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					   The Expectations Management Game: Do Analysts Act Independently of
                 Explicit Management Earnings Guidance?

                                               Julie Cotter
                                   University of Southern Queensland
                                 Toowoomba Queensland 4350, Australia
                                      E-mail: cotter@usq.edu.au

                                           A. Irem Tuna
                              University of Michigan Business School
                     701 Tappan Street, Ann Arbor, Michigan, 48109-1234, USA
                                     E-mail: atuna@umich.edu

                                         Peter D. Wysocki
                              University of Michigan Business School
                     701 Tappan Street, Ann Arbor, Michigan, 48109-1234, USA
                                   E-mail: wysockip@umich.edu

                                          June, 2000 - Preliminary

Abstract

The financial press and recent academic research have made claims that firms are guiding
security analysts toward "beatable" earnings forecasts. This paper re-examines these claims by
investigating security analysts' reactions to explicit management earnings guidance. We use a
large sample of quarterly management earnings forecasts to examine the content of management
guidance and the timing, extent and independence of analysts' reactions to this guidance. Our
empirical analysis shows that (a) management guidance tends to be overly pessimistic relative to
actual earnings outcomes, and (b) analysts react quickly to management guidance that contains
new information. However, we find that analysts do not simply "parrot" the information
provided by management. Analysts act independently and adjust their forecasts to compensate
for bias in management's earnings guidance. Further, while analysts tend to shift from optimism
to pessimism in response to ‘bad news’ management guidance, the opposite is true for ‘good



Key Words: Analysts, Expectations management, Management earnings forecasts.




_________
We would like to thank Stan Levine at First Call for providing the analyst and management forecast data and for
helpful discussions. The comments of Andreas Gintschel and Scott Richardson are gratefully acknowledged.
1. Introduction

       Recent research has argued that, during the 1990's, firms have successfully engaged in

earnings manipulation and expectations management games that allow them to consistently beat

analysts' earnings predictions. For example, Brown (1999), Brown and Higgins (1999), and

Burgstahler and Eames (1999) provide indirect evidence of earnings manipulation that allows

firms to beat analysts' targets. On the other hand, Matsumoto (1999) and Richardson, Teoh and

Wysocki (1999) provide indirect evidence that firms appear to be guiding analysts toward

"beatable" earnings forecasts. In particular, these studies document an apparent shift from

systematic analyst optimism at the beginning of a fiscal period to systematic analyst pessimism

just prior to an earnings announcement. These findings have been interpreted as prima facie

evidence that firms opportunistically guide analysts toward beatable targets. However, these

studies have not been able to provide direct evidence of actual management guidance. In

addition, it is implicitly assumed that analysts are naïve information disseminators who can be

easily directed toward any target.

       This study re-examines the evidence on expectations management games. Our approach

is new because we directly investigate security analysts' reactions to explicit management

earnings guidance. Our key finding is that analysts act independently of management when

issuing revised earnings forecasts following explicit management guidance. Analysts identify

and adjust for both pessimistic and optimistic bias in management earnings forecasts. Our direct

tests provide new insights into the role of security analysts as independent information

intermediaries in financial markets. Our approach also extends prior research on earnings

expectations management by documenting direct communications between firm management

and analysts instead of relying on indirect proxies for firm-provided guidance.




                                                2
       There is a widespread belief that "meeting or beating" analysts' consensus earnings target

is crucial for firm management. For example, Skinner and Sloan (1999) provide evidence that the

stock market severely punishes firms, especially growth firms, that miss an earnings target. This

emphasis on earnings targets motivates the empirical tests in this paper.

       We examine both the content of management guidance and the timing, extent and

independence of analysts' reactions to this guidance. This direct analysis breaks down the

expectations management game into its fundamental components. We first determine if

management is, in fact, providing pessimistic earnings guidance. We then examine whether

analysts react to management's guidance by issuing a revised earnings forecast, and the speed of

this reaction. For those analysts that do revise their forecasts, we investigate their independence

from management in terms of whether they merely “parrot” the forecasts of management.

Finally, we examine whether analysts become pessimistic relative to actual earnings outcomes. If

analysts are acting independently of management, we expect that they will discount any

excessive pessimism by managers.

       Our data sample consists of a large set of firms that issued quarterly management

earnings forecasts in the three months leading up to quarterly earnings announcements for the

period 1993-1999. We gather information on the actual date and content of all quarterly

management earnings forecasts from the comprehensive First Call database of management and

security analyst earnings forecasts. We then track the daily forecasting activities of individual

security analysts following explicit management guidance. The overall sample consists of 2,422

management earnings forecast events for 1,424 firms.

       As a first step, we examine explicit management guidance and determine whether

management is attempting to guide analysts toward a beatable target by being overly pessimistic




                                                 3
in their earnings forecasts (the first necessary part of the expectations management game).

Consistent with prior research, we find that most management earnings forecasts tend to contain

"bad news" relative to analysts' prior consensus estimates of quarterly earnings. Moreover, we

find that management guidance tends to be overly pessimistic relative to ex post actual earnings

outcomes (see also Soffer, Thiagaragan and Walther, 1999). This finding is consistent with

claims that management is trying to guide analysts toward beatable targets.

       The second important question is whether analysts "take the bait" and naïvely revise their

earnings forecasts to mirror managers' pessimistic forecasts. Our examination of this issue

involves the timing, extent and independence of analyst forecast revisions. In relation to the

timing of forecast revisions, we find that the vast majority of analyst activity in a fiscal quarter

takes place in direct response to explicit management communications with the market. This is

inconsistent with prior claims that analysts tend to revise their forecasts based on unobservable

guidance from management. In our sample, analysts appear to primarily react to explicit public

guidance from management. In regard to the extent of analyst revisions, we find that and

average of 41.6% of analysts following our sample firms revise their forecasts within five days

of a management guidance event. Analysts are more likely to react to management earnings

forecasts that contain new information, particularly if the guidance indicates that earnings will be

lower than expected (bad news).

       We then examine the independence of analysts' reactions to management earnings

guidance. First, we determine whether analysts merely "parrot" the information provided by

management. Our results indicate that analysts are more than mere information disseminators.

Indeed, they tend to adjust their forecasts to compensate for the excessive pessimism of




                                                 4
management. This result provides new evidence on the independence of analysts from

management. Analysts determine and then adjust for the direction of management bias.

       While our evidence that analysts do not simply “parrot” management forecasts indicates

that they act independently of management earnings guidance, management may still have been

successful in guiding analysts to “beatable” forecasts. That is, while analyst forecasts following

explicit management earnings guidance are less pessimistic than management’s forecasts they

may still be pessimistic relative to actual earnings outcomes. Therefore, our final set of tests

examines the extent of analyst pessimism following management earnings guidance. We find

that while analysts tend to be pessimistic following ‘bad news’ management guidance, they are

generally slightly optimistic following ‘good news’ management guidance. However, given that

the majority of management forecasts convey bad news, our results tend to indicate that

management is generally successful in guiding analysts towards forecasts that they can ‘meet’ or



       The remainder of the paper is organized as follows. Section 2 reviews the related

literature and develops the hypotheses. Section 3 describes the sample and our empirical

analysis, while section 4 concludes.



2. Hypothesis development and literature review

       Recent research has argued that firms have been engaging in a game of earnings

manipulation and expectations management that allows them to consistently beat analysts'

earnings predictions. There is evidence that investors tend to focus on earnings targets and

penalize firms that miss these targets. For example, Skinner and Sloan (1999) provide evidence

that the stock market severely punishes firms, especially growth firms, that miss an earnings




                                                5
target. Given these penalties, it has been argued that firm management has strong incentives to

avoid these negative surprises by manipulating earnings upward to meet or exceed analysts’

expectations. Indirect evidence of earnings manipulation to beat analysts' targets includes Brown

(1999), Brown and Higgins (1999), and Burgstahler and Eames (1999).

       On the other hand, firms can attempt to beat an earnings target by actually lowering the

target set by analysts. In other words, firm management can manage expectations downward.

Matsumoto (1999) and Richardson, Teoh and Wysocki (1999) provide indirect evidence that, in

recent years, firms have been guiding analysts toward "beatable" earnings forecasts. These

studies document an apparent shift from systematic analyst optimism at the beginning of a fiscal

period to systematic analyst pessimism just prior to an earnings announcement. This pattern has

become more pronounced during the 1990's. These findings have been interpreted as prima facie

evidence that management has been opportunistically guiding analysts toward beatable targets.

However, these studies have not been able to provide direct evidence of actual management

guidance. For example, Matsumoto (1999) asserts that "… much of the guidance provided to

analysts occurs in private conversations between managers and analysts, the content of which is

not directly observable."

       Implicit in prior work by Brown (1999), Burgstahler and Eames (1998), Masumoto

(1999), and Richardson et al. (1999), is the idea of a smooth and continual downward trend in the

level of optimism over a fiscal period that finally leads to pessimism at the end of the period.

This downward trend appears to be consistent with management gradually lowering expectations

of analysts through informal communications throughout the fiscal period.

       The purpose of this paper is to examine explicit guidance by management and determine

if prior theories on ex ante expectations management are correct. Past research on analyst




                                                6
forecast activity has shown that analyst activity tends to come in bursts, followed by long lulls in

activity. For example, Stickel (1989) finds that analysts tend to update around interim earnings

announcements. However, there has been little evidence on how analysts cluster around

unscheduled management announcements. 1 If analyst revisions tend to cluster around explicit

management earnings forecasts, then this is inconsistent with management gradually lowering

analysts' expectations through informal communications throughout the fiscal period.



2.1 Managers' attempts to lead analysts

         Our analysis begins with an examination of the nature of explicit management guidance.

In particular, we want to determine if management earnings forecasts contain overly-pessimistic

information intended to guide analysts toward a beatable target. If management is attempting to

deflate analysts' expectations and lead analysts toward a beatable target, then:



Hypothesis H1: Management forecasts are pessimistic relative to realized earnings.



2.2 Analysts’ reactions to explicit management earnings guidance

         Finding empirical evidence that managers are overly pessimistic in their earnings

forecasts would lend support to the claim that managers are explicitly trying to guide analysts

toward beatable targets. However, how analysts react to this guidance is the more critical

component to the game. If analysts act independently of management and fully adjust for

management bias in their revised forecasts, then the game falls apart. Our examination of




1
  Stan Levine of First Call Corporation has performed an analysis of the First Call dataset and also finds evidence
that analysts’ forecasts tend to cluster in time.


                                                          7
analysts’ reactions to management earnings guidance includes the timing and extent of analyst

forecast revisions, as well as the independence of analysts' reactions to this guidance.

        Stickel (1989) finds that analysts tend to revise their forecasts following interim earnings

announcements. Based on this finding, we expect that analysts will update their earnings

forecasts following other forms of explicit management guidance, including earnings forecasts.

If analysts participate in the expectations management game, then:



Hypothesis H2: Analysts issue revised earnings forecasts following explicit management

earnings guidance.



        We examine the independence of analysts by determining whether they react naively to

explicit management earnings guidance. The expectations management game implicitly assumes

that analysts act like "parrots" that simply disseminate information from management forecasts.

If this is the case, then:



Hypothesis H3a "Naive Analyst Hypothesis": Analysts simply repeat earnings numbers that

are forecasted by management.



        However, Chung and Jo (1996) propose that analysts act as independent monitors of

managers’ disclosures. Financial analysts play an important role as information intermediaries

between company management and capital markets (Schipper, 1991). Their stock of knowledge

about the firms that they follow allows them to assess the reasonableness of management

earnings forecasts. However, whether analysts adjust for any bias in management earnings




                                                 8
forecasts will be a function of their independence from management. Analysts have incentives to

nurture relationships with managers to ensure continued access to company information. If

analysts are independent of management, then:



Hypothesis H3b "Independent Analyst Hypothesis": Analysts’ earnings forecasts following

explicit management guidance are less biased than managers’ earnings forecasts.



2.3 Do analysts become pessimistic following explicit management guidance?

       Even if analysts act independently of management and adjust for management bias in

their forecast revisions, management may still have been successful in guiding analysts to

overly-pessimistic or “beatable” forecasts. That is, while analyst forecasts following explicit

management earnings guidance may be less pessimistic than management’s’ forecasts, they may

still be pessimistic relative to actual earnings outcomes. Prior research has shown that analysts

tend to be optimistic in their earnings forecasts, especially early in the year (O'Brien, 1988).

However, if managers can successfully guide analysts toward beatable targets just before an

earnings announcement, then:



Hypothesis H4: Analysts become pessimistic (relative to realized earnings) following explicit

management earnings guidance.



3. Analysis

       This section outlines the empirical tests of our hypotheses. In particular, we test the

relation between management earnings forecasts and analysts’ forecast revisions following these




                                                9
forecasts. The empirical tests examine each stage of the earnings expectations management

game and are divided into three main categories. We first test whether there is a pessimistic bias

in management's earnings guidance. In other words, is management attempting to lead analysts

toward beatable targets? This is a necessary condition for the existence of an expectations

management game. We then examine whether analysts naively follow management guidance.

This stage of the research commences with an examination of the timing and extent of analyst's

reactions to guidance events, followed by an examination of analyst independence from

management. Finally, we examine whether analyst forecasts become pessimistic following

explicit management guidance. That is, have managers been successful in guiding earnings

expectations to a level that they can meet or beat?



3.1 Sample and descriptive statistics

       In this study, we examine explicit earnings guidance provided by management to security

analysts. The data is obtained from the First Call database of “company issued guidelines"

(basically management forecasts). The sample is limited to forecasts of quarterly earnings per

share for the current quarter. Each quarter is defined by earnings announcements. Rather than

using the fiscal dates, we define the previous earnings announcement date as the start of the

quarter and the current earnings announcement date as the end of the quarter.

       We record the date and the exact value of each management earnings forecast. The date

of the management earnings forecast must take place after the announcement of last quarter's

earnings and be related to earnings for the current quarter. We merge this data with the First Call

database of analyst estimates of earnings per share for the current fiscal quarter. The date of each

analyst forecast revision is recorded. Analysts are allowed to make multiple revisions during the




                                                10
quarter. The data set is designed so that all events happen within a given quarter. The sample of

management and analysts forecasts are then merged with the First Call “official actuals” file that

contains the actual ex post earnings per share for the current fiscal quarter. Finally, the data is

merged with financial information from the Compustat database to obtain information on firm

characteristics.

        Panel A of Table 1 summarizes the sample for the final set of observations that have

complete information on management earnings forecasts, analyst revisions, and firm

characteristics. The final sample consists of 2,422 management guidance events for 1,424 firms.

There are a total of 6,760 analysts following our sample firms.

        Panel B of Table 1 shows descriptive statistics for our sample of firms issuing

management earnings forecasts. Nanalyst is a measure of the number analysts actively following

each sample firm in the quarter (tabulated as the number of individual analyst forecasts made

within 11 days of the previous quarter's earnings announcement). The mean (median) number of

analysts following each of our sample firms is 2.58 (2), with a range of 1 to 22. MVE is the

market value of common shareholders' equity at the beginning of the year. Growth captures

growth opportunities and is measured as the firm's market to book ratio. Our sample of firms

exhibits considerable variation in both size and growth.

        Panel C of Table 1 categorizes management earnings forecasts according to their “news

content”. The "news content" of earnings guidance is measured as the management forecast

value relative to analysts' prior consensus estimates of earnings. Good news (bad news, no news)

indicates that the management forecast value is higher than (lower than, equal to) the prior

consensus. Consistent with prior research (see Skinner 1994, 1997) there are substantially more

bad news forecasts than good news forecasts in our sample. 1119 of our sample of 2422




                                                11
management forecasts contain bad news, while 190 contain good news and 1113 contain no

news. That is, they are equal to the prior consensus.



3.2 Do managers attempt to lead analysts?

       To commence our analysis of the expectations management game, we first want to

determine if managers attempt to guide analysts toward a beatable earnings target. In particular,

we examine management earnings forecasts where there is explicit, public information

communicated to security analysts and investors. We examine the bias in management earnings

forecasts relative to actual ex post earnings outcomes for each management earnings forecast in

the sample. Pessimism is indicated where the management forecast is lower than ex post

earnings, optimism is indicated where the management forecast is higher than ex post earnings,

while unbiased management forecasts are equal to ex post earnings.

       A table cross tabulating the directional bias in management earnings forecasts and “news

content” is presented in Panel A of Table 2. The majority of management forecasts are

pessimistic relative to actual earnings outcomes. Indeed, 1351 of our 2422 (55.8%) of the

management forecasts in our sample are pessimistic, while 124 (5.1%) are unbiased and 947

(39.1%) are optimistic. The large number of pessimistic management forecasts is consistent with

anecdotal evidence and the commonly held perception that management may be leading analysts

toward beatable earnings targets. That is, this result provides evidence in support of hypothesis

one.

       Given the differing incentives of managers in relation to announcements of good and bad

news (see Skinner, 1994) we examine the impact of “news content” on managers’ propensity to

provide pessimistic forecasts. The results shown in Panel A of Table 2 indicate that the




                                                12
directional bias in management earnings forecasts is related to the news content of the forecast

(Chi-square = 27.346, p = 0.001). 128 (67.4%) of the 190 good news forecasts are pessimistic,

while only 568 (51%) of the 1113 bad news forecasts are pessimistic.

         Panel B of Table 2 shows the extent of bias in management earnings forecasts. MgmtBias

captures the magnitude of bias in management earnings forecasts, and is measured as the amount

of the management earnings forecast less actual earnings, all scaled by actual earnings. 2 The

mean value of our MgmtBias variable for our full sample of 2,422 management forecasts is

11.5% relative to actual earnings. An examination of the pessimistic and optimistic sub-samples

reveals that the average amount of optimism in management forecasts (76.3%) is much greater

than the average amount of pessimism (32.9%), although both of these are high.



3.3 How do analysts react to explicit management guidance?

         Based on our finding that managers tend to issue pessimistic guidance about future

earnings, we wish to determine whether analysts react to management's public information

releases, and if so, whether their reactions are naïve or independent. We commence our analysis

with an examination of the timing and extent of analyst reactions to guidance events. The content

of revised analyst forecasts is then examined to determine whether reacting analysts act

independently of management guidance.



3.3.1 The timing and extent of analysts reactions to management forecasts




2
  Very low realized earnings for some sample firms cause large absolute values for this measure and the analyst bias
measures discussed in the following sections. To overcome this problem, extreme outliers are winsorized. We also
calculate alternative measures for these variables that involve scaling by market value of equity rather than realized
earnings. However, we report the results using measures scaled by realized earnings, since the values are more
intuitive.


                                                         13
        Hypothesis two predicts that analysts issue revised earnings forecasts following explicit

management earnings guidance. In testing this hypothesis, we examine whether explicit

management guidance leads to forecast revisions by analysts, how quickly these revised

forecasts are issued following the management forecast, and the proportion of analysts revising

their forecasts.

        Based on the findings of Stickel (1989), we expect that most analysts will update their

earnings forecasts following explicit management communications including interim earnings

announcements and earnings forecasts. On a daily basis, we track the number of analyst forecast

revisions immediately following the last interim earnings announcement and in the days

surrounding a manangement guidance event. The results of this plot are presented in Figure 1.

We find a striking pattern in analyst activity. The vast majority of analyst activity occurs around

firms' public information releases. In the overall sample, we find that of all forecasts made

during a quarter, 34% of these individual analyst forecasts occur within 3 days of the last

quarterly earnings announcement. In addition, we find that for this sample of firms that had a

management forecast, another 41% occur on the day of and during the two days following the

management earnings forecasts. Analysts do not appear to foresee this unscheduled

announcement, but most analysts quickly react to the announcement event. We find a 17-fold

increase in analyst activity the day after a management forecast announcement compared to the

day before the announcement. In summary, the vast majority of analyst activity occurs in direct

response to explicit management announcements.

        We measure the extent of analyst reactions to management guidance by counting the

number of analysts issuing revised earnings forecasts following a guidance event relative to the

number of analysts following that firm. The total number of analysts' forecasts within 11 days




                                                14
after the announcement of the previous quarter's earnings captures the number of analysts

following each firm. While Figure 1 indicates that the majority of forecast revisions fall on the

day following interim earnings announcements, we consider eleven days following the

announcements to ensure that we capture all analysts following our sample firms.

       The number of analysts issuing revised earnings forecasts following a guidance event is

measured between the day of the management forecast and five days subsequent. This is the

period over which the majority of forecast revisions occur. There are a total of 6,284 analyst

forecast revisions during this post-management guidance period. However, not all of these are

issued by analysts that issue forecasts at the beginning of the quarter, and several analysts revise

more than once during this period. Of the 6,760 analysts following our sample, 3,302 (48.8%)

issue at least one revised forecast within this five day period. The majority of these are issued on

either the day of the management earnings forecast (819) or the day following (1,620). 96 of our

following analysts issue two forecasts over the post-management guidance period. However, we

include only the most recent forecast for these analysts.

       Frac is calculated for each management guidance event as the ratio of the number of

following analysts who revise their forecasts within 5 days after a management forecast to the

total number of analysts' forecasts within 11 days of the announcement of the previous quarter's

earnings. As for our examination of management guidance bias, our analysis of the extent of

analyst reactions to management guidance includes an examination of the impact of “news

content” on analysts’ propensity to issue revised earnings forecasts. The type of information

released in a management forecast is expected to affect the likelihood that analysts will react to

management guidance. In particular, analysts are expected to have incentives similar to those of

mangers in relation to the avoidance of potential legal liability and reputation costs associated




                                                 15
with overly optimistic forecasts. We therefore expect a greater extent of analyst revisions

following ‘bad news’ guidance events.

       Panel A of Table 3 shows that an average of 41.6% of analysts following each firm revise

their forecasts within five days of a management guidance event.        However, the extent of

forecast revisions differs dramatically with news content.     While an average of 65.3% of

following analysts revise in response to bad news management forecasts, only 37.1% respond to

good news forecasts. Further, a mere 18.8% of analysts respond to management forecasts that

simply confirm consensus analyst forecasts. Untabulated results confirm that these average

percentages are similar for the sub-sample of forecast revisions associated with pessimistic

management forecasts. We test the robustness of this relation by controlling for the extent of

management pessimism and several firm characteristics. Our regression model is:



       Frac = α + β1GoodNews + β2BadNews + β3MgmtBias + β4Nanalyst

              + β5Size + β6Growth + ε                                                    (1)



       where GoodNews and BadNews are categorical variables that equal one if the

management forecast is greater than or less than the prior consensus respectively, and zero where

it is the same (no news). MgmtBias captures the magnitude of bias in management earnings

forecasts, and is measured as the amount of the management earnings forecast less actual

earnings, all scaled by actual earnings. Nanalyst is a measure of the number analysts actively

following each sample firm in the quarter (tabulated as the total number of individual analyst

forecasts made within 11 days of the previous quarter's earnings announcement). Size is the

natural logarithm of market value of common shareholders' equity at the beginning of the year




                                               16
                                      C
(computed as [Compustat item # 199] * [ ompustat item # 25]). We measure firm growth

                                                                          C
opportunities, Growth, using the firm's market to book ratio (computed as [ ompustat item #

199] * [Compustat item # 25] / [Compustat item # 60]).

       The regression results are summarized in Panel B of Table 3. Again, analysts are more

likely to react to management earnings forecasts that contain new information, particularly if the

guidance indicates that earnings will be lower than expected (bad news). The extent of analyst

reaction is not significantly associated with the extent of management bias, the number of

analysts following each firm, or the firm characteristics examined. Overall, our evidence is

consistent with analysts quickly issuing revised earnings forecasts following explicit

management earnings guidance that contains new information, particularly bad news.



3.3.2 Are analysts' forecast revisions independent of management guidance?

       We have provided preliminary evidence that is consistent with the expectations

management game. First, management tends to understate expected performance in their public

earnings forecasts. Second, analysts quickly respond to newsworthy management guidance and

update their forecasts following these management announcements. The next part of the puzzle is

to determine whether analysts react naïvely or independently of management guidance.

       We measure how analysts react to management guidance by comparing each analyst’s

earnings forecast after a guidance event with the value of management's earnings forecast. The

results presented in Panel A of Table 4 provide strong evidence that analysts do not merely

"parrot" management's forecasts. Indeed, only 406 of the 3,302 analysts forecast revisions in the

five days following the management guidance are equal to the management forecasts. The

remainder are either more optimistic or more pessimistic than the management forecasts.




                                               17
Overall, revised analyst forecasts tend to be more optimistic (less pessimistic) than management

forecasts. 2419 (73.3%) of the revised analyst forecasts relate to bad news guidance from

management. For these bad news announcements, analysts are unbiased or more optimistic than

management 67.4% of the time.

       Panel B of Table 4 shows the extent of differences between analyst and management

earnings forecasts. GuidanceBias captures the magnitude of bias in revised analyst forecasts

relative to management earnings forecasts, and is measured as the difference between these two

forecasts scaled by actual earnings. For pessimistic management earnings forecasts, analysts tend

to be less pessimistic (more optimistic), while for optimistic management forecasts, analysts tend

to be less optimistic (more pessimistic). These results indicate that not only do analysts act

independently of management, but that they are able to determine and adjust for the direction of

management bias. 3

       We test for the robustness of this finding by using a multivariate regression model to

control for the news content of the announcement as well as several firm characteristics. Our

regression model is:



       GuidanceBias = α + β1 GoodNews + β2 BadNews + β3 MgmtBias + β4 Nanalyst

                       + β5 Size + β6Growth + ε                                           (2)



       where all variables are as previously defined. The regression results are summarized in

Panel C of Table 4. The result that analysts act independently of management by identifying and

adjusting for the extent of management bias is supported in a multivariate context. Further,




                                               18
analysts are more likely to be optimistic relative to managers after bad news guidance. In

addition, analysts are more likely to be optimistic when there is higher analyst following for a

given firm.

        Overall, the results above reject H3a, “naïve analyst hypothesis” and support H3b

“independent analysts hypothesis”.           There is a substantial difference between the analysts’

forecast value and management forecast value.                  In other words, analysts do not simply

disseminate the same information that is contained in management forecasts.                        Rather, their

forecast revisions exhibit independence from management guidance. Analysts identify and adjust

for both pessimistic and optimistic bias in explicit management earnings forecasts. Moreover, the

difference between the analysts’ forecasts and management forecasts is changing in the type of

news and number of analysts following the firm.



3.4 Do analysts become pessimistic following management guidance?

        The preceding results indicate that analysts tend to adjust for the bias in explicit

management guidance. However, analysts' revised forecasts could still be pessimistic enough to

create a "beatable" target for management at the end of the fiscal period. Therefore, we examine

revised analyst forecasts relative to actual ex post earnings to determine whether analysts become

pessimistic following management guidance. Hypothesis four predicts that analysts become

pessimistic following explicit management guidance.

        The results shown in Panel A of Table 5 support hypothesis four and indicate that

management was able to meet or beat a total of 2,113 of the 3,302 revised analyst forecasts

(64%). Therefore, while analysts tend to act independently of managers to the extent that they

3
  Deleting all but the most recent forecast revision for each analysts biases against us finding that analysts act
independently of management, since the median ‘second revision’ (0.047) is closer to the management forecast than



                                                        19
reduce the bias in management earnings forecasts, managers are still able to meet or beat revised

analysts forecasts in the majority of cases. Further, these results indicate that analyst bias is

associated with the news content of the management forecast; with analysts tending to become

overly optimistic following good news and overly pessimistic following bad news. Management

was less successful in beating analyst forecasts that were pre-empted by ‘good news’

announcements.

         Panel B of Table 5 shows the extent of bias in revised analyst forecasts. AnalystBias

captures the magnitude of bias in analyst earnings forecasts, and is measured as the amount of

the analyst earnings forecast less actual earnings, all scaled by actual earnings. While the mean

value of our AnalystBias variable is negative, the median of zero indicates that revised analysts

forecasts following explicit management guidance tend to be reasonably accurate indicators of

expected earnings. For pessimistic management earnings forecasts, analysts tend to be optimistic

relative to actual earnings; while for optimistic management forecasts, analysts tend to be

pessimistic relative to actual earnings. These results tend to indicate that analysts overcorrect in

their adjustments for management bias. That is, while they are able to determine the direction of

management forecast bias, they are less accurate in determining the extent of management bias.

         We test for the robustness of this finding by using a multivariate regression model to

control for the news content of the announcement as well as several firm characteristics. Our

regression model is:



AnalystBias = α + β1 GoodNews + β2 BadNews + β3 MgmtBias + β4 Nanalyst +

                   β5 Size + β6Growth + ε                                                   (3)




the first revision (0.323)

                                                20
         where all variables are as previously defined. The regression results are summarized in

Panel C of Table 5. These results confirm that the bias in analyst forecasts takes the opposite

direction to that in management forecasts. Further, analysts tend to be pessimistic in relation to

bad news management guidance and optimistic in relation to good news management guidance.

Finally, analysts tend to be more pessimistic when there are a greater number of analysts

following the firm. Analyst bias is not related to the size or growth characteristics of the firm.

         The final stage of our tests involves a determination of whether explicit management

guidance has caused a ‘switch’ from analyst optimism to pessimism. We examine our ‘good’ and

‘bad’ news groups separately, since by definition, analysts are overly optimistic prior to Bad

News management guidance and overly pessimistic prior to Good News guidance. Panel A of

Table 5 indicates that only 33.3% (805) of analysts remain optimistic following explicit bad

news guidance. The remainder issue revised forecasts that managers are able to meet or beat.

Indeed, Panel B of Table 5 indicate that the mean (median) extent of analysts forecast bias five

days after the management guidance is –0.110 (0.000). 4 There appears to have been a switch

from optimism to unbiased or slightly pessimistic analyst forecasts in relation to bad news

management guidance. On the other hand, the majority of revised analyst forecasts following

good news guidance are optimistic, with only 10.4% (23) remaining pessimistic. Further, the

mean (median) extent of analyst forecast bias five days after the management guidance is 0.016

(0.019), indicating a switch from pessimism to slight optimism in relation to good news

announcements. Overall, the results shown in Table 5 indicate a switch from optimism to slight

pessimism following bad news management guidance. However, they indicate the opposite trend

in relation to good news guidance.


4
  Our alternative measure of analyst bias that is scaled by market value rather than realized earnings shows similar
results.


                                                          21
4. Summary and conclusions

       This paper re-examines the claim that firms have been successfully guiding security

analysts toward "beatable" earnings forecasts. We use a novel approach to this empirical problem

by examining security analysts' reactions to explicit management earnings guidance. The

empirical tests are implemented using a sample of quarterly management earnings forecasts for a

large number of U.S. firms between 1993 and 1999. We examine the content of both

management's guidance and revised analysts' forecasts following this guidance.

       Our empirical tests examine each stage of the earnings expectations management game

and are divided into three main categories. We first test whether there is a pessimistic bias in

management's earnings guidance and find that the majority of management forecasts are

pessimistic. We then examine whether analysts naively follow management guidance. This stage

of the research commences with an examination of the timing and extent of analyst's reactions to

guidance events, followed by an examination of analyst independence from management. We

find that the vast majority of analyst activity occurs quickly and in direct response to explicit

management announcements. Further, we find that analysts are more likely to react to

management guidance that contains bad news.

       However, our results indicate that analysts are more than mere information disseminators.

Indeed, it appears that analysts act as monitors of management information releases and make

adjustments to management's overly pessimistic predictions of firm performance. Analysts

appear to limit manager's ability to mislead the market with negative forecasts. This is of

particular importance because management earnings forecasts are not "audited" by independent




                                               22
parties. Analysts are able to identify and adjust for both pessimistic and optimistic bias in explicit

management earnings forecasts.

       Finally, we examine whether analyst forecasts become pessimistic following explicit

management guidance. Despite the apparent independence of analysts, our findings are generally

consistent with prior claims that management has been successful in systematically guiding

analysts toward "beatable" earnings forecasts. However, this is not the case for good news

management guidance. Analysts tend to become overly optimistic following good news

guidance, causing a switch from analyst pessimism to slight optimism. Our results in relation to

bad news management guidance are consistent with Matsumoto (1999), Richardson et al. (1999),

and Brown (1999). That is, we find that analysts appear to shift from optimism to slight

pessimism in response to bad news management guidance.




                                                 23
References


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                                               24
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                                              25
                                                  Table 1
                                            Descriptive Statistics


Panel A: Sample Information
          Sample Period                                               1993-1999
    Number of management                                                2,422
        earnings forecasts
    Number of firms issuing                                              1,424
      management forecasts
    Total number of analysts                                             6760
     following sample firms

Panel B: Descriptive Statistics for Sample of 1424 Firms
Variable Name        Mean            Median        Std. Dev.                        Min                Max
   NAnalyst               2.58                2           2.49                               1                 22
     MVE                 2236               446          7037                               23            100231
   Growth                 3.36             2.52           2.69                            0.41              20.45

Panel C: News Content of Management Earnings Forecastsa
         Good News                                                        190
          Bad News                                                       1113
          No News                                                        1119
Total Number of Management
      Earnings Forecasts                                                 2422

Nanalyst captures the number of analysts actively following each firm, and is calculated as the number of analysts’
forecasts within 11 days after the previous quarter’s earnings announcement.

MVE is the beginning market value of equity [computed as Compustat Item #199 * Compustat Item #25].

Growth captures growth opportunities and is measured as the market to book ratio [computed as MVE / Compustat
Item #60].
a
  The news content of management earnings forecasts is determined by a comparison with the prior analyst
consensus. Good news (bad news, no news) indicates that the management forecast value is higher than (lower than,
equal to) the prior consensus.




                                                        26
                                       Table 2
    Bias in Management Earnings Forecasts Compared to Actual Earnings Outcomes for
                    Sample of 2422 Management Earnings Forecasts

Panel A: Bias in Management Earnings Forecasts by News Content

News Contenta                Pessimisticb            Unbiasedb              Optimisticb                Total

Good news                    128                            4                       58                  190
Bad news                     568                           72                      473                 1113
No news                      655                           48                      416                 1119
                            1351                          124                      947                 2422
Chi-square = 27.346, p = 0.001

Panel B: Extent of Bias in Management Earnings Forecasts (MgmtBias)
                                                                                            Total (includes
                                                 b                             b
                                    Pessimistic                   Optimistic              unbiased forecasts)

Mean                                   -0.329                        0.763                          0.115
Median                                 -0.200                        0.707                         -0.057
Standard Deviation                      0.328                        0.643                          0.705
N                                       1351                          947                           2422

a
  The news content of management earnings forecasts is determined by a comparison with the prior analyst
consensus. Good news (bad news, no news) indicates that the management forecast value is higher than (lower than,
equal to) the prior consensus.

b
  The directional bias in management forecasts is determined by a comparison with actual earnings. Pessimistic
(optimistic, unbiased) indicates that the management forecast value is lower than (higher than, equal to) the ex-post
earnings value for a given quarter.

MgmtBias captures the extent of bias in management earnings forecasts and is measured as the management
earnings forecast less actual earnings, all scaled by actual earnings.




                                                         27
                                             Table 3
            Fraction of Analysts Reacting to Management Earnings Forecast (FRAC)

Panel A: Descriptive Statistics for FRAC for Full Sample of 2422 Management Forecasts
 News content a      Mean            Median        Std. Dev.       Min          Max
  Good news             0.371            0.250           0.406           0                                          1
   Bad news             0.653            0.800           0.389           0                                          1
   No News              0.188                0           0.341           0                                          1
    Total               0.416            0.333           0.431           0                                          1

Panel B: Determinants of Fraction of Analysts Reacting to Management Earnings Forecast
(Frac = α + β1 GoodNews + β2 BadNews + β3 MgmtBias + β4 Nanalyst + β5Size + β6 Growth + ε)
          Coefficient              Estimate                t-statistic                p-value

Intercept                                         0.124                         3.592                         0.001

GoodNews                                          0.187                         6.441                         0.001

BadNews                                           0.470                        29.311                         0.001

MgmtBias                                         -0.017                        -1.590                         0.112

Nanalyst                                          0.004                         1.269                         0.204

Size                                              0.007                         1.342                         0.180

Growth                                            0.001                         0.472                         0.637

N                                                                                2422
Adj. R2                                                                         0.269
a
  The news content of management earnings forecasts is determined by a comparison with the prior analyst
consensus. Good news (bad news, no news) indicates that the management forecast value is higher than (lower than,
equal to) the prior consensus.
Frac is calculated as the ratio of the total number of individual analysts’ forecasts within 5 days after a management
forecast to the total number of individual analysts’ forecasts within 11 days after previous quarter’s earnings
announcement.
GoodNews is a categorical variable which equals 1 if the management forecast is greater than the prior consensus
and 0 otherwise.
BadNews is a categorical variable which equals 1 if the management forecast is less than the prior consensus and 0
otherwise.
MgmtBias captures the extent of bias in management earnings forecasts and is measured as the management
earnings forecast less actual earnings, all scaled by actual earnings.
Nanalyst captures the number of analysts actively following each firm, and is calculated as the number of analysts’
forecasts within 11 days after the previous quarter’s earnings announcement.
Size is proxied by Log(MVE) which is the natural logarithm of beginning market value of equity and is computed as
ln (Compustat Item #199 * Compustat Item #25).
Growth captures growth opportunities and is measured as the market to book ratio [computed as MVE / Compustat
Item #60].


                                                          28
                                           Table 4
                    Differences between Analyst and Management Forecasts

Panel A: Analyst Earnings Forecasts Compared to Management Earnings Forecasts by News Content
                          Analysts more                          Analysts more
               a
News Content               Pessimistic b      Unbiased    b
                                                                  Optimistic b           Total

Good news                       91                   18                   113              222
Bad news                       788                  327                  1304             2419
No news                        270                   61                   330              661
                              1149                  406                  1747             3302
χ2 = 25.732, p < 0.001

Panel B: Extent of differences between analyst and management earnings forecasts (GuidanceBias)
                                                                                        Total
                               Pessimistic                 Optimistic            (includes unbiased
                               Management                 Management                management
                                Forecasts                  Forecasts                  forecasts)

Mean                               0.300                      -0.524                  -0.057
Median                             0.167                      -0.333                   0.029
Standard Deviation                 0.381                       0.891                   0.749
N                                  1716                        1362                    3302

Panel C:
(GuidanceBias = α + β1 GoodNews + β2 BadNews + β3MgmtBias + β4Nanalyst + β5Size + β6 Growth + ε)
            Coefficient                Estimate                   t-statistic                  p-value

Intercept                                  0.041                        0.936                    0.349

GoodNews                                   -0.065                       -1.758                   0.079

BadNews                                    0.085                        4.019                    0.001

MgmtBias                                   -0.792                      -68.280                   0.001

Nanalyst                                   0.006                        3.314                    0.001

Size                                       -0.012                       -1.949                   0.051

Growth                                     0.000                        0.041                    0.967

N                                                                        3302
Adj. R2                                                                 0.599



                                                    29
a
  The news content of management earnings forecasts is determined by a comparison with the prior analyst
consensus. Good news (bad news, no news) indicates that the management forecast value is higher than (lower than,
equal to) the prior consensus.

b
  The directional bias in analyst forecasts relative to management forecasts is determined by a comparison of the
two. Analysts more pessimistic (optimistic, unbiased) indicates that the analysts forecast value is lower than (higher
than, equal to) the management forecast.

GuidanceBias captures the extent of bias in analyst forecasts relative to management forecasts and is measured as
the analysts forecasts less the management forecast, all scaled by actual earnings.

GoodNews is a categorical variable which equals 1 if the management forecast is greater than the prior consensus
and 0 otherwise.

BadNews is a categorical variable which equals 1 if the management forecast is less than the prior consensus and 0
otherwise.

MgmtBias captures the extent of bias in management earnings forecasts and is measured as the management
earnings forecast less actual earnings, all scaled by actual earnings.

Nanalyst captures the number of analysts actively following each firm, and is calculated as the number of analysts’
forecasts within 11 days after the previous quarter’s earnings announcement.

Size is proxied by Log(MVE) which is the natural logarithm of beginning market value of equity and is computed as
ln (Compustat Item #199 * Compustat Item #25).

Growth captures growth opportunities and is measured as the market to book ratio [computed as MVE / Compustat
Item #60].




                                                         30
                                          Table 5
             Analyst Earnings Forecasts Compared to Actual Earnings Outcomes

Panel A: Analyst Earnings Forecasts Compared to Actual Earnings Outcomes by News
Content

News Content a              Pessimistic b        Unbiasedb            Optimistic b     Total

Good news                          23                   74                 125          222
Bad news                          946                  668                 805         2419
No news                           225                  177                 259          661
                                 1194                  919                1189         3302
χ2 = 82.475, p < 0.001

Panel B: Extent of Bias in Revised Analyst Forecasts (AnalystBias)
                 Pessimistic     Optimistic     Bad News       Good News                Total
               Management Management Management Management
                 Forecasts       Forecasts      Forecasts       Forecasts

Mean                     0.024          -0.244               -0.110            0.016   -0.091
Median                   0.000          -0.042                0.000            0.019    0.000
Std Deviation            0.209           0.724                0.545            0.198    0.507
N                        1716            1362                 2419              222     3302

Panel C: Determinants of Extent of Analyst Bias
(AnalystBias = α + β1 GoodNews + β2 BadNews + β3MgmtBias + β4 Nanalyst + β5Size + β6 Growth + ε)
            Coefficient                     Estimate                  t-statistic        p-value

Intercept                                     -0.017                     -0.395               0.693

GoodNews                                       0.078                      2.157               0.031

BadNews                                       -0.091                     -4.371               0.001

MgmtBias                                      -0.270                    -23.691               0.001

Nanalyst                                      -0.006                     -3.222               0.001

Size                                           0.009                      1.485               0.138

Growth                                        -0.001                     -0.338               0.736

N                                                                          3302
Adj. R2                                                                   0.154


                                                       31
a
  The news content of management earnings forecasts is determined by a comparison with the prior analyst
consensus. Good news (bad news, no news) indicates that the management forecast value is higher than (lower than,
equal to) the prior consensus.

b
  The directional bias in analyst forecasts relative to management forecasts is determined by a comparison of the
two. Analysts more pessimistic (optimistic, unbiased) indicates that the analysts forecast value is lower than (higher
than, equal to) the management forecast.

AnalystBias is a categorical variable that equals –1 (1, 0) if the individual analyst’s forecast (after management
guidance) is lower than (higher than, equal to) the ex-post earnings value for a given quarter.

GoodNews is a categorical variable which equals 1 if the management forecast is greater than the prior consensus
and 0 otherwise.

BadNews is a categorical variable which equals 1 if the management forecast is less than the prior consensus and 0
otherwise.

MgmtBias captures the extent of bias in management earnings forecasts and is measured as the management
earnings forecast less actual earnings, all scaled by actual earnings.

Nanalyst captures the number of analysts actively following each firm, and is calculated as the number of analysts’
forecasts within 11 days after the previous quarter’s earnings announcement.

Size is proxied by Log(MVE) which is the natural logarithm of beginning market value of equity and is computed as
ln (Compustat Item #199 * Compustat Item #25).

Growth captures growth opportunities and is measured as the market to book ratio [computed as MVE / Compustat
Item #60].




                                                         32
                                        Figure 1: Frequency Distribution of Analysts' Forecasts


            4500



            4000


            3500



            3000
frequency




            2500



            2000


            1500



            1000


             500



               0
                    1    2    3    4   5    6     7   8   9   10   11          -5   -4   -3   -2   -1     0    1     2    3      4   5
                                                                        days




                   Day after earnings                                                                   Day of management
                   announcement for quarter q-1                                                         forecast for quarter q




                                                                   33

				
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