Peer Effects across Firms: Evidence from
Security Analysts
Jacelly Cespedes and Carlos Parra∗
January 2016
Abstract
We study peer effects among workers in the same occupation across firms in the context of
security analysts (i.e., we study whether the productivity of a worker is a function of her
peers in other firms). We use microdata concerning analysts as well as an identification
strategy that employs one feature of social networks: the existence of partially overlapping
peer group. The results show strong evidence of spillovers in terms of peers’ outcomes
and characteristics. We find a significant positive causal effect of peers’ accuracy on an
analysts’ accuracy. For example, a one standard deviation in peers’ earnings forecast
accuracy increases an analyst’s accuracy by 25.7%. We also find that peers’ characteristics
have a significant effect on earnings forecast accuracy, and the estimate of the social
multiplier is around 1.46.
∗
The University of Texas at Austin McCombs School of Business, 2110 Speedway, Stop B6600, Austin, TX
78712 ([email protected], [email protected]). I would like to thank Clemens Sialm, Jonathan
Cohn, Robert Parrino and seminar participants at the University of Texas at Austin, 16th Trans-Atlantic Doctoral
Conference at LBS, and18th IZA Summer School in Labor Economics.
1
1
Introduction
Is a worker’s productivity influenced by the productivity of her peers in other firms? The
complementarities between a worker’s productivity and her peers may arise for several reasons:
Individuals may learn from their peers, workers may be motivated to exert effort when they see
their peers performing well, or the incentive structure may encourage imitation, among other
reasons.1 For certain occupations, these complementarities could be important (e.g., academic
researchers, professional athletes, CEOs, portfolio managers). However, this kind of question
about peer effects is difficult to answer empirically because of two challenges the researcher
must overcome. The first challenge is the scarcity of data on individual productivity and peer
characteristics. The second is related to the challenges associated with estimating peer effects.2
In this paper, we empirically investigate how workers in the same occupation but in different
firms influence each other in the context of security analysts. We explore how the productivity
of a worker varies as a function of her peers in other firms. The analysis centers on the question
of how a security analyst in a firm is affected by her peers in other brokerage houses.
Security analysts are employed by brokerage houses to review firms in different industries and
generate information about them (e.g., earnings forecasts). Their clients include institutional
investors, for example. An analyst’s compensation depends crucially on an annual poll conducted
by institutional investors on the buy side.3 An analyst must balance the interests from the buy
side with the interests of the investment bankers of the brokerage house. Although an analyst’s
compensation includes fees from the trading volume that she generates and the investment
banking business that she brings in, her ability to do so in the long run depends in part on her
perceived forecasting ability (Hong et al., 2000).
To our knowledge, there is no prior evidence on peer effects among workers across firms. One
1
For example, Scharfstein and Stein (1990) and Trueman (1994) present models where individuals affect other
agents’ decisions because their incentive structure encourages imitation, thus peers outcomes affect analysts’
recommendations (i.e., endogenous effect).
2
The identification of social interactions remains problematic because of two well-known issues in the literature:
endogeneity, due either to peers’ self-selection or to common group (correlated) effects, and the reflection problem,
a particular case of simultaneity (Manski, 1993; Moffitt et al., 2001; Soetevent, 2006).
3
The analysts at the top of the poll are called All-Americans.
2
of the reasons why the market for security analysts is ideal for studying peer effects is the
abundance of microdata concerning analysts (e.g., their performance, their characteristics) that
are not easily accessible in other settings. Thus, the main contribution of the paper is to estimate
the spillover effects of agents in different firms.
In addition, the setting allows one to identify both the contextual effect (i.e., peers’ characteristics
effect) and the endogenous effect (i.e., peers’ outcome effect). This is not possible in most
settings due to perfectly overlapping peer groups. To identify the components of peer effects
in the security analyst setting, we follow Bramoullé et al. (2009) and De Giorgi et al. (2010).
Both studies take advantage of a common feature of social networks, namely the existence of
partially overlapping peer groups. By definition, groups are partially overlapped if the sets of
peers of two peers do not perfectly coincide. For our purposes, this refers to analysts that cover
similar industries, but not exactly the same group of industries. The key feature is the presence
of partially overlapping groups that generate peers of peers (or, excluded peers). These excluded
peers act as a restriction in the simultaneity problem (i.e., the reflection problem). To deal with
the correlated effects, the exogenous characteristics of excluded peers are used as instruments.
The findings show strong evidence of spillovers. For different peer groups definitions (i.e., different industry classifications), we find a significant positive causal effect of peers’ accuracy on
analysts’ accuracy. Specifically, we find that an increase of one standard deviation in the peers’
earnings forecast accuracy increases an analyst’s earnings forecast accuracy by approximately
25.7% (0.83 standard deviations), on average, for the sample period 1990-2013. However, the
effect is even greater (33.9% with respect to the mean) for the subsample 2000-2013, when the
Internet and other technologies became popular. In terms of the contextual effects, we find that
the number of industries followed by analysts’ peers, on average, has a significant negative effect
on accuracy, and both the brokerage house and general experience increase analysts’ accuracy.
Thus, a one standard deviation increase in the number of industries followed by peers leads to a
significant decrease in accuracy of approximately 6% on average. Peers’ industry experience and
peers’ brokerage size have a positive causal effect on the accuracy of the earnings forecast. For
example, an average increase of one standard deviation on peers’ industry experience leads to a
3
significant positive effect on accuracy of about 4%. To our knowledge, these findings about this
contextual effect have not been documented before in the security analyst literature. Finally,
the estimates of the social multiplier range from 1.31 to 1.46.
This paper is related to the literature on peer effects in the workplace. Recent studies have found
evidence that peer effects can be important in several work settings, including among workers
at a national supermarket chain (Mas and Moretti, 2009), among fruit pickers (Bandiera et al.,
2010), and among workers in a laboratory setting (Falk and Ichino, 2006). This paper is the
first to study the peer effects of workers across firms. In addition, the literature has focused on
identifying spillover effects on low-skill occupations. To our knowledge, this is the first article
that shows the existence of peer effects among highly skilled workers.
Finally, some papers show evidence that stock analysts’ earnings forecasts tend to cluster (e.g.,
(Hong et al., 2000)), and other papers question whether analysts’ forecasts cluster at all (e.g.,
Chen and Jiang, 2006). However, none of the articles in this literature attempts to relate
clustering to peer effects, and one consequence of this is that the literature does not deal with
the notoriously difficult problem of identifying spillover effects (i.e., correlated effects and the
reflection problem).
This paper is organized as follows: Section 2 reviews the literature on peer effects, Section 3
describes our empirical strategy, Section 4 presents our results, and Section 5 concludes.
2
Identification of Peer Effects
In recent years, studies on peer effects have been increasing. They have been applied to topics as
diverse as criminal activity (Glaeser and Sacerdote, 1996) school achievement (Sacerdote, 2001),
participation in retirement plans (Duflo and Saez, 2002), and productivity spillovers (Mas and
Moretti, 2009).
It has been documented that inner characteristics not only create private returns and increasing
earnings, but they also create externalities (i.e., they increase the productivity of other agents in
the economy). These externalities are well-known as peer effects (Acemoglu and Autor, 2011).
4
The most common estimated model in the peer effects literature is the linear-in-means model in
which the outcome y is a function of the characteristics of individual i characteristics, i’s peers’
average characteristics, and the individual’s peer average outcome:
yi = α + βE(y|Gi ) + γE(x|Gi ) + δxi + i
(1)
where yi represents the individual’s outcome, E(y|Gi ) represents her peers’ average outcome, xi
is a vector of the individual’s characteristics, and E(x|Gi ) is a vector of her peers’ average characteristics. This model encompasses both endogenous effects from the peers’ current outcomes
(β) and exogenous (or contextual) effects from the peers’ characteristics (γ). The fundamental
challenge in the literature is identification, since there are several endogeneity concerns with
this model (1). First, since the individual’s outcome (yi ) affects her peers’ outcomes, and vice
versa; β is subject to endogeneity concerns due simultaneity (i.e., the reflection problem). Second, in some settings, peers self-select into peer groups in a manner that is unobservable to
the econometrician, which could lead to bias in the point estimates of β and γ (i.e., correlated
effects). Third, (1) includes two types of effects: peers’ average outcome (E(y|Gi )) and peers’
average characteristics (E(x|Gi )); a separate identification of β and γ is difficult, since a peer’s
background in itself affects peer outcome (Manski, 1993). Finally, note that these endogenous
effects have the potential for social multipliers, since a small change for individual i will affect
the peer group, which will in turn reflect back to individual i, and so on.
3
3.1
Empirical Strategy
Data Source and Sample Selection
Data on security analysts come from the Institutional Brokers Estimates System (I/B/E/S)
database. The full sample covers the period 1990–2014. Following the literature, we focus on
annual earnings forecasts, because analysts revise their forecasts for annual earnings more frequently; therefore, the timing of forecast revisions is a less critical decision for annual earnings
than for quarterly earnings. Consistent with previous studies, we take the most recent forecast
5
of the annual earnings for each year (O’Brien and Bhushan, 1990; Sinha et al., 1997; Clement
and Tse, 2005). As a result, for each year, we have one forecast issued by each analyst covering
a stock. The data on U.S. firms come from the Center for Research in Security Prices (CRSP)
and Compustat. From the CRSP, we obtain monthly closing stock prices, monthly shares outstanding, and daily and monthly stock returns for the same period. From Compustat, we obtain
annual information on the book value of assets during the same period. To be included in the
sample, a firm must have the requisite financial data from both CRSP, Compustat, and I/B/E/S.
3.2
Research Design
In the literature on peer effects, as mentioned above, the two most important challenges for
identification are reflection and the correlated effects problem. We will now demonstrate how
we address the reflection problem using a linear-in-means model as follows:
yit = α + βE(y|Git ) + γE(x|Git ) + δxit + it
In this paper, yit is the analyst’s outcome variable related to the earnings forecast (i.e., the
accuracy and forecast error scaled by price) in year t, xit is a set of analyst’s characteristics,
and E(x|Git ) contains the averages of the x’s in the peer group of analyst i , who work for a
different brokerage houses, denoted by Git . The variable β measures the endogenous effect, and
γ measures the exogenous effects. For now, we assume E(it |Git , xit ) = 0 (i.e., no correlated
effects or self-selection into groups).
In the linear-in-means model, peer groups are fixed across individuals. In other words, if analysts
A and B both cover the same firms (“and, as a consequence, working within the same industry)
as C, it must follow that A and B are in the same group. This means that, if analysts i and j are
in the same peer group, then the two groups coincide (i.e., Git = Gjt ). In this case, endogenous
effects cannot be distinguished from exogenous effects (Manski, 1993). By averaging equation
(2) over group Git , it can be seen that E(y|Git ) is a linear combination of the other regressors :
E(y|Git ) =
α
1−β
!
!
γ+δ
+
E(x|Git )
1−β
6
Thus, with perfectly overlapping peer groups, E(y|Git ) does not vary at the analyst level, as it
is constant for all members of the same group. This problem prevents the separate identification
of endogenous and exogenous effects, even if the groups were randomly formed and even in
the absence of correlated effects. Using partially overlapping groups, peer groups are instead
individual-specific. This feature guarantees the existence of excluded peers (i.e., analysts who
are not in one’s peer group but are included in the groups of one’s peers). Therefore, the key is
the existence of the excluded peers (i.e., peers of peers) who generate within-group variation in
E(y|Git ), so if the only endogeneity were reflection it would not need to instrument for anything
else in order to identify the endogenous effect (β) and exogenous effect (γ).
Consider the simple case of only three analysts: Analysts A and B work for different brokerage
houses and cover the same set of firms (and therefore they cover the same industries). However,
B also covers another set of firms in a different industry, which is also covered by analyst C, who
also works for a different brokerage house and covers industries that are different from analyst
A. A’s peer group includes only B, while B’s peer group includes both A and C. In the standard
simultaneous equation model, at least one exogenous variable is excluded from each equation.
Here, A is excluded from the peer group of C, who is excluded from the peer group of A (See
Figure 1).
The main advantage of having partially overlapping groups is that we can now solve the reflection,
or simultaneity, problem. Thus, allowing peer groups to vary at the level of the single individual,
E(y|Git ) can be expressed as
E(y|Git ) = α + βE [E(y|Gjt )|Git ] + γE [E(x|Gjt )|Git ] + δE(x|Git )
(2)
where j is a generic member of i0 s peer group in year t. The key for identification is the fact that
i0 s peer group Git does not coincide with Gjt . The main assumption rests on the fact that the
excluded peer C does not interact with A directly. In our setting, this seems to be a plausible
assumption if A and C never cover the same industry. Alternatively, identification could also
be achieved in a setting in which direct interactions with one’s excluded peers are allowed, if
we simply assume that the strength of the interactions declines with distance in the network.
7
This is the approach taken by Calvó-Armengol et al. (2009), but it requires some additional
parametric assumptions.
However, in this setting, the assumption E(it |Git , x) = 0 does not hold; therefore, the presence
of correlated effects is a major concern. In other words, common unobservable shocks at the
group level may prevent the identification of endogenous and exogenous effects. In practice, the
error term it is of the following form:
it = ηi + λgt + uit
(3)
where λgt is the group fixed effect, ηi is an analyst fixed effect, and uit is an independently and
identically distributed random component.4 Unobservable group shocks may induce endogeneity
(i.e., Cov(E(y|Git ), λgt ) 6= 0, and Cov(E(x|Git ), λgt ) 6= 0) and impede the identification of β and
γ.
One way to solve the correlated effect problem, when Cov((E(y|Git ), λgt ) 6= 0, is to use instrumental variables.5 In this setting, a valid instrument could be the characteristics of peers of
peers who are not in the analyst’s own peer group. By construction, the x’s of analysts who are
excluded from i’s peer group, but are included in the group of one or more of i’s peers, are uncorrelated with the group fixed effect of i and are correlated with the mean outcome of i’s group
through endogenous interactions (i.e., the instrument satisfies the exclusion restriction). In this
sense, using the previous example, xC would be a valid instrument for yB in the estimation of
E(yA |Git ). The reason is that xC , which is uncorrelated with λA , affects yC ; and, since C is a
peer of B, yC also affects yB through endogenous effects. Following the same procedure, for this
triad xA , could be a valid instrument for yB in the estimation of E(yC |Git ).
In this setting, the group of peers of peers (i.e., excluded peers) for an analyst i includes all other
analysts who have never covered the same industry of i but have covered some of the industries
4
Equation 3 describes λgt as time varying. However, in the scenario in which this correlated effect is time
invariant, a simple group fixed effect could solve the endogeneity problem.
5
Even if λ were time invariant, industry-year FE would not be enough to deal with the correlated effects. If
the correlated effects vary at the industry level, then industry-year FE is all that is required. Since groups vary
at the individual level (i.e., correlated shocks should vary at the individual level), then we need an IV procedure
to deal with the correlated effect (i.e., instrument for the endogenous effect).
8
of i’s peers. This guarantees that the peers of peers of any analyst i have never covered any firm
in the same industry of i, regardless of how we define actual peers.
To deal with any endogeneity of the exogenous effect induced by correlated effects when
Cov(E(x|Git ), λgt ) 6= 0), we are using lags of x’s.
Finally, following Bramoullé et al. (2009), we can solve any correlation between individual effect
ηi and any endogenous effect (E(y|Git )) or exogenous effect (E(x|Git )) using a first difference
transformation or individual fixed effect.6
3.2.1
Peer Group Definition
The ideal grouping would be to define peers according to the firms covered by each analyst.
However, the process would become unwieldy and untraceable in terms of the matrix required
to define the peers of peers. As an alternative, we define peer groups in terms of industry.
However, as a robustness check, we will test whether the results hold for a different industry
classification.
In practice, research coverage by brokerage firm analysts is typically structured along industry classifications (Bhojraj et al., 2003). One approach to industry classification that enjoys
widespread use among investment practitioners is the Global Industry Classification System
(GICS). In the investment community, portfolio managers and analysts have gravitated to the
GICS as the standard approach (Chan et al., 2007). The GICS categorization is based not only
on a firm’s operational characteristics, but also on information regarding investors’ perceptions
as to what constitutes the firm’s main line of business. Given that GICS codes consider investors’ attitudes, we use this 4-digit classification for the peer group formation. We also use
the 2-digit SIC classification as peer a group definition as an alternative to the GICS, and also
to test whether our results are robust..
Analyst i’s peer group (Gi ) includes all analysts j who were assigned to cover same industries
as i and who also work for a different brokerage house than i. Moreover, i’s peers of peers (i.e.,
excluded peers) are those analysts who never covered the same industries as i, are peers of j,
6
A detailed proof can be found in Bramoullé et al. (2009).
9
and work for a different brokerage house than i. As an additional condition, i’s peers of peers
(excluded peers) cannot be peers of i’s coworkers.7 . Finally, we use a weighting scheme that
assigns a higher weight to analysts who cover more industries together to capture the intensity
of interactions.
Table 1 reports the group sizes for each industry classification. On average, the number of analysts by industry is 500 for GICS and 538 for SIC. Around 20 analysts follow each firm, on
average. Furthermore, on average, analysts follow around 4 industries and 11 firms. Finally,
on average, each analyst has 40 peers under GSIC classification and 43 under SIC classification. From Table 1, one can conclude that the group sizes are similar between both industry
classification definitions.
3.3
Variables of Interest
Since the main research question is related to how analysts’ peers who work for a different brokerage houses affect their main outcome, we define variable earnings forecast accuracy (Accuracy)
as the outcome .
We begin by constructing a measure of the absolute forecast accuracy of an analyst. We define
F Eijt as the most recent earnings (in dollars) per share (EPS) forecast of year-end earnings issued
by analyst i on stock j in year t. The measure of analyst i’s accuracy for firm j in year t is the
absolute difference between her forecast and the actual EPS of the firm, defined as Ajt :
AF Eijt =| Fijt − Ajt |
Following Clement and Tse (2005), we scale the forecast accuracy measure to be 0 for the
least accurate forecast (i.e., the highest absolute forecast error) and 1 for the most accurate
forecast (i.e., the lowest absolute forecast error). This ensures that the measures of forecasting
performance increase with forecast accuracy. The variable Accuracyijt is a measure of analyst
i’s forecast accuracy for firm j in year t, calculated as the maximum absolute forecast error for
7
This last condition ensures that the effect of the excluded peers on i is only through their effect on j.
10
analysts who follow firm j in year t minus the absolute forecast error of analyst i following firm
j in year t, and this difference is scaled by the range of absolute forecast errors for analysts
following firm j in year t:
Accuracyijt =
AF Emaxjt -AF Eijt
AF Emaxjt -AF Eminjt
Following Hong and Kubik (2003), we also use forecast error scaled by price (F EP ) as an
alternative dependent variable. The variable F EPijt is analyst i’s forecast error for firm j in
year t scaled by the end-of-day stock price two days prior to the revision.:
F EP ijt =
| Fijt − Ajt |
Pit
To estimate the contextual (exogenous) effect, we define xi as the set of analyst characteristics.
Following Clement and Tse (2005) and Hong and Kubik (2003),8 we include the following characteristics: The variable BrokerSizeijt is a measure of the analyst’s brokerage size, calculated
as the number of analysts employed by the brokerage firm that employs analyst i following
firm j in year t. The variable GenExperienceijt is a measure of analyst i’s general experience,
calculated as the number of years of experience for analyst i who follows firm j in year t. We
only examine analysts for whom we can calculate the number of years they have been working
as analysts. Because the I/B/E/S dataset begins in 1983, we know the experience level of all
analysts who began their careers after 1983. Therefore, we exclude all left-censored analysts
from our subsequent analysis (i.e., all analysts whose first year as an analyst was not 1984).
The variable F irmExperienceijt is a measure of analyst i’s firm-specific experience, calculated
as the number of years of firm-specific experience for analyst i who follows firm j in year t.
The variable Companiesijt is a measure of the number of companies analyst i follows in year
t, calculated as the number of companies followed by analyst i who follows firm j in year t.
The variable Industriesijt is a measure of the number of industries analyst i follows in year t,
calculated as the number of industries followed by analyst i who follows firm j in year t.
8
Also following Mikhail et al. (1997), Clement (1999), and Jacob et al. (1999).
11
Following the literature, we control for N umAnalystijt , which is the natural logarithm of the
number of analysts who issue EPS forecasts for firm j in year t.
Finally, we compute the specific characteristics of each analyst, her peers’ mean characteristics,
and the mean characteristics of her peers of peers. We also compute the mean of our two
dependent variables for each analyst as well as the mean of her peers in order to estimate the
endogenous effect (i.e., how the mean of the accuracy of her peers affects her own accuracy).
Table 2 reports the descriptive statistics.
4
Results
Since the main goal of this paper is to estimate the endogenous and exogenous peer effects, the
estimating equation is
Accuracyijt = ηi + τt + βAAPijt + γMCPijt−1 + δXijt−1 + ρZijt + ijt
(4)
where AAPijt is the average accuracy of analyst i’s peers in industry j and year t. The variable
MCPijt−1 is the set of mean characteristics described in the last section in year t − 1 for analyst
i’s peers for each industry j that the analyst i is covering, and Xijt−1 is the set of characteristics
of analyst i in industry j and year t − 1. The set of controls described in the last section
for each analyst i in industry j and year t is denoted by Zijt . Finally, ηi are individual fixed
effects to control for any time-invariant unobservables at the individual level, which can induce
self-selection; and τt are year fixed effects.
We use the general experience of the peers of analyst i’s peers as an excluded instrument for
AAP , which satisfies the exclusion restriction if the peers of analyst i’s peers never interact with
analyst i. Since the general experience of the excluded peer is not correlated with any group
fixed effect, we argue that the exclusion restriction is satisfied.
Table 3 reports the results of Equation (4), the estimation for two definitions of peer groups: the
4-digit GICS group (Panel A) and the 2-digit SIC group (Panel B). For each of these definitions,
we estimate the model under two different specifications. The first is a pooled fixed effect model
(i.e., individual and year fixed effects) (Columns 1-2) and 2SLS (Columns 4-5), using the general
12
experience of the excluded peers (i.e., peers of peers) as an instrument. Then, to control for any
time-varying unobservables at the industry level that may still bias the estimates, Columns (3)
and (6) include industry-by-year fixed effects.
IV estimates are considerably larger than OLS point estimates, and they clearly indicate the
presence of significant endogenous peer effects in the accuracy of earnings forecasts. In the
OLS specification (Column 1) for the GICS industry classification, the estimates indicate that
an increase of one standard deviation in peers’ accuracy increases an analyst’s accuracy by
approximately 2.9% (0.1185 x 0.2490), on average. This represents an increase of 9.5% with
respect to the mean. However, the effect is even greater for the subsample 2000-2013, in which
the peers’ accuracy increases an analyst’s accuracy by approximately 7.27% (0.2920 x 0.2490),
on average. This represents an increase of 23.5% with respect to the mean. Finally, when
industry-by-year fixed effects are included (in addition with analyst and year fixed effects, which
are included in all specifications), there is a positive and significant effect of 11.4% with respect
to the mean, which is qualitatively similar to the result shown in Column (1).
When correlated effects are controlled through instrumental variables in the 2SLS specifications
(Columns 4-6), the estimates are relatively higher. Column (4) shows that an increase of one
standard deviation in peers’ accuracy increases an analyst’s accuracy by 7.9% (0.319 x 0.249)
on average, which represents an increase of 25.7% with respect to the mean. Similar to the OLS
specification, the estimate is higher for the subsample 2000-2013 (which shows an increase of
33.9% with respect to the mean) but similar to the estimate that results when industry-by-year
fixed effects are included (i.e., 29.5%).
For the SIC industry classification (Table 3 Panel B), the estimates indicate that an increase
of one standard deviation in peers’ accuracy increases an analyst’s accuracy, on average, by
approximately 7.4% with respect to the mean for the OLS specification (Column 1) and 19.4%
with respect to the mean for the IV specification.
In terms of the exogenous (or contextual) effect, Table 3 reports that the number of industries
followed by analysts’ peers, on average, has a significant negative effect on earnings forecast
accuracy. An average increase of one standard deviation in the number of industries followed by
13
peers leads to a significant decrease in accuracy of approximately 4%, on average, for the GICS
definition and 6% for the SIC definition. However, peers’ general experience and brokerage size
have a positive effect on the accuracy of earnings forecasts. For example, an average increase
of one standard deviation in peers’ general experience leads to a significant positive effect on
accuracy of about 6% for the GICS industry classification and 8% for the SIC classification. The
above findings have not been documented before in the analyst literature.9 Finally, consistent
to the findings in Clement and Tse (2005), analyst characteristics (e.g., industry experience,
brokerage size) have a significant effect on earnings forecast accuracy.
Overall, the estimates in Table 3 indicate the presence of economic and statistically significant
endogenous peer effects. These effects are estimated using peers of peers’ general experience as
an excluded instrument, which appears to be very significant in explaining the endogenous term.
The F-test of the excluded instrument from the first stage regression, reported at the bottom
of Table 3, is large for all models. Table 4 shows the complete first-stage regressions for both of
the 2SLS specifications as well as the reduced-form estimates.
In order to test whether the results are robust for an alternative dependent variable specification,
we estimate Equation (4) for forecast error scaled by price (F EP ) as a robustness check. Similar
to Table 3, Table 5 reports that 2SLS estimates are considerably larger than OLS estimates.
The coefficients for IV specification indicate that an increase of one standard deviation in peers’
forecast error increases an analyst’s forecast error, on average, by approximately 0.24 percentage
points (0.10098 x 0.024), which represents an increase of 18.6% with respect to the mean for the
GICS industry classification (Panel A) and 0.3 percentage points (0.12482 x 0.024) for SIC (Panel
B), which represents an increase of 23% with respect to the mean. However, the coefficients for
OLS specification are 6.4% and 11.5% for GICS and SIC industry classifications, respectively.
Table 5 also shows the peers’ characteristics that affect forecast error. Peers’ characteristics, such
as the number of industries, have a significant positive relation to forecast error. That is, on
average, forecast error increases by approximately 8% as the average number of industries that
9
These findings are also important for the peers effect literature, since peers effect across firms has not been
studied in any setting before.
14
are followed by analyst’s peers increases, on average, by one standard deviation. In addition,
on average, forecast error decreases by 2% and 5% as industry experience and the number of
brokerage houses increases by one standard deviation, on average.
Finally, Table 6 reports the complete first-stage regressions for both 2SLS specifications and the
reduced-form analysis for this alternative dependent variable.
4.0.1
Discussion
As mentioned in Section 3.2, a substantial portion of the peer effects literature can only identify a
coefficient for both the endogenous and contextual effects together. Thus, we also estimate only
the endogenous effect (i.e., peers’ accuracy) through a 2SLS that uses the general experience of
the excluded peers as instrument. Appendix Table 1 shows the results. As the literature predicts,
this basic model overestimates the true endogenous peer effects, since it captures not only the
impact of endogenous effects but also the contextual effects. These estimates are upwardly
biased (2.6 times) with respect to the results in Column (4) of Table 3.
Tables 3 and 5 highlight some key findings. In both tables, Column (3) estimates the model in
Equation (4), assuming that both the self-selection and the correlated effects are time invariant.
Therefore, including individual and industry-by-year fixed effects should be sufficient to deal
with both endogeneity concerns. Under these very strong identifying assumptions, the estimates
in Column (1) are downwardly biased (1.20 times).
Moreover, Columns (4) and (5) in Tables 3 and 5 show that the OLS estimates are downwardly
biased (around 2.7 times) under the three main identifying assumptions that (1) the self-selection
problem is an individual FE, (2) the correlated effects are not correlated with the contextual
effects, and (3) the instrumental variable of the general experience of excluded peers satisfies
the exclusion restriction . This effect is even stronger (34%) when we restrict the sample to the
period 2000-2013 in Column (5). This may be related to the Internet expansion, the introduction
of Bloomberg terminals, or the adoption of other communication technologies.
In the peer effects literature, it is believed that the OLS results should overestimate the true
endogenous peer effects, since they capture not only the impact of endogenous effects but also the
group shocks from correlated effects. The implicit assumption is that these two effects influence
15
the dependent variable in the same direction for all individuals. However, in this setting, the
group shock is a vector of all the industry shocks that affect each analyst in every industry she
follows. Since each of the industry shocks can have different signs, it is impossible to predict
unambiguously whether the OLS estimator would be larger or smaller than the IV.
When industry-by-year fixed effects are included, the peer’s accuracy coefficient is slightly
higher; however, in terms of the forecast error scaled by price, they are not significantly different.
In addition, we are concerned that using the lags of peer characteristics is not enough to deal with
the endogeneity of peer characteristics and their correlated effect (i.e., Cov(E(x|Git ), λgt ) 6= 0).
Column (6) shows that when industry-by-year fixed effects are included, the estimates for peer
characteristics (i.e., the contextual effect) appear similar to the those in Column (4).
4.1
Possible Channels
The literature shows that peer effects may be driven by learning, envy, or imitation. In this
context, analysts may learn from each others (e.g., from their peers through reading their reports,
through the questions their peers ask during conference calls). Workers may also be motivated to
exert effort when they see their peer performing well (i.e., the envy channel), or some incentive
structures in the analyst’s setting may encourage imitation (i.e., via the imitation channel).
10
However, in this setting, it is quite challenging to disentangle learning from imitation.
Table 3 shows that peers’ experience and the number of industries followed by peers have
significant effects on analyst accuracy. Then, it could be the case that the level of peers’ specialization may play a role in analyst accuracy. To study this hypothesis, we test whether the
endogenous effect is stronger in those cases in which at least 50% of an analyst’s peers are focused
in fewer industries (i.e., below the median). Table 7 reports the coefficient of interest for two different subsamples, one for when the number of industries followed by peers is below the median,
and another for above the median. For both industry classifications, the coefficient of interest
is higher when peers cover fewer industries. One potential explanation is that those specialized
10
A substantial portion of analysts’ compensation depends on an annual All-Americans poll conducted by
Institutional Investors of the buy side.
16
peers may have a better knowledge of the industry, thus there is more learning experience from
those peers.
From our results, we find that peers’ brokerage size has a significant effect on peers’ accuracy.
Larger brokerage firms have presumably more resources for research, and thus their forecast could
reflect more firms’ fundamentals. To test whether analysts learn from those peers who work in
larger brokerage houses, and thus increase their accuracy; we estimate the endogenous effect
when an analyst has at least one peer who works for a brokerage house larger than the median
(measured as the number of analysts working for each brokerage house). Table 7 reports the
coefficient of interest for both subsamples. For both industry classifications, the effect of peers’
accuracy is larger when an analyst has at least one peer who works for a brokerage house that
is larger than the median.
We expect to study other channels in the next iteration of this paper.
4.2
Social Multiplier
The presence of positive social interactions, or strategic complementarities, implies the existence
of a social multiplier. The intuition is that small changes at the individual level are magnified
through the social interaction process. Presumably, the larger the average peer’s group size, the
greater the share of social influences. Thus, the social multiplier should increase with the size
of the group. Additionally, with the existence of a social multiplier, aggregate coefficients (e.g.,
wages regressed on years of schooling at the state level) are greater than individual coefficients
(e.g., wages regressed on years of schooling at the individual level). Thus, it is problematic to
use aggregate variation to infer individual-level parameters when there are positive (or negative)
spillovers. In the setting of a security analyst, the social multiplier is of interest since it can give
us an estimate of the following question: If an exogenous shock induces one additional analyst
to take action “A” (e.g., increase her forecast), how many total analysts will take action “A” in
equilibrium?
We follow Glaeser and Sacerdote (1996), who define from their model the expected value of
the social multiplier as (N − 1 + β) / [(1 − β) (N − 1) + β], where N is the group size (i.e., the
17
average group size), and β is the social interaction parameter from the endogenous effect (i.e.,
the peers’ outcome effect).
Table 8 reports the social multiplier for both outcome variables:, accuracy and forecast error.
The results show that the estimate of the social multiplier is about 1.31 to 1.46. This point
estimate supports the existence of a social multiplier in the context of sell-side analysts.Finally,
the values suggest that social interactions are important in the setting of a security analyst.
5
Conclusion
This paper studies peer effects of workers across firms. The context of security analysts in
different brokerage houses allows one to overcome the two problems required to achieve the
objectives of this paper. The first problem relates to the availability of data on individual
productivity and peer characteristics, and the second problem lies in the estimation of peer
effects. To identify the components of peer effects, the empirical strategy takes advantage of a
common feature of social networks: the existence of partially overlapping groups of peers. Thus,
the advantage of having partially overlapping groups is that they allow us to generate excluded
peers. These excluded peers act as a restriction in the simultaneity problem. Additionally, the
exogenous characteristics of excluded peers are used as instruments to deal with the correlated
effects.
The findings show strong evidence of spillovers in terms of peers’ outcome and characteristics.
In the case of the endogenous effect, we find a significant positive causal effect of peers’ accuracy
on analysts’ accuracy: A one standard deviation increase in peers’ earnings forecast accuracy
increases analyst’s accuracy by 25.7%. In the case of the contextual effects, for example, we
find that the number of industries followed by analysts’ peers has, on average, a significant
negative effect on accuracy. Finally, the point estimates are significantly larger from 2SLS than
the estimates from OLS regressions that do not correct for the endogeneity. Therefore, without
taking into account the endogeneity concerns when estimating peer effects (i.e., the correlated
effects and the reflection problem), the OLS estimates can underestimate the true causal effect
18
of interest. Finally, we estimate the multiplier effect, and we find that the social multiplier effect
is around 1.46. The current findings are the first step of the project. The next steps are to
examine the underlying mechanisms (i.e., distinguish between specific forms of peer effects that
could be at work) and study possible nonlinearities in the peer effects.
19
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22
Figure 1 Example of Having Partially Overlapping Groups
Consider the simple case of only three analysts: Analysts A and B work for different brokerage houses and cover the same industry
Software and Services. However, B also covers another industry (i.e., Technology Hardware and Equipment), which is also covered
by analyst C, who also works for a different brokerage house and covers an industry that is different from analyst A. A’s peer
group includes only B, while B’s peer group includes both A and C. Here, A is excluded from the peer group of C, who is excluded
from the peer group of A.
Table 1 Descriptive Statistics
This table reports the mean and standard deviation of the number of analysts following each industry and firm, and the number
of industries and firms followed on average by each analyst. It also reports the size of peer groups given each industry
classification. The period under analysis is from 1990-2013. Data come from detailed I/B/E/S.
Number of analysts by industry
GSIC
SIC
Number of analysts by firm
Mean
(1)
499.73
537.95
Std Dev
(2)
352.82
403.69
Min
(3)
6.00
5.00
P 25
(4)
197.00
193.00
P 50
(5)
287.00
393.00
P 75
(6)
778.00
822.00
19.77
13.01
1.00
10.00
17.00
27.00
Size of peer groups
GSIC
SIC
39.74
43.35
29.19
52.74
2.00
3.00
19.00
26.00
31.00
48.00
52.00
89.00
Number peers of peers
GSIC
SIC
268.25
223.03
66.14
74.52
18.00
26.00
241.00
169.00
293.00
231.00
313.00
2867.00
Table 2 Descriptive Statistics on Main Variables of Interest
This table reports descriptive statistics for 659,837 analysts forecast observations from 1990 to 2013. Analyst and forecast
characteristics are derived from detailed I/B/E/S data. I restrict the sample to the last forecast issued by the analyst for a
particular firm in each sample year. The characteristics are: BrokerSize, the number of analysts in the analyst’s brokerage in
each year; FirmExperience, the analyst’s years of experience forecasting a particular firm’s earnings; GenExperience, the analyst’s
overall years of forecasting experience; Companies, the number of companies the analyst follows in each year; and Industries, the
number industries the analyst follows in each year. The first section reports the summary statistics for each analyst. The second
section shows the descriptive statistics for the peer group, which is those analyst who cover the same industries but work for a
different brokerage house. Finally, the last section presents the descriptive statistics for the peers of peers group
Variable
Mean
(2)
Std Dev
(3)
Min
(4)
P 25
(5)
P 50
(6)
P 75
(7)
Max
(8)
Outcome Variables
Accuracy
Forecast error scaled by price
0.309
0.013
0.249
0.024
0.000
0.000
0.129
0.007
0.243
0.018
0.421
0.041
1.000
1.421
Characteristics
Brokerage size
Companies
Industries (GSIC)
Industries (SIC)
General experience
Firm experience
Industry experience (GSIC)
Industry experience (SIC)
14.353
10.508
3.628
3.882
11.427
5.090
7.975
8.015
12.648
9.637
3.011
3.036
9.324
3.720
3.978
3.971
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
3.000
12.000
2.000
2.000
7.000
3.000
4.000
4.000
10.000
17.000
3.000
3.000
11.000
5.000
8.000
8.000
25.000
24.000
5.000
5.000
15.000
8.000
11.000
11.000
102.000
193.000
37.000
36.000
27.000
27.000
14.000
14.000
Table 3 Peers’ Accuracy on Analysts’ Accuracy
This table reports the association between forecast accuracy, analyst characteristics, analyst peers forecast accuracy and analyst
peers characteristics, along with two alternative peer group definitions (GICS Panel A and SIC Panel B). The characteristics
(which are all lagged one period) are: BrokerSize, the number of analysts in the analyst’s brokerage in each year; Firm Experience,
the analyst’s years of experience forecasting a particular firm’s earnings; Industry Experience, the analyst’s years of experience
following a particular industry; General Experience, the analyst’s overall years of forecasting experience; Companies, the number
of companies the analyst follows in each year; and Industries, the number of industries the analyst follows in each year. Peers
of peers General Experience is used as excluded instrument. The model includes controls such as: number of analyst following
each firm, book to market and firm size. The estimation also includes individual fixed effects and year fixed effects, for columns
(3) and (6) industry year fixed effect is also included. Data come from detailed I/B/E/S. Robust standard errors in parentheses,
clustered at the analyst level. ***, ** and * indicate p-values of 1%, 5%, and 10%, respectively
Panel A
Dependent Variable/Industry
VARIABLES
Peers accuracy
Peers characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
Analyst characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
Accuracy/GICS
OLS
2SLS
(3)
(4)
OLS
(1)
OLS
(2)
2SLS
(5)
2SLS
(6)
0.1185***
(0.02895)
0.2921***
(0.03725)
0.1420***
(0.04340)
0.3191***
(0.03445)
0.4212***
(0.03205)
0.3663***
(0.03465)
0.0042***
(0.00095)
0.0094*
(0.00496)
0.0226***
(0.00516)
0.0137***
(0.00528)
-0.0007***
(0.00024)
-0.0029*
(0.00162)
0.0050***
(0.00104)
0.0305***
(0.00708)
0.0153**
(0.00675)
0.0340***
(0.00764)
-0.0008**
(0.00038)
-0.0127***
(0.00299)
0.0053***
(0.00118)
0.0242***
(0.00623)
0.0140**
(0.00593)
0.0164**
(0.00644)
-0.0006**
(0.00022)
-0.0020**
(0.00070)
0.0039***
(0.00019)
0.0035**
(0.00143)
0.0240***
(0.00113)
0.0055***
(0.00105)
-0.0016***
(0.00021)
-0.0031***
(0.00079)
0.0036*
(0.00022)
0.0066*
(0.00319)
0.0226***
(0.00143)
0.0014
(0.00154)
-0.0018***
(0.00028)
-0.0051***
(0.00111)
0.0032***
(0.00019)
0.0029**
(0.00138)
0.0233***
(0.00112)
0.0071***
(0.00103)
-0.0014***
(0.00020)
-0.0055***
(0.00078)
0.0039***
(0.00019)
0.0022*
(0.00093)
0.0240***
(0.00113)
0.0055***
(0.00105)
-0.0016***
(0.00021)
-0.0031***
(0.00079)
0.0036*
(0.00022)
0.0039*
(0.00164)
0.0226***
(0.00143)
0.0014
(0.00154)
-0.0018***
(0.00028)
-0.0051***
(0.00111)
0.0032***
(0.00019)
0.0028
(0.000221)
0.0233***
(0.00112)
0.0071***
(0.00103)
-0.0014***
(0.00020)
-0.0055***
(0.00078)
0.0042***
(0.00095)
0.0094*
(0.00496)
0.0226***
(0.00516)
0.0137***
(0.00528)
-0.0007***
(0.00024)
-0.0029*
(0.00162)
0.0050***
(0.00104)
0.0305***
(0.00708)
0.0153**
(0.00675)
0.0340***
(0.00764)
-0.0008**
(0.00038)
-0.0127***
(0.00299)
0.0053***
(0.00118)
0.0062***
(0.00054)
0.0140**
(0.00593)
0.0164**
(0.00644)
-0.0006**
(0.00022)
-0.0020**
(0.00070)
61.93
49.23
56.17
First stage F-test
Controls
Analyst FE
Year FE
Industry-Year FE
Period
Observations
Yes
Yes
Yes
No
1990-2013
Yes
Yes
Yes
No
2000-2013
Yes
Yes
No
Yes
1990-2013
Yes
Yes
Yes
No
1990-2013
Yes
Yes
Yes
No
2000-2013
Yes
Yes
No
Yes
1990-2013
659,837
426,348
659,177
646,271
426,348
646,126
(continued Table 3)
Panel B
Dependent Variable/Industry
VARIABLES
Peers accuracy
Peers characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
Analyst characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
Accuracy/SIC
OLS
2SLS
(9)
(10)
OLS
(7)
OLS
(8)
2SLS
(11)
2SLS
(12)
0.0921***
(0.0313)
0.1782***
(0.0419)
0.1271***
(0.0443)
0.2405***
(0.0283)
0.3585***
(0.0271)
0.2845***
(0.0291)
0.0040***
(0.0010)
0.00612
(0.0055)
0.0240***
(0.0052)
0.0152**
(0.0060)
-0.0008***
(0.0002)
-0.0049***
(0.0017)
0.0055***
(0.0012)
0.0236***
(0.0083)
0.0218***
(0.0069)
0.0337***
(0.0091)
-0.0010***
(0.0003)
-0.0149***
(0.0035)
0.0061***
(0.0013)
0.0056
(0.0067)
0.0309***
(0.0058)
0.0072
(0.0074)
-0.0010***
(0.0003)
-0.0035*
(0.0021)
0.0040***
(0.0014)
0.0054***
(0.0007)
0.0270***
(0.0074)
0.0042
(0.0075)
-0.0054***
(0.0005)
-0.0012
(0.0023)
0.0038**
(0.0015)
0.0045***
(0.0007)
0.0017
(0.0085)
0.0248***
(0.0089)
-0.0045***
(0.0005)
-0.0094***
(0.0032)
-0.0031*
(0.0014)
0.0068***
(0.0007)
0.0286***
(0.0075)
0.0038
(0.0076)
-0.0083***
(0.0007)
-0.0039
(0.0023)
0.0019**
(0.0009)
0.0013*
(0.0007)
0.0222***
(0.0011)
0.0050***
(0.0011)
-0.0016***
(0.0002)
-0.0036***
(0.0008)
0.0036**
(0.0016)
0.0018*
(0.0010)
0.0211***
(0.0014)
0.0058
(0.0016)
-0.0020***
(0.0003)
-0.0068***
(0.0012)
0.0027*
(0.0016)
0.0016*
(0.0009)
0.0215***
(0.0011)
0.0068***
(0.0011)
-0.0012***
(0.0002)
-0.0057***
(0.0008)
0.0051**
(0.0023)
0.0026**
(0.0011)
0.0023***
(0.0001)
0.0043***
(0.0013)
-0.0010***
(0.0002)
-0.0037***
(0.0010)
0.0072*
(0.0039)
0.0034**
(0.0016)
0.0023***
(0.0001)
0.0037**
(0.0014)
-0.0011***
(0.0003)
-0.0043***
(0.0011)
0.0017
(0.0014)
0.0029*
(0.0016)
0.0022***
(0.0001)
0.0052***
(0.0010)
-0.0011***
(0.0002)
-0.0051***
(0.0009)
166.41
137.28
160.83
First stage F-test
Controls
Analyst FE
Year FE
Industry-Year FE
Period
Observations
Yes
Yes
Yes
No
1990-2013
Yes
Yes
Yes
No
2000-2013
Yes
Yes
No
Yes
1990-2013
Yes
Yes
Yes
No
1990-2013
Yes
Yes
Yes
No
2000-2013
Yes
Yes
No
Yes
1990-2013
659,837
426,348
659,177
646,271
426,348
646,126
Table 4 First Stage and Reduced Form Analysis For Analysts’ Accuracy
This table reports the first stage OLS, reduced form and 2SLS. Columns (1)-(2) shows the first stage using as instrumental
variable peers of peers general experience. The dependent variable equals analysts forecast accuracy in columns (3)-(8). The
model includes controls such as: number of analyst following each firm. The estimation also includes individual fixed effects and
year fixed effects. Data come from detailed I/B/E/S for the period 1990-2013. Robust standard errors in parentheses, clustered
at the analyst level. ***, ** and * indicate p-values of 1%, 5%, and 10%, respectively
First stage
Peers Accuracy
GICS
SIC
(1)
(2)
Peers accuracy
0.1185***
(0.02895)
General experience
(peers of peers)
0.0919***
(0.01167)
0.0823***
(0.00637)
First-Stage F-stat
61.93
166.41
Yes
Yes
Yes
Yes
Yes
Yes
Controls
Analyst FE
Year FE
OLS
Accuracy
GICS
SIC
(3)
(4)
Yes
Yes
Yes
RF
GICS
(5)
2SLS
SIC
(6)
0.0921***
(0.0313)
Yes
Yes
Yes
0.0293***
(0.00020)
0.0197***
(0.00013)
Yes
Yes
Yes
Yes
Yes
Yes
GICS
(7)
SIC
(8)
0.3191***
(0.03445)
0.2405***
(0.0283)
Yes
Yes
Yes
Yes
Yes
Yes
Table 5 Peer Effects in Analysts’ Forecast Error Scaled by Price
This table reports the association between forecast error scaled by price, analyst characteristics, analyst peers forecast accuracy
and analyst peers characteristics, along with two alternative peer group definitions (GICS Panel A and SIC Panel B). The
characteristics (which are all lagged one period) are: BrokerSize, the number of analysts in the analyst’s brokerage in each year;
Firm Experience, the analyst’s years of experience forecasting a particular firm’s earnings; Industry Experience, the analyst’s years
of experience following a particular industry;General Experience, the analyst’s overall years of forecasting experience; Companies,
the number of companies the analyst follows in each year; and Industries, the number of industries the analyst follows in each
year. Peers of peers General experience is used as excluded instrument. The model includes controls such as: number of analyst
following each firm. The estimation also includes individual fixed effects and year fixed effects, for columns (3), (6) industry year
fixed effect is also included. Data come from detailed I/B/E/S. Robust standard errors in parentheses, clustered at the analyst
level. ***, ** and * indicate p-values of 1%, 5%, and 10%, respectively
Panel A
Dependent Variable/Industry
Peers FEP
Peers characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
Analyst characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
FEP/GICS
OLS
2SLS
(3)
(4)
OLS
(1)
OLS
(2)
2SLS
(5)
2SLS
(6)
0.03468*
(0.01721)
0.05508**
(0.01734)
0.03896*
(0.02032)
0.10098***
(0.02958)
0.06631**
(0.02754)
0.10302**
(0.00408)
-0.00016*
(0.00009)
-0.00147
(0.00109)
-0.00013**
(0.00004)
-0.01300
(0.01370)
0.00830*
(0.04900)
0.00126*
(0.00712)
-0.00003*
(0.00003)
-0.00131
(0.00100)
-0.00079***
(0.00009)
-0.02100
(0.01750)
0.00100
(0.03140)
0.00164
(0.00102)
-0.00015**
(0.00007)
-0.00062
(0.00161)
-0.00021***
(0.00006)
-0.00761
(0.01890)
0.00704
(0.05070)
0.00293
(0.00889)
-0.00214*
(0.00109)
-0.00035
(0.00056)
-0.00463**
(0.00196)
-0.00060
(0.00087)
0.00261
(0.00164)
0.00182**
(0.00047)
-0.00306*
(0.00150)
-0.00050
(0.00080)
-0.00661**
(0.00280)
-0.00086
(0.00124)
0.00373
(0.00234)
0.00180**
(0.00070)
-0.00045*
(0.00022)
-0.00093***
(0.00017)
-0.00034*
(0.00016)
-0.00093***
(0.00022)
0.00232***
(0.00052)
0.00005
(0.00006)
-0.00647*
(0.00384)
-0.05400
(0.08280)
-0.00417**
(0.00201)
-0.00005**
(0.00002)
0.00069**
(0.000313)
0.00594*
(0.00337)
-0.00086*
(0.00049)
-0.04390
(0.04970)
-0.00109*
(0.00065)
-0.00001*
(0.00001)
0.00018
(0.00016)
0.00779
(0.00481)
-0.00374*
(0.00224)
-0.03020
(0.03490)
-0.00112*
(0.00061)
-0.00003*
(0.00000)
0.00045*
(0.00025)
0.00139
(0.00421)
-0.00508***
(0.00144)
-0.04220
(0.01208)
-0.00558***
(0.00156)
-0.00206***
(0.00057)
0.00084
(0.00191)
0.00184
(0.00229)
-0.00314**
(0.00127)
-0.03420
(0.01353)
-0.00414**
(0.00163)
-0.00178**
(0.00069)
0.00160
(0.00607)
0.00352
(0.00728)
-0.00429***
(0.00117)
-0.00251
(0.00195)
-0.00212*
(0.00109)
-0.00296***
(0.00062)
0.00002
(0.00057)
0.00161**
(0.00063)
16.43
12.36
21.99
Yes
Yes
Yes
No
1990-2013
642,157
Yes
Yes
Yes
No
2000-2013
422,998
Yes
Yes
Yes
Yes
1990-2013
641,294
First stage F-test
Controls
Analyst FE
Year FE
Industry-Year FE
Observations
Yes
Yes
Yes
No
1990-2013
642,157
Yes
Yes
Yes
No
2000-2013
422,998
Yes
Yes
Yes
Yes
1990-2013
641,294
(continued Table 5)
Panel B
Dependent Variable/Industry
Peers FEP
Peers characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
Analyst characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
FEP/SIC
2SLS
(10)
OLS
(7)
OLS
(8)
OLS
(9)
2SLS
(11)
2SLS
(12)
0.06241***
(0.02015)
0.0845***
(0.23920)
0.06112**
(0.02405)
0.12482***
(0.00260)
0.13132***
(0.00325)
0.13195***
(0.00260)
-0.00031**
(0.00016)
-0.00004
(0.00012)
-0.00122***
(0.00010)
-0.00019
(0.00012)
0.00011***
(0.00002)
0.00025***
(0.00008)
-0.00092
(0.00001)
-0.00063
(0.00001)
-0.00131***
(0.00008)
-0.00015*
(0.00009)
0.00081***
(0.00001)
0.00016***
(0.00006)
-0.00021***
(0.00001)
-0.000441
(0.00001)
-0.00122***
(0.00007)
-0.00021***
(0.00008)
0.00072***
(0.00001)
0.00029***
(0.00005)
-0.00179***
(0.00035)
-0.00409***
(0.00124)
-0.00984***
(0.00203)
-0.00772***
(0.00185)
0.00022***
(0.00005)
0.00222***
(0.00048)
-0.000174**
(0.00008)
-0.000224
(0.00038)
-0.00119***
(0.00042)
-0.00025
(0.00045)
0.00004***
(0.00001)
0.00014
(0.00012)
-0.00211***
(0.00042)
-0.00459***
(0.00145)
-0.0105***
(0.00227)
-0.00849***
(0.00220)
0.00011***
(0.00004)
0.00214***
(0.00058)
-0.00019**
(0.00008)
-0.00121**
(0.00053)
-0.00160**
(0.00038)
-0.00325***
(0.00064)
0.00065***
(0.00024)
0.00111***
(0.00025)
-0.00013*
(0.00007)
-0.00077*
(0.00041)
-0.00648***
(0.000462)
-0.00232***
(0.00051)
0.00047**
(0.00015)
0.00066***
(0.00017)
-0.00016*
(0.00009)
-0.00080**
(0.00040)
-0.00115***
(0.000431)
-0.00175***
(0.00050)
0.00023
(0.00017)
0.00166***
(0.00013)
-0.00019
(0.00004)
-0.00100***
(0.00021)
-0.00050**
(0.00021)
-0.00091**
(0.00045)
0.00004
(0.00004)
0.000128
(0.00016)
-0.00077
(0.00001)
-0.00122***
(0.00008)
-0.00018**
(0.00008)
-0.00136**
(0.00061)
0.00008***
(0.00001)
0.00016***
(0.00005)
-0.00028
(0.00004)
-0.00100***
(0.00023)
-0.00062***
(0.00023)
-0.00103*
(0.00059)
0.00002
(0.00004)
0.00002
(0.00018)
235.62
136.87
181.02
Yes
Yes
Yes
No
1990-2013
642,157
Yes
Yes
Yes
No
2000-2013
422,998
Yes
Yes
Yes
Yes
1990-2013
641,294
First stage F-test
Controls
Analyst FE
Year FE
Industry-Year FE
Observations
Yes
Yes
Yes
No
1990-2013
642,157
Yes
Yes
Yes
No
2000-2013
422,998
Yes
Yes
Yes
Yes
1990-2013
641,294
Table 6 First Stage and Reduced Form Analysis for Analysts’ Forecast Error Scaled by Price
This table reports the first stage OLS, reduced form and 2SLS. Columns (1)-(2) shows the first stage using as instrumental variable
peers of peers general experience. The dependent variable equals analysts forecast error scaled by price in columns (3)-(8). The
model includes controls such as: number of analyst following each firm. The estimation also includes individual fixed effects and
year fixed effects. Data come from detailed I/B/E/S. Robust standard errors in parentheses, clustered at the analyst level. ***,
** and * indicate p-values of 1%, 5%, and 10%, respectively
First stage
Peers FEP
GICS
SIC
(1)
(2)
Peers FEP
GICS
(3)
0.03468*
(0.01721)
General experience
(peers of peers)
-0.01615***
(0.00398)
-0.01291***
(0.00008)
First-Stage F-stat
16.43
235.62
Yes
Yes
Yes
Yes
Yes
Yes
Controls
Analyst FE
Year FE
OLS
Yes
Yes
Yes
RF
FEP
SIC
(4)
GICS
(5)
2SLS
SIC
(6)
0.06241***
(0.02015)
Yes
Yes
Yes
-0.001621**
(0.00060)
-0.00161***
(0.00042)
Yes
Yes
Yes
Yes
Yes
Yes
GICS
(7)
SIC
(8)
0.10098***
(0.02958)
0.12482***
(0.00260)
Yes
Yes
Yes
Yes
Yes
Yes
Table 7 Peer Effects Heterogeneity
This table reports the association between forecast accuracy, analyst characteristics, analyst peers forecast accuracy and analyst
peers characteristics, along with two alternative peer group definitions (GICS and SIC). Each specification includes peers characteristics and analyst’s characteristics such in Table 3. The model includes controls such as: number of analyst following each
firm, book to market and firm size. The estimation also includes individual fixed effects and year fixed effects. Panel A shows
the effect of peers accuracy when the number of industries followed by at least 50 percent of peers is below and above the median
respectively. Panel B shows the effect of peers accuracy when at least one of the analyst’s peers work for a brokerage house higher
than the median. Data come from detailed I/B/E/S. Robust standard errors in parentheses, clustered at the analyst level. ***,
** and * indicate p-values of 1%, 5%, and 10%, respectively
Panel A
Dependent Variable/Industry
Peers accuracy
Accuracy/GICS
2SLS
(1)
(2)
Accuracy/SIC
2SLS
(3)
(4)
0.4312***
(0.1086)
0.2851***
(0.0731)
0.2894**
(0.1093)
0.1972**
(0.0725)
below median
above median
below median
above median
Yes
Yes
Yes
Yes
Yes
1990-2013
Yes
Yes
Yes
Yes
Yes
1990-2013
Yes
Yes
Yes
Yes
Yes
1990-2013
Yes
Yes
Yes
Yes
Yes
1990-2013
Observations
Panel B
Dependent Variable/Industry
211,147
448,688
209,791
450,045
Peers accuracy
0.2681*
(0.1414)
0.3379**
(0.0966)
0.2252***
(0.0459)
0.2936***
(0.0572)
below median
above median
below median
above median
Yes
Yes
Yes
Yes
Yes
1990-2013
Yes
Yes
Yes
Yes
Yes
1990-2013
Yes
Yes
Yes
Yes
Yes
1990-2013
Yes
Yes
Yes
Yes
Yes
1990-2013
290,328
369,508
278,751
367,520
Industries followed
Peers characteristics
Analyst characteristics
Controls
Analyst FE
Year FE
Period
Peers brokerage house size
Peers characteristics
Analyst characteristics
Controls
Analyst FE
Year FE
Period
Observations
Accuracy/GICS
2SLS
(1)
(2)
Accuracy/SIC
2SLS
(3)
(4)
Table 8 Social Multiplier Estimation
This table reports the social multiplier for both variables of interest (accuracy, FEP) for each industry classification. From Glaeser
et al. (2003), the expected value of the social multiplier is defined as (N − 1 + β) / [(1 − β) (N − 1) + β], where N is the group
size (i.e., average group size) and β is the social interaction parameter from the endogenous effect (i.e., peers’ outcome effect).
Social Multiplier’s Accuracy Effect
Social Multiplier’s FEP effect
GSIC
SIC
1.4627
1.5480
1.4868
1.4934
Appendix Table 1 Endogenous Effect in Analysts’ Accuracy: Basic Model
This table reports the association between forecast accuracy, analyst characteristics, analyst peers forecast accuracy along with
two alternative peer group definitions (GICS and SIC). The characteristics (which are all lagged one period) are: BrokerSize,
the number of analysts in the analyst’s brokerage in each year; Firm Experience, the analyst’s years of experience forecasting
a particular firm’s earnings; Industry Experience, the analyst’s years of experience following a particular industry; General
Experience, the analyst’s overall years of forecasting experience; Companies, the number of companies the analyst follows in each
year; and Industries, the number of industries the analyst follows in each year. Peers of peers General Experience is used as
excluded instrument. The model includes controls such as: number of analyst following each firm, book to market and firm size.
The estimation also includes individual fixed effects and year fixed effects. Data come from detailed I/B/E/S. Robust standard
errors in parentheses, clustered at the analyst level. ***, ** and * indicate p-values of 1%, 5%, and 10%, respectively
Dependent Variable/Industry
Accuracy/GICS
2SLS
(1)
Accuracy/SIC
2SLS
(2)
0.824***
(0.0943)
0.936***
(0.07121)
0.00129***
(0.000239)
0.0236***
(0.00144)
0.00459***
(0.00133)
0.00763***
(0.00266)
-0.000235
(0.00104)
-0.00802***
(0.00298)
0.00112***
(0.00026)
0.0228***
(0.00164)
0.00381**
(0.00154)
0.00482***
(0.00112)
-0.000895***
(0.000280)
-0.00179*
(0.00108)
179.02
57.00
Controls
Analyst FE
Year FE
Period
Yes
Yes
Yes
1990-2013
Yes
Yes
Yes
1990-2013
Observations
646,271
646,271
VARIABLES
Peers accuracy
Analyst characteristics
Brokerage size
Firm experience
General experience
Industry experience
Companies
Industries
First stage F-test