Analysts' Forecastscapital markets research on earnings and stock prices. In recent years,...

COMMENTARYKatherine Schipper

onAnalysts' Forecasts

INTRODUCTIONThe purpose of this commentary is to draw

together the results of some research on ana-lysts' eamings forecasts and analysts' decisionprocesses and to discuss their implications.^ Iwill focus first on how analysts' eamings fore-casts are used in empirical accounting re-search on the relation between eamings andstock prices and second on what is known bothabout the statistical properties of these fore-casts and about the decision processes of ana-lysts and the incentives they face.

Overall, these remarks are intended tosuggest that a focus on analysts primarily assources of earnings predictions seems undulylimiting. Just as we study auditor decisionprocesses because of the intimate relation be-tween auditing and accounting on thepreparer side, it makes sense to study ana-lyst decision processes because analysts areamong the primary users of financial account-ing information. (Indeed, this user focus ap-pears to be the motivation for at least part ofthe judgment/decision making literaturewhich uses analysts as subjects.^) Given theirimportance as intermediaries who receive andprocess financial information for investors, itmakes sense to view analysts—sophisticatedusers—as representative of the group to whomfinancial reporting is and should be addressed.Under this perspective, accoimtants have apolicy-based stake in understanding how ana-lysts actually use financial information.

In addition, just as some research intoauditor judgments has implications for theeducation of future practicing professionalauditors, so too could we draw on results ofresearch into analyst decision processes toshape the education of future users of account-ing disclosiires, again represented by finan-cial analysts. There is no logical reason whywe should focus heavily on auditor judgments

and decision processes and take relativelylittle interest in the judgments of analysts, inhow they process information and in how we(as educators) can better prepare them fortheir careers.^ In addition, a clear understand-ing of how financial accounting information

Katherine Schipper is Professor of Accounting atthe University of Chicago.

I appreciate useful conversations with MaureenMcNichols and Mark Zmijewski, and comments on anearlier draft from Jeffrey Abarbanell, Stanley Biggs,Marinus Bouwman, Lawrence Brown, John Burton,Barry Lewis, Thomas Lys, Laureen Maines, MaureenMcNichola, Richard Mendenhall, Patricia O'Brien, Dou-glas Schroeder, and Mark Zmijewksi.

'This commentary is not intended to provide a reviewof the literature on analysts' forecasts. Papers dis-cussed are chosen merely to illustrate certain points,many or all of which might appear in other publishedand unpublished research as well. For a review of theliterature to 1983, see D. Givoly and J. Lakonishok,"Properties of Analjrsts' Forecasts of Earnings: A Re-view and Analysis of the Research," Joumal of Account-ing Uterature 3,1984, pp. 117-152.

'For a review and analysis of the judgment/decisionmaking literature which focuses on individual users(inclu<£ng, for example, student subjects, lending of-ficers and analysts), see L. Maines, "Judgment andDecision-Making Research on External Users at theIndividual Level," working paper. Duke University,July 1991.

^While no logical reason precludes detailed investiga-tions of financial analysts, certain logistical and his-toarical conditions make such research relatively moredifBcult than research using auditors. First, some firmssimply preclude their analysts from participating inquestionnaire studies. Second, the absence of continu-ing education requirements means that analysts neednot gather periodically (as auditors must) for trainingaway fTom their day-to-day responsibilities. Third, re-searchers in accounting (as well as other areas) havenot so far succeeded in convincing employers (^finan-cial analysts that time and energy should be allocatedto participation in completing experimental tasks de-veloped for academic research. Finally, to the extentthat accounting academics have prior work experienceas auditors, and not as analysis, they may be moreattuned to research which bears more directly on au-diting.

106 Accounting Horizons/December 1991

is actually used should improve our ability toteach financial accovinting courses to all stu-dents, including those planning careers thatinvolve the preparation (as opposed to the use)of financial statements.

This commentary has several specific ob-jectives. One is to consider apparent motiva-tions for first, our uses of analysts' earningsforecasts as inputs into empirical research onthe earnings-price relation, and second, ourinvestigations of the forecasts' properties. InHght of this consideration, a second objectiveis to lay out some questions that may serve tobroaden our investigations. A third objectiveof this commentary is to point out what ap-pear to be neglected relations between judg-ment/decision making research on analysts'decision strategies and capital markets-basedresearch that either focuses on or uses ana-lysts' forecasts. I believe that accounting re-search could draw on these relations to de-velop more insightful investigations of howfinancial information is processed by sophis-ticated users. The results of such investiga-tions would have immediate applicability tothe standard-setting process, given the userorientation articulated by the IVueblood Com-mittee and the Financial Accounting Stan-dards Board.

Relatedly, I will discuss how a direct con-sideration of what decisions and judgmentsanalysts actually make and what incentivesthey face might help to shape fiiture investi-gations. Specifically, the focus of acco\intingresearch on analysts' forecasts is essentiallya focus on just one part ofthe total responsi-bilities of a financial analyst; the questionarises, how would we view these forecs^ts andtheir properties if we studied them as an in-put to the ultimate analyst juc^ment—^whatrecommendation to make on a stock.

In considering the role of earnings fore-casts in shaping the analyst's recommenda-tion about a stock, it sometimes makes senseto distinguish between buy-side and sell-sideanalysts. While both make recommendationsabout which stocks to buy, sell and hold, sell-side analysts are the primary producers ofearnings forecasts. Buy-side analysts tend tobe employed by money management firms or

institutional investors while seU-side analyststend to be employed at broker/dealer firmsthat serve individual and institutional inves-tors. Thus the buy-side analyst may in fact bea user of sell-side analyst reports as one in-put into his own decision process. Clearly,some but not all of the tas]<^ are similar forbuy-side and sell-side analysts; because theiremployers are so different, they most likelyface dissimilar incentives. For the most part,capital markets research discussed in thiscommentary deals with sell-side analysts,while judgment/decision making research usesbuy-side analysts as subjects. In some experi-mental research, I was not able to tell fromthe context whether the analyst-subjects werebuy-side or sell-side analysts. However, thestock recommendation decision is common toboth."

This commentary contains six sections.The following section discusses a common useof analysts' earnings forecasts in capital mar-kets research; the third section comments onempirical investigations ofthe statistical prop-erties of analysts' forecasts; the fourth sectionplaces the forecasting task within a largerdecision context; the fifth section discussesjudgment/decision making research on ana-lysts' decision processes. A final section con-tains some conclusions.

Uses of analysts' forecasts in empiricalcapital markets research on earnings and stockprices. In recent years, accounting research-ers have adopted analysts' forecasts as a proxyof choice for market earnings expectations; theidea is to identify with greater precision andaccuracy the unanticipated portion of earnings(i.e., the earnings forecast error). In a typicalapplication, an earnings forecast error is com-puted as the difference between actual earn-ings per share and a measure based on ana-lysts' forecasts. In some cases, the measure isa "consensus" forecast prepared from a n\im-ber of forecasts submitted by analysts em-

*For buy-ade analysts, reccHnmendations to buy a stockmay be executed immediately as part of a planned in-vestment strat QT. For sell-side analysts, reccHnmen-dations involve placing stocks on a "buy" list, dissemi-nating written reports, and making recommendationsto appropriate clients.

Commentary on Analysts' Forecasts 107

ployed at brokerage firms and provided by avendor such as the IBES service of Lynch,Jones and Ryan. In other cases, the measxireis a single forecast provided by a commercialinvestment advisory service such as ValueLine. Various approaches are used to stan-dardize the earnings forecast error, includingdivision by the earnings number and divisionby share price. The standardized earningsforecast error is then an input into an inves-tigation of some question associated with therelation between earnings and share retums.

An altemative proxy for market earningsexpectations is a prediction from a statisticalmodel based on past realizations of earnings.Such a proxy has the advantage of beingreadily computable for any firm with a his-tory of earnings—for a simple random walkmodel, only one past earnings release is re-quired. Analysts' forecasts, in contrast, are notavailable in machine-readable form until themid-to-late 1970s and they are not availableat all for firms that are not followed by ana-lysts (usually smaller and newer firms). Thequestion then arises, what gives rise to thepreference for analysts' forecasts? The pref-erence seems to stem fi*om two considerations:accuracy and association with stock retumprediction errors (abnormal retums) at earn-ings announcements.^ That is, analysts haveconsistently been shown to forecast earningsmore accurately than do mechanical modelsand at least some research has found a stron-ger association between the market responseto earnings and forecast errors based on ana-lysts' forecasts than between the market re-sponse to earnings and forecast errors gener-ated from mechanical models.

It would be exceedingly siuprising if ana-lysts lacked the capability to forecast earningsat least as accurately as a mechanical modelbased only on historical earnings. Analystshave access to more information to project fu-ture earning^ than the accounting system hasto produce the earnings niunber; an exampleof such information is order backlog^. In ad-dition, analysts have direct access to the sta-tistical models themselves. Therefore, a svir-prising degree of incompetence or an exoticallyperverse objective function would be required

for analysts to lack the capability to equal orexceed tê predictive accuracy of models basedonly on past earnings most or all of the time.''

The market association issue is a bit lessclear than the predictive accuracy question.The reason is that aggregate (i.e., market)expectations are iinobservable by definition,and all our inferences about these expecta-tions are of necessity drawn firom a priori rea-soning and indirect tests. An example of theformer is: analysts influence investors withtheir forecasts and recommendations, so weexpect their views will mirror or summarizethose of investors generally. An example of the

'For a description of the various sources of analysts'forecasts and the degree to which the forecasts corre-spond to commercially-available sources of earningsannouncements (such as Compustat), see D. Philbrickand W. Ricks, "Using Value line and IBES Forecastsin Accounting Research," Joumal of Accounting Re-search, 29:2, Autumn 1991.

•For evidence on the predictive accuracy of analysts'forecasts relative to those generated &om statisticalmodels and on the association between forecast errorsand abnormal retums, see L. Brown, P. Griffin, L.Hagerman and M. Zmijewski, "Security Analyst Su-periority Relative to Univariate Time-Series Modelsin Forecasting Earnings," Joumal of Accounting andEconomics 9:1, r i l 1987, pp. 61-88, and "An Evalua-tion of Altemative Proxies for the Market's Assessmentof Unexpected Earnings," Joumal (Âccounting andEconomics 9:2, July 1987, pp. 159-194.^This argument assumes that the analyst wishes tomaximize forecast accuracy unconditionally, and vari-ous incentives faced by the analyst may undercut thisgoal. In addition, there is some empirical evidence thatmight be viewed as inconsistent with superior suialystforecast accuracy. First, not all empirical comparisonsof analyst and model predictive ability have found ana-lysts to be superior. Tat a discussion, see L. Brown andM. Rozeff, "The Superiority of Analyst Forecasts asMeasures of Expectations: Evidence from Earnings,"Joumal of Finance IQCXIII: 1, March 1978, pp. 1-16.Second, research on diagnosis has in some cases foundthat linear models provide better diagnoses than ex-perts. See, for example, R. Dawes and B. Corrigan,"Linear Models in Decision Making," PsychoUîcalBuUetin 81:2, Febmary 1974, pp. 95-106 and R. Dawes,D. Faust and P. Meehl, "Clinical versus Actuarial Judg-ment," Science 243, March 31, 1989, pp. 1668-1674.Note that in a diagnosis task the linear models in ques-tion differ from historical earnings models in that time-liness is not an issue and the Unear models are notrestricted to just one kind of infcHination. Thus, thepossibility exists that a linear model (i.e., a lens model)incorporating variables in addition to past earningsrealizations could prove more accurate than analystsin forecasting earnings.


latter is examinations of the properties of anearnings-return relation estimated using vari-ous proxies for earnings expectations." Unam-biguous interpretations of the indirect testsare, however, impeded by several pieces of re-lated evidence. This evidence supports theviews that share returns at earnings an-nouncements are in fact not fully responsiveto current earnings information; price re-sponses at earning^ announcements appear tobe consistent with earnings expectations de-rived from a relatively naive mechanicalmodel (as opp(»ied to an analyst forecast) andearnings themselves appear to explain ex-tremely modest percentages of the cross-sec-tioned variation in retums at earnings an-nouncements.^ If we do not completely under-stand what is driving the market response toearnings annoiuxcements, then perhaps weshould be cautious in using that response todraw definitive conclusions about the pre-ferred proxy for pre-announcement earningsexpectations.

Our uses of analysts' earnings forecastsimply a certain decision process on the partof investors, but this implication has not beendirectly examined. For example, while ac-counting researchers use earnings forecastswithout adjustment and often without othermeasures to measure or proxy for market ex-pectations of earnings, there is no compellingreason to believe that market participants usesuch forecasts the same way. Investors may,for example, adjust the reported forecasts insome way to take account of other informa-tion about either the firm in question or theforecasting process itself. " In particular, in-stitutional money managers would be imlikelyto use analysts' forecasts as received, becausesuch behavior would imply no special analyti-cal expertise on their part. It seems morelikely that money managers examine the con-tents of several analysts' reports, including theearnings forecasts and specific stock recom-mendations, and form their own judgments."

The usual regression approach to evalu-ating the earnings-share price relation, basedon a standardized earnings forecast error,implies a linear loss function based on earn-ings forecasts as reported. But this function

may not be descriptive; some circumstantialevidence that investor loss functions are notlinear in forecast accuracy is found by consid-ering the empirical regularity that share price

*See, for example, L. Brown et al. (JAB, July 1987). Theyexamine the earnings-return relation using five prox-ies for market expectations. Four of the proxies arebased on statistical models and Iê fifth is Value Ldneforecasts. They report that pnades based rai Value Lineforecasts have the highest assodations with abnormalretums, especially when the proxies are adjusted formeasurement error. In contrast, O'Brien (1988) findsthat forecast errors from a quarterly autoregressivemodel of earnings have a stronger association withabnormal retums than do errors based on analysts'forecasts obtained from IBES. See P. O'Brien, "Ana-lysts' Forecasts as Earnings Expectations," Journal ofAccounting and Biconomics 10:1, January 1988, pp. 53-83.

'Numerous studies document what appears to be a pre-dictable lagged share price response to quarterly earn-ings announcements (i.e., a piost-eamings-announce-ment drift). See, for example, V. Bernard and J. Tho-mas, "Post-Earnings-Announcement Drift: DelayedPrice Resp<»ise or Risk Premium?" Joumal {Âccount-ing Research 27 supplement 1989, pp. 1-36. This studyreports that share reactions to earnings announce-ments are consistent with expectatitms based on a sea-sonal random walk model. For a summary and discus-sion of the modest explanatory power of earnings forretums, see, for example, B. Lev, "On the Usefulnessof Earnings and Elamings Research: Lessens and Di-rections from Two Decades of Empirical Research,"Joumal of Accounting Research 27 supplement, 1989,pp. 153-192.

"There is some evidence that investtnrs adjust reportedearnings to take account of other information; see, forexample, B. Lev, op. cit. Lev finds that in a regressionof residual retums on change in earnings, the explana-tory power of the regression increases firom .06 to .09when earnings are adjusted for the effects of early adop-tion of SFAS 87 (the usual effect of this early adoptionwas to increase earnings but not cash flows because ofthe partiid write-(ôf the access of pension assets overpension lisJiiliiaes). There is also some evidence thatthe usefulness of analysts' forecasts as proxies formarket expectations is enhanced if tê researcher ad-justs for information released after the forecast an-nouncement but before the earnings announcement byincludii^ the share retums during this interval. SeeBrown et al. (JAE, r i l 1987).

"This conjecture about the likely behavior of sophisti-cated investors begs the question of whetJier profitabletrading strategies can be constructed from analysts'forecasts. Research based on price or retums fore-casts—aa opposed to earnings forecasts—indicates t ^ tsuch trading strategies are possible. For an example,see L. Brown, G. Richardson and C. TV^nka, "Strong-form Elfficiency on the Ibronto Stock Exchange: AnExamination of Analyst Price Forecasts," Contempo-rary Accounting Research 7:2, Spring 1991, pp. 323-346.


responses to unfavorable earnings surprisesare not mere mirror images of responses tofavorable surprises—this evidence is not con-clusive, but it is suggestive of the possibilitythat our current assumptions about how in-vestors use earnings forecasts are not richenough to capture their behavior.

It appears, then, that relying on analysts'earnings forecasts as proxies for market ex-pectations is supported by eviden<» based onaccuracy and market association tests. Butsome assumptions about analysts' and inves-tors' loss functions and decision processes un-derlying this reliance are rarely if ever explic-itly spelled out and examined by researchers,and these assumptions may in fact be incon-sistent with how forecasts are actually gener-ated and used. Instead, accounting research-ers have focussed on describing the propertiesof the forecasts and related error metrics, as Idiscuss in the next section.

Investigations of the properties of analysts'earnings forecasts. A number of detailed in-vestigations of analysts' earnings forecastshave been published. ^ This research hastended to focus on issues like dispersionamong forecasts, accuracy of forecasts as afunction of timeliness, bias, the characteris-tics of various forecast error metrics, and de-gree of association between forecast disclo-sxires and share price reactions. The motiva-tion for these investigations appears to me tostem from thinking of analysts' forecasts pri-marily or even exclusively as proxies for mar-ket earnings expectations. For example.Brown and Rozeff(1978) motivate their inves-tigation of forecast accuracy by stating (p. 1)"Accurate measurement of earnings expecta-tions is essential for studies of... the relation-ship between unanticipated earnings andstock prices." In other words, the primary driv-ing force for the investigation of forecast ac-curacy is not curiosity about the functioningof the decision process which generates theforecasts; instead it is the forecasts them-selves, and in a particular use, that are of spe-dal interest.

There is, however, an emerging theme inthis research which takes the form of a move-ment away from documentations of the sta-

tistical properties of forecasts and measiiresof forecast errors and toward investigationsof what information appears to be impoundedin these forecasts. The motivations for suchinvestigations are closer to a decision contextsince they include uncovering the sources ofanalysts' forecast superiority relative to me-chanical models and testing for whether ana-lysts appear to make full and rational use ofall information available to them.

An example of research into the factorswhich generally determine, or at least are gen-erally associated with, analyst earnings fore-cast superiority is Brown, Richardson andSchwager (1987). ' The theoretical vinderpin-nings of their investigation relate analyst fore-cast superiority to very general constructs thatare in no way specific to a particular kind offirm or decision setting: the dimensionality ofthe information set (how much there is toknow), inherent variability of the underlyingseries and the degree to which the various bitsof information available to the analyst are in-dependent. The empirical proxies used arefirm size, dispersion of forecasts and the num-ber of lines of business of the firm whose earn-ings are being predicted. While it is not pos-sible given available evidence to investigatethe degree to which these proxies captiire theunderlying constructs of interest, it is clearthat these variables— in particular, firm size—have been shown to be associated with other

e, for example, P. Brown, G. Foster and E. Noreen,Security Analyst Multi-Year Earnings Forecaste andthe Capital Market, 1985, American Accounting Asso-ciation, Sarasota, Florida; Brown et al. (1987a and1987b), and O'Brien (1988). Different studies investi-gate the properties of different databases; the natureof the question asked is in part a function of the stmc-ture of the database. For example, IBES data are usu-ally used in academic research as "consensus" num-bers formed from earnings forecasts supplied by ana-lysts at bndcerage houses; an exception is O'Brien'sresearch based on the detailed data underlying theccmsensus forecasts. As another example, after 1983the Standard and Poor Earnings Forecaster providesforecasts only from S&P (which has no investmentbanking or brokerage activities), often several forecastsfor a given firm.

"See L. Brown, G. Richardson and S. Schwager, "AnInformation Interpretation of Financial Analyst Supe-riority in Forecasting Earnings," Joumal of Account-ing Research 25:1, Spring 1987, pp. 49-67.


firm characteristics. On the other hand, it isdifficult to see how more specific proxies couldbe found for the very general constructs be-ing considered; thus the generality of the in-vestigation reinforces the credibility of theassociation tests while making it difficult toprovide precise and unambiguous interpreta-tions.

At the cost of a potentially severe loss ofgenerality, it may be possible to avoid at leastsome problems associated with using empiri-cal proxies for very general forces believed tobe driving analyst forecasting behavior (andthe superiority of analysts' forecasts to thosederived from, mechanical models) by consid-ering, for certain well-defined cases, how ana-lysts deal with specific information situations.Two examples that illustrate this kind of re-search are Kim and Schroeder (1990) andBiddle and Ricks (1988),"

Kim and Schroeder investigate whetheranalysts act as if they both believe the boniismaximization explanation for earnings man-agement and a<ûst their earnings forecaststo take account of accruals manipulations bymanagers. Their tests are predicated on thenotion that if earnings management behaviorarises from bonus incentives, then forecasterrors in the discretionary accruals portion ofearnings should either offset or reinforce cashflow forecast errors, depending on where theearnings fall relative to the bounds of short-term income-based bonus plans. This investi-gation presumes analysts are shrewdly awareof various forces affecting reported earningsand capable of using available information totake account of these forces in developing fore-casts. Clearly, no mechanical model is likelyto capture these effects, so their ability to ad-just for earnings man£êment behavior maywell be a force driving analysts' earnings fore-cast superiority.

In contrast to the Kim and Schroeder find-ing, which focussed on ongoing behavior,Biddle and Ricks dociunent that analysts sys-tematically overestimated the 1974 earningsof the 1974 LIFO adopters, even when theanalysts knew of the LIFO adoptions. Obvi-ously, while a mechanical model could nothave easily been constructed to deal with this

situation either, Biddle and Ricks find evi-dence that analysts do not always fully im-poimd the effects of a one-time interventionin the earnings process in their forecasts.Given the one-time nature of the forecastingtask, it is not possible to determine whetherthe substantial earnings forecast errora weredue simply to an abrupt shift in inflation thatwas not predictable given the best availableinformation or to a failxire to take full accountof all available information.

Recent investigations of information usedby analysts have begun to focus on settingsthat are still highly structured (as in Kim-Schroeder and Biddle-Ricks) but more gen-erai— in particular, whether analysts' fore-casts and forecast revisions appear to impoundall the informaticm in prior stock price changesand earnings releases, and whether forecastscontain information even when they are asso-ciated in time with either other forecasts orfirm-specific disclosures. Results, discussed inmore detail below, support the informationcontent (ânalysts' forecasts regardless of thepresence of comi)eting disclosures and alsoindicate that analysts' forecasts do not im-pound all available earnings and stock retuminformation.

\^th regard to competing disclosures, Lysand Sohn (1990) investigate several aspectsof the relation between analysts' forecast re-visions and share price reactions.^^ They findthat while forecast revisions are positivelyassociated witii prior share retums and mar-ket retums (indicating that the revisions takeaccount of some or all of the information im-potinded in these retums), the association isnot complete. That is, the earnings forecasterror based on the revised forecast is associ-

»*See K. Km and D. Schroeder, "Analysts' Use of Mana-gerial Bonus Incentives in Forecastini; Earnings,"Joumal cf Accounting andEconomics 13:1, May 1990,pp. 3-24, and G. Biddle and W. Ricks, "Analyst Fore-cast Errors and Stock Price Behavior Near the Earn-ings Announcement Dates of LIFO Adi^ters," Jour-nal tÂccounHng Research 26-:2,A\A\rrcm 1988, pp. 169-194.

T. Lys and S. Sohn, "The Association between Re-visions of Analysts' Earnings Forecasts and Security-Price Changes," Joumal of Accounting and Econom-ics 13:4, December 1990, pp. 341-363.


ated with share retums and market retumspreceding the revision, consistent with theview that analysts do not incorporate in theirearnings forecasts all infomiation available inshare retums £ind market retums. The mag-nitude of the "missing" explanatory power isabout 34 percent of the total explanatorypower ofthe regression of revisions on shareretums and market retums. Based on simi-lar regression analyses, Lys and Sohn con-clude that analysts' forecasts are based on in-formation that is partly independent acrossanalysts and partly independent of corporatedisclosures.

Using different databases and differentresearch designs to answer somewhat differ-ent questions, both Klein (1990) andAbarbanell (1991) confirm the Lys and Sohnresults.^" Abarbanell's research is motivateddirectly by the use of analysts' forecasts asproxies for market earnings expectations. Hepoints out that the extent to which analysts'forecast revisions merely reflect the informa-tion impounded in share retums preceding therevisions affects how such revised forecastscan be interpreted as market earnings expec-tations. Using Value Line data, he finds anassociation between prior retums and bothforecast revisions and forecast errors based onthose revisions, and he interprets these resultsin a way that reinforces the interpretationsof Lys and Sohn.

Klein's research is motivated by a cogni-tive bias theory put forward by DeBondt andThaler (1987, 1990) to explain what they re-gard as an overreaction to extremes of earn-ings changes and retums.^^ She tests whetheranalysts systematically underpredict earningsfollowing large share price declines andoverpredict earnings after large price in-creases, and finds no evidence that they do.The evidence instead supports nonsymmetricforecasting behavior that seems to tend tooptimism in the face of bad news; specifically,forecasts after price declines are optimistic(i.e., the forecasts exceed the actual earnings)and forecasts after price increases are neitheroptimistic nor pessimistic (i.e., there is no par-ticular bias in the forecast errors).

A related series of papers has consideredhow analysts respond to earnings information

(as opposed to share price change information)in making and revising forecasts. These pa-pers appear to be motivated both by researchon the forecast revision-share retum relationand by evidence of anomalous stock price be-havior around earnings announcements. Forexample, Mendenhall (1991) finds evidence ofpositive serial correlation in Value Line earn-ings forecast errors, as well as a positive rela-tion between forecast revisions and retumsaround the subsequent earnings announce-ment. ^ He interprets his results as indicat-ing that analysts underreact to earnings in-formation in forming their forecasts.

In research which investigates the poten-tial links between analyst forecasting behav-ior and post-earnings announcement drifts,Abarbanell and Bernard (1991) bring togetherseveral strands ofthe research just discussed.Using Value Line earnings forecasts (whererelatively precise dating is possible), they findthat forecast extremes (relative to actual earn-ings) are eliminated after the first quarterlyreport.^' Th\is, analyst overreaction to infor-mation (which might be earnings information)is apparent only in the early portion of theyear. In addition, they find (consistent withKlein) that the most optimistic earnings fore-casts (the biggest overestimates) are associ-ated with firms that had weak earnings per-formance the previous year—in contrast, over-

A. Klein, "A Direct TBSt of the Cognitive BiasThe<nry of Share Price Reversals," Joumal (^Account-ing and Economics 13:2, July 1990, pp. 155-166, andJ. Abarfoanell, "Do Analysts' Earnings Forecasts Incor-porate Information in Prior Stock Price Changes?"Joumal of Accounting and Economics 14:2, June 1991,pp. 147-166.

"See W. DeBondt and R. Thaler, "Further Evidence ofInvestor Overreaction and Stock Market Seasonality,"Joumal cf Finance 42:3, July 1987, pp. 557-582 andDeBondt and Thaler, "Do Security Analysts Overre-act?" American Economic Review 80:2, May 1990, pp.52-57.

"R. Mendenhall, "Evidence on the PossibleUnderweighting of Earnings-Related Information,"Joumal (^Accounting Research 29:1, Spring 1991, pp.170-179.

"See J. Abaii>anell and V. Bernard, "Analysts' Overre-actionAJnderreaction to Earnings Information as anExplanation for Anomalous Stock Price Behaviors,"University of Michigan, Working Paper, May 1991.

112 Accounting Horizons I December 1991

reaction to earnings information implies thatpoor past earnings would lead to under^ti-mation of future earnings (i.e., pessimisticforecasts). Ultimately, they conclude, as didMendenhall, that analysts appear tounderreact to past earnings signals—espe-cially negative signals.

Ib summarize, it appears that analyst fore-casts tend to be optimistic on average (thatis, they appear to overestimate future earn-ings) when researchers examine samples offorecast ernnrs without conditioning on priorinformation.*" When the forecast errors areconditioned on previous share retum or earn-ings information, it appears that optimism ismost prevalent in the face of bad news. In de-scribing the complexity of the situation,Abarbanell and Bernard remark that a com-plete understanding of the results will prob-ably require understanding why analysts, whoare sophisticated market agents, would con-sistently appear to make the same kind ofearnings forecasting error. One suggestionthey ofifer to explain this behavior involves theincentive structure &ced by financial analysts.Understanding analysts' incentives in fore-casting earnings requires pladng t l^ forecast-ing task within the context of what the ana-lyst does; the next section takes up this issueand offers some reasons why analysts mighttend to be optimistic.

What do financial analysts actually do,and why do they forecast earnings? The gen-eral focus of accoimting research on accuracyand bias of analysts' earnings forecasts hasyet to capitalize on whatever opp(»rtunities forinsights might arise from considering theseforecasts in the context of what the analystdoes and, relatedly, what incentives he faces.Both context and incentives will shape theproperties of analysts' work products gener-ally and their forecasts in particular. For ex-ample, one possibility is that incentives notcontemplated in the usual tests of analyst fore-cast accuracy and bias operate to produce sys-tematically biased forecasts. Tb explore thispossibility, I will consider first the generalanalyst decision context and second two kindsof incentives faced by analysts. This focus oncontext and incentives may not necessarily be

consistent with some psychological theories ofinformation processing which predict that in-dividuals engaged in forecasting will be sub-ject to psychological forces that are not likelyto be strongly influenced by incentives.^^

The basic responsibility of an analyst is tofollow stocks—10 to 20 stocks in a given in-dustry or economic sector. The ultimate prod-uct of sell-side financial analysis is a reportevaluating a firm's securities; for our pur-poses, we can assume the analyst is focussingon common stock. \^thin the analyst's report,the ultimate ju(^ment recommending an ex-plicit action is "buy, sell, or hold." TD quotefrom a classic text on security analysis, "se-curity analysis provides the basis for select-ing individual issu^ to buy or sell from amongavailable choices."^ Note that for a i^ven ana-lyst, the available choices are concentratedamong related stocks, so the analyst in effect

their research monograph. Brown, Foster andN(»reen op. dt. report that for their sample and timeperiod (1976-1980) the mean consensus forecast erroris negative and tiie median is positive, indicating thatthe forecast errors arising from overestimates surelarger than those arising from underei^mates. Thereis, therefore, some evidence supporting optimism inthis research. Aten^ncy fat c^timism in analysts' fore-casts is also reported for the 1983-1966 period by K.Butler and L. Lang, "The Forecast Accuracy of Indi-vidual Analysts: Evidence (^Systematic Optimism andPessimism," Journal of Accounting Research 29:1,Spring 1991, pp. 150-156.See, for example, J. Hunter and T. Co^n, "AnalystJud^ent: The Efficient Market Hypothesis versus aPtfychological Theoiy of Humsui Judgment," Organi-zational Behavior and Human Decision Processes 42:3,December 1988, pp. 284-302. Using samples of ana-lysts' forecasts, they test a prediction of perstmal con-struct theory: financial ana]3rsts' judgments in mak-ing forecasts are influenced by their personal tiieoriesof earnings, taken to be the currently dimiinant finan-cial theory. Hunter and Coggin also test whetl^r ana-lysts appear to overuse data cmisistent with the cur-rently dominant theory. For the early 1960s, when timeseries theories of earnings growth were dtnninant, theyfind that analysts' forecasts are sensitive to histmca!earnings growth. FOT the period 1979-1983, when theo-ries of market efficiency were dominant, they find thatanalysts' forecasts are sensitive to systematic risk fromthe Capital Asset Pricing Moctel. They reject the hy-pothesis that forecasts are based on all available in-formation.

S. Cottle, R. Murray, and F. Block, Graham andDodd's Security Analysis, fifth edition. New York:McGraw Hill, 1988, page 34.


is engaging in prospective relative perfor-mance evaluation.

The process of forecasting earnings entersinto the job of the financial analyst becausethe forecast is one of numerous pieces of in-formation used to arrive at a recommenda-tion.^' Thus, when we focus on earnings fore-casts we are considering not a final productbut rather an input into generating a finalproduct. As one investment professional putsit: "Focusing on EPS forecasts, for example,makes as much sense as.. .judging a car manu-facturer on his ability to forecast car sales,rather than to make better cars than his com-petition."** Clearly, understanding how manycars will be sold is not immaterial to the ulti-mate objective, but it is not the ultimate ob-jective. In fact, forecasting earnings is by defi-nition subordinate to the goal of picking stocksand writing reports which support those judg-ments.^ This goal, combined with the incen-tives faced and information available, shapesthe £inalyst's decision process, of which fore-casting earnings is just one component.

The analyst's overall decision process hasa loss function which in turn dictates the lossfunction associated with earnings forecasts.This function may or may not be captured bythe usual measures we iise for evaluating ana-lysts' earnings forecasts, such as meansquared error. Consider, for example, thetradeoff between timeliness and accuracy offorecasting. Ib the extent having the forecastsooner (even at the cost of less accuracy) im-plies greater investing profits to consumersof analysts' earnings forecasts, the loss func-tion implied by pleasing customers will cre-ate a preference for timeliness. Alternatively,readers of analyst reports may use forecastaccuracy as a quantitative measure of thequality of the overall report; this effect willcreate a preference for accuracy, possibly evenat the expense of timeliness.

Nor is it transparent how earnings fore-casts are affected by the incentives faced byanalysts. It is dear from the amoiint of atten-tion devoted to earnings forecasts in the fi-nancial press, and the presence of commercialvendors of such forecasts, that these numbersare especially salient for investors. But it isnot so clear how this salience is reflected inthe overall incentive structxire ofthe analyst's

job within a firm. Specifically, sell-side ana-lysts' research reports and recommendationsare often part of a group of bundled servicesoffered by full-service investment bankingfirms. Such firms offer merger and divestitureadvice, brokerage services and underwritingservices; they may have their own tradingoperations as well. For these firms, analystresearch is overhead and does not in itselfgenerate any sales revenues. Instead, rev-enues must come fixtm providing brokerageservices, merger and divestiture advice andunderwriting, at least two of which have thepotential to infiuence analysts' behavior.

A key revenue source for firms offeringbrokerage services is customer interest infirms they follow. Increasingly, institutions arethe customers; within these institutions, buy-side analysts regard the reports of sell-sideanalysts as an important source of informa-tion.** Therefore, analyst reports could be used

''The following description of the analyst's tasks is takenfrom a description of how top management can workeffectively with financial analysts following the com-pany. Analysts study the industry (including marketresearch, industry statistics and the trade press), in-terview and possibly visit customers and suppliers,vidt the company, interview management, analyze thefinandal statements to identify valuation cbivers, valuethe stock and write a report containing a recommen-dation. Earnings forecasts are mentioned only as theyaffect the final report and the process of forecastingearnings is accorded only a minor role in the descrip-tion. See S. Balog, "What an Analyst Wants from You,"Financial Executive, July/August 1991, pp. 47-52.

' See P. Heitner, "Isn't It Time to Measure Analysts'IVack Reccnrds?" Financial Analysts Joumal 47:3, May/June 1991, pp. 5-6.

"I do not mean to imply that earnings forecasts are otherthan an important, possibly essential, step in the taskof preparing a report. Fcac example, empirical evidencethat analysts incorporate their earnings forecasts intotheir price forecasts is presented in S. Ban^opadhyay,L. Brown, and G. Riduordson, "Tlie Importance of Earn-ings Forecasts to Analysts' Stock Price Forecasts,"working paper, SUNY at Buffalo, August 1991. For asample of Canadian analysts' forecasts and Value Lineforecasts covering the same firms, during 1983-1988,they find that analysts' earnings forecast revisionsexplain 48 to 72 percent of the variance in their priceforecast revisions.

"Other analysts are listed as the seamd most impor-tant source of information (after annual reports) bybuy-side professionals in a survey reported in SRI In-tematioiud. Investor Information Needs and the An-nual Report, 1987, Financial Executives ResearchFoundation, Morristown, New Jersey.


as part of a marketing effort to induce insti-tutions to increase their holdings of a particu-lar firm or firms. In turn, institutional inter-est in a particular firm provides an incentivefor an analyst to begin following it. Thus, thedecision processes of analysts associated withbrokerage houses are linked with those of in-stitutional investors and studying either inisolation ignores the feedback effects of thelinkage. For example, certain properties ofanalysts' forecasts (such as timeliness, diver-gence from a consensus or quick revisions)may lead to greater institutional propensityto trade in the stock.

The joint analyst-institution decision pro-cess was investigated by O'Brien and Bhushan(1990).' '' They examined year-to-year changesin analyst following and institutional owner-ship in a simultaneous equations frameworkand found that analysts prefer to follow firmswhere volatility is low and so is competitionfrom other analysts, while institutions tendto prefer firms with pre-existing analyst fol-lowing and with increasing size and risk. Inparticular, a previously-dociimented relationbetween analyst following and firm size iseliminated once institutions' decisions are si-multaneously considered. Their results sup-port the view that institutions' investmentchoices—which are generally not consideredin investigations of analysts' forecasting be-havior—causally influence at least one deci-sion made by analysts, the decision of whichfirms to follow. The question then arises: doesthe presence of the institutional investor alsoaffect the nature of the analyst's reports and(by implication) the properties of his earningsforecasts?

In terms of the demands and preferencesof their customers, financial services firmsthat offer both imderwriting services and ana-lyst research face another infiuence that con-stitutes a potential conflict of interest: favor-able reports and buy recommendations willhelp generate demand for the securities be-ing underwritten and may help to solidifylong-term business relationships. Thus, theanalyst knows that his employer's revenuesare infiuenced by how favorably he evaluatesthe client firm.

The conflict of interest generated by com-bining underwriting and analysis is not un-like the one faced by large public accountingfirms who offer both audit services and con-sulting or management advisory services(MAS). In both cases, a professional judgmentthat is presumably used by market agents inmaking investment decisions could be subjectto potential infiuence by the presence of a sec-ond, related income-generating activity, be-cause the kind of judgment rendered couldinfiuence the client's perception of the attrac-tiveness of the second activity.^ Also in bothcases, it is possible to argue that the presenceof both activities in the same firm has ben-efits that outweigh the likely co3ts of any con-fiict of interest. Specifically, auditors and con-sultants from the same firm can (it is argued)pool information about a client and analystswho are associated with underwriters willhave better access to management and hencethe basis for superior analysis and recommen-dations.^'

The analyst, however, faces a somewhatdifferent and possibly more complicated situ-ation than the auditor. Although the under-writing client can take his underwriting ser-vices business elsewhere if he is displeasedwith some aspect of the analysis and cover-age provided (just as the audit client can takehis MAS business elsewhere if he doesn't likethe audit judgments provided) the imderwrit-ing client has another source of infiuence overthe analyst: access to management. Specifi-cally, irritated managers can make it very dif-ficult for out-of-favor analysts to maintain any

"See P. OBrien and R. Bhushan, "Analyst Following andInstitutional Ownership," Joumal of Accounting Re-search 28 supplement 1990, pp. 55-76.

'"This point is made explicitly by Steven Balog, op. cit..Associate Director of Research at Lehman Brothers.

^Analytic examinations of the potential for conflict gen-erated by the provision of audit services and MAS ser-vices by the same firm include, for example, R. Antleand J. Demski, "Contracting Frictions, R^ulaticm andthe Structure of CPA Firms," forthcooiing, Joumal ofAccounting Research supplement 1991. For a discus-sion of the potential benefits of joint supply of auditservices and MAS, see D. Simunic, "Auditing, Consult-ing, and Auditor Independence," Joumal of Account-ing Research 22:2, Autumn 1984, pp. 679-702.


reasonable kind of contact.^" This source ofinfluence on analyst reports would have nodifferential effect on analysts employed byfirms with and without other kinds of busi-ness relationships with client firms. To theextent this influence exists and has wide-spread e£fects, it may help to account for thetendency for analysts in general to issue opti-mistic earnings forecasts, especially in the faceof negative share retiims and earnings sig-nals.

While empirical investigations of the au-dit-MAS potential conflict are hampered by ageneral lack of objective outcome measures,^^and direct investigations of the possibility thatanalysts shade their forecasts to maintaingood relations with and ready access to man-agers are impeded by an inability to identifyand distinguish cases where such infiuencesare and are not expected to exist, it is pos-sible to conduct empirical investigations of theassociation between underwriting activitiesand the properties of analysts' earnings fore-casts. One such investigation is Lin andMcNichols (1991), who consider whether theforecasts of analyst-underwriters are moreacciirate or more optimistic than other fore-casts and whether there is a difference inmarket reactions to forecasts depending on thepresence of an underwriting relationship.^^

Based on preliminary results, Lin andMcNichols find that analyst-iinderwriters aremore optimistic but not more accurate thanother analysts. However, this optimistic biasdoes not destroy the information content of theanalyst-underwriters' forecasts, and weak evi-dence indicates that such forecasts may beviewed as more informative than those ofother analysts. They do not investigate thebuy, sell and hold recommendations of ana-lysts; however, to the extent that optimisticforecasts are associated with relatively morefrequent buy recommendations, Lin andMcNichols' results would indicate that ana-lyst-underwriters are influenced to be rela-tively more favorable about their employers'undenfmting clients. The degree of optimismin analysts' recommendations, as well as theextent to which investors adjust for any suchbias, are, of course, empirical questions.

There are thus two distinct but relatedconsiderations which seem likely to influencethe properties of analysts' forecasts. The firstinvolves incentives; analysts are subject to atleast two kinds of pressures that arise fi*omthe nature of their jobs. Both buy-side and sell-side analysts rank company managementamong the top five sources of information usedin making judgments and decisions, so itseems reasonable to expect that they wouldlike to maintain good relations with, and readyaccess to, managers.^^ In addition, to the ex-tent their employers have other, revenue-gen-erating business relationships with the firmsthe analysts follow, there is an incentive forthe analyst to produce a report and recommen-dation which solidifies the revenue-generat-ing relationship.

The second consideration is the notion thatanalysts are not paid merely to forecast eam-

^A loss of access to management is costly if analystsrely on management for information. "Company man-agement" is listed along with financial reports as a keysource of information for sell-rade analysts; buy-sideanalysts rank "other analysts" as key in addition. Pro-fessional investors in general (including brokers andportfolio managers in addition to analysts) rank "com-pany management" as the most important informationsource. See SRI Intemational, op. dt. In addition, thepopular financial press has carried stories that pro-vide anecdotal evidence supporting both the impor-tance of analysts' access to company management andthe retaliatory denial of such access. See, for example,J. Laderman, C. Hawkins, and I. Recio, "How MuchShould You IVust Your Analyst?" Business Week, July23, 1990, pp. 54-56; E. Berg, "Risks for the AnalystsWho Dare to Say Sell," New York Times, May 15,1990,pp. cl-c6.

^Kliven a sufficiently structured setting that is simulta-neously rich enough to capture both the economic in-centives and the institutional ccmstraints facing firmsproviding both audit services and MAS, it may be pos-sible to use laboratory m£u*kets approaches to investi-gate these issues. For an example (rf'such an approach,see N. Dopuch and R. King, "The Impact of MAS onAuditors' Independence: An Experimental MarketsStudy," forthcoming, Joumal of Accounting Researchsupplement, 1991. For a discussion of the pitfalls andproblems associated with this approach, see J. Berg,"Discussion of The Impact of MAS on Auditors' Inde-pendence: An Experimental Markets Study,'" forthcom-ing, c/ottma/o/!Accoun<(7i /Ze8earcA supplement, 1991.

'"See H. Lan and M. McNichols, "Underwriting Relation-ships and Analysts' Earnings Forecasts," wwldng pa-per, Stanford University, May 1991.

' See Table 4.5 in SRI Intemational, op. cit.


ings. Rather, they are paid to pick stocks andto write reports which cogently support theirrecommendations. Therefore, it makes senseto consider the eamings forecasting processas one portion of a more (implicated (^dsionprocess ciilminating in a report and recom-mendation. Somejudgmentanddedsion-mak-ing research, discussed in the next sedi(»x, hasfocussed on this kind of consideration.

Judgment and decision-making researchon analysts' decision process. Intuitively, itseems clear that one potentially useful ap-proach to understanding analysts' decisionprocesses would be to present them with de-cision and judgment tasks that capture theinformation and incentive characteristics oftheir day-to-day activities. It also seems clearthat this approach is likely to be even moredifficult to make operational for analysts thanit is for auditors, and it is certainly quite dif-ficult there. For example, judgment and deci-sion-making research involving auditorsdoesn't always present them with tasks thathave the same degree of complexity they faceon the job, and only fairly recently have re-searchers attempted to provide explicit incen-tives for auditor subgects. * In addition, whilepracticing auditors have been quite willing toparticipate in academic research as subjects,practicing financial analysts have been rathermore elusive in this regard.

I will discuss three basic approaches toresearch on analysts' decision processes thathave appeared in the literature: regressionanalyses of judgments based on informationsupplied by the researcher; content analysisof analysts' reports; protocol analyses. In theresearch I will discuss to illustrate the firstand third cases, no explicit incentives are sup-plied and no potential conflict of interest isestablished (e.g., between a truthful adverseevaluation and the possible loss of client good-will). In the second case (content analysis),these issues do not arise in the same way, be-cause there is no eliciting of responses fromsubjects— the research materials are essen-tially archival, not experimental.

In the area of regression analyses, Mearand Firth have published a series of papersinvestigating the decision processes by which

38 market professionals (analysts, portfoliomanagers, brokers) rated each of 30 stocks onrisk and predicted 12-month retums for thesame stocks. The materials supplied by theresearchers were several market variablesand accounting ratios plus one industry vari-able, chosen based on interviews with profes-sionals and reviews of the literature. In anevaluation of the degree to which the subjectscan explicitly express how they make deci-sions, the authors compared the subjects'weightings of the variables with weights im-plied by a regression model of their decisions(a Brunswik lens model). Spedficedly, subjects'predicted risk and retum measxires were re-gressed on the researcher-supplied informa-tion variables. Self-insight was measured asthe correlation between the subject-suppliedweights and the regression weights on thevariables.^ As in other judgment and dedsion-making research, Mear and Firth found thattheir subjects overestimated the weights theyplaced on minor cues and underestimated theweights on m£gor cues.

This research, like the statistical researchon analysts' forecasts, focusses on what ap-pear to be inputs to the work product of fi-nandal analysis (risk and retum assessments)rather than the end product itself, the recom-mendation and the supporting report. In ad-dition, the researchers severely limited theamount of information available to the sub-jects, possibly because they wished to use aBrunswik lens framework which employs allthe available cues (researcher-supplied pieces

"For an example of research involving auditors whichincorporates financial incentives, feedback about pastperformance and a justification requirement—all ofwhich are posited to be features of the auditor's workenvironment—see R. Ashttm, "Pressure and Perfor-mance in Accounting Deciraon Settings: ParadoxicalEffects of Incentives, Feedback and Justification,"Joumal of Accounting Research 28 supplement, 1990,pp. 148-180. On the other hand, no attempt was madeto provide a task that could be expected to capture thekind of work auditors do; see V. Heiman, "Discussionof 'Pressure and Performance in Accounting DecisionSettings: Paradindcal Effects of Incentives, Feedbackand Justificatiim,'" Journal ofAccounting Research 28supplement, 1990, pp. 181-186.

»See R. Mear and M. Firth, "Cue Usage and Self-in-sight of Financial Analysts," The Accounting ReviewLXII:1, January 1987, pp. 176-182.


of information) as right-hand side variablesin a regression model. The advantage of suchan approach is that the researcher can obtainstatistical weights on all the cues; one cost ofthe approach is that the researcher mxist limitand quantify the information supplied.

In another inquiry based an. the same data,Mear and Firth report that suiayects differedconsiderably in the cues they used and theweights they appeared to place on variouscues.^ While each subject was fairly consis-tent in his risk and retum judgments acrossthe 30 cases considered, between-subject vari-ability in these judgments was rather high.Based on various statistical analyses, the au-thors conclude these differences are relatedto differences in emphasis among the cues pro-vided— for example, some subjects focussedmuch more on profitability information thanothers. These results must be interpreted inlight of the fact that the subjects were con-strained in terms of available information;information usage pattems might shift in thepresence of different and broader informationsets.

The regression analysis of intermediatejudgments based on researcher- supplied in-formation has the advantage of control andfairly strong precision of analysis. The ex-amples described here contain relativelysparse information environments, so that itis not clear to what extent the restdts dl>tainedin such studies would carry over to richer en-vironments. In contrast, content analysis ofanalysts' reports offers no control by the re-searcher, inevitably leads to some imprecisionbecause of the need to code and quantify quali-tative information, and offers a direct focuson the end product as it is actually produced.

One example of a direct examination ofanalysts' research reports is Govindarsgan(1980). '' This research is motivated by an in-terest in whether financial statement usersare primarily interested in cash flows or inearnings in making decisions. (Clearly, al-though the approaches differ, the motivationis related to that underlying the numerouscapital markets studies of the incrementalinformation content of cash flows and accru-als.) The contents of 976 analyst researdi re-

ports published in 1976-77 in The Wall StreetTranscript were coded 1 to 6 depending ontheir relative emphasis on cash flows (l=onlycash was analyzed) and earnings (6=oiily earn-ings was analyzed). Of these reports, 86.5 per-cent were rated 4, 5 or 6, indicating that intheir written reports, analysts favored discus-sions of earnings.

What do these results mean for a consid-eration of analysts' decision processes? AsGovindarcgan points out, the reports are notrecords of the decision processes; they are in-stead the formal explanations of the recom-mendations. He notes that an examination ofintemal records would be reqmred to distin-guish between analysts' decision processesand the presentation of the outcomes of thoseprocesses. But this assessment may be toosevere. First, a necessary condition for inclu-sion in the report is inclusion in the decisionprocess, so the report sets a lower bo\ind onthe information items analyzed. (Clearly, theanalyst may have used information in prepar-ing the report that is not explicitly disclosedin the report itself.) Second, if the reports areregarded as justifications for the judgments,then perhaps their structure can be studiedwithin the framework of the effect of justifi-cation requirements on decision processes.Third, some clues to the nature of and rea-sons for the apparent tendency to optimismmight be found in the information providedand the way it is presented. Finally, protocolanalysis is an altemative to examinations ofintemal records—such records could be cha-otic since analysts do not, for example, face arequirement that they prepare workpapers (asauditors do).

Two published protocol analyses involvingfinancial analysts are Biggs (1984) andBouwman, Frishkoff and Frishkoff (1987).^

>*See R. Mear and M. Firth, "A Parsimonious Descrip-tion of Individual Differences in Financial AnalystJudgment, * Joumal of Accounting, Auditing and Fi-nance 5:4, Fall 1990, pp. 501-620.

"See V. Govindarigan, "The Objectives of FinancialStatements: An Empirical Study of the Use of Cash-Flow and Earnings by Security Analysts," Accounting,Organizations and Society 5:4,1980, pp. 383-392."See S. Biggs, "Financial Analysts' Information Searchin ihe Assessment of Corporate Earning Power," Ac-

(Continued on next page)


Based on information in the papers, itthat the subjects were buy-side analysts.Biggs' protocol analysis was based on 11 ana-lysts who were asked to choose which of fivefirms would have the greatest ability to ^ n -erate income over the next three to five years;eight of the 11 subjects chose the same firm.He supplied 10 years of finandal statementsbut no notes or schedules. While all subjectscalculated ratios and trends, only four of thesubjects predicted eamings exphdtly.

The relative performance evaluation as-pect of Biggs' task seems to be based on theway analysts follow firms; that is, they followan industry or sector and try to choose the bestperformers firom among these firms. On theother hand, the amount of information sup-plied was quite limited relative to what ananalyst might actually be expected to use, andinvolved only accounting data. Interestingly,more than half the subjects made their choiceswithout explidtly predicting earning^; giventhe nattire of the task and the set-up of theresearch, it is not dear why this was so. Forexample, those who did not make an explidteamings prediction may have wished to savetime; they may have ladled some or all of theinformation they usually use to predict eam-ings; or they may have thought no eamingsprediction was necessary for the task at hand.Note also that buy-side analysts may in factpurchase eamings forecasts from sell-sideanalysts, so that they may not be accustomedto making these projections as part of theirjobs.

For whatever reason, these protocol analy-ses indicate that predicting eamings per sewas apparently not regarded as a necessarystep in the subjects' dedsion processes. In-stead, the m£gority of the analysts based theirrelative performance evaluation on an evalu-ation of historical information. However, theprotocol analysis as reported in the paper nei-ther suggests nor rules out a combined deci-sion process and model of eamini^ such thatthe analyst does a cross-sectional evaluationof historical data to obtain an implidt eam-ings prediction which be(X)mes part of the ba-sis for the final judgments.

Bouwman, Frishkoff and Frishkoff explic-itly motivate their protocol analysis study of

12 financial analysts as an inquiry into thededsion making processes used in finandalstatement analysis. Their task— to analyze acompany and dedde whether to eliminate itas a possible investment candidate or to pro-cess further—is clearly a part of the analyst'sjob. The subjects, all buy-side analysts, re-ceived a significant amount of accounting andother information—for example, several yearsof finandal statements, a proxy statement.Standard and Poor's industry analysis, timelyWall Street Joumal market information.

Bouwman et al. identified these goals asdescribing the behavior of their subject ana-lysts: become familiar with the company, lookfor imusual items, look for reasons to rejectthe company as an investment candidate. Themost firequently used information came fromthe income statement, performance ratios, andsegment data. While income statement datawere relatively heavily used as the analystsfamiliarized themselves with the company butnot as they reasoned to a conclusion, segmentdata were fairly heavily used in the familiar-ization stage and they were the m(^t heavilytised items in the reasoning portions of thetask. This finding, taken in conjunction withthe fact that Biggs omitted notes and sched-ules firom the information supplied to subjects,suggests that his analysts may have had tomodify their dedsion strategies because someinformation they habitually use was missing.

In doing the task, the analysts used whatBouwman et al. call "templates" or images assummaries of many firm characteristics; forexample, one analyst characterized the firmas "cydical," a kind of shorthand device thataggregates many features. Clearly, develop-ing such templates or images involves train-ing, experience and memory, and it may bethat the quality and compl^ty of an analyst'stemplates is one measure of expertise. As inthe Biggs protocol analysis, eamings forecast-ing did not play a dominant role.

(Footnote 38 continued)counting. Organizations and Society 9:3/4, 1984, pp.313-323 and M. Bouwman, P. Frishkoff and P.FrishkofT, "How Do Financial Analysts Make Deci-sions? A Process Model of the Investsnent ScreeningDecision," Accounting, Organizations and Society 12:1,1987, pp. 1-29.


Neither of the two protocol analyses siun-maiized here bears directly on questions thathave occupied capital-markets based researchinvolving analysts. It is possible, however, thatthe approach of asking an analyst to "thinkout loud" in forecasting earnings could bestructured to shed some light on the reasonsfor the tendency to optimism in analysts' earn-ings forecasts. Given restilts which indicatethat optimism seems to be most pronouncedin forecasts preceded by share price declinesor earnings declines, it may make sense toinvestigate decision strategies in extremecases like these as well as more typical casesto ascertain whether and how the firm's finan-cial condition affects decisions even in a labo-ratory setting where job-related incentives areabsent.

Conclusions. Research on financial ana-lysts using capital markets approaches hasremained distinct from judgment and deci-sion-making research in this area. The capi-tal markets research appears to be largelydriven by accountants' preference for usinganalysts' earnings forecasts as proxies formarket expectations in investigations of theearnings-share retum relation. As a result,there has historically been an emphasis ondocumenting the statistical properties of theseforecasts. This emphasis is now beginning toshift, in tbat some researchers are beginningto consider the decision contexts in which fore-casts are made and tbe incentives facing ana-lysts. In addition, some questions of analysts'information use are being investigated indi-rectly.

In contrast, judgment and decision-mak-ing research on analysts' decision processeshas largely ignored forecasting behavior infavor of general considerations of what infor-mation analysts use, and how. In part, this

focus is refiected in tbe use of buy-side ana-lysts as subjects; tbese persons may consumethe earnings forecasts of sell-side analysts in-stead of or in addition to making tbeir ownprqjections. Wbile tbis research bas so far notattempted to place analysts in realistic set-tings in terms of incentives and conflicts ofinterest, it has in some cases focussed on tbeanalysts' final product (tbe recommendationand related report). In addition, tbe paperssxunmarized bere indicate tbat analysts tendto focus on earnings more tban cash flows intbeir reports, tbat tbey do not necessarily ex-plicitly predict earnings as an intermediatestep in choosing a stock to recommend fix>m agiven group, and tbat tbey pay substantialattention to segment data wben these are of-fered. Accuracy and bias, two m^or preoccu-pations of capital markets researcb, are notusually the focusses of judgment and dedsion-making research involving analysts.

Tbe capital markets and judgment/ded-sion-making strands of analyst research maybe drawn together if investigations of analystunder- or overreaction to price and earningsinformation are extended. Based on statisti-cal evidence from capital markets researcbthat suggests certain information is or is notfully reflected in forecast revisions, a detailedbehavioral investigation in a controlled set-ting may be able to discover wbetber the ana-lysts are (to take one example) using a par-ticular dedsion rule tbat results in optimisticforecasts (i.e., systematic earnings overesti-mates) in tbe presence of negative price orearnings information. An altemative explana-tion, of course, is tbat analysts experiencepressiire fi*om managers of companies tbeyfollow to be favorable even wben at least someof tbe objective evidence woiild suggest other-wise.

120 Accounting Horizons /December 1991

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