst supposton s that there s a sngle agent who has the followng payoff functon: u ( a, θ ) ( a θ ) where θ s the unobservable true value of the underlyng state, and a s agent s belef of θ. The closer a s to θ, the better for agent. Agent receves both free publc nformaton and collects prvate nformaton y = θ +η x = θ + ε where η s normally dstrbuted, ndependent of θ, wth mean zero and precson α (.e., Var[ η] ). ε s normally dstrbuted, ndependent of θ and η, wth mean zero and precson λ (.e., ). α and λ are known to agent. Var[ ε ] To maxmze hs expected payoff E [ u ( a, θ )], agent wll choose a accordng to the followng Bayesan updatng rule 5 : αy + λx â =. α + λ The maxmzed E [ u ( a, θ )], E [ u ( aˆ, θ )], s αy + λx E[ u ( aˆ, θ )] = E[( α + λ =. α + λ θ ) As one can see, when prvate nformaton becomes more precse (.e., when λ goes up), the expected payoff to agent ncreases as well. However, there s cost assocated wth collectng prvate nformaton. The cost s assumed to be a lnear functon of λ : ] 5 Bayesan updatng rules are explaned on page 08 of Lyons (00). 3

14 C = φλ where φ s a postve constant. Accordng to the cost functon, as prvate nformaton becomes more precse, the total producton cost of obtanng prvate nformaton ncreases. The agent then needs to balance the beneft and cost of gettng more precse prvate nformaton. Specfcally, he optmally chooses λ by maxmzng the followng payoff, whch ncludes both the beneft and the cost of prvate nformaton: V ( λ) = E[ u ( aˆ, θ )] C = φλ. α + λ The frst order condton for maxmzng V (λ) mples that he wll choose The equaton for λˆ ˆ λ = α φ. shows that as publc nformaton becomes more precse ( α ncreases), the demand for prvate nformaton accuracy drops. In partcular, the crowdng out effect between α and Evaluated at λˆ, λˆ s one-to-one. E [ u ( aˆ, θ )] = = α + ˆ λ whch does not depend on α. Note that n ths model, E [ u ( aˆ, θ )] can be nterpreted as how accurate agent s forecast of θ s. Because E [ ( ˆ, θ )] does not depend on α, the precson of publc nformaton wll not affect agent s forecast accuracy for θ. In ths basc model, the one-to-one crowdng out between α and u φ a suggests that the ˆ λ crowdng out effect wll be the same no matter how large α s. That s to say, =. α Emprcal results of ths study, however, fnd that the crowdng out effect s larger when α s smaller. 6 ˆ λ That s to say, > 0. Next, dfferent functon forms for the cost of α producng prvate nformaton are examned. Wth the nformaton cost functon λˆ 6 Ths study fnds that the crowdng out effect s bgger n developng countres than n developed countres. Developed countres tend to have hgher publc transparency (hgherα ) than developng countres. Ths suggests that the crowdng out effect decreases as α ncreases. 4

15 C = φλ s ˆ λ ˆ λ, when s>, the crowdng decreases as α goes up ( < 0, > 0 ), and the α α E[ u ( ˆ, )] net effect of α on nformaton accuracy s postve ( a θ > 0 ). However, when α ˆ λ ˆ λ s<, the crowdng ncreases as α goes up ( < 0, < 0 ), and the net effect of α on α α E[ u ( ˆ, )] nformaton accuracy s negatve ( a θ < 0 ). α 3.. Model wth Strategc Interacton The basc model outlned above studes a sngle representatve agent. What f there s more than one agent n the economy? Can we say anythng about the dsperson of belefs across multple agents? To address ths queston, we need to move from a sngleagent model to a model that ncludes a group of agents. Prevous work has placed partcular emphass on the herdng and peer effects among nvestors. 7 In the present study, the way n whch publc and prvate nformaton nteract when herdng effects exst, and how the nteracton affects nformaton accuracy and dsperson, are examned. The startng pont for ths multple-agent model s the work of Morrs and Shn (00), who develop a strategc nteracton model to study the socal values of publc nformaton. Morrs and Shn (00) provde a useful model for the followng three reasons: Frst, ther model s a smple statc coordnaton model, and does not depend on the fne detals of the tmng structure that exsts n the prevous herdng lterature. Second, ther model ncludes both publc and prvate nformaton. Thrd, ther model has a unque equlbrum, so s sutable for comparatve analyss. However, Morrs and Shn (00) treat prvate nformaton acquston as exogenous or pre-determned, and they do not explctly study nformaton dsperson. Morrs and Shn s (00) model s adapted n two ways: Frst, ther model s extended by endogenzng the producton of prvate nformaton as dependent on the precson of publc nformaton. Second, how the crowdng out between publc and prvate nformaton affects nformaton dsperson s examned. When publc nformaton s not 7 Prevous works on herdng and nformaton cascades nclude Banerjee (99), Bkhchandan, Hrshlefer and Welch (99) and Cao and Hrshlefer (000). 5

16 precse, I fnd that the crowdng out effect wll shrnk the nfluence of publc nformaton on nformaton dsperson. Moreover, owng to strategc nteracton among agents, the net nfluence of publc nformaton precson on nformaton accuracy can be negatve even when the nformaton cost s lnear. We now start wth the assumptons artculated n Morrs and Shn (00). Suppose there s a contnuum of agents, ndexed by the unt nterval [0, ]. Agent chooses an acton to maxmze hs payoff functon: a u ( a, θ ) ( r)( a where r s a constant, wth 0 < r <, and L 0 θ ) r( L L) ( a a ) dj, L L dj j The payoff functon for ndvdual has two components. The frst s a standard quadratc payoff n the dstance between hs acton a and the unobservable underlyng state θ. The second component s the beauty contest term, whch provdes the motvaton for herdng. s ncreasng n the average dstance between agent s acton and the acton profle of the entre populaton. The parameter r gves the weght on the second-guessng ncentve. L Agent wll receve both publc nformaton 0 j y = θ + η, and prvate nformaton x = θ + ε. η s normally dstrbuted, ndependent of θ, wth mean zero and precson α. ε s normally dstrbuted, ndependent of θ, η and ε j, wth mean zero and precson β. Morrs and Shn (00) argue that when β s n a certan range, the socal welfare can decrease as α ncreases, where the socal welfare s W = ( a θ ) d. 0 In ths study, I defne nformaton accuracy as E[( ˆ θ ) ], whch s the expected a value of the socal welfare W. Informaton dsperson s defned as Dsperson ( E[ ( a a) d ]) where a s the average of a. Appendx I derves

17 ( r) β Dsperson = β ( r) + α whch suggests that as publc nformaton precson α ncreases, nformaton dsperson wll decrease. However, Morrs and Shn (00) treat β as exogenous. I wll extend ther model by endogenzng the producton of prvate nformaton. Moreover, the producton cost functon for prvate nformaton s assumed to be C = φβ. Appendx I shows that ncreased publc nformaton precson wll crowd out the ncentve for acqurng prvate nformaton. The optmal prvate nformaton precson ( βˆ ) n the equlbrum s Therefore, unless ˆ α β =. φ r r = 0, the crowdng out s greater than one-to-one. In the equlbrum, agents nformaton accuracy s E[( aˆ = αrφ ( r) θ ) ] φ. In ths way, hgher publc nformaton precson wll actually decrease nformaton accuracy. Smlarly, n the equlbrum, nformaton dsperson becomes Dsperson = 0.5 αφ φ r whch mples that ncreased precson of publc nformaton wll decrease nformaton dsperson. When r α <, the effect of α on dsperson wll be smaller than f no φ crowdng out exsted. However, when r α >, the effect of α on dsperson wll be φ bgger than f no crowdng out were present Testable Implcatons To test the theoretcal results derved above, the followng three emprcal models are proposed. The frst model s 7

18 E [( a θ ) ] = π 0 + π α + π β( α) + ν where E[( θ ) ] s nformaton accuracy, and ν s a dsturbance term. The basc a theoretcal model suggests that E[( a θ ) ] wll not depend on the precson of publc nformaton when the cost functon s lnear. Therefore, the frst null hypothess s β π + π = 0. α The second model s 0.5 ( E [ ( a a) d ]) = π 0 + π α + π β( α) + ν 0 where 0.5 E [ ( a a) d ]) s nformaton dsperson, and 0 ( ν s a dsturbance term. The herdng model suggests that π can be ether postve, negatve, or zero, dependng on r r whether α > or α <. The second null hypothess s then 0 φ φ π =. The thrd emprcal model s β + = π 30 + π 3α ν 3 where ν 3 s a dsturbance term. Both the basc model and the herdng model suggest that the precson of prvate nformaton ( β ) wll be smaller when α s larger. The thrd null hypothess s π 3 < 0. The basc model also suggests that wth dfferent cost functon forms, the crowdng out effect can be smaller/the same/bgger as α becomes larger. To π test ths pont, the fourth null hypothess wll be 3 = 0. α I now test these four hypotheses by examnng data on the earnngs forecasts of stock market analysts. I use analyst forecasts for the followng reasons. Frst, nternatonal standards and codes are desgned to help global nvestors understand local markets and to facltate cross-board captal flows. Analysts forecasts consttute an mportant channel through whch nternatonal nvestors acqure nformaton about local stock markets. If the accuracy of analysts forecasts does not mprove after local frms mplement transparency reforms, then there may be reason to doubt the clam that such reforms wll 8

19 sgnfcantly enhance the qualty and quantty of nformaton avalable to nternatonal nvestors. Second, the accuracy of analysts forecasts s closely related to the transparency of government polcy and nformaton about the publc sector fnances. To forecast a frm s earnngs (profts), t s necessary to understand the economc stuaton of the country n whch t operates. If the country wll be n recesson, then the frm s sales are lkely to declne, reducng ts profts. If the government plans to deprecate the currency, then the profts for exportng frms are lkely to go up, whle the profts for mportng frms are lkely to go down. Thrd, and crtcally for present purposes, the stock analyst data base records the ext and entry of analysts for a certan stock, whch helps us to estmate the crowdng effect by lookng at the analyst number for a stock. 4. Data and Varables Ths secton descrbes my data sources and defnes the dependent and explanatory varables. 4.. Data Descrpton Stock analyst forecastng data were obtaned from the Insttutonal Brokers Estmates System (IBES) data base. The IBES data base contans analyst-by-analyst estmates for companes n 60 countres for more than 5 years. Varables nclude company name, data type ndcator (e.g., earnngs per share), forecast perod ndcator, broker code, analyst code, estmate date, estmate value, actual value report date, actual reported value, currency, ndustry name, 5-year growth of the measure, 5-year stablty of the measure, stock prce, and shares outstandng. The accountng research mentoned earler, such as Lang and Lundholm (996) and Hope (003), has also reled on IBES. Created n the 970s, IBES now covers over 8,000 companes n 60 countres more than any other source. In addton, ts lst of contrbutors ncludes more than 7,000 fnancal analysts from over,000 nsttutons. Because of the broad coverage of IBES, t s commonly assumed that the number of analysts covered by IBES for a stock s the actual number of analysts who follow that 9

20 stock. However, before adoptng ths assumpton, some of the characterstcs of the IBES database should be examned to determne whether they affect the results. Accordng to Rajan and Servaes (997), IBES collects all forecasts from a group of analysts who agree to provde ther estmates n return for free use of IBES products or data. Some bases may therefore enter nto IBES's choce of analysts. For example, t may be easer for IBES to obtan forecasts from analysts of the major brokerage houses than from those of small brokerages n remote areas. The former, n turn, may be more lkely to gnore small frms tradng on regonal exchanges. In ths case, there are two reasons why a specfc frm may not be followed: Ether analysts deem the frm unworthy of beng followed, or IBES does not receve forecasts from the analysts most lkely to follow the frm. Rajan and Servaes (997) focus on the analyst s followng of IPOs, employng Heckman's (979) selecton model to correct for the selecton bas n the case where there s no record of analyst followng for an IPO. Ths selecton bas problem s less relevant n the case of my study, whch focuses on the forecast accuracy for a stock already recorded n the IBES database, rather than on whether a stock wll be covered by IBES. Moreover, I am concerned wth how forecast accuracy and dsperson may affect domestc and nternatonal nvestors behavor. For many nvestors, IBES real-tme and hstorcal forecastng data are mportant sources for forecastng data, especally for nternatonal stocks. If one analyst s forecast s not ncluded n the IBES database, then hs/her forecast has much less chance of beng known and of affectng nvestors market expectatons and behavor. Data on SDDS fulfllment were obtaned from the webste of the IMF, whch records the dates when a country subscrbed to the SDDS, began postng data, and met all SDDS requrements. Ths study focuses on the date at whch a country met all requrements. When these are satsfed, a country s sad to have fulflled the requrements of the SDDS. The IAS adopton varable s constructed from the 000 and 00 GAAP surveys, whch benchmarked natonal accountng rules aganst IAS. These surveys asked large accountng frms n more than 60 countres to benchmark ther local wrtten requrements aganst approxmately 80 accountng measures, focusng on standards (both IAS and natonal) n force for the fnancal reportng perod endng 3 December 000 or 00. The surveys dentfed, for the selected accountng measures, nstances n whch a 0

21 country would not allow or would not requre IAS treatment. The 00 survey also dentfed progress that had been made n achevng convergence n the years 000 and 00, as well as possble progress n 00. I also referred to to dentfy when a country had adopted certan IAS rules, snce ths webste traces some countres as far back as 997. In what follows I examne analysts forecastng data for stocks n 60 countres between 990 and 00. Stocks from Canada, Japan, the Unted Kngdom, and the Unted States are not ncluded, because these four markets have far more stocks than other markets. If ncluded, these markets mght domnate the estmates, makng t dffcult to estmate the effects of transparency on smaller economes. 8 Markets n whch fewer than 0 stocks were covered, typcally very small countres such as Slovena and Latva, are also excluded from the sample. 4.. Varables 4... Dependent varables Number of analysts Analysts sometmes provde multple-perod forecasts of earnngs per share. For example, IBM s annual accountng year ends n December 3. For accountng year 000, analysts may gve estmates back n 998 (n the case of a three-year forecastng horzon), 999 (a two-year forecastng horzon), and 000 (a one-year forecastng horzon). In the emprcal works that follows I focus on the one-year-ahead forecasts. Thus, for every accountng year of IBM, the number of analysts who have gven forecasts for the corporaton for that year s calculated. Forecast dsperson and error Forecast dsperson s based on the sample standard devaton of analysts forecasts for a stock n a gven accountng year. To facltate cross-country comparson, the sample standard devaton dvded by the absolute value of the mean earnngs forecast s used as the measure of forecast dsperson. Forecast error s defned n the followng manner: 8 Another possblty s to randomly select some stocks from these three countres. However, IBES tends to cover bg rather than small stocks, so cauton s requred n settng the selecton crtera.

22 Forcast Error( t) = Estamted Earnngs( t) Actual( t) Actual( t) For the accountng year endng n December, some analysts provde forecasts n March, and some provde these n November. Intutvely, the estmates offered n November wll have smaller forecast errors. To control for ths forecastng-tme effect, I regress the forecast error on the tme dfference from the forecastng month to the end of the accountng year. 9 I do ths usng a pooled regresson for all stocks over the whole sample perod. For each stock per accountng year, the mean of the resduals from the above regresson s calculated as the average forecast error Transparency varables IAS adopton I base my measure of IAS adopton on accountng rules that are adopted by at least 0% and no more than of 80% of the countres n the sample. If all countres (or nearly all countres) are adoptng or not adoptng a certan accountng rule, then there s nsuffcent varaton across countres to analyze ts effects; ths s why I dsregard rules adopted by fewer than 0 per cent or more than 80 per cent of sample countres. I frst construct a dummy varable for each accountng rule, whch equals when that rule s adopted. The square root of the sum over these adopton dummes s then calculated to construct a sngle composte ndex of IAS adopton. The IAS adopton data cover the perod from 997 through 00. Before 997, I do not have nformaton on when a country started usng a certan IAS rule. Although some IAS rules were developed n the early 990s, but the GAAP surveys and IASPLUS webste together cover only the perod To gve a flavor of the resultng composte ndex, Table presents the values for 00. SDDS fulfllment My measure of SDDS fulfllment equals when a country meets all the requrements of the SDDS, and zero otherwse. Note that we are estmatng the average forecast error 9 Note that the forecastng error so far s defned as a postve number. 0 Note that there can be multple estmates and thus resduals for each stock per accountng year. These accountng rules are IAS, IAS, IAS7, IAS8, IAS, IAS4, IAS6, IAS7, IAS8, IAS, IAS4, IAS7, IAS8, IAS33, and IAS35.

23 for each stock per accountng year. The data base tells me the date when the frst analyst s estmate s provded for a gven accountng year for each stock. (Usually ths wll be a date n March or Aprl.) If the SDDS s fulflled at that tme, I record SDDS as for that accountng year; otherwse, I record 0. Of course, the SDDS can be mplemented n the mddle of the year, after the frst estmate of earnngs per share has been gven. Ths may rase some concerns about a tme dscrepancy,.e., t n the regresson model s n the year frequency, but the mplementaton of the SDDS s n daly frequency. As a robustness check, I replace the average forecast error over a year n the regresson model wth the forecast error for each sngle estmate. In ths case the SDDS dummy s constructed by comparng the SDDS fulfllment date wth the forecastng date. Ths robustness check yelds very smlar results for SDDS mplementaton. Another robustness check s performed: SDDS s recorded as f SDDS mplementaton s completed before July st, and as 0 otherwse. Agan, the emprcal results for the SDDS are very smlar Control varables Industry dummes. There are eleven ndustres, e.g., fnance and consumer products. Frm sze. Frm sze s defned as the log value of the market captalzaton, whch s the stock prce (n U.S. dollar terms) multpled by the shares outstandng. Snce the stock prce vares over the sample perod, medan frm sze over the sample perod 990 to 003 s used. In ths way, the frm sze varable wll be the same over the sample perod for every stock. Earnngs surprse. Ths s the absolute value of the percentage change n the actual earnngs per share (n dollar terms). The formula for the earnngs surprse s Earnngs surprse( t) = Actual( t) Actual( t ) Actual( t) The earnngs surprse s ncluded to control for the fact that when earnngs surprse s hgh, forecast error s also lkely to be hgh. The defnton of the earnngs surprse here s dfferent from the tradtonal defnton, where ths varable s defned as the unexpected shock to a varable nstead of the change of 3

24 that varable over tme. However, the defnton here s consstent wth the lterature on analyst forecast errors. Subsequently, n the secton on senstvty analyss, I drop the earnngs-surprse varable and show that the results are lttle changed. Loss. Ths s a dummy varable equalng one when a frm has negatve earnngs per share n the current perod and zero otherwse. In the emprcal models that follow, Loss s lagged by a year. Tme dummes. Year dummes are used to control for the extent to whch global stock markets are dong well or badly n a gven year. Macroeconomc varables, such as GDP per capta, nflaton rate, GDP growth rate, and the number of lsted domestc stocks n a country. In the estmates that follow I use one-year lags of these varables. 5. Emprcal Models and Results The frst emprcal model, of the determnants of forecast errors, s Mean Forcast Error jt = ω + ω Transparency + ω Loss 6 j, t jt + ω AnalystNumber 7 + ω Sze + ω Industry + ω Surprse 3 jt 4 + ω Tme + ω Macro 8 t 9 5 jt jt + ε, jt where, j and t stands for stock n country j at tme t. Moulton (990) argues that, when estmatng the effects of aggregate varables (such as IAS adopton) on a mcro dataset, dsturbance terms ε, jt may not be ndependent over the group where the value of the aggregate varable s defned as the same. Therefore, robust standard errors, wth clusterng consdered, wll be estmated to control for the possble nterdependence of ε,jt across j. The second model studes the dsperson of analyst forecasts: Forecast Dsperson jt = ω + ω Transparency + ω Loss 6 j, t + ω AnalystNumber 7 jt + ω Sze + ω Industry + ω Surprse 3 jt 4 + ω Tme + ω Macro 8 t 9 5 jt jt + ε, jt Fnally, the thrd model studes the analyst followng (.e., the number of analysts): Analyst Number jt = ω + ω Transparency 3 + ω Loss 35 3 j, t jt + ω Sze + ω Industry + ω Tme + ω Macro 36 t jt + ε 3, jt 4

25 Analyst number s a proxy for market partcpants nvestments n acqurng prvate nformaton ( β n the theoretcal model), whle Transparen cy s the proxy for the precson of publc nformaton ( α n the theoretcal model). These three models are estmated separately for IAS adopton and SDDS mplementaton. The emprcal results are as follows. 5.. IAS Adopton Benchmark results for IAS adopton are n Table, 3 and 4. Table records the drect and net effects of IAS adopton on forecast error n developng countres. Table 3 records the effect of IAS adopton on analyst followng n developng countres. The sample s then expanded to nclude developed countres to study how the crowdng out effect changes wth a country s developng level (Table 4). Snce IAS adopton does not change over tme for most countres between 997 and 00, the panel regresson estmates reported n these three tables are equvalent to pooled cross secton analyss. The results, n Table, show that IAS adopton drectly reduces forecast error. The estmated value of the drect effect, at -.8, s sgnfcantly dfferent from zero at the 9% confdence level (frst column). Ths s consstent wth the vew that tmely and comprehensve accountng reports gve analysts more accurate nformaton on a frm s operaton. The medan value of forecast error for each stock s 0.7. Therefore, f the composte IAS adopton ndex goes up by one, the forecast error wll drop by 40%. 3 Another result derved from Table s that hgher analyst followng wll decrease forecast error. The coeffcent on analyst number (square root) s -0.3, dfferent from 0 at a 0.% sgnfcance level. As the number of analysts (square root) ncreases by, the forecast error wll drop by 0.3, whch s 33% of the orgnal forecast error. In the sample, the number of analysts per stock vares from to 8. Because an addtonal analyst where there was prevously only one s very unlkely to have the same nfluence as where there were already 0 analysts, the square root s used to control for the possble Standard errors here are adjusted accordng to Moulton (990). 3 For the developng countres, the composte ndex of IAS adopton s.73 at the 5% quantle and 3.3 at the 75% quantle. 5