Global Expertise of Financial Analysts

Guang Ma
NUS Business School, National University of Singapore

Stanimir Markov
Cox School of Business, Southern Methodist University

Joanna Shuang Wu
Simon Business School, University of Rochester

October 2016

We gratefully acknowledge the financial support provided by the National University ofSingapore, the Cox School of Business, Southern Methodist University and the Simon Business
School at the University of Rochester. We thank Ekkehart Boehmer, Dashan Huang, Bin Ke,
Edmund Keung, Hao Liang, Roger Loh, Shuqing Luo, Srini Sankaraguruswamy, Charles Shi, Jun
Tu, Rong Wang, and workshop participants at the National University of Singapore and Singapore
Management University for their comments.

Global Expertise of Financial Analysts

Abstract

We describe the challenges of forecasting earnings in a globally interconnected marketplace, andwe document inefficient use of information regarding foreign country exposures and expected
country GDP growth at the consensus and individual forecast levels. A country’s proximity to theUS, importance to the firm, and visibility, as well as availability of more precise information about
foreign country exposures, contribute to consensus forecast efficiency. We identify a dimensionof individual analyst global expertise—similarity in exposure between the firm and the rest of thefirms in the analyst portfolio—and show that it contributes to forecast efficiency, accuracy, andinformativeness and that it helps the analyst achieve the coveted all-star rank, suggesting that
globalization not only poses a challenge but also creates an opportunity for research providers andanalysts to distinguish themselves.

Keywords: financial analysts; earnings forecasts; global expertiseJEL Classification: G10, G17, M41

“(I)t is no longer enough for analysts covering stocks in developed economies to focus on theirlocal economy, or even on developed economies. For example, for many US companies a
growing share of their business is likely to come from the developing world. So to do your job
today as a US analyst, it is important to understand what is going on in these developing
countries and to be able to identify which US companies are likely to be able to compete
effectively in these markets.” (Healy [2014], p.15)

1. Introduction
The task of forecasting corporate earnings in a global world is truly challenging. An
accurate forecast must incorporate information about all foreign economies that a company is
exposed to as well as the rates at which these economies grow. This challenge is further
compounded by the fact that companies continuously adjust their foreign activities and that foreign
economies grow at different rates at different times. In this study, we examine whether sell-side
equity analysts rise to the challenge of forecasting earnings in a global world.
Equity analyst forecasts are generally superior to statistical forecasts (e.g., Bradshaw,
Drake, Myers and Myers [2012], Brown and Rozeff [1978]) and are informative to investors (Lysand Sohn [1990]); at the same time, they are inefficient and biased (Abarbanell and Bernard[1992], DeBondt and Thaler [1985]). Prior evidence of forecast inefficiency raises the possibility
that analysts may fail to fully rise to the challenge of forecasting earnings in a global world. It is
important to understand, within a global setting, the degree of analyst forecast (in)efficiency, itsvariation, and the consequences for investors and analysts themselves. These issues are more
pertinent now than ever because companies operate in an increasingly global world. For example,
in 2014, 34% of all publicly listed US companies reported foreign sales that, on average, accounted
for 40% of their total sales (see Figure 1 for indicators of the rising significance of US companies’
foreign operations over time).

1

To shed light on analyst global expertise, we address three questions. At the consensus
forecast level, what country-level factors determine whether information about foreign country
exposures and expected performance is used efficiently? At the individual analyst level, what
analyst and broker-level factors determine whether this information is used efficiently? What are
the capital market and labor market consequences of inefficient information use?
We measure a company’s foreign country exposure as the ratio of foreign country sales tototal sales, extracted from the Compustat Segment file (Li, Richardson and Tuna [2014]). The sum
product of foreign country exposures and one-year-ahead International Monetary Fund’s (IMF)
country GDP growth forecasts yields a simple but admittedly noisy summary measure of what an
analyst must know to forecast earnings in a global world. We refer to this measure as MACRO_F.
To maximize the power of our tests, we focus on firms with substantial foreign sales thatare more than 25% of total sales. From 1998 to 2014, the number of these companies increased
from 607 to 970, and the percentage of their foreign sales increased from 42% to 55%. The
importance of these firms for the health of the US stock market and economy has become immense:
in 2014, these companies accounted for 40% of the total US market capitalization.
Analyzing one-year-ahead consensus EPS forecasts, we find evidence that analysts
underreact to information about foreign country exposures and expected country performance. A
one standard deviation increase in MACRO_F corresponds to an approximately 0.25 percent
increase in the consensus forecast error, which is approximately 27% of the mean consensus
forecast error. Furthermore, we document systematic patterns in information use. Specifically, theconsensus forecast is more efficient at impounding information about countries that are
geographically closer to the US, that account for a larger share of the company’s sales, and thathave higher analyst following. We conclude that a country’s geographic proximity to the US, its
2

economic importance to the firm, and its visibility facilitate efficient information use, consistent
with theories of limited attention and information processing cost (Sims [2003], Hirshleifer andTeoh [2003]). Finally, the consensus forecast is more efficient when a firm discloses foreign sales
at the country level than at the regional level, consistent with the idea that having more precise
information about foreign sales facilitates the task of forecasting earnings.
Turning to individual analyst-level analysis, we expect that individual analysts’ use of
information about country exposures and expected country growth in forecasting company j’s
earnings, which we refer to as analyst global expertise, likely depends on the similarity between
company j’s country exposures and the country exposures of the rest of the companies in theanalyst’s portfolio. Intuitively, an analyst’s ability to solve the forecasting problem posed by firm
j would be enhanced if the rest of the firms pose similar forecasting problems because of economies
of scale in the acquisition and production of information.
Empirically, we start by measuring pairwise distances in foreign country exposures (sales
in foreign country divided by total firm sales) between firm j and the other firms in the analyst’s
portfolio. The weighted average (by the other firms’ market values) of all these distances
multiplied by −1, which we label SIMEXP_F, captures the notion of the similarity between firm
j’s foreign country exposures and those of the other firms in the analyst’s portfolio. (For anumerical example of the calculations, see Appendix II.) We expect SIMEXP_F to encapsulate animportant dimension of analysts’ global expertise and to be associated with a more efficient use of
global information because a greater similarity in foreign exposures among covered stocks allows
an analyst to take advantage of greater economies of scale in information gathering and production
and to make more efficient resource allocation decisions. We note that our tests include as controls
various firm, broker and analyst characteristics. In particular, we include analysts’ country-level3and industry-level concentrations (Herfindahl Index measures). Therefore, our SIMEXP_F
variable captures a dimension of analyst global specialization and expertise that is distinct fromthe country and industry concentration measures seen in prior literature (e.g., Sonney [2009]).
To investigate whether SIMEXP_F facilitates the efficient impounding of global
information in analyst forecasts, we run regressions of individual analyst forecast errors on
MACRO_F and its interactions with SIMEXP_F. A negative coefficient of the interaction term
MACRO_F×SIMEXP_F suggests that similarity within an analyst’s portfolio regarding firms’
foreign exposures helps mitigate the inefficiency in incorporating MACRO_F in analyst forecasts.
We find results consistent with our prediction: analyst inefficiency in using information about afirm’s country exposure and expected country performance, MACRO_F, is decreasing with thesimilarity in firm foreign exposures within the analyst’s portfolio. A one standard deviation
increase in SIMEXP_F from the sample mean reduces the inefficiency in MACRO_F by 35%,
which is statistically and economically significant.
Viewing SIMEXP_F as a measure of analyst global expertise, we explore whether analyst
global expertise is positively related to forecast accuracy and price impact. A positive relation
cannot be presumed based on our earlier result that analysts with greater SIMEXP_F make more
efficient use of foreign exposures and foreign country expected growth information. To the extent
that analysts make tradeoffs among different tasks when allocating resources, an analyst who
efficiently uses one type of information may inefficiently use another. 1
0 F0F

Our results indicate that analysts with higher global expertise as measured by SIMEXP_F
indeed issue forecasts with higher accuracy and higher price impact. A one standard deviation

1

For example, Luo and Nagarajan [2015] show that supplier chain analysts make superior forecasts for supplier firms
but low-quality forecasts for other firms in their portfolios.

4increase in SIMEXP_F relative to her peers will increase her relative accuracy by 1.2%. In
comparison, a one standard deviation increase in an analyst’s experience will increase her relative
accuracy by 1.4%. Furthermore, a one standard deviation increase in SIMEXP_F increases themarket response to forecast news by approximately 11%. In comparison, the analyst’s experience
does not significantly increase the market response to forecast news.
Finally, we explore whether analyst global expertise as measured by SIMEXP_F ispositively associated with the likelihood of an analyst being ranked as an all-star in the Institutional
Investor Magazine’s annual poll. All-star ranking is a widely accepted measure of analyst
reputation and a determinant of analyst compensation even after controlling for other important
performance metrics (Groysberg, Healy and Maber [2011]), making it an effective proxy for
analyst incentives. Here we find that an analyst with higher SIMEXP_F is more likely to be ranked
as an all-star and less likely to lose her all-star ranking. A one standard deviation increase in
SIMEXP_F from the sample mean increases the probability of being ranked as an all-star by 0.5
percent and decreases the probability of losing all-star ranking by 3.3 percent. Note that thecorresponding sample probabilities are 11.2 percent and 27.3 percent, respectively. This meansthat a one standard deviation increase in global expertise results in 5% and 12% changes in therespective probabilities of being an all-star and losing the all-star position, which are economicallysignificant.
In supplemental analyses, we show that our results are robust to alternative constructs ofthe similarity measure. Alternatively, we view all firms an analyst covers as a single (hypothetical)
firm, and we aggregate sales in each country from these firms. We then calculate its similarity withfirm j. All our results are retained with this alternative measure. Finally, we conduct our analyses
for the subsample of initially covered firms and our inferences remain the same.
5

A vast body of literature in accounting and finance examines the properties andconsequences of analyst forecasts but largely overlooks the increasingly interconnected global
nature of today’s product and financial markets (see recent surveys by Kothari, So and Verdi[2016] and Beyer, Cohen, Lys and Walther [2010]). We contribute to this literature by describing
the challenges of forecasting earnings in a globally interconnected marketplace, assessing theextent to which analysts accomplish this task, identifying factors that determine analyst global
expertise, and showing that the latter is rewarded by the labor market.
Our study fits well with the nascent literature on how globalization affects market priceefficiency (e.g., Claus and Thomas [2001], Li et al. [2014], Albuquerque, Ramadorai and
Watugala [2015], Huang [2015]). The emerging consensus in this literature is that the market isinefficient with respect to information that concerns various aspects of a firm’s global activities
and exposures. Our results that equity analyst forecasts are inefficient with respect to information
about foreign country exposures and expected country growth forecasts naturally complement the
Li et al. [2014] finding that the market is inefficient with respect to this information. Importantly,
we extend this literature by focusing on how analyst global expertise can help mitigate theinefficient use of global information.
While a number of prior studies have examined analyst research in a global setting (seeSection 2 for a summary), they answer different research questions and rely on different test
designs. For example, Khurana, Pereira and Raman [2003] and Duru and Reeb [2002] show thatanalysts are less efficient in impounding information about US firms’ foreign (as opposed todomestic) operations. While these studies make no distinction among the sources of a firm’s
foreign exposures, we show that analyst global expertise is highly context-specific, as it varies
with the characteristics of each foreign country (e.g., its geographic proximity to the US, its
6

visibility and its significance to a firm’s overall operations) and with the commonality within ananalyst’s research portfolio in terms of geographic exposure. Our analyses illustrate that the issueof analyst forecast efficiency in the global setting is highly nuanced and more textured thanpreviously entertained in the literature.
Our results should be interpreted with the following caveats in mind. First, we focus onfirms incorporated in the US and their foreign exposures. While the basis of our predictions may
apply to multinational firms in general, we caution that our inferences may not extend to firms
incorporated outside of the US. Second, we quantify the information germane to an analyst’s
understanding and forecast of a firm’s foreign operations through two vectors: i) the ratio of
foreign country sales to total sales to reflect a firm’s foreign country exposures and ii) country
GDP growth forecasts. Even though this information structure (following Li et al. [2014]) is simple
to implement and intuitive to understand, it is not designed to capture all information dimensions
pertaining to a firm’s foreign operations. Furthermore, the sales-ratio-based (GDP-based) measure
likely captures foreign exposure (country growth) with error.
The rest of the paper proceeds as follows. Section 2 summarizes related research. Section
3 discusses the framework of predicting earnings using firms’ country exposures and GDP growthforecasts, and it replicates Li et al. [2014]. Section 4 explores whether consensus forecasts fully
incorporate such information and factors that affect the efficient use of information. Section 5
explores broker and analyst factors that facilitate the use of macro information at the individual
analyst level and examines the consequences of analyst global expertise in the stock market andlabor market. Section 6 concludes.
2. Related Studies on Analyst Research in the Global Setting
Several earlier studies document that analysts forecast the foreign earnings component of7

US firms inefficiently. Specifically, Khurana et al. [2003] show that analysts underestimate thepersistence of US firms’ foreign earnings. Duru and Reeb [2002] find that analyst forecasts exhibit
lower accuracy and higher optimism for US firms with greater international operations. Thesestudies combine a firm’s foreign operations, treating them as a single, homogenous component
and not distinguishing among the firm’s exposures to different parts of the world. Our analysis, on
the other hand, recognizes the complexity of an analyst’s task, differentiating the various sourcesof a firm’s foreign earnings and accounting for economic growth heterogeneity. It tackles issues
shown in prior work to bear on the efficiency of security prices (Li et al. [2014]).
Our paper is related to a number of prior studies that examine analyst research in theinternational setting. Kini, Mian, Rebello and Venkateswaran [2009] and Sonney [2009] study the
relation between forecast accuracy and analyst country (and industry) specialization, but theyreport conflicting results. 2 Both studies analyze samples of international firms, where a firm is
1F1F

identified with a single home country based on its headquarters location. Our research setting isfundamentally different. We focus on US firms, but our analysis recognizes the fact that many ofthese firms are global in nature and that the headquarters location is inadequate in defining thesefirms geographically. Despite these differences, we construct and include as controls country andindustry concentration measures (based on the Herfindahl Index and analogous to those in Sonney[2009]). The results on our main variable of analyst global expertise (SIMEXP_F) remain robust,suggesting that our measure captures a distinct aspect of analyst specialization and expertise fromthe country and industry concentration measures in prior literature.

2

Sonney [2009] constructs country and industry concentration measures based on the Herfindahl Index to gauge
analyst specialization and find a positive relation between analyst forecast accuracy and their country specialization
for a sample of European firms. Kini et al. [2009] use a simple count of the number of countries (industries) coveredby an analyst to measure his/her country (industry) diversification and find a positive relation between analyst forecast
accuracy and their country diversification for firms in more than 50 countries, including the US.

8

Finally, our study is related to Malloy [2005] and Bae, Stulz and Tan [2008], who showthat analyst proximity to a firm’s headquarters in the US (Malloy [2005]) and internationally (Baeet al. [2008]) enhances forecast accuracy. While our focus is on US firms with global operations,our findings that a country’s proximity to the US facilitates forecast efficiency and accuracy are
consistent with the idea that geographic distance matters.
3. Forecasting Earnings of a Global Company
3.1. Globalization trend
Figure 1 explores the trend toward globalization for US firms in the period from 1998 to2014 (see Table 1 for details about the sample). The percentage of firms with non-zero foreign
sales and significant (25% or more) foreign sales steadily increased from 17% and 8% in 1998 to
34% and 24% in 2014, respectively. The percentage of foreign sales for firms that report non-zeroforeign sales also increased from 27% in 1998 to 40% in 2014. The economic importance of firms
with substantial foreign sales in the US capital market cannot be overstated. In 2014, these firms
accounted for 40% of the total US market capitalization.
3.2. Earnings forecasting framework
A global company has exposures to multiple economies (we use economy and country
interchangeably). Our measurement approach exploits geographic segment sales disclosures madeby US firms and available in the Compustat Annual Fundamental Historical Segments dataset forthe period 1998–2014. 3 Firms report sales at the country (e.g., US) or at the regional (e.g., North
2 F2F

America) level. We use country level disclosures when available. When absent, we allocate

3

SFAS No. 131 (effective 1997) changes the way a segment is defined and reported compared to the previous rules
under SFAS 14. To ensure comparability and consistency in geographic segment data, we focus on the post-SFAS
131 period.

9

regional sales to country sales using a GDP-based weighting scheme, which allocates more sales
to economically more important countries (Roberts [1989], Li et al. [2014]). We assume that firm
j’s exposure to country n in year t−1 can be measured by the firm’s country sales, scaled by total
1

2

sales, s nj,t 1 . We denote the vector of firm j’s country exposures with S j ,t 1 = (s j ,t 1 , s j ,t 1 ,, s Nj ,t 1 ) .

Our empirical approach to measuring country exposures is prone to measurement error.
We do not consider how other components of income may depend on country performance, andthere is much subjectivity both in firm reporting of geographic sales and in allocating geographic
sales to countries. According to Balakrishnan, Harris and Sen [1990], however, the allocation ofgeographic sales to country sales is reasonably close to the actual country-level sales.
Prior studies document that firm performance is associated with macroeconomic condition
variables (Li, Vassalou and Xing [2006], Vassalou [2003]). As a summary indicator ofmacroeconomic conditions, real GDP growth forecasts are useful in predicting firm performance(Li et al. [2014]). We obtain country-level annual real GDP (in US dollars) and GDP growthforecasts from the International Monetary Fund (IMF) World Economic Outlook database. 4 Prior
3F3 F

studies have shown that IMF GDP growth forecasts are accurate and rational (Ashiya [2006],
Tsuchiya [2013]). GDP growth forecasts for calendar years t and t+1 are published in April andn

September of year t. We create a GDP growth forecast for firm j country n in fiscal year t, g t ,using the latest IMF forecasts 5 . We denote the vector of GDP growth forecasts with
4F4F

The data are publicly available at IMF’s website: http://www.imf.org/external/pubs/ft/weo/2016/01/weodata/index.
aspx.
5
If a firm’s fiscal year end is different from December, we combine the one-year-ahead and two-year-ahead IMF
4n

forecasts to create g t . For example, if firm j’s fiscal year ends in March, then g jt  9 12  ft  3 12  ft 1 , where
n

n

n

n

ft and ft 1 are the latest IMF GDP growth forecasts for country n in calendar years t and t+1.

10n

G jt  ( g1jt , g 2jt , , g Njt ) . Finally, we assume that the dot product of firm j’s country exposure vector,
S j ,t 1 , and country GDP growth forecast vector, G jt , i.e., S j ,t 1  G jt   n1 s nj ,t 1  g njt , which weNcall MACRO, summarizes what an analyst must know to forecast the earnings of global firm j. We
further decompose MACRO into its domestic, MACRO_D, and foreign, MACRO_F, components
to assess whether the predictive ability comes from the foreign component. That is, MACRO_Djt= s j ,t 1  g jt , and MACRO_Fjt =  nUS s nj ,t 1  g njt .
US

US

N

Li et al. [2014] demonstrate that MACRO and MACRO_F, in particular, are useful inforecasting earnings, but given the differences in sample composition and research designs, weseek to validate this assumption below.
3.3. Predicting future earnings with MACROAt the beginning of each year t, we predict firm j’s year t earnings, calculated as actual
earnings per share (EPS) in year t scaled by stock price at the beginning of year t, Ajt, as a functionof MACROjt or its components MACRO_Fjt and MACRO_Djt:

Ajt     MACRO jt  Firm Controls   jt

(1)

Ajt    1MACRO _ Fjt  2 MACRO _ D jt  Firm Controls   jt

(2)

We also control for other firm characteristics, including firm j’s actual EPS in year t−1 scaled bystock price at the beginning of year t−1, Aj,t−1; firm j’s market capitalization at the end of year t−1,
LogMVj,t−1; firm j’s book-to-market ratio at the end of year t−1, BTMj,t−1; an indicator for negative
earnings before extraordinary items for firm j in year t−1, LOSSj,t−1; an indicator of firm j paying
dividends in year t−1, D_DIVj,t−1; and firm j’s dividend yield in year t−1, DYj,t−1; as well as
industry and year fixed effects. See Appendix I for detailed variable definitions.

11

We estimate equations (1) and (2) on a sample of 9,843 firm-year observations. A key
sample requirement imposed to increase power is that foreign sales be 25% or more of total sales.
See Table 1 for more information about the sample and the steps taken to derive it.
Table 2 panel A reports sample statistics. The average firm has earnings of 3.752% of its
stock price, a market capitalization of $7.5 billion, a 0.494 book-to-market ratio, and 62.5% of
institutional ownership, suggesting the sample mainly consists of large firms. The variable ofinterest, MACROjt, has a mean (median) of 2.462% (2.692%) with a standard deviation of 1.653%,
and its foreign and domestic components, MACRO_Fjt and MACRO_Djt, have means (medians) of
1.358% (1.231%) and 1.119% (1.178%) with standard deviations of 1.228% and 1.009%
respectively. Note that these numbers differ from Li et al. [2014] because their sample includes
firms from 45 countries over the 1998–2010 period, while ours covers only US firms for the period
1998–2014.
Table 3 columns (1) and (2) report the OLS estimation results of equations (1) and (2). We
find that the coefficient on our variable of interest, MACROjt, is positive at 0.347 and significant
at the 1% level, suggesting that a one standard deviation increase in MACROjt predicts an increaseof 0.57 percent in future earnings, which translates to a 15.3% increase from the sample mean.
When we decompose MACROjt into domestic and foreign components, we find that the coefficienton the foreign component, MACRO_Fjt, is 0.383, significant at the 1% level, while the coefficientof the domestic component, MACRO_Djt, is positive but insignificant. MACRO_Fjt is also
economically more significant. A one standard deviation increase in MACRO_Fjt predicts anincrease of 0.47 percent in future earnings, which translates to a 12.5% increase from the sample
mean.
In columns (3) and (4), we examine whether our results are robust to controlling for the12analyst consensus, Consjt,, defined as the first consensus forecast for firm j in year t after year t−1’searnings announcement, scaled by stock price at the beginning of year t. The consensus forecast
loads significantly and positively, with a coefficient of approximately 0.42, and it is significant at
the 1% level. While the magnitudes of the coefficients of MACROjt and MACRO_Fjt are reduced
to 0.212 and 0.230, respectively, they remain significant at the 1% level, suggesting that MACROjt
and MACRO_Fjt are not subsumed by the analyst consensus. We note that the result regarding
Consjt is consistent with that of prior studies with US firms (Givoly and Lakonishok [1979]) but isdifferent from Li et al. [2014], where the consensus forecast is insignificant in predicting returnon assets, presumably due to sample composition, e.g., our sample includes only US firms overthe period 1998–2014, whereas theirs includes international firms over the period 1998–2010.
The coefficients on the control variables are generally consistent with the findings of Li etal. [2014]. Specifically, lagged actual earnings, Ajt−1, size, LogMVjt−1, and the dividend paying
indicator, D_DIVjt−1, load positively and significantly, while book-to-market ratio, BTMjt−1,
LOSSjt−1, and dividend yield, DYjt−1, load negatively and significantly.
4. Consensus Forecast Analysis
We examine two questions: 1) whether analyst consensus forecasts fully incorporate
information about country exposures and expected country GDP growth and 2) what factors
influence efficient information use. To address the first question, we regress consensus forecast
errors, CFEjt, on MACROjt or MACRO_Fjt and test for a non-zero slope coefficient; we expect ahigher slope coefficient on MACRO_Fjt, which considers only foreign countries:

CFE jt    1MACRO _ Fjt  2 MACRO _ D jt  Firm Controls   jt

(3)

To address the second question, we first sort countries into two groups based on a country’s

13economic importance to the firm, proximity to the US, and visibility. We calculate MACRO_Fjt
for each group and test for coefficient equality across the two groups. Second, we separate firmreported countries from regions and calculate MACRO_Fjt for each group. Third, we sort firms
into two groups based on their total foreign exposure, calculate MACRO_Fjt for each group, andtest for coefficient equality across the two groups:
CFE jt     1MACRO _ F _ HighGroup jt   2 MACRO _ F _ LowGroup jt  2 MACRO _ D jt  Firm Controls   jt

(4)

The motivations for this sorting as well as details on the research design are provided
below.
4.1. Factors that influence efficient information use
Assuming limited attention and information processing resources, forecasting performance
should be improved by shifting attention and resources away from countries to which the firm has
minimal exposures to countries to which the firm has large exposures. We construct
MACRO_F_Top5jt, which considers only the top five countries with the largest shares of firm total
sales, and MACRO_F_NonTop5jt, which excludes these five countries; we expect more efficientuse of MACRO_F_Top5jt than MACRO_F_NonTop5jt.
There is evidence to suggest that forecast accuracy is inversely related to geographic
distance (Malloy [2005], Bae et al. [2008]). We construct MACRO_F_Close5jt, which focuses on
the five countries whose capital cities are closest to Washington DC, and its complement
MACRO_F_NonClose5jt; we expect more efficient use of MACRO_F_Close5jt than
MACRO_F_NonClose5jt.
Analyst coverage is an often-used measure of information availability and visibility at thefirm level. Furthermore, many market anomalies are weaker for companies with greater analyst
14

coverage (Brennan and Hughes [1991], Hong, Kubik and Solomon [2000], Gleason and Lee[2003]), consistent with a positive relation between analyst coverage and information use. Buildingon this line of research, we construct a measure of analyst coverage of a country by summing overanalyst coverage of US firms with non-zero exposures to the respective country and
MACRO_F_Cov5jt, which considers the five countries with highest analyst coverage, and its
complement, MACRO_F_NonCov5jt. We predict more efficient use of MACRO_F_Cov5jt than
MACRO_F_NonCov5jt.
Forecasts should be more efficient when information about foreign sales is more precise:that is, when a firm reports foreign sales at the country level as opposed to at the regional level.
We construct MACRO_F_Cntryjt using country level disclosures and MACRO_F_Regjt using
regional level disclosures, and test whether analysts use MACRO_F_Cntryjt more efficiently than
MACRO_F_Regjt.
As a firm’s overall foreign exposure increases, the importance of accurately assessing
foreign country growth rates and a firm’s exposures also increases. Analysts will accordingly
allocate more of their efforts and resources to firms with higher overall foreign exposure. We
therefore sort firms into quartiles each year based on their overall foreign exposure. Denoting
membership

in

the

(MACRO_F_LoXpsjt),

top
wequartile
predict(remaining
more

efficientquartiles)
use

of

with

MACRO_F_HiXpsjt

MACRO_F_HiXpsjt

than

MACRO_F_LoXpsjt.
4.2. Results
We define consensus forecast errors, CFEjt, as actual EPS minus analysts’ consensus
forecast EPS, scaled by stock price at the beginning of year t, then multiplied by 100 (inpercentage), i.e., Ajt − Consjt. Control variables include lagged earnings (DeBondt and Thaler15

[1985]), Aj,t−1; annual size-adjusted abnormal returns (Abarbanell [1991]), ANNRETj,t−1; accruals(Bradshaw, Richardson and Sloan [2001]), ACCRUALj,t−1; size (Doukas, Kim and Pantzalis[2002]), LogMVj,t−1; institutional ownership (O'Brien and Bhushan [1990]), IORj,t−1, book-tomarket (Doukas et al. [2002]), BTMj,t−1; loss (Duru and Reeb [2002]), LOSSj,t−1; standard deviationof ROA (Duru and Reeb [2002]), STDROAj,t−1; financial leverage (Hutton, Lee and Shu [2012]),
LEVj,t−1; and the cross-sectional dispersion of GDP growth forecasts, GDPDISPjt.
Table 2 panel A reports descriptive statistics for the sample used in this section. The mean
(median) consensus forecast error is −0.914% (−0.019%), with a standard deviation of 4.824%.
The means (medians) of MACRO_F_Top5jt and MACRO_F_NonTop5jt are 0.803% (0.557%) and
0.557% (0.550%), respectively, suggesting that the top five countries with the largest sales sharescontribute significantly to MACRO_Fjt.
Table 4 columns (1) and (2) report results from our test of equation (3)—whether theconsensus incorporates information about MACROjt and its components. The coefficient on
MACROjt in column (1) is 0.169, significant at the 10% level, suggesting that analysts, on average,
underreact to MACROjt information. In column (2), the coefficient on MACRO_Fjt is positive at
0.202 and significant at the 5% level, while the coefficient of the domestic component,
MACRO_Djt, is positive but insignificant. A one standard deviation increase in MACRO_Fjt
increases consensus forecast error by 0.25 percent, which translates to a 27% increase from themean consensus forecast error. The coefficient of MACRO_Fjt is larger than that of MACROjt and
MACRO_Djt, but the difference is not statistically significant.
Table 4 columns (3) to (7) report the estimation results of equation (4)—what factors
influence efficient use of information about country exposures and expected country GDP growthforecasts. The coefficient on MACRO_F_NonTop5jt is positive, 0.530, and significant at the 1%16

level, while the coefficient of MACRO_F_Top5jt is insignificant. The difference between the twocoefficients is significant at the 5% level. A one standard deviation increase in
MACRO_F_NonTopjt5 corresponds to a 0.61 percent increase in consensus forecast error, whichtranslates to a 66.4% increase from its sample mean. Similar patterns exist for the pairs
MACRO_F_Close5jt

and

MACRO_F_NonClose5jt,

MACRO_F_Cov5jt

and

MACRO_F_NonCov5jt, and MACRO_F_Cntryjt and MACRO_F_Regjt, except that the coefficientof MACRO_F_Cov5jt is not significantly different from that of MACRO_F_NonCov5jt. The
coefficients of MACRO_F_HiXpsjt and MACRO_F_LoXpsjt are 0.209 and 0.393, significant at the
5% and 10% levels, respectively, suggesting the inefficiency exists both for firms with high andlow foreign exposure. The difference between the two coefficients is statistically significant at the
5% level, which implies smaller forecast inefficiency when the firm has greater overall foreign
exposure.
To sum up, the evidence in Table 4 suggests that analysts do not efficiently use information
about country exposures and GDP growth forecasts and such inefficiency is reduced for countries
that are more important, geographically closer to the US, and more visible, for firms whose overall
foreign exposure is higher, and when the information about exposure is more precise, consistent
with the implications of theories of limited attention and information processing resources (e.g.,
Corwin and Coughenour [2008], Hirshleifer and Teoh [2003], Sims [2003]).
5. Individual Analyst Forecast Analysis
5.1. Are individual forecasts efficient with respect to MACRO?
In this section, we explore what broker and analyst factors facilitate the use of MACRO at
the individual level. Knowledge of how the use of MACRO varies as a function of observablebroker and analyst characteristics should be helpful to investors in adjusting for forecast
17deficiencies and to analysts and research directors in organizing analyst research portfolios.
A unique feature of the task of covering global firms is that an analyst must estimate
country exposures for each firm in her portfolio. Suppose an analyst follows three firms, A, B, andC, each generating earnings in three countries. If these firms have no overlapping country
exposures, then the analyst would have to estimate nine country exposures. If these firms have
exposures to the same three countries, and furthermore, if these exposures are the same (orapproximately the same), the analyst would have to estimate only three country exposures.
Intuitively, the task magnitude is greatly reduced when the analyst must solve the same problem
three times compared to when she must solve three different problems at once. The reduction intask magnitude can also be framed as economy of scale benefits: the quantity of information usefulin assessing a firm’s exposure triples when firms A, B, and C have the same country exposures.
We calculate similarity in exposure between firm j and all other firms in analyst i’s
portfolio in year t, SIMEXPijt, in two steps. We first calculate the distance in exposures between
firm j and firm k as DIST j ,k ,t  (s  s ) . Then, we calculate the value-weighted average
N

n 1

n
j ,t

n 2
k ,t

of DISTj,k,t,, with the weights given by firm k’s market value divided by the collective market value,
multiplied by −1. 6 By construction, 0  SIMEXPijt   2 . We expect more efficient use of
5F5F

MACRO when SIMEXP is higher.
We decompose SIMEXP into similarity in domestic exposure, SIMEXP_D, and similarity
in foreign exposure, SIMEXP_F, just like we decompose MACRO into MACRO_D and6

We assume that more attention and resources are devoted to firms with greater market value. If firm B has larger
market capitalization than firm C, we posit greater economy of scales benefits when firm A’s exposure is more likefirm B’s exposure (as opposed to a simple average of B’s and C’s exposures). Alternatively, we use the equal-weighted
average of DISTjkt multiplied by −1; our results are slightly weaker but qualitatively similar.

18

MACRO_F. Specifically, we first calculate the distance in domestic exposures and foreignUS
exposures between firm j and firm k in analyst i’s portfolio in year t as DIST jD,k ,t  sUS
j ,t  sk ,t and

DIST jF,k ,t 



N
n US

(s nj ,t – skn,t )2 , respectively. We then calculate the market-value-weighted

average of DIST jD,k ,t and DIST jF,k ,t , multiplied by −1.
We also explore the roles of country concentration and industry concentration of theanalyst’s research portfolio in reducing the inefficient use of MACRO and MACRO_F. Sonney[2009] and Kini et al. [2009] document that the country and industry structure of an analyst’s
portfolio affects forecast accuracy differentially (see discussion in Section 2). It is possible that
our measures of exposure similarity capture some degree of analyst country specialization. By
including the country and industry concentrations, we differentiate our measures from the country
specialization documented by Sonney [2009] and Kini et al. [2009]. We calculate the country
concentration of analyst i’s portfolio in year t, HI_Cntryit, as the sum of the squared sales
percentage from each country for all firms in analyst i’s portfolio,  n 1 ( sitn )2 , where sin,t is theNratio of firms’ sales in country n in year t to firms’ total sales. We calculate the industry
concentration, HI_Indit, in a similar fashion. We denote the ratio of firms’ sales in industry m tototal sales with eit , HI _ Indit   m1 (eitm )2 . We classify firms in industries using two-digit SIC
mM

codes and industry segment data from Compustat.
Our measures of country (industry) concentration differ from Sonney’s specialization
measures or Kini et al.’s diversification measures, as those studies assign each firm to a single
country (industry). Due to these differences and Sonney [2009] and Kini et al. [2009]’s conflicting
conclusions on the relation between specialization (diversification) and forecast accuracy, we do

19not make directional predictions regarding the relation between country (industry) concentrationand efficient use of MACRO_F.
We also consider the following determinants of forecast efficiency and accuracy: firmspecific experience (Lim [2001]), LogFIRMEXPijt; broker size (Clement [1999]), LogBRKSZit;
number of firms followed by the analyst (Clement, Frankel and Miller [2003]), LogNFIRMSit,
number of industries followed by the analyst (Clement et al. [2003]), LogNINDit; and forecasthorizon (Clement and Tse [2005]), LogHRZNijt. Finally, we include firm characteristics that affectthe consensus forecast errors (see Section 4). We estimate the following regressions:
FEijt     MACRO jt   SIMEXPij ,t 1   MACRO jt  SIMEXPij ,t 1
 Analyst Controls  Firm Controls   ijt(5)FEijt    1MACRO _ Fjt   1SIMEXP _ Fij ,t 1  1MACRO _ F jt  SIMEXP _ Fij ,t 1
  2 MACRO _ D jt   2 SIMEXP _ Dij ,t 1   2 MACRO _ D jt  SIMEXP _ Dij ,t 1(6)

 Analyst Controls  Firm Controls   ijt

where the dependent variable is analyst i’s forecast error for firm j in year t, scaled by stock price;
independent variables are defined above and in detail in Appendix I. Our variables of interest are
the interaction terms MACROjt×SIMEXPij,t−1 and MACRO_Fjt×SIMEXP_Fij,t−1. Based on thefinding in Section 4 that analysts underreact to MACRO and MACRO_F information, we expect
β>0 and β1>0. Therefore, more efficient use of MACRO and MACRO_F indicates ρ<0 in equation(5) and ρ1<0 in equation (6).
Table 2 panel B reports the descriptive statistics for the individual forecast sample used inthis test. The mean individual forecast error is −0.027, the mean exposure similarity, SIMEXPij,t−1,
is −0.250 and the mean foreign exposure similarity, SIMEXP_F ij,t−1, is −0.146. The average
analyst has a country concentration of 0.338, an industry concentration of 0.578, follows 3.095
industries and 9.548 firms, makes forecasts at the horizon of 91 days, and has been following the20

firm for 3.709 years. The average broker employs 44 analysts. Similar to consensus forecast errors,
individual forecast errors are positively correlated with MACRO (Table 2 panel D).
Table 5 reports the estimation results for equations (5) and (6). Column (1) reports thebaseline regression. Consistent with our Section 4 findings of inefficient use of information at theconsensus level, individual analysts significantly underreact to MACRO information, as evidenced
by a slope coefficient of 0.066, significant at the 1% level. In column (2), where we estimate
equation (5), the coefficient of the interaction term MACROjt×SIMEXPij,t−1 is statisticallysignificant at −0.149, consistent with our prediction that analysts who follow firms with similarcountry exposures use MACRO information more efficiently. The main effect of SIMEXPij,t−1 ispositive and significant, 0.604 with a t-value of 3.48, suggesting that analysts whose portfolios are
more similar in country exposures are, on average, less optimistic.
In column (3), we include MACRO interacted with country concentration and industry
concentration. The coefficient on MACROjt×SIMEXPij,t−1 remains significant at the 5% level; itsvalue, −0.139, is similar to that in column (2). The coefficient on MACROjt×HI_Cntryi,t−1 isnegative but insignificant, while the coefficient of MACROjt×HI_Indi,t−1 is positive and significant,suggesting that either analyst’s country concentration or industry concentration helps to reduce theinefficiency in use of MACRO information. In column (4), we further interact MACRO with otheranalyst characteristics and find that analyst’s firm-specific experience and number of firms
followed help reduce the inefficiency, but our variable of interest, MACROjt×SIMEXPij,t−1,
remains significant at the 5% level, and the magnitude of its coefficient remains approximately thesame. The evidence in columns (3) and (4) suggests that our measure of country exposure
similarity helps explain forecast efficiency beyond the analyst’s country and industry
concentrations and traditional analyst characteristics.
21

In columns (5)–(7) of Table 5, we estimate equation (6). The results indicate that individual
analysts underreact to the foreign component of MACRO, MACRO_F, but the similarity in foreign
exposures among analyst’s research portfolios helps reduce such inefficiency. The coefficient on
MACRO_Fjt×SIMEXP_Fij,t−1, is −0.243, significant at the 5% level, while the coefficient of
MACRO_Djt×SIMEXP_Dij,t−1 is insignificant. A one standard deviation increase in SIMEXP_Fij,t−1
from the sample mean corresponds to a reduction in the inefficiency regarding MACRO_F by
0.027, which is equivalent to a 35% reduction in the inefficiency 7. An analyst’s firm-specific
6 F6Fexperience and the number of firms followed also help reduce the inefficiency related to
MACRO_F, while country and industry concentration do not.
In summary, individual forecasts are inefficient with respect to MACRO and MACRO_F.
The inefficiency is reduced when analysts follow firms with similar foreign exposures, consistent
with our conjecture that analysts benefit from economies of scale in the acquisition and processingof information about the foreign activities of the firms they cover.
5.2. Global expertise and forecast accuracy
In this section, we examine whether similarity in exposures (SIMEXP) is positively relatedwith forecast accuracy. Our evidence that SIMEXP is positively associated with forecast efficiency
regarding MACRO does not necessarily imply that it has a positive relation with accuracy: ananalyst who efficiently uses one type of information may use another inefficiently (see Footnote#1), and the net effect on forecast accuracy remains uncertain.

7

The 35% is calculated by taking the first-order derivative of FEijt with respect to MACRO_Fjt in equation (6) andplugging in the sample mean and standard deviation of SIMEXP_Fij,t−1:
0.243  standard deviation of SIMEXP _ Fij ,t 1
0.243  0.112
 35%
0.043  0.243  mean of SIMEXP _ Fij ,t 1
0.043  0.243   0.146 .

22

Following Clement [1999], we control for the heterogeneity across firm years bydemeaning the dependent and independent variables. 8 Specifically, we measure forecast accuracy
7F7F

as the proportional mean absolute forecast error, PMAFEijt  AFEijt / AFE jt  1 , where AFEijt isthe absolute forecast error for analyst i’s forecast for firm j in year t, and AFE jt is the mean
absolute forecast error for firm j in year t. We then estimate the following regressions:

PMAFEijt     SIMEXPij ,t 1  Analyst Controls   ijt(7)

PMAFEijt    1SIMEXP _ Fij ,t 1  2 SIMEXP _ Dij ,t 1  Analyst Controls   ijt .

(8)

We include LogFIRMEXPijt, LogNFIRMSit, LogNINDit, LogBRKSZit, and LogHRZNijt as analyst
controls.
Table 6 column (1) reports the estimation of equations (7). The coefficient of the global
expertise variable, SIMEXP, is −0.137, significant at the 1% level. Firm-specific experience andbroker size also help improve forecast accuracy, while number of industries followed and forecasthorizon reduce forecast accuracy. In column (2), we include country concentration, HI_Cntryi,t−1,
and industry concentration, HI_Indi,t−1, and our global expertise variable remains significantly
negative, −0.183, significant at the 1% level. Country and industry concentrations also help
improve forecast accuracy.
Column (3) reports the estimation of equation (8). SIMEXP_F is negative, −0.377, andsignificant at the 5% level. A one standard deviation increase in SIMEXP_F corresponds to a 1.6%
decrease in absolute forecast errors. SIMEXP_D is also significant and negative, but its economic
significance is much smaller, with a 0.5% decrease in absolute forecast errors for a one-standard
deviation increase in SIMEXP_D. In column (4), we include HI_Cntryi,t−1 and HI_Indi,t−1 and

8This approach is analogous to estimating a model with firm-year dummies.

23

reestimate equation (8). SIMEXP_F remains significant and negative, although its economic
significance drops slightly. A one standard deviation increase in SIMEXP_F corresponds to a 1.2%
decrease in absolute forecast errors. In comparison, a one standard deviation increase in ananalyst’s experience, LogFIRMEXP, corresponds to a 1.4% decrease in absolute forecast errors,
and a one standard deviation increase in country concentration, HI_Cntryi,t−1, (industry
concentration, HI_Indi,t−1) corresponds to a 1.4% (2.0%) decrease in absolute forecast errors. Thisevidence suggests that an analyst’s global expertise improves overall forecast accuracy, and theimprovement is comparable to and beyond that of country and industry concentrations.
5.3. Global expertise and forecast informativenessWe now investigate whether analysts with global expertise issue more informative
forecasts. 9 We estimate the following regressions of three-day market reactions to analyst forecast
8F8F

revisions on forecast revisions interacted with global expertise:
CARijt     NEWSijt   SIMEXPij ,t 1   NEWSijt  SIMEXPij ,t 1
 Firm Controls  Analyst Controls   ijt(9)

CARijt    1 NEWS _ Fijt   1SIMEXP _ Fij ,t 1  1 NEWS _ Fijt  SIMEXP _ Fij ,t 1
  2 NEWS _ Dijt   2 SIMEXP _ Dij ,t 1  2 NEWS _ Dijt  SIMEXP _ Dij ,t 1(10)

 Firm Controls  Analyst Controls   ijt

where the dependent variable, CARijt, is three-day cumulative abnormal returns, adjusted with theFama-French three factors (Fama and French [1993]) and the momentum factor (Carhart [1997]),
around analyst i’s EPS forecast revision for firm j in year t, multiplied by 100. NEWSijt is thedifference between analyst i’s EPS forecast for firm j in year t and the prevailing consensus
forecast, scaled by stock price at the beginning of year t, then multiplied by 100. We further

9

Our results that analysts with global expertise incorporate MACRO information more efficiently and produce moreaccurate forecasts suggest but do not guarantee that these forecasts convey new information to the market.

24decompose NEWSijt into domestic and foreign components, NEWS_Dijt and NEWS_Fijt, by
multiplying it with percentages of firm j’s domestic and foreign sales in year t−1, respectively.
We expect the coefficients on the interaction terms, φ and φ1, to be positive. We include the analyst
and firm controls appearing in the previous sections: LogFIRMEXPijt, LogBRKSZit, LogNFIRMSit,
LogNINDit, LogHRZNijt, GDPDISPijt, ∆ROAj,t−1, STDROAj,t−1, LOGMVj,t−1, BTMj,t−1, LEVj,t−1,
IORj,t−1, LOSSj,t−1, ANNRETj,t−1, and ACCRUA j,t−1, as well as industry and year fixed effects.
The mean (median) three-day market reaction to individual forecast revisions, CARijt, is−9.8 (7.1) basis points, and the standard deviation is 7.297 percent, as reported in Table 2 panel B.
The mean news contained in individual forecast revisions is −0.089% of the stock price, and the
median is zero. The mean foreign (domestic) news is −0.049% (−0.040%) of the stock price. The
correlation between CARijt and NEWSijt is significantly positive, as expected.
Table 7 columns (1) and (4) report the estimation of equations (9) and (10), respectively.
The coefficients on NEWSijt, NEWS_Fijt, and NEWS_Dijt are significantly positive, suggesting that
forecast revisions convey new information to the market. The coefficient φ of the interaction
between news and global expertise, NEWSijt × SIMEXPij,t−1, is positive at 0.765 and significant at
the 1% level, and the coefficient φ1 of the interaction, NEWS_Fijt × SIMEXP_Fij,t−1, is positive at
1.978 and significant at the 1% level, suggesting that the market reacts more strongly to forecasts
issued by analysts with global expertise. A one standard deviation increase in SIMEXP_Fij,t−1 fromthe sample mean increases the market reaction to forecast revisions by 11%. 10 In columns (2) and
9F9F(5), we include interactions of NEWSijt and NEWS_Fijt with country and industry concentration,

10

The 11% is calculated by taking the first-order derivative of CARijt with respect to NEWS_Fijt in equation (10) andplugging in the sample mean and standard deviation of SIMEXP_Fij,t-1:
1.978  standard deviation of SIMEXP _ Fij ,t 1
1.978  0.112
= 11%.
2.375  1.978  mean of SIMEXP _ Fij ,t 12.375  1.978   0.146 

25

and in columns (3) and (6), we further include interactions of NEWSijt and NEWS_Fijt with otheranalyst characteristics. We find no evidence that country and industry concentrations, firm-specificexperience, broker size, or number of firms followed is associated with forecast informativeness.
The evidence suggests that analysts with global expertise produce more informative forecasts.
5.4. Global expertise and all-star ranking
Finally, we explore whether analyst global expertise is positively associated with thelikelihood of an analyst being ranked as an all-star (First, Second, or Runner-Up) in theInstitutional Investor Magazine annual poll. All-star ranking is a widely accepted measure ofanalyst reputation and a determinant of analyst compensation even after controlling for otherimportant performance metrics (Groysberg et al. [2011]), making it an effective proxy for analyst
incentives.
We model the probability of an analyst being ranked as all-star in year t+1 as a function ofanalyst characteristics measured in year t:

Logit (STARi ,t 1 )     SIMEXPit  Analyst Controls   i ,t 1(11)

Logit (STARi ,t 1 )    1SIMEXP _ Fit  2 SIMEXP _ Dit  Analyst Controls   i,t 1(12)where STARi,t+1 equals one if analyst i is ranked as First, Second, or Runner-Up by Institutional
Investor magazine in year t+1, and zero otherwise. We measure SIMEXPit, SIMEXP_Fit, and
SIMEXP_Dit as the means of SIMEXPijt, SIMEXP_Fijt, and SIMEXP_Dijt across all firms j thatanalyst i covers in year t. As controls, we include country and industry concentrations, HI_Cntryit
and HI_Indit; broker size, LogBRKSZit; number of firms followed, LogNFIRMSit; number ofindustries followed, LogNINDit; and analyst’s general experience, LogGENEXPit. We also control
for Leader-Follower ratio (Cooper, Day and Lewis [2001]), LFRit; forecast accuracy (Hong et al.

26[2000]), ACCit; boldness (Rees, Sharp and Twedt [2014]), BOLDit; forecast frequency (Jacob, Lys
and Neale [1999], Emery and Li [2009]), FREQit; percentage of optimistic forecasts issued (Hongand Kubik [2003]), OPTit; firm size (Stickel [1995]), LogMVit; and institutional holding (O'Brien
and Bhushan [1990]), IORit. See Appendix I for detailed variable definitions. Table 1 panel C
reports the descriptive statistics for this sample.
Table 8 columns (1) and (4) report the estimation of equations (11) and (12), respectively.
The coefficients on SIMEXPit and SIMEXP_Fit are 1.015 and 1.585, respectively, both significant
at the 1% level, suggesting that an analyst with global expertise is more likely to be ranked as anall-star. A one standard deviation increase in SIMEXP_Fit increases the probability of being ranked
all-star by 0.5 percent, a 5% increase in the probability of being ranked from the sample mean of
11.2 percent.
We also examine the changes in all-star rankings. We define STAR_UPi,t+1 = 1 if analyst i
is not ranked all-star in year t but ranked in year t+1, and zero otherwise; STAR_DOWNi,t+1 = 1 ifanalyst i is ranked all-star in year t but not ranked in year t+1, and zero otherwise. We reestimate
equations (11) and (12) with STAR_UPi,t+1 and STAR_DOWNi,t+1 as dependent variables and report
the results in Table 8 columns (2)–(3) and (5)–(6). We find a significant and negative effect of
global expertise in losing all-star ranking. The coefficient on SIMEXP_Fit in the regression of
STAR_DOWNi,t+1 is −1.552, significant at the 5% level. A one-standard deviation increase in
SIMEXP_Fit results in a 3.3 percent reduction in the probability of losing all-star ranking, a 12%
decrease from the mean probability of 27.3 percent. We find no evidence that global expertise isassociated with gaining all-star ranking. Overall, the evidence suggests that brokerages andinstitutional investors value analysts’ global expertise and that analysts can develop global
expertise to distinguish themselves from other analysts.
27

5.5. Robustness checks
In our main analysis, we value-weight pair-wise similarity measures to construct our global
expertise measure. An alternative measure of similarity, SIMEXP2ijt, can be constructed byviewing all firms in analyst i’s portfolio as a single hypothetical firm and calculating the distance
between firm j’s exposure and the hypothetical firm’s exposure. See Appendix II for illustrations.
We replicate our analyses in Sections 5.1–5.4 using this alternative measure and report the resultsin Table 9. All our previous inferences are maintained. In particular, we find that SIMEXP2 isassociated with greater forecast efficiency regarding MACRO information, higher forecast
accuracy, greater forecast news informativeness, and generally better analyst career outcomes.
In untabulated analysis, we conduct tests on a subsample of firm-years where analysts
initiate new coverage to illustrate that our similarity measure (SIMEXP) captures analyst expertisethat applies to new tasks and not just familiarity with existing coverage. Consistent with our
expectation, for this sample of newly added stocks, we find that analyst global expertise issignificantly associated with greater efficiency in analyst use of MACRO information, higher
forecast accuracy and higher informativeness of forecast news.
6. Conclusions
While there is much interest in understanding the determinants of analyst earnings forecast
performance, relatively little is known about how globalization affects the task of forecastingearnings and basic performance measures such as efficiency, accuracy, and price informativeness.
As US firms increasingly derive their profits in foreign markets, research in this area takes on
added urgency.
A unique forecasting challenge posed by globalization is that information relevant to28

forecasting earnings increasingly transcends national borders, which means that to forecast
earnings, the analyst must accurately assess firm exposures to multiple foreign countries and all ofthese countries’ growth prospects. We show that sell-side consensus and sell-side individual
forecasts do not efficiently use available information regarding country exposures and expected
country GDP growth. Country-specific factors that contribute to consensus forecast efficiency
include proximity to the US, economic importance to the firm, and visibility, consistent withtheories of limited attention and information processing cost (Hirshleifer and Teoh [2003], Sims
[2003]). At the individual forecast level, forecast efficiency is positively related to the similarity
in exposure between the firm and the rest of the firms in the analyst portfolio, suggesting economy
of scales benefits from covering similar firms. Finally, following firms with similar country
exposures is associated with higher accuracy, informativeness, and likelihood of being ranked as
an all-star analyst in the Institutional Investor Magazine annual poll. Our results that an
appropriately structured research portfolio allows an analyst to issue more informative forecasts
and achieve the coveted all-star rank suggest globalization not only poses a challenge but also
creates an opportunity for research providers and analysts to distinguish themselves.

29References

ABARBANELL, J. S. "Do Analysts' Earnings Forecasts Incorporate Information in Prior Stock Price
Changes?" Journal of Accounting and Economics 14 (1991): 147-165.
ABARBANELL, J. S., AND V. L. BERNARD. "Tests of Analysts' Overreaction/Underreaction to
Earnings Information as an Explanation for Anomalous Stock Price Behavior." The
Journal of Finance 47 (1992): 1181-1207.
ALBUQUERQUE, R., T. RAMADORAI, AND S. W. WATUGALA. "Trade Credit and Cross-Country
Predictable Firm Returns." Journal of Financial Economics 115 (2015): 592-613.
ASHIYA, M. "Testing the Rationality of Forecast Revisions Made by the IMF and the OECD."
Journal of Forecasting 25 (2006): 25-36.
BAE, K.-H., R. M. STULZ, AND H. TAN. "Do Local Analysts Know More? A Cross-Country Studyof the Performance of Local Analysts and Foreign Analysts." Journal of Financial
Economics 88 (2008): 581-606.
BALAKRISHNAN, R., T. S. HARRIS, AND P. K. SEN. "The Predictive Ability of Geographic Segment
Disclosures." Journal of Accounting Research 28 (1990): 305.
BEYER, A., D. A. COHEN, T. Z. LYS, AND B. R. WALTHER. "The Financial Reporting Environment:
Review of the Recent Literature." Journal of Accounting and Economics 50 (2010): 296343.
BRADSHAW, M. T., M. S. DRAKE, J. N. MYERS, AND L. A. MYERS. "A Re-Examination of Analysts’
Superiority over Time-Series Forecasts of Annual Earnings." Review of Accounting Studies
17 (2012): 944-968.
BRADSHAW, M. T., S. A. RICHARDSON, AND R. G. SLOAN. "Do Analysts and Auditors Use
Information in Accruals?" Journal of Accounting Research 39 (2001): 45-74.
BRENNAN, M. J., AND P. J. HUGHES. "Stock Prices and the Supply of Information." The Journal of
Finance 46 (1991): 1665-1691.
BROWN, L. D., AND M. S. ROZEFF. "The Superiority of Analyst Forecasts as Measures of
Expectations: Evidence from Earnings." The Journal of Finance 33 (1978): 1-16.
CARHART, M. M. "On Persistence in Mutual Fund Performance." The Journal of Finance 52(1997): 57-82.
CLAUS, J., AND J. THOMAS. "Equity Premia as Low as Three Percent? Evidence from Analysts'
Earnings Forecasts for Domestic and International Stock Markets." The Journal of Finance
56 (2001): 1629-1666.
CLEMENT, M. B. "Analyst Forecast Accuracy: Do Ability, Resources, and Portfolio Complexity
Matter?" Journal of Accounting and Economics 27 (1999): 285-303.
30

CLEMENT, M. B., R. FRANKEL, AND J. MILLER. "Confirming Management Earnings Forecasts,
Earnings Uncertainty, and Stock Returns." Journal of Accounting Research 41 (2003):
653-679.
CLEMENT, M. B., AND S. Y. TSE. "Financial Analyst Characteristics and Herding Behavior in
Forecasting." The Journal of Finance 60 (2005): 307-341.
COOPER, R. A., T. E. DAY, AND C. M. LEWIS. "Following the Leader: A Study of Individual
Analysts’ Earnings Forecasts." Journal of Financial Economics 61 (2001): 383-416.
CORWIN, S. A., AND J. F. COUGHENOUR. "Limited Attention and the Allocation of Effort in
Securities Trading." The Journal of Finance 63 (2008): 3031-3067.
DEBONDT, W., AND R. THALER. "Does the Stock Market Overreact?" The Journal of Finance 40(1985): 793.
DOUKAS, J. A., C. KIM, AND C. PANTZALIS. "A Test of the Errors-in-Expectations Explanation of
the Value/Glamour Stock Returns Performance: Evidence from Analysts' Forecasts." The
Journal of Finance 57 (2002): 2143-2165.
DURU, A., AND D. M. REEB. "International Diversification and Analysts' Forecast Accuracy andBias." The Accounting Review 77 (2002): 415-433.
EMERY, D. R., AND X. LI. "Are the Wall Street Analyst Rankings Popularity Contests?" Journal of
Financial and Quantitative Analysis 44 (2009): 411-437.
FAMA, E. F., AND K. R. FRENCH. "Common Risk Factors in the Returns on Stocks and Bonds."
Journal of Financial Economics 33 (1993): 3-56.
GIVOLY, D., AND J. LAKONISHOK. "The Information Content of Financial Analysts' Forecasts ofEarnings." Journal of Accounting and Economics 1 (1979): 165-185.
GLEASON, C. A., AND C. M. C. LEE. "Analyst Forecast Revisions and Market Price Discovery."
The Accounting Review 78 (2003): 193-225.
GROYSBERG, B., P. M. HEALY, AND D. A. MABER. "What Drives Sell-Side Analyst Compensation
at High-Status Investment Banks?" Journal of Accounting Research 49 (2011): 969-1000.
HEALY, P. "Wall Street Research." Journal of Applied Finance 24 (2014): 6-16.
HIRSHLEIFER, D., AND S. H. TEOH. "Limited Attention, Information Disclosure, and Financial
Reporting." Journal of Accounting and Economics 36 (2003): 337-386.
HONG, H., AND J. D. KUBIK. "Analyzing the Analysts: Career Concerns and Biased Earnings
Forecasts." The Journal of Finance 58 (2003): 313-351.
HONG, H., J. D. KUBIK, AND A. SOLOMON. "Security Analysts' Career Concerns and Herding of
Earnings Forecasts." The RAND Journal of Economics 31 (2000): 121-144.
31

HUANG, X. "Thinking Outside the Borders: Investors' Underreaction to Foreign Operations
Information." The Review of Financial Studies 28 (2015): 3109.
HUTTON, A. P., L. F. LEE, AND S. Z. SHU. "Do Managers Always Know Better? The Relative
Accuracy of Management and Analyst Forecasts." Journal of Accounting Research 50(2012): 1217-1244.
JACOB, J., T. Z. LYS, AND M. A. NEALE. "Expertise in Forecasting Performance of Security
Analysts." Journal of Accounting and Economics 28 (1999): 51-82.
KHURANA, I. K., R. PEREIRA, AND K. K. RAMAN. "Does Analyst Behavior Explain Market
Mispricing of Foreign Earnings for U.S. Multinational Firms?" Journal of Accounting,
Auditing & Finance 18 (2003): 453.
KINI, O., S. MIAN, M. REBELLO, AND A. VENKATESWARAN. "On the Structure of Analyst Research
Portfolios and Forecast Accuracy." Journal of Accounting Research 47 (2009): 867-909.
KOTHARI, S. P., E. C. SO, AND R. S. VERDI. "Analysts’ Forecasts and Asset Pricing: A Survey."
Working paper, Massachusetts Institute of Technology (MIT), 2016. Available at
http://ssrn.com/abstract=2748072.
LI, N., S. RICHARDSON, AND İ. TUNA. "Macro to Micro: Country Exposures, Firm Fundamentals
and Stock Returns." Journal of Accounting and Economics 58 (2014): 1-20.
LI, Q., M. VASSALOU, AND Y. XING. "Sector Investment Growth Rates and the Cross Section of
Equity Returns." The Journal of Business 79 (2006): 1637-1665.
LIM, T. "Rationality and Analysts' Forecast Bias." The Journal of Finance 56 (2001): 369-385.
LUO, S., AND N. J. NAGARAJAN. "Information Complementarities and Supply Chain Analysts." The
Accounting Review 90 (2015): 1995-2029.
LYS, T., AND S. SOHN. "The Association between Revisions of Financial Analysts' Earnings
Forecasts and Security-Price Changes." Journal of Accounting and Economics 13 (1990):
341-363.
MALLOY, C. J. "The Geography of Equity Analysis." The Journal of Finance 60 (2005): 719-755.
O'BRIEN, P. C., AND R. BHUSHAN. "Analyst Following and Institutional Ownership." Journal of
Accounting Research 28 (1990): 55-76.
REES, L., N. SHARP, AND B. TWEDT. "Who’s Heard on the Street? Determinants and Consequencesof Financial Analyst Coverage in the Business Press." Review of Accounting Studies(2014): 1-37.
ROBERTS, C. B. "Forecasting Earnings Using Geographical Segment Data: Some Uk Evidence."
Journal of International Financial Management & Accounting 1 (1989): 130-151.

32

SIMS, C. A. "Implications of Rational Inattention." Journal of Monetary Economics 50 (2003):
665-690.
SONNEY, F. "Financial Analysts' Performance: Sector Versus Country Specialization." Review of
Financial Studies 22 (2009): 2087-2131.
STICKEL, S. E. "The Anatomy of the Performance of Buy and Sell Recommendations." Financial
Analysts Journal 51 (1995): 25.
TSUCHIYA, Y. "Are Government and IMF Forecasts Useful? An Application of a New MarketTiming Test." Economics Letters 118 (2013): 118-120.
VASSALOU, M. "News Related to Future GDP Growth as a Risk Factor in Equity Returns." Journalof Financial Economics 68 (2003): 47-73.

33

Appendix I. Variable Definitions
Variable
Definition
Consensus and individual forecast sample
Ajt= Actual earnings per share (EPS) of firm j in year t, scaled by stock price at the
beginning of year t, then multiplied by 100 (in percentage).
Fijt= Analyst i’s EPS forecast for firm j in year t, scaled by stock price at the beginning ofyear t, then multiplied by 100 (in percentage). Analyst i’s EPS forecast is taken as
the last forecast before year t end.
Consjt= Consensus (median) forecast for firm j in year t, scaled by stock price at the
beginning of year t, then multiplied by 100 (in percentage). The consensus isconstructed using the first forecast issued by each analyst right after year t−1’searnings announcements.
FEijt= Forecast error for analyst i’s EPS forecast for firm j in year t, calculated as actual
EPS minus analyst i’s forecast EPS, scaled by stock price at the beginning of year t,then multiplied by 100 (in percentage), i.e., Ajt − Fijt. We keep analyst i’s last forecast
before year t end but after year t−1’s earnings announcements.
CFEjt= Consensus forecast error for firm j in year t, calculated as actual EPS minus analysts’
consensus forecast EPS, scaled by stock price at the beginning of year t, thenmultiplied by 100 (in percentage), i.e., Ajt − Consjt.
PMAFEijt= Proportional mean forecast error for analyst i’s forecast for firm j in year t, calculated
as AFEijt / AFE jt  1 , where AFEijt is the absolute forecast error for analyst i’s

CARijt=NEWSijt=

NEWS_Dijt=

NEWS_Fijt=

MACROjt=

forecast for firm j in year t, and AFE jt is the mean absolute forecast error for firm j
in year t.
three-day cumulative abnormal returns adjusted for Fama-French’s Four Factors in
the window [−1,+1] of analyst i’s EPS forecast for firm j in year t, in percentage.
Difference between analyst i’s EPS forecast for firm j in year t and prevailingconsensus forecast, scaled by stock price at the beginning of year t, then multipliedby 100.
Domestic component of news contained in analyst i’s EPS forecast for firm j in yeart, calculated as NEWSijt multiplied by percentage of domestic sales of firm j in year
t−1.
Foreign component of news contained in analyst i’s EPS forecast for firm j in year t,
calculated as NEWSijt multiplied by percentage of foreign sales of firm j in year t−1.
Sum product of firm j's N country exposures and the respective one year ahead
International Monetary Fund (IMF) country GDP growth forecasts. Country
exposures is the vector S j ,t 1  s1j ,t 1 , s 2j ,t 1 , , s Nj ,t 1 , where each element is firm j's







country sales in year t−1, scaled by total sales. G jt  g 1jt , g 2jt ,, g Njt

 is the vectorof GDP growth forecasts in year t, in percentage. MACROjt = S j ,t 1  G jt
  n 1 s nj ,t 1  g njt . The country sales data are extracted from Compustat Segment
N

Data. For sales reported at a regional level, we decompose it into the region’s
member countries using a GDP-based weighting scheme. See Appendix II for a
simple example.

34

Appendix I. Variable Definitions (cont’d)
Variable
MACRO_Djt=

MACRO_Fjt=

Definition
Product of firm j’s domestic exposure in year t−1 and the one year
ahead domestic GDP growth forecast, in percentage.
Sum product of firm j's foreign country exposures and the one year
ahead respective foreign country GDP growth forecasts,


MACRO_F_Top5jt=

MACRO_F_NonTop5jt=

MACRO_F_Close5jt=

MACRO_F_NonClose5jt=

MACRO_F_Cov5jt=

MACRO_F_NonCov5jt=

MACRO_F_Cntryjt=

MACRO_F_Regjt=

MACRO_F_HiXpsjt=

MACRO_F_LoXpsjt=

SIMEXPijt

=

N
n US

s nj ,t 1  g njt , in percentage. MACRO_Fjt +MACRO_Djt=MACROjt.

Sum product of firm j's five largest foreign country exposures and theone year ahead respective GDP growth forecasts.
Complement of MACRO_F_Top5jt, calculated as MACRO_Fjt −MACRO_F_Top5jt.
Sum product of firm j's exposures to the five foreign countries closest
to the US in year t−1 and the one year ahead respective country GDP
growth forecasts.
Complement of MACRO_F_Close5jt, calculated as MACRO_Fjt −MACRO_F_Close5jt.
Sum product of firm j's exposures to the five foreign countries with thehighest analyst coverage in year t−1 and the one year ahead GDP
growth forecast of the respective countries. We calculate a country’s
analyst coverage by summing over the coverage of all firms with
exposures to the respective country.
Complement of MACRO_F_Cov5jt, calculated as MACRO_Fjt −MACRO_F_Cov5jt.
MACRO_Fjt only for countries reported by firm j at the specific country
level in year t−1.
MACRO_Fjt only for regions reported by firm j at the regional level inyear t−1. We construct the regional GDP growth forecasts fromindividual member countries’ GDP shares and growth forecasts.
MACRO_F_Cntryjt + MACRO_F_Regjt = MACRO_Fjt.
MACRO_Fjt, if firm j’s percentage of foreign sales is in the top quartileof the sample in year t−1; 0 otherwise.
MACRO_Fjt, if firm j’s percentage of foreign sales is in the bottom
three quartiles of the sample in year t−1; 0 otherwise.
Similarity in country exposures between firm j and all other firms inanalyst i’s portfolio in year t. We first calculate the distance in
exposures between firm j and firm k as DISTjkt  (s  s ) .
N

n 1

n
jt

n 2
kt

We then calculate the value-weighted average of DISTj,k,t,, with theweights given by firm k’s market value divided by the collective market
value, multiplied by −1. See Appendix II for a simple example.

35

Appendix I. Variable Definitions (cont’d)
Variable
SIMEXP_Dijt=

Definition
Similarity between firm j’s domestic exposure and all other firms in analyst i’s
portfolio in year t. We first calculate the distance in domestic exposures between
firm j and firm k as DISTjktD  sUS
 sktUS . We then calculate the value-weighted
jt
D

average of DISTjkt , with the weights given by firm k’s market value divided by
SIMEXP_Fijt=

the collective market value, multiplied by −1.
Similarity between firm j’s foreign exposure and all other firms in analyst i’s
portfolio in year t. We first calculate the distance in foreign exposures betweenfirm j and firm k as DISTjkt 
F

N
n US

( s njt – sknt )2 . We then calculate the value-Fweighted average of DISTj , k ,t , with the weights given by firm k’s market value
SIMEXP2ijt=

divided by the collective market value, multiplied by −1.
Similarity in country exposures between firm j, Sjt, and all the firms in analyst i’s
portfolio, viewed as a single hypothetical firm, Sit,  Sit  S jt SIMEXP2_Dijt=

 (s  s ) , where each element of S , sitn , is the ratio of the firms’ sales
N

n 1

n
it

n 2
jt

it

in country n in year t divided by firms’ total sales. See Appendix II for a simple
example.
Similarity between firm j’s domestic exposure and all other firms in analyst i’s
portfolio when viewed as a single hypothetical firm,  sitUS  sUS, where sit is thejtUSratio of firms’ sales in the US in year t divided by firms’ total sales.
SIMEXP2_Fijt=

F

Similarity between firm j’s foreign exposures, S jt , and all the firms in analyst i’s
portfolio when viewed as a single hypothetical firm, S it ,  sitUS  sUS
jt
F





N

( sitn  s njt ) 2 , where sit is the ratio of firms’ sales in foreign country n in
n

n US

year t divided by firms’ total sales.
HI_Cntryit=

Country concentration of analyst i's portfolio in year t, calculated by  n 1 ( sitn ) 2 ,Nnwhere sit is the ratio of firms’ sales in country n in year t to firms’ total sales.
HI_Indit=

Industry concentration of analyst i’s portfolio in year t, calculated similarly tom

HI_Cntryit. Denoting the ratio of firms’ sales in industry m to total sales with eit ,HI _ Indit   m1 (eitm ) 2 . Industry classification is based on two-digit SIC code,
M

HRZNijt=

BRKSZit=and we identify all industries each firm operates in using industry segment datafrom Compustat.
Analyst i’s forecast horizon for firm j in year t, defined as the number of days
from the analyst’s forecast to the fiscal year end.
Brokerage size, defined as the number of analysts employed by the brokerage atyear t.

36

Appendix I. Variable Definitions (cont’d)
Variable
FIRMEXPijt=

NFIRMSit
NINDit==GDPDISPjt=∆ROAjtSTDROAjt
LogMVjt
BTMjt
LOSSjt=====Definition
Firm-specific experience, defined as the number of years analyst i has covered
firm j.
Number of firms followed by analyst i in year t.
Number of industries followed by analyst i in year t. Industry classification isbased on two-digit SIC code of each firm’s primary industry.
Cross-sectional dispersion of GDP growth forecasts for year t for all countries
firm j operates in.
Change of ROA for firm j in year t.
Standard deviation of ROA for firm j in the past five years t−4 to t.
Natural logarithm of firm j’s market capitalization at the end of year t.
Firm j’s book-to-market ratio at the end of year t.
1 if firm j’s earnings before extraordinary items are negative in year t; 0
otherwise.
Firm j’s financial leverage at the beginning of year t.
Institutional ownership of firm j at the beginning of year t.
Firm j’s accruals in year t, scaled by book value of equity at the end of year t.
Dummy for negative earnings for firm j in year t.
Dummy for firm j paying dividends in year t.
Annual size-adjusted abnormal returns for firm j in year t.
Firm j’s dividend yield in year t.

LEVjt
=
IORjt
=
ACCRUALjt=
LOSSjt=
D_DIVjt=
ANNRETjt=
DYjt=
All-star ranking sample
STARi,t+1= 1 if analyst i is ranked as First, Second, or Runner-Up by Institutional Investor
magazine in year t+1, and zero otherwise.
STAR_UPi,t+1= 1 if analyst i is not ranked by Institutional Investor magazine in year t but
ranked in year t+1, and zero otherwise. This variable is missing for analysts
who are ranked in year t.
STAR_DOWNi,t+1= 1 if analyst i is ranked by Institutional Investor magazine in year t but not
ranked in year t+1, and zero otherwise. This variable is missing for analysts
who are not ranked in year t.
LFRit= Leader-Follower ratio of analyst i in year t. Following Cooper et al. [2001], foreach forecast made by analyst i in year t, we identify the five preceding andfive subsequent forecasts issued by other analysts. We calculate the forecast’s
LFR as T0/T1, where T0 (T1) is the cumulative number of days by which thepreceding (following) forecasts lead (lag) the forecast of interest. LFRit is theaverage of LFRs of all forecasts issued by analyst i in year t.
ACCit= Analyst i's accuracy in year t. Following Hong et al. [2000], we sort analysts
that follow firm j in year t based on their absolute forecast errors. We
transform the sorting into a ranking, assigning 1 (0) to the highest (lowest)
accuracy, and then average the ranks across all firms followed by analyst i inyear t.

37

Appendix I. Variable Definitions (cont’d)
Variable
BOLDit

=

OPTit

=

LogGENEXPit=

FMVRit=FREQit=Definition
Boldness. We sort all forecasts for firm j in year t based on the absolute valueof their deviation from the consensus, defined as Rees et al. [2014]. We
transform the sorting into a ranking, assigning 1 (0) to the largest (smallest)
deviations. Averaging these ranks over all firms followed by analyst i in year tyields BOLDit.
Percentage of optimistic (greater than actual earnings) forecasts issued byanalyst i in year t.
General experience, defined as the natural logarithm of the number of years
since analyst i first appears in IBES.
First mover measure. The first analyst who issues a forecast for firm j in year t
is given a score of 1; all other analysts are given scores of zero. Averaging
scores across all firms followed by analyst i in year t yields FMVRit.
Forecast frequency. We first calculate the difference between the number offorecasts issued by analyst i for firm j in year t and the average number offorecasts issued by all other analysts for the same firm-year, FREQijt. We thenaverage across all firms followed by analyst i in year t.

38

Appendix II. A Numerical Example
We provide a simple numerical example to illustrate the calculation of the key variables, MACRO, SIMEXP,
and SIMEXP2. In view of the simplicity of the example, we do not use any subscripts.
An analyst forecasts the earnings of firms A, B, and C. These firms have sales in the US, the UK, and China,
as follows (percentage in parentheses):
Firm A
Firm B
Firm CUS sales$50M (50%)$10M (20%)$25M (33%)
UK sales$30M (30%)$40M (80%)
China sales$20M (20%)$50M (67%)
Total sales$100M (100%)$50M (100%)$75M (100%)Market value
$80M
$40M
$60M
Furthermore, assume that the GDP growth forecasts for the US, UK, and China in year t are 2%, 5%, and10%, respectively.
MACROA
Firm A’s exposures to the US, UK, and China are measured by firm A’s US, UK, and China sales, eachscaled by firm A’s total sales: 50%, 30%, and 20%, respectively.
We calculate MACROA as the product of the country exposures and country GDP growth forecasts:
MACROA  50%  2%  30%  5%  20% 10%  4.5% .
The domestic and foreign portions of MACROA are MACRO _ DA  50%  2%  1% and MACRO _ FA
 30%  5%  20%10%  3.5% .
SIMEXPAThe distance between A’s and B’s and between A’s and C’s country exposures are calculated as:
DISTA, B  (50%  20%) 2  (30%  80%) 2  (20%  0%)2  0.6164
DISTA,C  (50%  33%)2  (30%  0%) 2  (20%  67%) 2  0.5994 .

The similarity in exposures between firm A and the other firms in the analyst portfolio, B and C, is
SIMEXPA, which is the value-weighted sum of DISTA,B and DISTA,C, with the weights given by firm B andC’s market values as percentages of their collective market value:
60
 40

SIMEXPA   DISTA, B  DISTA,C   (1)  0.6062 .
40  60
 40  60
SIMEXP2A
Viewing firms A, B, and C as a single hypothetical firm, ABC, we calculate ABC’s country exposures as:  50  10  25  100  50  75    37.8% 

S


 

  30  40  0  100  50  75     31.1%  .
  20  0  50  100  50  75    31.1%  Intuitively, ABC’s exposure to the US is the sum of A, B, and C’s US sales, divided by the sum of A, B,
and C’s total sales. The similarity between Firm A’s exposure and ABC’s exposure, therefore, is:
SIMEXP2 A  (50%  37.8%)2  (30%  31.1%)2  (20%  31.1%)2   1  0.1653 .

39

Figure 1. Globalization Trend of US Firms

10%

20%

30%

40%

This figure shows the globalization trend of US firms from 1998 to 2014. The sample consists of all US
firms listed on the NYSE or NASDAQ with data available from Compustat. This figure shows the timetrends of the following: percentage of firms with foreign sales, percentage of firms with significant (>=25%)
foreign sales, mean percentage of foreign sales for firms with foreign sales, and market capitalization offirms with significant foreign sales as a percentage of total market capitalization.

1998

2002

2006

2010

% firms with foreign sales% firms with significant foreign sales (>=25%)% foreign sales for firms with foreign sales% market cap for firms with significant foreign sales

40

2014

Table 1. Sample Selection
This table reports the sample selection procedures and the number of observations, firms, and analysts thatremained after each step.
Procedure
Compustat1.
Observations from Compustat Annual Fundamental North
America with non-missing sales and total assets in the period
1998–20142.
Merge with CRSP permno and IBES ticker
3.
Delete observations with missing earnings announcement
dates for year t and year t−1.
4.
Merge with Geographic Segment data.
5.
Delete firm-years whose aggregated segment sales differ fromconsolidated sales by more than 5%, foreign sales are larger
than 100%.
 Sample for Figure 1.
6.
Delete observations with foreign sales less than 25%.
7.
Delete observations with stock price less than $1, total assets
less than $5 million, book value of equity less than $1 million,
and total market value less than $10 million.
8.
Merge with IMF GDP growth forecasts, construct MACRO,
and delete observations with missing MACRO.
I/B/E/S
9.
Individual EPS forecasts for year t between year t−1’searnings announcement date and year t’s fiscal year end withactual EPS data available for both years from I/B/E/S US filefor the period 1999–2014.
10. Construct the first consensus immediately after year t−1’searnings announcements for each firm year.
11. Keep only the last forecast from each individual analyst foreach firm year.
12. Merge with Compustat data from step 8.
13. Merge with CRSP stock price and return data.
 Keep one observation per firm year, and construct the
sample analyzed in Tables 3–4 (Consensus Sample).
14. Construct SIMEXP and delete observations with missing
SIMEXP.
 Sample analyzed in Tables 5–7 (Individual Analyst
Sample)15. Merge with Institutional Investor all-star ranking.
 Keep one observation per analyst-year and construct the
all-star ranking sample analyzed in Table 8 (All-star
Ranking Sample).

41

#Obs.

#Firms #Analysts

99,274

13,20991,42584,312

11,132
10,646

61,862
55,246

8,682
8,545

18,87817,503

3,322
3,160

13,0592,505

1,672,617

6,692

13,522

1,672,617

6,692

13,522465,853

6,692

13,522

106,756
106,042
9,843

1,714
1,672
1,672

7,788
7,736

82,754

1,554

5,103

82,75422,737

1,554

5,103
5,103

Table 2. Descriptive Statistics
This table reports the descriptive statistics for the consensus forecasts sample (panel A), the individual
forecasts sample (panel B), the analyst all-star ranking sample (panel C), and pair-wise correlations for theindividual forecasts sample (panel D) for the period 1999–2014. See Appendix I for variable definitions.
Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.

Panel A. Consensus forecast sample

Ajt
Consjt
CFEjt
MACROjt
MACRO_Djt
MACRO_Fjt
MACRO_F_Top5jt
MACRO_F_NonTop5jt
MACRO_F_Close5jt
MACRO_F_NonClose5jt
MACRO_F_Cov5jt
MACRO_F_NonCov5jt
MACRO_F_Cntryjt
MACRO_F_Regjt
MACRO_F_HiXpsjt
MACRO_F_LoXpsjt
LogMVj,t−1
BTMj,t−1
LOSSj,t−1
D_DIVj,t−1
DYj,t−1
STDROAj,t−1
LEVj,t−1
IORj,t−1
ANNRETj,t−1
ACCRUALj,t−1
GDPDISPjt

Count
9843
9843
9843
9843
9734
9835
9835
9843
9835
9532
9835
9528
9843
9843
9836
9842
9843
9843
9843
9843
9843
9016
9787
9843
9841
9842
9834

Mean
3.752
4.455
-0.914
2.462
1.119
1.358
0.803
0.557
0.081
1.299
0.242
1.125
0.471
0.942
0.565
0.792
7.273
0.494
0.225
0.419
2.464
0.063
0.176
0.625
0.073
-0.147
3.249

42

Std. Dev.
6.576
7.257
4.824
1.653
1.009
1.228
1.145
0.477
0.542
1.171
0.504
1.088
1.137
0.897
1.316
0.820
1.767
0.329
0.418
0.493
8.194
0.070
0.165
0.289
0.515
0.260
1.141

P25
2.196
2.949
-1.091
2.007
0.527
0.790
0.339
0.291
0.001
0.769
0.095
0.575
0.000
0.417
0.000
0.000
5.985
0.257
0.000
0.000
0.000
0.019
0.008
0.477
-0.230-0.1802.576

Median4.856
5.189-0.019
2.692
1.178
1.231
0.557
0.550
0.002
1.210
0.179
0.988
0.000
0.919
0.000
0.871
7.123
0.422
0.000
0.000
0.000
0.038
0.152
0.707
-0.013-0.091
2.975

P75
7.030
7.094
0.445
3.332
1.733
1.827
0.937
0.846
0.006
1.783
0.293
1.535
0.458
1.421
0.000
1.339
8.425
0.642
0.000
1.000
0.879
0.079
0.287
0.841
0.243
-0.0303.778

Panel B. Individual forecast sample

FEijt
CARijtNEWSijt
NEWS_Dijt
NEWS_Fijt
MACROjt
MACRO_Djt
MACRO_Fijt
SIMEXPij,t−1
SIMEXP_Dij,t−1
SIMEXP_Fij,t−1
SIMEXP2ij,t−1
SIMEXP2_Dij,t−1
SIMEXP2_Fij,t−1
HI_Cntryi,t−1
HI_Indi,t−1
BRKSZitHRZNijt
FIRMEXPijt
NFIRMSit
NINDit

Count
82754
82754
81413
81413
81413
82754
82754
82754
82754
82754
82754
82754
82754
82754
82754
82754
82754
82754
82754
82754
82754

Mean
-0.027-0.098-0.089-0.040-0.049
2.501
1.076
1.427
-0.250-0.171-0.146-0.226-0.160-0.1280.338
0.578
44.42991.040
3.709
9.548
3.095

Std. Dev.
1.236
7.297
0.864
0.421
0.519
1.637
0.968
1.213
0.133
0.110
0.112
0.140
0.131
0.108
0.198
0.260
30.26970.268
2.796
4.738
1.907

43

P25
-0.082-3.416-0.165-0.068-0.080
2.081
0.540
0.883
-0.314-0.224-0.197-0.297-0.226-0.1700.195
0.363
18.00057.000
2.000
6.000
2.000

Median
0.048
0.071
0.000
0.000
0.000
2.764
1.158
1.319
-0.223-0.148-0.107-0.197-0.126-0.0910.292
0.522
38.00067.000
3.000
9.000
3.000

P75
0.232
3.593
0.140
0.060
0.067
3.339
1.645
1.897
-0.154-0.093-0.065-0.119-0.060-0.0530.437
0.820
71.00087.0005.000
12.0004.000

Panel C. Analyst all-star ranking sample

STARi,t+1
STAR_UPi,t+1
STAR_DOWNi,t+1
SIMEXPit
SIMEXP_Dit
SIMEXP_Fit
SIMEXP2it
SIMEXP2_Dit
SIMEXP2_Fit
HI_Cntryit
HI_Indit
LFRit
ACCit
BOLDitOPTit
NFIRMSit
NINDit
GENEXPit
BRKSZit
FMVRitFREQitLogMVit
IORit

Count
22737
19857
2880
22737
22567
22722
22737
22705
22737
22737
22737
22737
22737
22737
22522
22737
22737
22737
22737
22737
22737
22731
22737

Mean
0.112
0.022
0.273
-0.281-0.210-0.149-0.251-0.186-0.1340.388
0.599
10.324
3.376
0.486
0.505
6.788
2.654
5.464
43.262
0.142
0.111
8.258
0.665

Std. Dev.
0.315
0.148
0.445
0.129
0.110
0.109
0.128
0.115
0.099
0.208
0.262
17.717
1.039
0.190
0.183
3.775
1.567
3.729
31.234
0.249
1.431
1.421
0.199

44

P25
0.000
0.000
0.000
-0.346-0.262-0.191-0.307-0.232-0.171
0.230
0.382
0.722
2.773
0.370
0.390
4.000
1.000
3.000
16.0000.000-0.6847.312
0.571

Median
0.000
0.000
0.000
-0.262-0.192-0.117-0.225-0.158-0.104
0.358
0.549
3.348
3.584
0.500
0.500
6.000
2.000
4.000
36.000
0.000
0.072
8.301
0.694

P75
0.000
0.000
1.000
-0.193-0.137-0.075-0.162-0.111-0.0680.513
0.850
11.861
4.263
0.610
0.620
9.000
3.000
7.000
71.000
0.200
0.841
9.244
0.800

Panel D. Pearson (below the diagonal) and Spearman (above the diagonal) correlation matrix for the individual forecasts sample
(1)

(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
(1) Ajt
0.20
0.01
0.05
0.17
0.02 -0.08
0.07
0.03
0.03
0.02
0.03
0.03
0.04 -0.07 -0.10***
***
***
***
***
***
***
***
***
**
***
***
(2) FEijt
0.34
0.13
0.05 -0.06
0.04 -0.01
0.02 -0.01
0.01 -0.02
0.00
0.01 -0.01*** -0.06*** 0.01***
***
***
***
***
**
(3) PMAFEijt
0.00 -0.00-0.00 -0.07 -0.00
0.02 -0.00 -0.01 -0.04
0.01 -0.00 -0.02*** 0.01*** 0.01*** -0.03***
***
***
***
***
***
(4) CARijt
0.07
0.05
0.00
0.28
0.01 -0.00
0.00
0.02
0.01
0.01*** 0.02*** 0.01*** 0.02*** 0.00
0.01*
***
***
***
***
***
***
***
***
***
***
**
***
**
(5) NEWSijt
0.29 -0.06 -0.09
0.22
0.04
0.04 -0.00
0.04
0.01
0.03
0.03
0.01
0.04 -0.01
0.02***(6) MACROjt
0.02*** 0.01** 0.00 -0.00
0.01*
0.43*** 0.66*** -0.11*** 0.04*** -0.16*** -0.09*** 0.03*** -0.15*** -0.16*** 0.05***
***
**
**
***
***
(7) MACRO_Djt-0.02 -0.01
0.00 -0.01
0.02
0.67
-0.21*** 0.25*** -0.07*** 0.35*** 0.16*** -0.11*** 0.35*** 0.30*** -0.03***
***
***
**
***
***
(8) MACRO_Fjt
0.05
0.02 -0.00
0.00 -0.01
0.80
0.09
-0.26*** 0.06*** -0.36*** -0.15*** 0.11*** -0.36*** -0.37*** 0.05***
***
***
***
***
***
***
***
***
(9) SIMEXPij,t−1
0.07
0.02 -0.01
0.02
0.04 -0.09
0.19 -0.27
0.67*** 0.76*** 0.70*** 0.37*** 0.73*** 0.06*** 0.02***
***
***
***
***
***
***
***
***
(10) SIMEXP_Dij,t−1 0.05
0.02 -0.01
0.01
0.01
0.02 -0.01
0.03
0.71
0.16*** 0.41*** 0.58*** 0.18*** -0.28*** 0.13***
***
***
***
***
***
***
***
***
***
(11) SIMEXP_Fij,t−1 0.06
0.01 -0.00
0.01
0.05 -0.14
0.26 -0.40
0.78
0.16
0.57*** 0.07*** 0.90*** 0.33*** -0.04***(12) SIMEXP2ij,t−1
0.07*** 0.03*** -0.01** 0.02*** 0.04*** -0.07*** 0.12*** -0.19*** 0.76*** 0.50*** 0.62***0.73*** 0.64*** 0.08*** -0.09***
***
***
***
***
***
***
***
***
***
***
***
***
(13) SIMEXP2_Dij,t−1 0.05
0.02 -0.01
0.01
0.02
0.02 -0.05
0.07
0.44
0.62
0.09
0.78
0.11*** -0.14*** -0.01*
***
***
***
***
***
***
***
***
***
***
***
***
(14) SIMEXP2_Fij,t−1 0.07
0.02 -0.00
0.02
0.05 -0.13
0.26 -0.39
0.76
0.19
0.93
0.68
0.12
0.22*** -0.09***
***
***
***
***
***
***
***
***
***
***
***
(15) HI_Cntryi,t−1-0.06 -0.04 -0.01 -0.00 -0.00 -0.07
0.19 -0.26 -0.03 -0.31
0.25 -0.04 -0.20
0.16-0.05***
***
***
***
***
***
***
***
***
***
***
***
***
***
(16) HI_Indi,t−1-0.07
0.01 -0.01
0.01 -0.00
0.03 -0.02
0.07
0.01
0.09 -0.06 -0.08 -0.03 -0.08 -0.02

45

Table 3. Predicting Firm Earnings with MACRO
This table reports the results from OLS regressions of future earnings (Ajt) on company-specificfundamental forecast, MACROjt, its components MACRO_Fjt and MACRO_Djt, and control variables. See
Appendix I for variable definitions. The t-statistics are calculated based on standard errors clustered by firm
and year. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.

Dependent variable = Ajt
MACROjt(1)
0.347***(3.77)(2)
0.383***(3.96)
0.173(1.12)

MACRO_Fjt
MACRO_Djt
ConsjtAj,t−1
LogMVj,t−1
BTM j,t−1
LOSS j,t−1
D_DIV j,t−1
DY j,t−1
Industry & Year
Adj.R2N(3)0.212***(2.61)

0.563***(25.67)0.304***(7.14)-1.316***(-4.45)-0.888***(-4.42)0.720***(5.51)-0.019***(-2.72)
Yes
0.422
9843

46

0.562***(25.54)0.309***(7.22)-1.285***(-4.30)-0.912***(-4.51)0.684***(5.22)-0.019***(-2.70)
Yes
0.422
9726

0.422***(4.79)
0.279***(5.19)
0.322***(8.41)-1.013***(-4.16)-0.780***(-4.18)0.474***(3.51)-0.019***(-2.92)
Yes
0.556
9843

(4)
0.230***(2.64)
0.120(0.95)
0.419***(4.73)
0.281***(5.23)
0.322***(8.40)-1.014***(-4.15)-0.784***(-4.15)0.446***(3.33)-0.019***(-2.88)
Yes
0.555
9726

Table 4. Predicting Consensus Forecast Error with MACRO
This table reports the results from OLS regressions of consensus forecast errors, CFEjt, on MACROjt,
MACRO_Fjt, and components of MACRO_Fjt that incorporate information about a country’s economic
significance (Top5 vs NonTop5), proximity to the US (Close5 vs NonClose5), analyst coverage (Cov5 vsNonCov5), information precision (Cntry vs Reg), and total foreign exposure (HiXps vs LoXps). See
Appendix I for variable definitions. The t-statistics are calculated based on standard errors clustered by firm
and year. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
Dep. Var. = CFEjt
MACROjt(1)0.169*(1.85)

β1 MACRO_Fjt

(2)

(3)

(4)

(5)

(6)

0.202**(2.17)

γ1 MACRO_F_Top5jt

0.135(1.20)
0.530***(2.78)

γ2 MACRO_F_NonTop5jt
γ1 MACRO_F_Close5jt-0.177(-1.02)0.191**(2.19)

γ2 MACRO_F_NonClose5jt
γ1 MACRO_F_Cov5jt

0.576(1.45)
0.194**(2.23)

γ2 MACRO_F_NonCov5jt
γ1 MACRO_F_Cntryjt

0.102(1.28)
0.267**(2.32)

γ2 MACRO_F_Regjt
γ1 MACRO_F_HiXpsjt
γ2 MACRO_F_LoXpsjt
β2 MACRO_Djt
Aj,t−1
LogMVj,t−1
BTMj,t−1
LOSSj,t−1
STDROAj,t−1
LEVj,t−1
IORj,t−1
ANNRETj,t−1(7)0.085***(3.57)
0.167***(3.47)-2.411***(-6.54)0.357(1.55)-2.795***(-3.22)-2.280***(-3.94)0.602***(2.95)
0.399***(3.18)

0.076(0.61)
0.082***(3.45)
0.171***(3.62)-2.349***(-6.39)0.364(1.56)-2.800***(-3.31)-2.213***(-3.81)0.701***(3.38)
0.397***(3.11)

47

0.097(0.63)
0.083***(3.33)
0.161***(3.95)-2.336***(-5.66)0.360(1.62)-2.648***(-2.83)-2.231***(-4.99)0.704***(2.88)
0.403***(3.97)

0.068(0.59)
0.077***(3.32)
0.147***(3.22)-2.302***(-6.23)0.322(1.40)-2.779***(-3.31)-2.228***(-3.93)0.676***(3.17)
0.408***(3.12)

0.101(0.87)
0.078***(3.33)
0.151***(3.30)-2.297***(-6.24)0.326(1.40)-2.716***(-3.18)-2.238***(-3.96)0.682***(3.19)
0.408***(3.11)

0.057(0.47)
0.083***(3.67)
0.166***(3.79)-2.335***(-5.81)0.362(1.50)-2.641***(-3.15)-2.224***(-4.48)0.698***(3.22)
0.404***(2.83)

0.209**(2.16)
0.393***(3.24)
0.021(0.16)
0.083***(3.47)
0.165***(3.48)-2.353***(-6.40)0.361(1.56)-2.751***(-3.22)-2.226***(-3.85)0.697***(3.34)
0.397***(3.11)

ACCRUALj,t−1
GDPDISPjt
Industry & Year
Adj.R2
N
Test: β1 = β2p-value
Test: γ1 = γ2p-value

0.606*(1.74)
0.044(0.35)
Yes
0.097
8953

0.601*(1.73)
0.019(0.15)
Yes
0.096
8868
2.328
(0.132)48

0.603*(1.65)-0.006(-0.06)
Yes
0.096
8868

0.469
(1.43)-0.071(-0.88)
Yes
0.093
8654

0.465
(1.42)-0.050(-0.65)
Yes
0.093
8654

0.601
(1.63)
0.007(0.05)
Yes
0.096
8868

0.605*(1.74)
0.009(0.07)
Yes
0.096
8868

5.044
(0.025)5.554(0.019)0.945(0.332)3.711(0.055)4.765(0.030)Table 5. Predicting Individual Analyst Forecast Errors with MACRO
This table reports the results from regressions of individual analyst forecast errors, FE, on macroeconomic
information, MACRO, and MACRO interacted with analyst and broker attributes that potentially explain
the use of MACRO, e.g., SIMEXP. See Appendix I for variable definitions. The t-statistics are calculated
based on standard errors clustered by analyst and year. Significance levels: * p < 0.10, ** p < 0.05, *** p <0.01.

Dependent variable = FEijt
MACROjt
MACROjt× SIMEXPij,t−1(1)0.066***(4.53)(2)
0.039**(2.19)-0.149**(-2.41)

MACROjt× HI_Cntryi,t−1
MACROjt× HI_Indi,t−1

(3)
(4)
0.009
0.127***(0.41)(3.13)-0.139** -0.141**(-2.24)(-2.30)-0.012-0.038(-0.55)(-1.62)0.057***
0.009(3.10)(0.34)(5)

0.043**(2.12)-0.243**(-2.44)

MACRO_Fjt
MACRO_Fjt× SIMEXP_Fij,t−1
MACRO_Fjt× HI_Cntryi,t−1
MACRO_Fjt× HI_Indi,t−1
MACRO_Djt

0.020(0.92)-0.081(-0.93)

MACRO_Djt× SIMEXP_Dij,t−1
MACRO_Djt× HI_Cntryi,t−1
MACRO_Djt× HI_Indi,t−1

(6)

(7)

-0.034
0.109**(-1.14)(2.00)-0.242** -0.240**(-2.37)(-2.36)0.024-0.006(0.63)(-0.16)
0.113***
0.051(4.51)(1.56)
0.043
0.128**(1.49)(2.24)-0.059-0.058(-0.67)(-0.65)-0.011-0.027(-0.30)(-0.73)-0.028-0.049(-1.08)(-1.28)-0.012**(-1.99)0.006(1.02)-0.035***(-2.99)-0.016(-1.40)

MACROjt × LogFIRMEXPijt
MACROjt × LogBRKSZit
MACROjt × LogNFIRMSit
MACROjt × LogNINDit-0.016*(-1.90)0.012(1.61)-0.045***(-2.87)-0.023(-1.47)-0.005(-0.49)MACRO_Fjt×LogFIRMEXPijt
MACRO_Fjt × LogBRKSZit
MACRO_Fjt × LogNFIRMSit
MACRO_Fjt × LogNINDit
MACRO_Djt × LogFIRMEXPijt

49

MACRO_Djt × LogBRKSZit-0.003(-0.45)-0.023(-1.40)-0.003(-0.15)

MACRO_Djt × LogNFIRMSit
MACRO_Djt × LogNINDit
0.604***(3.48)SIMEXPij,t−1

0.565***(3.24)

0.569***(3.28)SIMEXP_Fij,t−1
SIMEXP_Dij,t−1
HI_Cntryi,t−1
HI_Indi,t−1
LogFIRMEXPijt
LogNFIRMSit
LogNINDit
LogBRKSZit
LogHRZNit
Aj,t−1
STDROAj,t−1
LogMVj,t−1
BTMj,t−1
LEVj,t−1
IORj,t−1
LOSSj,t−1
ANNRETj,t−1
ACCRUALj,t−1
GDPDISPjt
Industry & Year
Adj.R2N-0.003(-0.32)0.069***(4.21)-0.039**(-2.26)0.030***(3.87)-0.138***(-13.45)0.017***(4.63)-0.382***(-2.67)0.028***(5.26)-0.299***(-5.77)-0.394***(-6.13)0.227***(7.71)
0.082***(2.63)
0.089***(5.33)-0.080(-1.64)0.005(0.34)
Yes
0.056
82555

-0.004(-0.46)0.060***(3.66)-0.031*(-1.78)0.030***(3.82)-0.139***(-13.54)0.017***(4.61)-0.364**(-2.54)0.024***(4.35)-0.300***(-5.81)-0.403***(-6.23)0.222***(7.58)
0.080***(2.58)
0.092***(5.44)-0.077(-1.58)0.003(0.26)
Yes
0.057
82555

50

-0.025(-0.37)-0.225***(-3.42)-0.008(-0.99)0.059***(3.58)-0.051**(-2.51)0.029***(3.75)-0.141***(-13.67)
0.017***(4.59)-0.361**(-2.52)0.023***(4.18)-0.306***(-5.89)-0.403***(-6.23)0.223***(7.61)
0.079**(2.56)
0.092***(5.46)-0.078(-1.60)0.004(0.32)
Yes
0.057
82555

0.036
(0.51)-0.108(-1.26)0.022(1.22)
0.146***(4.16)-0.011(-0.30)0.015(0.92)-0.140***(-13.66)
0.017***(4.56)-0.359**(-2.50)0.023***(4.14)-0.310***(-5.98)-0.403***(-6.24)0.223***(7.59)
0.079**(2.55)
0.090***(5.34)-0.078(-1.61)0.004(0.29)
Yes
0.057
82555

0.890***(4.51)
0.020(0.17)-0.105***(-2.66)-0.087**(-2.32)-0.010(-1.19)0.060***(3.63)-0.053***(-2.63)0.029***(3.74)-0.141***(-13.67)
0.017***(4.61)-0.345**(-2.40)0.021***(3.75)-0.301***(-5.83)-0.406***(-6.27)0.226***(6.98)
0.079**(2.56)
0.094***(5.53)-0.075(-1.54)0.006(0.43)
Yes
0.057
82555

0.876***(4.44)-0.015(-0.12)-0.119*(-1.72)-0.214***(-3.28)-0.010(-1.17)0.058***(3.51)-0.051**(-2.54)0.030***(3.83)-0.141***(-13.70)0.017***(4.61)-0.330**(-2.29)0.021***(3.89)-0.300***(-5.82)-0.408***(-6.30)0.222***(6.82)
0.080**(2.57)
0.095***(5.58)-0.075(-1.55)0.006(0.47)
Yes
0.058
82555

0.875***(4.45)-0.019(-0.15)-0.061(-0.86)-0.106(-1.23)0.018(0.98)
0.146***(4.16)-0.017(-0.44)0.017(1.04)-0.141***(-13.74)
0.017***(4.57)-0.327**(-2.26)0.021***(3.86)-0.303***(-5.89)-0.409***(-6.32)0.223***(6.86)
0.079**(2.56)
0.093***(5.49)-0.076(-1.57)0.006(0.44)
Yes
0.058
82555

Table 6. Analyst’s Global Expertise and Forecast Accuracy
This table reports the results from OLS regressions of analyst forecast accuracy, PMAFE, on analysts’
global expertise, SIMEXP and SIMEXP_F, and other accuracy determinants. See Appendix I for variable
definitions. The t-statistics are calculated based on standard errors clustered by analyst and year.
Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
Dependent variable = PMAFEijt
SIMEXPij,t−1(1)-0.137***(-2.60)(2)-0.183***(-3.53)SIMEXP_Fij,t−1
SIMEXP_Dij,t−1
HI_Cntryi,t−1
HI_Indi,t−1
LogFIRMEXPijt
LogNFIRMSit
LogNINDit
LogBRKSZit
LogHRZNijt
Constant
Adj.R2N-0.017***(-3.01)-0.002(-0.30)0.389***(18.29)-0.149***(-13.90)0.042***(3.17)-0.086***(-22.12)
0.142
82483

51

-0.100***(-4.74)-0.118***(-5.32)-0.024***(-4.34)-0.003(-0.38)0.387***(18.33)-0.150***(-13.78)
0.018(1.32)-0.086***(-22.13)
0.143
82483

(3)

(4)

-0.377**(-2.44)-0.082*(-1.68)-0.295*(-1.94)-0.132***(-3.08)-0.096***(-5.39)-0.117***(-5.28)-0.024***(-4.30)-0.003(-0.37)0.387***(18.31)-0.151***(-13.58)
0.018(1.34)-0.086***(-22.15)
0.143
82483

-0.017***(-3.02)-0.002(-0.28)0.388***(18.29)-0.150***(-13.66)
0.042***(3.16)-0.085***(-21.97)
0.142
82483

Table 7. Global Expertise and Forecast InformativenessThis table reports the results from OLS regressions of the 3-day market reaction to analyst forecast
revisions, CAR, on forecast news interacted with global expertise: NEWS × SIMEXP and NEWS_F×
SIMEXP_F. We include but do not tabulate LogFIRMEXP, LogBRKSZ, LogNFIRMS, LogNIND,
LogHRZN, GDPDISP, ∆ROA, STDROA, LOGMV, BTM, LEV, IOR, LOSS, ANNRET, and ACCRUAL. See
Appendix I for variable definitions. The t-statistics are calculated based on standard errors clustered byanalyst and year. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
Dependent variable = CARijt
NEWSijt
NEWSijt × SIMEXPij,t−1
NEWSijt × HI_Cntryi,t−1
NEWSijt × HI_Indi,t−1(1)2.096***(10.83)0.765***(2.80)(2)(3)
2.444***
1.195***(8.68)(2.86)
0.781***
0.734***(3.11)(3.14)-0.0880.032(-0.23)(0.08)-0.535*-0.041(-1.73)(-0.12)(4)

2.375***(6.48)
1.978***(3.87)

NEWS_Fijt
NEWS_Fijt× SIMEXP_Fij,t−1
NEWS_Fijt× HI_Cntryi,t−1
NEWS_Fijt × HI_Indi,t−1

1.688***(6.11)-0.563(-0.77)

NEWS_Dijt
NEWS_Dijt × SIMEXP_Dij,t−1
NEWS_Dijt × HI_Cntryi,t−1
NEWS_Dijt × HI_Indi,t−1
NEWSijt × LogFIRMEXPijt

(5)

(6)

2.648***
1.659**(4.71)(2.50)
1.733***
1.526***(3.42)(3.02)
1.228
0.870(1.25)(0.87)-1.097**0.287(-2.08)(0.55)
2.193***
0.510(4.28)(0.55)-0.0340.062(-0.04)(0.08)-1.865**-1.124(-2.56)(-1.42)0.395-0.307(0.62)(-0.48)-0.074(-0.57)0.061(0.88)
0.255**(2.04)
0.270*(1.74)

NEWSijt × LogBRKSZit
NEWSijt × LogNFIRMSit
NEWSijt × LogNINDit
NEWS_Fijt × LogFIRMEXPijt

0.311(1.48)
0.053(0.40)-0.596*(-1.86)
1.052***(3.42)-0.565*(-1.83)

NEWS_Fijt × LogBRKSZit
NEWS_Fijt × LogNFIRMSit
NEWS_Fijt × LogNINDit
NEWS_Dijt × LogFIRMEXPijt

52

NEWS_Dijt × LogBRKSZit

0.084(0.54)
1.517***(4.16)-0.921***(-3.34)

NEWS_Dijt × LogNFIRMSit
NEWS_Dijt × LogNINDit
SIMEXP ij,t−1

0.686**(2.01)

0.689**(2.03)

0.682**(2.02)SIMEXP_F ij,t−1
SIMEXP_Dij,t−1
HI_Cntryi,t−1
HI_Indi,t−1
Main effects
Other controls
Industry & Year
Adj.R2N

0.021(0.08)-0.067(-0.43)
Yes
Yes
Yes
0.053
80459

0.020
(0.08)-0.118(-0.76)
Yes
Yes
Yes
0.054
80459

53

0.037
(0.15)-0.064(-0.38)
Yes
Yes
Yes
0.054
80459

0.364
(0.55)
0.793*(1.78)
0.090(0.30)-0.081(-0.51)
Yes
Yes
Yes
0.054
80459

0.342
(0.52)
0.782*(1.77)
0.063(0.21)-0.122(-0.77)
Yes
Yes
Yes
0.054
80459

0.305
(0.46)
0.785*(1.77)
0.085(0.29)-0.076(-0.45)
Yes
Yes
Yes
0.056
80459

Table 8. All-star Ranking and Global ExpertiseThis table reports the results from logit regressions of annual all-star ranking, Star, and changes in annual
ranking, Star_Up and Star_Down, on analyst’s global expertise, SIMEXP and SIMEXP_F. For each analyst
i year t, we calculate the means of SIMEXPijt, SIMEXP_Fijt, SIMEXP_Dijt, LogMVjt, and IORjt across all
firms j that analyst i covers in year t as SIMEXPit, SIMEXP_Fit, SIMEXP_Dit, LogMVit, and IORit. See
Appendix I for other variable definitions. The t-statistics are calculated based on standard errors clusteredby brokerage and year. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.

SIMEXPit

(1)
(2)
(3)
Stari,t+1
Star_Upi,t+1 Star_Downi,t+1
1.015***
0.140-0.998**(2.88)(0.33)(-2.29)SIMEXP_Fit
SIMEXP_Dit
HI_Cntryit
HI_Indit
LFRit
LogBRKSZit
ACCit
BOLDitOPTit
LogNFIRMSit
LogNINDit
LogGENEXPit
FMVRitFREQitLogMVit
IORit
Constant
Adj.R2N

1.278***(6.04)-0.610***(-2.86)-0.003**(-2.11)2.142***(17.32)
1.035***(6.71)
0.082(0.54)-0.163*(-1.85)0.456***(5.19)-0.078(-0.75)0.367***(5.92)-0.154(-1.16)
0.196***(7.70)
0.290***(8.65)-0.106(-0.54)-14.667***(-22.24)
0.270
22516

1.197***(4.28)-0.512*(-1.77)0.001(0.28)
2.146***(15.12)0.884***(3.13)
0.041(0.15)-0.163(-1.08)0.421***(3.62)-0.118(-0.87)-0.241***(-2.80)0.417*(1.91)
0.135***(3.77)
0.296***(6.54)
0.389(1.40)-15.857***(-17.55)
0.186
19645

-0.749***(-2.88)0.318(1.21)
0.003(1.05)-0.152*(-1.78)-0.703***(-2.59)-0.103(-0.41)0.211(1.49)
0.063(0.56)
0.122(0.97)
0.090(1.21)
0.283(1.28)-0.108***(-3.63)0.072(1.64)
0.040(0.14)-1.186*(-1.65)
0.015
2871

54

(4)
Stari,t+1
1.585***(2.96)
0.391(1.02)
1.199***(5.42)-0.588***(-2.76)-0.003**(-2.12)2.141***(17.36)1.019***(6.58)
0.102(0.67)-0.139(-1.57)0.451***(5.11)-0.092(-0.87)0.366***(5.92)-0.144(-1.08)0.201***(7.84)
0.280***(8.47)-0.219(-1.06)-14.451***(-21.69)
0.271
22335

(5)
(6)
Star_Upi,t+1 Star_Downi,t+1-0.559(-0.92)0.693(1.30)
1.336***(4.75)-0.497*(-1.72)0.001(0.31)
2.151***(15.16)0.885***(3.12)
0.043(0.15)-0.158(-1.04)0.400***(3.43)-0.100(-0.73)-0.241***(-2.80)0.439**(1.99)
0.137***(3.79)
0.302***(6.72)
0.356(1.21)-15.922***(-17.60)
0.187
19467

-1.552**(-2.54)-0.390(-0.80)-0.643**(-2.34)0.300(1.14)
0.003(1.12)-0.144*(-1.68)-0.719***(-2.64)-0.112(-0.44)0.185(1.30)
0.040(0.35)
0.143(1.12)
0.093(1.24)
0.285(1.29)-0.107***(-3.60)0.084*(1.92)
0.042(0.15)-1.343*(-1.85)0.016
2868

Table 9. Alternative Measures of Global ExpertiseThis table replicates results in Tables 5-8 using an alternative measure of global expertise, SIMEXP2. PanelA replicates columns (2) and (5) of Table 5, panel B replicates columns (2) and (4) of Table 6, panel C
replicates columns (1) and (4) of Table 7, and panel D replicates Table 8. See Appendix I for variable
definitions. The t-statistics are calculated based on standard errors clustered by analyst and year (panels A,
B, and C) and by brokerage and year (panel C). Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
Panel A. Individual forecast errors
Dependent variable = FEijt
MACROjt(1)0.050***(3.20)
0.517***(3.64)-0.085**(-2.02)SIMEXP2ij,t−1
MACROjt × SIMEXP2ij,t−1
MACRO_Fjt
SIMEXP2_Fij,t−1
MACRO_Fjt× SIMEXP2_Fij,t−1
MACRO_Djt
SIMEXP2_Dij,t−1
MACRO_Djt× SIMEXP2_Dij,t−1
HI_Cntryi,t−1-0.050(-1.27)-0.082**(-2.18)-0.012(-1.44)0.064***(3.87)-0.052***(-2.59)0.030***(3.80)-0.140***(-13.65)
Yes
Yes
0.057
82555

HI_Indi,t−1
LogFIRMEXPijt
LogNFIRMSit
LogNINDit
LogBRKSZit
LogHRZNijt
Other controls
Industry & Year
Adj.R2N55(2)0.047**(2.42)
1.020***(4.77)-0.178**(-2.37)0.023(1.14)-0.062(-0.62)0.004(0.06)-0.084**(-2.13)-0.084**(-2.23)-0.014(-1.61)
0.064***(3.89)-0.053***(-2.63)0.030***(3.82)-0.141***(-13.69)
Yes
Yes
0.057
82555

Panel B. Forecast accuracy
Dependent variable = PMAFEijt
SIMEXP2ij,t−1(1)-0.148***(-2.70)SIMEXP2_Fij,t−1
SIMEXP2_Dij,t−1
HI_Cntryi,t−1
HI_Indi,t−1
LogFIRMEXPijt
LogNFIRMSit
LogNINDit
LogBRKSZit
LogHRZNijt
Constant
Adj.R2N-0.119***(-4.13)-0.156***(-4.93)-0.029***(-3.45)-0.201***(-12.69)0.024(1.24)-0.003(-0.29)0.477***(17.03)-0.025***(-5.77)
0.134
82483

56

(2)
-0.235**(-2.09)-0.088(-1.39)-0.115***(-4.12)-0.155***(-4.91)-0.028***(-3.43)-0.202***(-12.71)0.024(1.24)-0.003(-0.28)0.477***(17.03)-0.024***(-5.75)
0.134
82483

Panel C. Forecast informativeness
Dependent variable = CARijt
NEWSijt(1)
2.107***(21.49)0.881***(2.96)

NEWSijt × SIMEXP2ij,t−1(2)2.221***(9.46)
1.660***(2.68)
2.013***(9.70)
0.908(1.32)

NEWS_Fijt
NEWS_Fijt× SIMEXP2_Fij,t−1
NEWS_Dijt
NEWS_Dijt× SIMEXP2_Dij,t−1
0.599***(2.82)SIMEXP2ij,t−1
SIMEXP2_Fij,t−1
SIMEXP2_Dij,t−1
HI_Cntryi,t−1

0.118(0.73)
0.055(0.37)
Yes
Yes
0.058
80459

HI_Indi,t−1
Other controls
Industry & Year
Adj.R2N

57

0.666*(1.95)
0.406*(1.85)
0.119(0.73)
0.051(0.35)
Yes
Yes
0.058
80459

Panel D. All-star ranking

SIMEXP2it(1)
Stari,t+1
1.278***(5.49)(2)
Star_Upi,t+1
0.731**(2.23)(3)
Star_Downi,t+1-1.488***(-4.28)SIMEXP2_Fit
SIMEXP2_Dit
HI_Cntryit
HI_Indit
LFRit
LogBRKSZit
ACCit
BOLDitOPTit
LogNFIRMSit
LogNINDit
LogGENEXPit
FMVRitFREQitLogMVit
IORit
Constant
Adj.R2N

1.309***(6.76)-0.567***(-2.84)-0.003*(-1.90)1.046***(5.62)
0.079(0.49)-0.171(-1.59)0.403***(3.65)-0.064(-0.61)0.357***(3.56)
2.145***(5.36)-0.155(-1.29)0.195***(4.07)
0.288***(4.79)-0.184(-0.84)-14.519***(-8.21)
0.270
22516

1.090***(3.26)-0.523**(-2.38)0.001(0.30)
0.799**(2.04)-0.111(-0.54)-0.091(-0.45)0.496***(4.64)-0.145(-1.07)-0.331***(-3.05)2.088***(6.42)
0.308(1.46)
0.174***(3.20)
0.324***(5.37)
0.153(0.40)-15.542***(-9.18)
0.187
19645

-0.808***(-3.21)
0.277(0.91)
0.003(1.13)-0.704***(-2.98)-0.095(-0.32)0.211(1.61)
0.127(1.18)
0.108(0.92)
0.102(1.36)-0.153(-1.43)0.303(1.36)-0.111***(-2.95)0.081**(2.10)
0.142(0.49)-1.503*(-1.90)
0.017
2871

58

(4)
Stari,t+1(5)
Star_Upi,t+1(6)
Star_Downi,t+1

1.528***(3.59)
0.744***(3.09)
1.261***(6.30)-0.562***(-2.81)-0.003*(-1.91)1.039***(5.52)
0.074(0.46)-0.155(-1.45)0.411***(3.74)-0.073(-0.69)0.359***(3.56)
2.145***(5.36)-0.153(-1.27)
0.198***(4.09)
0.281***(4.73)-0.184(-0.83)-14.437***(-8.07)
0.271
22486

0.596
(1.19)
0.276(1.06)
1.133***(3.32)-0.523**(-2.39)0.001(0.31)
0.798**(2.04)-0.116(-0.56)-0.095(-0.48)0.489***(4.58)-0.136(-1.00)-0.334***(-3.07)2.090***(6.43)
0.318(1.52)
0.174***(3.21)
0.328***(5.35)
0.173(0.44)-15.598***(-9.14)
0.187
19616

-1.267**(-2.13)-1.081***(-3.22)-0.800***(-3.02)0.277(0.90)
0.003(1.15)-0.700***(-2.95)-0.098(-0.33)0.206(1.56)
0.115(1.06)
0.113(0.96)
0.101(1.33)-0.154(-1.45)0.292(1.32)-0.110***(-2.92)0.082**(2.27)
0.118(0.41)-1.467*(-1.88)0.017
2870