The Association Between the Legal and Financial Reporting
Environments and Forecast Performance of Individual Analysts
Ran Barniv
Kent State University
Graduate School of Management, CBA
Kent, OH 44242
Phone: 330-672-1112
Fax: 330-672-2545
E-mail: rbarniv@bsa3.kent.edu
Mark Myring
Ball State University
College of Business
Muncie, IN 47306
Phone: 765-285-5108
Fax: 765-285-8024
E-mail: mmyring@bsu.edu
Wayne B. Thomas*
University of Oklahoma
Michael F. Price College of Business
307 W. Brooks, Room 212B
Norman, OK 73019
Phone: 405-325-5789
Fax: 405-325-7348
E-mail: wthomas@ou.edu
* Contact author.
We appreciate the helpful comments of two anonymous reviewers, Patricia O’Brien (associate
editor), A. Amir, S. Beninga, workshop participants at the universities of Cincinnati,
Pennsylvania State, and Tel Aviv, and participants in a concurrent session of the AAA annual
meeting, Hawaii, August 2003. We also acknowledge Thomson Financial for providing I/B/E/S
International and U.S. Detail History data.
The Association Between the Legal and Financial Reporting
Environments and Forecast Performance of Individual Analysts
Abstract
We test the ability of analyst characteristics to explain relative forecast accuracy
across legal origins (common law versus civil law). Common law countries
generally have more effective corporate governance mechanisms, including
stronger investor protection laws and inputs provided through higher-quality
financial reporting systems. In this type of environment, we predict that analysts
with superior ability and resources in common law countries will more
consistently outperform their peers because appropriate market-based incentives
exist. In civil law countries, where the demand for earnings information is
reduced because of weaker corporate governance mechanisms and lower-quality
financial reporting, we predict that analysts with superior ability will less
consistently provide superior forecasts. Results are consistent with our
expectations and suggest an association between legal and financial reporting
environments and analysts’ forecast behavior.
Keywords Analysts’ characteristics, relative forecast performance, common law,
civil law.
JEL Descriptors G38, K22, M41
The Association Between the Legal and Financial Reporting
Environments and Forecast Performance of Individual Analysts
1. Introduction
We test the ability of analyst characteristics to explain relative forecast
accuracy across legal origins. In common law countries, where investor protection
laws are stronger and financial reporting is generally perceived to have higher
quality (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998, 2000a;
Ball, Kothari, and Robin 2000), the increased demand by investors for earnings
information may create incentives for analysts to provide that information
accurately. Analysts with superior characteristics (e.g., ability, effort, experience,
resources, etc.) are more likely to issue a superior forecast relative to their peers.
In civil law countries, weaker investor protection laws and lower-quality financial
reporting may reduce the economic incentives of analysts to incur costly activities
to provide a superior earnings forecast. We expect that it will be more difficult to
relate individual analysts’ characteristics to relative forecast performance in civil
law countries.
Examining the relation between relative forecast performance and analyst
characteristics across legal regimes provides evidence outside the United States,
where the bulk of this research has been conducted.1 Understanding analyst
behavior in other environments provides additional insight into how analysts’
efforts in accurately forecasting earnings can contribute to the informational
efficiency of financial markets (Frankel, Kothari, and Weber 2002). The results
also contribute to our understanding of the relation between investors’ demands
and analysts’ behavior (Defond and Hung 2002). As the value relevance of
reported earnings declines, investors may have less demand for analysts’ earnings
forecasts and demand other sources of information such as cash flow forecasts.2
Our results may also be helpful in investigating other related research issues, such
as the value relevance of accounting numbers across countries. Prior research has
focused primarily on estimating the relation between earnings and stock prices to
understand investors’ demand for accounting earnings (e.g., Ball, Kothari, and
Robin 2000; Ali and Hwang 2000). We extend this literature by examining
whether the relation between analyst characteristics and relative forecast accuracy
differs across legal origins consistent with investors’ demand for earnings
information.
Consistent with expectations, we find that the relation between analyst
characteristics and relative forecast accuracy is stronger in common law countries.
These results are consistent with analysts’ forecast behavior responding to the
demand by investors for earnings information. In common law countries where
investor protection laws are stronger, financial reporting is higher-quality, and the
demand by investors for earnings information is greater, analysts with superior
abilities tend to distinguish themselves more clearly. In civil law countries, it is
more difficult to explain analysts’ relative forecast accuracy. Overall, we find that
2
the relation is strongest in the United States, followed by non-U.S. common law
countries. The relation is weakest in the civil law countries. Results within the
three origins of the civil law classification (French, German, and Scandinavian)
suggest that the quality of financial reporting systems plays a role in these
relations beyond the influence of investor protection laws. Finally, we find some
empirical support for the notion that cash flow forecasts may substitute for
earnings forecasts when earnings are less relevant (Defond and Hung 2002). The
relation between analysts’ characteristics and relative cash flow forecast accuracy
is stronger in civil law countries than in common law countries.
The remainder of the paper is organized as follows. Section 2 develops the
hypotheses. Section 3 outlines the research design and section 4 details the data
and sample selection. Section 5 reports results and section 6 provides additional
analyses. The paper concludes in section 7.
2. Hypotheses
We provide the following rationale for our tests. Common law countries
are generally perceived to have stronger investor protection laws (La Porta,
Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998, 2000a)3 and higher-quality
financial reporting (Ball, Kothari, and Robin 2000).4 In these settings, earnings
information can play a more prominent role in corporate governance mechanisms
and therefore have greater value relevance.5 The greater value relevance of
3
earnings information increases investors’ demand for that information when
making decisions. The increased demand by investors offers proper economic
incentives for analysts to compete in providing accurate forecasts of earnings.
Those analysts having the ability and resources to outperform other analysts will,
on average, do so because the market-based reward structure established by
investor demand offers analysts fair incentives (Schipper 1991). In other words,
the rewards for making accurate forecasts fairly outweigh the cost of gathering
and processing information when investor protection laws are strong and the
quality of the financial reporting system is good. For common law countries, we
expect analysts with superior characteristics (ability, effort, experience, resources,
etc.) to more consistently outperform their peers, resulting in a stronger relation
between analysts’ characteristics and relative forecast accuracy.
In civil law countries, financial accounting systems are generally
perceived to be of lower quality in terms of their ability to reflect accurately the
underlying economic activity of the firm (Ball, Kothari, and Robin 2000;
Guenther and Young 2000; Bhattacharya, Daouk, and Welker 2003; Francis,
Khurana, and Pereira 2004). Financial accounting practices in civil law countries
are oriented less toward serving the needs of outside investors (O’Brien 1998;
Lang, Lins, and Miller 2004) and investor protection laws are weaker (La Porta,
Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998, 2000a). These factors likely
weaken the demand by investors for earnings information, which reduces the
4
economic incentives of superior analysts to outperform their peers. Providing a
superior earnings forecast is costly and analysts with superior abilities and
resources will incur the incremental costs of gathering information only when
they expect to be equitably rewarded. We expect that the reduction in incentives
of superior analysts to make superior forecasts will lead to a weaker relation
between analyst characteristics and relative forecast performance in civil law
countries (i.e., relative forecast accuracy occurs more randomly in civil law
countries).
Furthermore, among the common law countries, prior studies cited in the
preceding paragraphs suggest that the United States has some of the strongest
investor protection laws and higher-quality financial reporting. If investor
protection laws and quality of financial reporting affect the relevance of
accounting earnings to investors, one would expect the demand for earnings
information by investors and the incentives of analysts to compete and provide
that information accurately to be greater in the United States than in most of the
non-U.S. common law countries. Similarly, as discussed in the preceding
paragraphs, we expect that analysts in non-U.S. common law countries will have
more incentive to compete and provide more accurate forecasts relative to their
peers than will analysts in civil law countries.
Overall, the preceding ideas lead to our first three hypotheses.
5
H1: Analyst characteristics better explain relative forecast accuracy in the
common law countries than in civil law countries.
H2: Analyst characteristics better explain relative forecast accuracy in the
United States than in non-U.S. common law countries.
H3: Analyst characteristics better explain relative forecast accuracy in
non-U.S. common law countries than in civil law countries.
It is also interesting to consider whether the strength of investor protection
laws or the quality of the financial reporting system offers the greater motivation
to analysts to provide superior forecasts. We examine whether analyst
characteristics are useful for explaining relative forecast accuracy across three
groups of civil law countries. Within the civil law origin, La Porta, Lopez-deSilanes, Shleifer, and Vishny (1997, 1998) find that countries of the French origin
have weaker investor protection laws than do countries of the German origin.
However, countries of the French origin have higher quality and more transparent
financial accounting information than do countries of the German origin (Ball,
Kothari, and Robin 2000; Francis, Khurana, and Pereira 2004). Thus, the
incentives for analysts to provide superior forecasts might be stronger in the
German origin countries because of better investor protection laws or stronger in
the French origin countries because of higher-quality financial reporting. By
estimating the ability of analyst characteristics to explain relative forecast
accuracy in the French versus German origins, we expect to obtain some
6
indication of the impact that investor protection laws, on the one hand, versus
quality of financial reporting, on the other hand, has on the behavior of analysts.
Relative to other civil law origin countries, the Scandinavian origin has the
better investor protection laws (La Porta, Lopez-de-Silanes, Shleifer, and Vishny
1997, 1998) and the higher-quality financial reporting (Ball, Kothari, and Robin
2000; Francis, Khurana, and Pereira 2004). We therefore predict that analyst
characteristics will have greater explanatory power for this group of civil law
countries. Thus,
H4: Analyst characteristics differentially explain the relative forecast
accuracy across the three civil law origins.
Table 1 summarizes the strength of investor protection laws and the
quality and transparency of financial reporting and their expected impact on the
relative performance of analysts across legal origins.
3. Research design and determinants of relative forecast accuracy
Extending the model of Jacob, Lys, and Neale (1999), we examine the
impact of analyst activity, experience, portfolio complexity, specialization, and
internal environmental factors on the ability of analysts to produce superior
forecasts of earnings relative to their peers. One possible limitation of using their
model is that it was developed in a U.S. context. While there could be other
important analyst characteristics in other countries, we believe the United States
7
provides a good setting for establishing a benchmark model of the way in which
analyst characteristics explain relative forecast accuracy when the demand for
earnings information is high.6
We estimate the following model, where the first ten variables are those
used in Jacob, Lys, and Neale (1999) and the final three represent additional
international attributes of analysts and their brokerage firms.
(AFEk,j,t/MAFEj,t)-1 = 0 + 1*HORIZk,j,t + 2*CHANGEk,j,t + 3*EXPk,j,t +
4*COMPk,j,t
+ 5*SPECk,j,t + 6*FREQk,j,t + 7*B-SIZEk,j,t + 8*B-INDk,j,t + 9*PINk,j,t
+ 10*POUTk,j,t + 11*C-EXPk,j,t + 12*C-SPECk,j,t + 13*B-Ck,j,t + k,j,t
The dependent variable measures the relative forecast accuracy of analyst
k to all other analysts following company j in year t. AFE is the absolute value of
analyst k’s forecast error and MAFE is the mean absolute forecast error of all
analysts issuing a forecast for company j in year t.7
The independent variables are defined as follows:
HORIZ = The number of calendar days between the forecast issue date and the
earnings announcement date.
CHANGE = Dummy variable that takes a value of 1 (0 otherwise) when there has
been a change in the assignment of specific analyst k following company j
for a particular brokerage in year t.8
EXP = The natural log of the number of years analyst k has issued forecasts for
company j.
COMP = The number of companies followed by analyst k in the calendar year in
which the forecast was issued.
SPEC = Percentage of companies followed by analyst k with the same I/B/E/S
industry code as company j.
FREQ = Number of forecasts issued by analyst k for company j in year t.
8
B-SIZE = Percentile ranking of the total number of analysts employed by the
brokerage house to which analyst k belongs in the calendar year in which
the forecast was issued, relative to other brokerage houses.
B-IND = Percentage of analyst k’s brokerage house analysts which follows
company j’s industry in the calendar year in which the forecast was issued.
PIN = Portion of new analysts that come from outside the brokerage house
relative to the total number of analysts who worked for analysts k’s
brokerage house during the calendar year in which the forecast was issued.
POUT = Portion of analysts who left analyst k’s brokerage house relative to the
total number of analysts who worked for analysts k’s brokerage house
during the calendar year in which the forecast was issued.
C-EXP = Dummy variable that takes a value of 1 (0 otherwise) when analyst k
has issued forecasts for more than three years for any company in a
country.9
C-SPEC = Percentage of companies followed by analyst k in the same country
where the analyst has issued forecasts for company j in year t.
B-C = Percentage of analyst k’s brokerage house analysts which follow company
j’s country in the calendar year in which the forecast was issued.
The variables HORIZ and FREQ represent analyst activity, EXP and CEXP stand for experience, COMP represents portfolio complexity, while SPEC
and C-SPEC correspond to specialization. Finally, internal environmental factors
include B-SIZE, B-IND, B-C, PIN, and POUT. Consistent with prior research, we
subtract the mean of the independent variable for each year for the empirical tests
(Clement 1999; Jacob, Lys, and Neale 1999). For the common law sample, we
expect HORIZ, COMP, PIN, and POUT to have a positive relation with relative
forecast error and CHANGE, EXP, SPEC, FREQ, B-SIZE, B-IND, C-EXP, CSPEC, and B-C to have a negative relation. We expect the significance of the
relations to be higher for the U.S. sample than for the non-U.S. common law
9
sample. For our civil law sample, we predict that the significance of the relations
will be further reduced or become insignificant.
4. Data and sample selection
The data are obtained from I/B/E/S for the period 1984-2001 (for
companies with fiscal year-end between January 1984 and December 2000). We
use the International edition and U.S. edition of the I/B/E/S Detail History files.10
Summary statistics for the final sample used in our study are reported in Table 2.
The results are aggregated for 12 common law countries and 21 civil law
countries. We base our aggregation of the results on legal origins (La Porta,
Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998, 2000a). The common law
countries are further separated into the United States and non-U.S. common law
countries.11 We further classify the civil law countries into French origin (11),
German origin (6), and Scandinavian origin (4).
Including only the analyst’s most recent annual forecast for each
company-year, the database includes 1,038,329 annual earnings forecasts issued
by 30,966 analysts for 27,379 companies. We first exclude observations for
countries not included in our data, that are team forecasts, and that do not have
actual annual earnings available. This reduces the sample to 28,738 analysts in
1,321 brokerage firms who provide 1,012,189 company-year forecasts for 25,933
10
companies in the 33 countries. We further exclude observations where there are
less than three analysts following the company in that year.12
Panel A of Table 2 reports summary statistics for the final sample. The
final sample consists of 673,817 annual forecasts for 15,220 companies, issued by
27,082 analysts who work for 1,151 brokerage houses. The majority of forecasts
(79%) were for companies in common law countries.13 The average number of
analyst following per firm-year is greatest in the U.S. (10.55), followed by nonU.S. common law countries (8.65) and civil law countries (8.23).14 Panel B of
Table 2 shows additional descriptive statistics regarding firm characteristics for
each of the legal regimes. For our sample, we report that civil law companies are,
on average, larger than common law companies. This is important because an
overweighting of larger firms in the common law sample could bias results in
favor of our hypotheses. The enhanced information environment of larger firms
increases demand for their securities (Merton 1987), offering greater economic
incentives to superior analysts to outperform their peers. In this case, civil law
countries (i.e., larger firms in our study) rather than common law countries would
be expected to exhibit a stronger association between relative forecast accuracy
and analyst characteristics, absent any effects of the legal origin (e.g., investor
protection laws, quality of accounting). We also show that the common law firms
have higher earnings to price ratio and greater analyst forecast errors than the civil
law firms. Forecast dispersion among analysts is approximately the same across
11
origins. We further report descriptive statistics for U.S. versus non-US companies
and for companies in the three civil law origins.
5. Results
Univariate statistics
Table 3 shows the means and medians for variables representing analyst
characteristics for each legal origin. The reported unadjusted amounts provide
descriptive statistics for the independent variables and comparisons between (1)
common law and civil law countries, (2) the U.S. and non-U.S. common law
countries, (3) non-U.S. common law countries and civil law countries, and (4)
pairs of the three civil law origin countries.15 In general, the common law analysts
provide significantly shorter forecast horizons, present fewer changes in analysts
following a company, have more experience, follow fewer companies, specialize
more in the same industry, and provide more forecasts for each company than do
the analysts in the civil law countries. They are employed in larger brokerage
houses with larger percentages of analysts following companies in the same
industry, and these houses have smaller portions of new and outgoing analysts
than do civil law analysts. Finally, common law analysts have more countryspecific experience, greater specialization in a particular country, and greater
brokerage house specialization within a country.16
12
Further analyses of the descriptive statistics show significant differences
between all characteristics of analysts in the United States and analysts in nonU.S. common law countries. For example, U.S. analysts have more experience
and follow more companies in the same industry, but they provide fewer forecasts
for each company than analysts in the non-U.S. common law countries. Similarly,
all characteristics of analysts in the non-U.S. common law countries differ
significantly from those of the civil law countries. For instance, analysts in the
civil law countries are less experienced, have less country-specific experience,
provide less forecasts for each company, and have a greater forecast horizon.
Finally, the comparisons between the three civil law origin countries show
significant t-statistics and Wilcoxon Z-statistics for all of the 13 independent
variables.
Test of hypothesis 1
Table 4 presents OLS results for the common law countries and the civil
law countries.17 Eleven coefficients are statistically significant in the predicted
direction for the common law sample, but only six coefficients are significant in
the predicted direction for the civil law sample. The coefficients for the forecast
horizon (HORIZ) and the analyst-broker turnover variables (PIN and POUT) are
positive (as predicted) and they are significant in the common law and the civil
law countries. The estimated coefficient for portfolio complexity (COMP) is
13
positive and statistically significant for the common law countries, suggesting that
the larger number of companies followed by an analyst reduces forecast accuracy.
This coefficient is not significant for the civil law countries. The coefficients for
frequency (FREQ) are significant and negative (as predicted) for both origins. The
coefficients for analyst specialization (SPEC), broker size (B-SIZE), and brokerindustry specialization (B-IND) are significantly negative (as predicted) for the
common law countries, while the coefficients for B-SIZE and SPEC are not
significant (at p <0.05) and B-IND is significant in the unpredicted direction for
the civil law countries. These results suggest that internal environment factors in
brokerage houses in civil law countries do not have the predicted impact on
analysts’ relative forecast accuracy as they do in common law countries. The
regression coefficients for experience (EXP) are not statistically significant across
the two groups of countries.
The coefficients for the three international variables are statistically
significant for the common law countries. The coefficients for C-SPEC and B-C
are significantly negative (as predicted) for both origins. The coefficient for CEXP, however, is significantly positive (not negative as predicted) for the
common law origin and insignificant for the civil law origin. This result indicates
that analysts in common law countries may have reasons to extend their
forecasting activity to other countries (e.g., they may possess private knowledge)
and this extension leads to more accurate forecasting in the first couple of years.
14
The R2 for common law countries is 0.1491 and the R2 for civil law
countries is 0.0811, suggesting that analyst characteristics better explain relative
forecast accuracy in the common law countries than in civil law countries. The
Chow test (F = 237.6) suggests that model coefficients are significantly different
across origins.18 The t-statistics for tests of differences in individual coefficients
are also reported in Table 4.19 The results show that portfolio complexity,
specialization, analyst activity, and internal environment factors, but not
experience, more significantly explain (in the predicted directions) analysts’
relative forecast accuracy in the common law countries than in the civil law
countries. Eight of the estimated coefficients in the common law countries have
significantly greater magnitudes, in the predicted directions, than do the
corresponding coefficients in the civil law countries. For example, relative
forecast accuracy increases by 0.37 percent per day as the forecast age decreases
in the common law countries, which is significantly faster (t = 36.3) than the 0.23
percent increase per day in the civil law countries, controlling for other variables
in the model. This result suggests that common law analysts provide more effort
and compete more aggressively in utilizing recent information than do civil law
analysts. The relative forecast accuracy increases by 3.97 percent for each
additional forecast issued by analysts (FREQ) in the common law countries,
which is significantly greater (t = 4.97) than the 1.56 percent increase in relative
forecast accuracy in the civil law countries. The differences in the coefficients are
15
not significant for the three international variables. This result suggests that
domestic characteristics have a greater impact on the relative forecast accuracy in
the common law countries than in the civil law countries, but the international
characteristics provide similar effects on the relative forecast accuracy in both
origins.
In sum, the empirical evidence supports our first hypothesis that analyst
characteristics better explain the relative forecast accuracy in common law
countries than in civil law countries. It seems that analysts in common law
countries compete to provide superior forecasts more than do analysts in civil law
countries, and those analysts with superior abilities and resources tend to
outperform their peers.
Test of hypothesis 2
Table 4 also reports the results for comparing the regression model between
the United States and non-U.S. common law countries. In the United States, ten
coefficients are statistically significant in the predicted direction. One coefficient
(C-EXP) is significant in the unpredicted direction.20 Eight coefficients are
significant in the predicted directions for the non-U.S. common law countries.
The coefficient for COMP is significant in the unpredicted direction.
The R2s are 0.1494 for the United States and 0.1271 for the non-U.S.
common law countries and the Chow test of differences in model coefficients is
16
significant (F = 75.5).21 Furthermore, of the ten coefficients significant in the
predicted direction for the United States, eight have a significantly greater
magnitude than the corresponding coefficients for the non-U.S. common law
sample. For example, the magnitude of the coefficient for B-SIZE is three times
greater for the U.S. sample than for the non-U.S. common law sample (t = –4.70).
The coefficients for international specialization (C-SPEC) and brokercountry factor (B-C) are significantly negative (as predicted). The coefficient for
B-C is significantly more negative for the United States than for the non-U.S.
common law countries (t = –4.09). The coefficients for C-SPEC, however, are
not significantly different. The coefficient for country-specific experience (CEXP) is significantly positive (not negative as predicted) for the United States,
and it is not significantly different from the coefficient for the non-U.S. common
law countries. In sum, our results support the second hypothesis. In non-U.S.
common law countries, where investor protection laws and financial reporting are
somewhat weaker than in the United States, we find less evidence that superior
analysts distinguish themselves.
Test of hypothesis 3
On the final right column of Table 4, we report results for comparing the
regression model between the non-U.S. common law countries and the civil law
countries. The R2 for the non-U.S. common law countries (0.1271) is greater than
17
that of the civil law countries (0.0811). The significant Chow test (F = 76.0)
suggests that model coefficients are not equal across origins.
Four of the variables have a significantly greater magnitude with the
predicted signs for the non-U.S. common law countries than for the civil law
countries. The estimated coefficient for B-C is the only one that is more
significant in the civil law countries. Overall, the results support the third
hypothesis, indicating that analyst characteristics have less ability to distinguish
superior analysts in the civil law countries than in the non-U.S. common law
countries.
The results in Table 4 support the first three hypotheses. We conclude that
analyst characteristics better explain relative forecast accuracy in countries with
stronger investor protection laws and higher-quality financial reporting. In the
United States, where investor protection laws and financial reporting are generally
considered the strongest, we find the most evidence that analyst characteristics
distinguish superior analysts. Furthermore, the evidence is stronger for non-U.S.
common law countries than for civil law countries.
Test of hypothesis 4
Analyses within the civil law countries provide an interesting setting to test
the relative influence of investor protection laws and the quality of financial
reporting systems. Compared to German origin countries, those of French origin
18
have weaker investor protection laws (La Porta, Lopez-de-Silanes, Shleifer, and
Vishny 1997) but higher-quality financial reporting (Ball, Kothari, and Robin
2000; Francis, Khurana, and Pereira 2004). Which environment provides superior
analysts with stronger incentives to distinguish themselves? If analyst forecast
behavior is related more to quality of financial reporting (strength of investor
protection laws), we would then expect superior analysts to distinguish
themselves better in French (German) origin countries. Therefore, tests within
French and German origin classifications provide some insights into the relative
influence investor protection laws and quality of financial reporting can have on
investors’ demand for earnings information and analysts’ forecast activities. Since
Scandinavian countries have stronger investor protection laws and higher-quality
financial reporting systems (relative to other civil law countries), we expect this
group to provide the strongest evidence of analysts’ differential forecast ability.
Table 5 reports regression results by the three civil law origins. The R2s are
0.0980, 0.0881, and 0.0711, respectively, for countries of the Scandinavian,
French, and Germanic origins. Chow tests are significant, suggesting differences
in model coefficients across origins. Six (five) regression coefficients are
significant for French (German) origin countries in the predicted direction.22 Of
the six significant coefficients for the French sample, three have a significantly
greater magnitude in the predicted direction than those for the German sample.
For example, the estimated positive coefficient for HORIZ in the French origin
19
countries is significantly greater than the positive coefficient in the German origin
countries (t = 8.29). In addition, the negative coefficient for EXP (as predicted,
but significant only at p < 0.10) for the French sample is significantly smaller than
the significant positive coefficient for the German sample (t = –3.76).
The greater explanatory power for the Scandinavian sample provides
evidence consistent with analyst behavior being affected by the strength of
investor protection laws and quality of financial reporting. Five coefficients are
significant in the predicted direction and three (two) of these have significantly
larger magnitudes in the predicted directions than those reported for the French
(German) sample. The differences between the estimated coefficient of all other
variables in the Scandinavian origin and the other two origins are not significant.
None of the coefficients for the Scandinavian sample is significant in the
unpredicted direction, whereas two coefficients are significant in the unpredicted
direction for the French and German countries. We do not consider coefficients in
the unpredicted direction to constitute evidence consistent with analysts
responding to investors’ demands for more accurate forecasts. We base the
analyst characteristics used in the model on a reasonable understanding of the
factors likely to distinguish superior analysts. These variables have received
empirical support in the literature. Therefore, the significance of the analyst
characteristics in the predicted direction provides a basis upon which to evaluate
20
the forecast behavior of analysts in different legal and financial reporting
environments.
In summary, within the civil law countries, the greatest and most consistent
evidence that superior analysts distinguish themselves comes from the
Scandinavian origin countries, which have higher-quality financial reporting and
stronger investor protection laws. Countries with stronger investor protection laws
but lower-quality financial reporting (i.e., German origin) show the weakest
evidence that analysts strive to outperform their peers. The evidence for the
countries with higher-quality financial reporting but weak investor protection
laws (i.e., French origin) appears to be somewhere in the middle. The differences
in the impact of analyst characteristics on relative forecast accuracy across the
civil law countries suggest that while investor protection laws can influence
analyst behavior, they are not the only factors affecting relative forecast accuracy.
The quality, timeliness, and transparency of financial reporting systems certainly
play a role in the demand for earnings information and the incentives of analysts
to distinguish themselves from their peers. These results support the view of
Bushman and Smith (2001) that financial accounting information is an important
input to corporate control mechanism.
6. Additional analyses
Impact of analyst characteristics on the relative accuracy of cash flow forecasts
21
Cash flow forecasts may substitute for earnings forecasts in countries
where earnings forecasts are less value-relevant to investors (Defond and Hung
2002). Hung (2000) reports that accruals reduce the value relevance of earnings in
countries with weak shareholder protection (civil law countries), but not in
countries with strong shareholder protection (common law countries). The
reduced relevance of accruals in civil law countries likely decreases the relevance
of earnings information to investors and increases the relevance of cash flow
information. As investor demand for information moves from earnings to cash
flows, analysts may spend relatively more time and resources on forecasting cash
flows. If this is so, then analyst characteristics may better explain relative cash
flow forecast accuracy in civil law countries. We provide an analysis of cash flow
relative forecast accuracy using the same methodology used for earnings
forecasts. We modify some relevant independent variables (e.g., horizon,
experience, frequency, and country-specific experience) for cash flow forecasts.
The other independent variables are the same as those used for the earnings
analyses.
The untabulated results show greater explanatory power for the 13variable regression in the civil law countries than in the common law countries.
The R2s are 0.146 for the civil law countries and 0.075 for the common law
countries. The Chow test is significant (F = 117.7). The explanatory powers are
approximately the same for the United States and the non-U.S. common law
22
countries. These findings provide some support for the alternate conjecture that
some analysts in civil law countries may spend more time and resources on
forecasting cash flow than on earnings, as those with superior characteristics tend
to more consistently outperform their peers. Again, these results are consistent
with analysts responding to investors’ demand for information.23
Forecast performance and termination
We test whether analysts are less likely to continue forecasting when their
performance is bad. We select the worst analyst (i.e., the one with the greatest
forecast error) for each firm-year and estimate the probability that the analyst will
forecast earnings for that same company in the following year. We find that 54.1
percent of the worst analysts from civil law countries provide forecasts for the
same firm next year. Only 34.8 percent and 41.0 percent of the worst analysts
continue to forecast for the same firm in the following year for the United States
and non-U.S. common law countries, respectively. The percentages of worst
analysts that continue to forecast for the same firm are similar across the civil law
groups of countries. These results are consistent with the analyst forecast
environment being more competitive in common law countries. Analysts that
provide bad forecasts and therefore fail to meet the information demands of
investors are less likely to continue forecasting earnings for these firms.
23
7. Summary and conclusions
In this study, we test the ability of analyst characteristics to explain relative
forecast accuracy across legal origins (common law versus civil law). Common
law countries typically have higher-quality financial reporting systems and
stronger investor protection laws. In this type of environment, the increased
demand by investors for earnings information increases the economic incentives
of analysts to provide accurate earnings forecasts. We expect analysts with
superior characteristics (e.g., ability, effort, experience, resources, etc.) to
outperform their peers in common law countries. In civil law countries, the
demand for earnings information is reduced because of weaker investor protection
laws and lower-quality financial reporting. The reduced demand by investors for
earnings information reduces the incentives for analysts to provide accurate
forecasts. Superior analysts may not be motivated to provide more accurate
forecasts if they have no expectation of being equitably rewarded for their efforts
and costs. Thus, we expect the relative performance of individual analysts to be
less systematic, making the relation between analyst characteristics and relative
forecast accuracy weaker in civil law countries.
We find results consistent with analyst behavior being related to legal
origins. In common law countries, analyst characteristics better explain relative
forecast accuracy. Analyst characteristics show the weakest association with
relative forecast accuracy in civil law countries. The strongest evidence that
24
superior analysts have incentives to outperform their peers comes from the United
States, where investor protection laws are arguably more effective and where
financial reporting has higher quality. The evidence of superior analysts
outperforming their peers in non-U.S. common law countries is greater than that
in civil law countries but less than that in the United States. Additional sensitivity
analyses support these conclusions and provide further insights into the impact of
legal origin on financial analysts’ activities.
We also examine the relation between relative forecast performance and
analyst characteristics within the civil law countries. Those of French origin have
higher-quality financial reporting systems, while those of German origin have
stronger investor protection laws. The Scandinavian origin countries have higherquality financial reporting and stronger investor protection laws relative to other
civil law countries. The evidence most consistent with superior analysts
outperforming their peers comes from the Scandinavian origin countries, followed
by the French origin countries. German origin countries provide the least
consistent evidence that analysts actively compete to outperform their peers.
These results constitute initial evidence that quality of financial reporting plays an
incremental and perhaps larger role in affecting analysts’ forecast behavior and
investors’ demand for earnings information than does the strength of investor
protection laws.
25
Future research can further understand the impact that legal origin and
financial reporting quality have on investors’ demand for earnings information
and analysts’ activities by considering additional analyst characteristics. In our
paper, we use a model developed in a U.S. context and incorporate some
“international” variables. While the U.S. environment is a natural starting point
for a model relating investor demand for earnings information to analysts’
activities, this represents a possible limitation of the study. Other variables in
other countries may lead to additional insights in this area. Another potential for
future research would be to consider how differences in information asymmetry
across countries affects the incentives of superior analysts to outperform other
analysts. One might expect superior analysts to have greater incentives when
information asymmetry is high because the gains to exploiting accurate forecasts
may be highest under such situations. However, this expectation would need to be
reconciled to the findings presented in this paper that superior analysts better
distinguish themselves in common law countries, where information asymmetry is
generally perceived to be lower.
26
Endnotes:
1
Early studies on the relative forecast accuracy of individual analysts revealed no systematic
differences in abilities (e.g., O’Brien 1987; Coggin and Hunter 1989; O’Brien 1990; Butler and
Lang 1991). More recent research has, however, found some differences (e.g., see Stickel 1992;
Sinha, Brown, and Das 1997). Subsequent research has sought to explain these differences
(Mikhail, Walther, and Willis 1997; Clement 1999; Jacob, Lys, and Neale 1999). In an
international context, Rees, Swanson, and Clement (2003) associate forecast accuracy with
cultural factors in several countries.
2
Francis, Schipper, and Vincent (2002) find a complementary, rather then substitutional, relation
between earnings announcements and analyst reports. Similarly, we assume that the roles of
financial accounting systems and analyst activities are complements rather than substitutes.
Analysts provide forecasts of earnings based on mandatory GAAP in each country, and
forecasting lower-quality earnings cannot substitute for reporting lower-quality earnings.
3
Research shows that greater legal protection is associated with more valuable stock markets and
a larger number of listed firms (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1997), larger
listed firms (Kumar, Rajan, and Zingales1999), greater cross-listing of foreign companies
(Pagano, Randl, Roell, and Zechner 2001), higher valuations for listed firms (Claessens,
Djankov, Fan, and Lang 1999; La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1999a), greater
dividend payouts (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 2000b), lower concentration
of ownership and control (La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1999b; Claessens,
Djankov, and Lang 2000), lower private benefits of control (Zingales 1994; Nenova 1999), and
higher correlation between investment opportunities and actual investments (Wurgler 2000).
Prior findings on investor protection and equity markets have also received some theoretical
support (Shleifer and Wolfenzon 2000).
4
The development of financial markets is directly related to the quality of financial accounting
information (Black 2001). Countries with more developed financial markets have better
accounting disclosures (Levine 2001; Hope 2003a, b, c), more timely and transparent
accounting information (Ali and Hwang 2000; Ball, Kothari, and Robin 2000; Guenther and
Young 2000), less management of reported earnings (Leuz, Nanda, and Wysocki 2003;
Bhattacharya, Daouk, and Welker 2003; Fulkerson, Jackson, and Meek 2002), higher-quality
earnings (Hung 2000; Fulkerson, Jackson, and Meek 2002), and greater demand for auditing
services (Francis, Khurana, and Pereira 2004).
5
Bushman and Smith (2001) suggest that the corporate control mechanisms can include both
internal mechanisms (e.g., managerial incentive plans) and external mechanisms (e.g.,
monitoring by outside shareholders or debtholders and securities laws that protect outside
investors against expropriation by corporate insiders). Financial accounting systems provide
direct and indirect inputs into corporate control mechanisms.
6
In the United States, the high demand by investors for earnings information has lead to strong
competition among analysts to forecast that information accurately. In civil law countries, the
relation between analyst behavior and the usefulness of financial accounting information is less
obvious (O’Brien 1998). As the demand for future earnings information weakens, analysts have
fewer economic incentives to outperform their peers and characteristics identified by the
benchmark model should have less ability to explain relative forecast accuracy.
7
We do not focus on whether consensus analyst forecast errors differ across legal regimes.
Analysts’ forecast errors can be affected by a number of factors including economic and cultural
conditions, income smoothing, industry composition, earnings management, management of
analysts’ forecasts, or level of economic development (Basu, Hwang, and Jan 1998). Our
27
within-firm design allows us to identify whether analysts with superior abilities and resources
reliably outperform their peers, while controlling for these potentially confounding effects.
8
The variable CHANGE refers neither to a change in the overall number of analysts following a
firm nor to a change in number of analyst following in a particular brokerage firm. CHANGE
refers to our firm-year-analyst observations and takes the value one in the first year t when
particular analyst k is assigned or replaced by the brokerage firm for forecasting earnings for
firm j.
9
We use a cut off of three years because it is about the 75th percentile of analysts in most
countries. We assume that the highest quartile could be considered as experienced analysts in a
country. We also measure C-EXP as a continuous variable consistent with EXP, but the two
variables are highly correlated.
10
On a global scale, I/B/E/S currently receives forecasts from more than 7,000 analysts
representing 800 brokerage firms providing forecasts for approximately 18,000 companies.
Many analysts in our database who provided forecasts in previous years are currently inactive.
11
Results for the United States are reported separately for testing the second hypothesis and for
two other reasons. First, U.S. observations make up approximately 58 percent of our global
sample. Second, reporting separate results for the U.S. sample facilitates comparisons with prior
U.S. studies (Jacob, Lys, and Neale 1999; Clement 1999).
12
Similar to Jacob, Lys, and Neale (1999) and Clement (1999), we require at least three analysts
so that our measure of relative forecast performance is meaningful.
13
Less than ten percent of the analysts provide forecasts for firms in more than one country.
Observations are classified into legal origin based on firm location rather than analyst location.
As a sensitivity test, we control for differences in sample sizes by randomly selecting
observations from the full common law sample, the U.S. sample, and the non-U.S. common law
sample so that all samples sizes would equal the number of observations for the civil law
sample. The results for the random samples resemble those for the complete samples reported in
Table 4, suggesting that differences in sample size do not confound the results.
14
Presumably, as the incentives of analysts to follow a firm increase, more analysts will follow the
firm and forecast accuracy will increase (Alford and Berger 1999). We calculate the correlations
(1) between forecast accuracy and number of analysts and (2) between the change in EPS and
the change in the number of analysts following the firm. We find these correlations are stronger
for the United States and non-U.S. common law countries than for civil law countries. These
results provide additional support for the notion that the incentives of individual analyst to
forecast more accurately are stronger in common law countries.
15
While we use the mean-adjusted amounts in the regression analyses, we report unadjusted raw
amounts for each descriptive statistic for the independent variables since subtracting the annual
means from each independent variable produces means equal to zero.
16
I/B/E/S history is longer for the United States than for non-U.S. countries. We re-estimate the
results for the sub-periods 1991-2001 and 1995-2001. These more recent samples minimize the
effect of analysts with reported one or two years of experience in the first or second year of
available data. Univariate and OLS results for these subperiods are very similar to those
reported in Tables 3 and 4, respectively.
17
We also examine the model including year-intercept dummies that are not statistically
significant for the regressions reported in Tables 4 and 5. Therefore, these results are not
reported.
18
The Chow F-test (Greene 2003) is used to test whether some or all the regression coefficients
are equal between the common law and civil law origins.
28
19
The reported t-statistics examine the significance of the difference in slope coefficients between
the civil law and common law regressions. We do not report an alternative presentation of
pooling the observations and using interactive binary variables to test the differences between
the estimated coefficients of the two samples (Kennedy, 1992, pp. 220-225). These results are
not reported for two reasons. First, the t-tests for the differences in coefficients reported in Table
4 and 5 are the same using either method. Thus, results of testing the incremental significance of
the interactions and the method reported in our paper are econometrically equivalent. Second,
the R2s for each sample would not be available using the pooled model with interactive binary
variables.
20
We also compare our results with Jacob, Lys, and Neale (1999) using their ten-variable OLS
regression model. Our untabulated results in the United States resemble theirs. We report greater
explanatory power than them and we obtain significant estimated coefficients for CHANGE and
PIN and insignificant coefficient for EXP. We interpret these findings as results of using more
recent data for 1984-2001 (Jacob, Lys, and Neale used 1981-1992), using annual data -quarterly data are not available for countries other than the United States (they used quarterly
data), and employing I/B/E/S data (while they use Zack’s database). Our results are similar to
findings reported by Clement (1999) who uses I/B/E/S U.S. annual data for 1985-1994 and
reports adjusted R2s closer to ours, even though four of his seven variables differ from Jacob,
Lys, and Neale.
21
We also examine results for Canada using 28,437 analyst-company-year observations. The
results (not tabulated) show that the R2 and most estimated coefficients resemble those of the
non-U.S. common law countries rather those reported for the United States.
22
The coefficients for B-IND are significantly positive (not negative as predicted) for the French
and the German origin countries. This result suggests that industry specialization in the
brokerage house increases the relative forecast error rather than decreases it, unlike in the
United States. Contrary to the predictions, analyst experience (EXP) increases relative forecast
error in the German origin countries, and increasing the number of companies followed
(COMP) decreases relative forecast error in the French origin countries.
23
The results should be interpreted with caution since our final cash flow sample includes only
49,654 analyst-firm-year observations with complete data for all variables. Analysts’ cash flow
coverage is very limited in the United States (Defond and Hung 2002). Our cash flow sample
size is less than 1% of the U.S. earnings sample, about 15% of the non-U.S. common law
earnings sample, and about 19% of the civil law earnings sample. We report results after
winsorizing relative cash flow forecast accuracy at the 1st and 99th percentiles.
29
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33
TABLE 1
Investor protection laws, quality and transparency of financial reporting, and their expected impact on the ability of analyst characteristics to
explain relative forecast accuracy
Expected Impact on
Relative Forecasts
Quality and Transparency of
Accuracy
Origin
Investor Protection Laws
Financial Reporting
Efficiency
Antidirector Creditor of Judicial
Accrual Disclosure
Audit
Rightsa
Rightsa
Systemb
Indexc
Indexd
Spendinge
Common Law f
Stronger
4.0
3.11
8.15
Higher
0.76
70.6
0.27
Greater
Civil Lawg
Average
2.4
1.83
7.39
Average
0.58
65.1
0.14
Less
United States
Non-U.S. Common
Frenchh
Germani
Scandinavianj
Strongest
Stronger
5.0
3.9
1.00
3.23
10.0
8.04
Highest
Higher
0.86
0.74
71.0
70.5
0.24
0.27
Greatest
Greater
Weaker
Average
Above Avg.
2.3
2.3
3.0
1.58
2.33
2.00
6.56
8.54
10.0
Average
Below Avg.
Higher
0.64
0.43
0.63
62.1
62.7
74.0
0.12
0.12
0.22
Below average
Below average
Average
Numbers reported represent mean amounts obtained from the following studies:
a
La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) – higher number indicates more rights, creditor rights may be classified separately.
b
La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) – higher number indicates more efficiency.
c
Hung (2000) – higher number indicates greater use of accruals.
d
Francis, Khurana, and Pereira (2004) – higher number indicates better disclosure.
e
Jaffe (1992) as reported in Francis, Khurana, and Pereira (2004) – higher number indicates more audit spending.
f
Countries in the common law origin are Australia, Canada, Hong Kong, India, Ireland, Malaysia, New Zealand, Singapore, South Africa, Thailand, United
Kingdom, and United States.
g
Countries in the civil law origin are Argentina, Austria, Belgium, Denmark, Finland, France, Germany, Indonesia, Italy, Japan, South Korea, Mexico,
Netherlands, Norway, Philippines, Portugal, Spain, Sweden, Switzerland, Taiwan, and Turkey.
h
Countries in the French origin are Argentina, Belgium, France, Indonesia, Italy, Mexico, Netherlands, Philippines, Portugal, Spain, and Turkey.
i
Countries in the German origin are Austria, Germany, Japan, South Korea, Switzerland, and Taiwan.
j
Countries in the Scandinavian origin are Denmark, Finland, Norway, and Sweden.
34
TABLE 2
I/B/E/S summary statistics for our sample, sample period 1984-2001.
Panel A: Number of analysts and forecasts across legal origins
Number of
Analystsa
Legal Origin
Common law Origind
Civil law Origine
Final Observations Used
Number of
Firm-Year
Forecastsb
Number of Firm
yearsc
Average
Number of
Analysts
Following
18,009
9,073
27,082
535,462
138,355
673,817
53,785
16,806
70,591
9.96
8.23
US
Non-U.S. common law Origin
9,198
8,811
390,121
145,341
36,985
16,800
10.55
8.66
French Originf
German Origing
Scandinavian Originh
4,735
3,129
1,209
70,011
52,951
15,393
7,488
7,469
1,849
9.35
7.09
8.33
Panel B: Firm characteristics across legal origins.
Common Law Origin
Civil Law Origin
U.S.
Non-U.S. Common Law
Origin
MVE
Mean(Med)
1004.10
(444.61)
1199.18
(643.13)
1060.78
(486.33)
879.38
(365.57)
EPS/PRICE
Mean(Med)
0.0905
(0.061)
0.0487
(0.005)
0.0318
(0.0554)
0.2209
(0.0814)
SD(CON)/PRICE
Mean(Med)
0.0168
(0.002)
0.0177
(0.001)
0.0043
(0.0010)
0.0446
(0.0094)
AFE/PRICE
Mean(Med)
0.0706
(0.1620)
0.0310
(0.001)
0.0666
(0.0430)
0.0795
(0.2188)
0.0873
(0.0134)
0.0131
(0.0034)
0.0360
(0.0530)
0.0305
(0.0028)
0.0042
(0.0004)
0.0205
(0.0082)
0.0565
(0.0022)
0.0076
(0.0006)
0.0301
(0.0068)
18.1*
77.3*
-1.8
9.7*
27.9*
122.6*
-79.5*
-62.9*
-89.0*
-133.0*
-8.7*
-78.7*
French Origin
965.15
(419.17)
German Origin
1540.25
(1102.75)
Scandinavian Origin
786.78
(378.32)
Common-Law Origin versus Civil-Law Origin
t-statastic
-18.7*
Wilcoxon
-18.9*
U.S. versus non-U.S. Common-Law Origin
t-statastic
17.0*
Wilcoxon
17.0*
(Table 2 continued on the next page)
35
TABLE 2 (continued)
I/B/E/S summary statistics for our sample, sample period 1984-2001.
Panel B (continued): Firm characteristics across legal origins.
Non-U.S. Common-Law Origin versus Civil-Law Origin
t-statastic
-25.3*
25.3*
Wilcoxon
-25.2*
84.6*
French Origin versus German Origin
t-statastic
-28.8*
20.5*
Wilcoxon
-33.6*
20.4*
French Origin versus Scandinavian Origin
t-statastic
6.2*
7.1*
Wilcoxon
1.3
-13.0*
German Origin versus Scandinavian Origin
t-statastic
23.9*
-8.8*
Wilcoxon
25.2*
-35.4*
a
52.1*
80.9*
24.1*
65.8*
27.4*
26.6*
21.0*
24.2*
5.1*
-21.3*
5.6*
-15.4*
-24.4*
-46.6*
-13.7*
-38.0*
The number of analysts includes all analysts that provide at least one forecast for one company during the period.
Many analysts provide forecasts for many companies.
b
Only the most recent forecast is included in the reported number of firm-year forecasts.
c
We report only forecasts for companies followed by at least three analysts.
d
Common law countries are Australia, Canada, Hong Kong, India, Ireland, Malaysia, New Zealand, Singapore,
South Africa, Thailand, United Kingdom, and United States.
e
Civil law countries are Argentina, Austria, Belgium, Denmark, Finland, France, Germany, Indonesia, Italy, Japan,
South Korea, Mexico, Netherlands, Norway, Philippines, Portugal, Spain, Sweden, Switzerland, Taiwan, and
Turkey.
f
Countries in the French origin are Argentina, Belgium, France, Indonesia, Italy, Mexico, Netherlands, Philippines,
Portugal, Spain, and Turkey.
g
Countries in the German origin are Austria, Germany, Japan, South Korea, Switzerland, and Taiwan.
h
Countries in the Scandinavian origin are Denmark, Finland, Norway, and Sweden.
MVE = Market value of equity in million U.S. dollars.
EPS/PRICE = EPS deflated by price (including negative EPS).
SD(CON)/PRICE = Standard deviation of I/B/E/S consensus deflated by price.
AFE/PRICE = Absolute forecast error deflated by price.
36
TABLE 3
Raw univariate statistics: Means (medians) by origin, sample period: 1984-2001a
Panel A: Common law origin, civil law origin, United States, and non-U.S. common law
origin.
U.S. vs.
Non-U.S.
Non-U.S. Common
Non-U.S
Common
Common Civil
Common
vs. Civil
common
vs. Civil
Law
Law
United
Law
t-stat.
t- stat.
t-stat.
b
c
d
e
e
Variables
Origin
Origin
States
Origin
(Z- stat.)
(Z- stat.)
(Z-stat.)e
HORIZ
123.3
134.6
124.1
121.3
-38.3 *
9.44*
-31.9*
(95.0)
(111.0)
(95.0)
(93.0)
(-27.6) *
(23.8)*
(-29.8)*
CHANGE
0.20
0.21
0.24
0.19
-4.90 *
12.5*
-11.4*
(0.00)
(0.00)
(0.00)
(0.00)
(-4.93) *
(12.4)*
(-11.4)*
EXP
0.80
0.59
0.84
0.71
106.1 *
58.3*
48.6*
(0.69)
(0.69)
(0.69)
(0.69)
(88.6) *
(48.5)*
(45.6)*
COMP
25.2
26.2
22.3
32.9
-9.17 *
-85.6*
36.2*
(17.0)
(14.0)
(18.0)
(16.0)
(90.7) *
(30.4)*
(47.7)*
SPEC
0.67
0.51
0.71
0.58
174.2 *
133.7*
51.8*
(0.80)
(0.50)
(0.85)
(0.64)
(166.3) *
(141.9)*
(48.9)*
FREQ
3.56
2.96
3.11
4.78
65.6 *
-113.0*
104.2*
(3.00)
(2.00)
(3.00)
(3.00)
(67.4) *
(-39.6)*
(82.2)*
B-SIZE
0.87
0.84
0.88
0.87
68.9 *
20.0*
18.7*
(0.94)
(0.90)
(0.94)
(0.91)
(67.4) *
(48.1)*
(10.6)*
B-IND
0.13
0.12
0.14
0.10
28.5 *
127.0*
-41.1*
(0.09)
(0.09)
(0.10)
(0.08)
(8.23) *
(90.6)*
(-32.0)*
PIN
0.29
0.38
0.27
0.33
-133.2 *
-88.5*
-46.0*
(0.25)
(0.33)
(0.24)
(0.27)
(-136.7) *
(-69.2)*
(-52.4)*
POUT
0.25
0.29
0.24
0.29
-65.9 *
-81.7*
-6.16*
(0.21)
(0.24)
(0.20)
(0.23)
(-61.6) *
(-75.4)*
(-9.20)*
C-EXP
0.56
0.31
0.61
0.42
177.5 *
170.5*
61.4*
(1.00)
(0.00)
(1.00)
(0.00)
(167.0) *
(166.9)*
(60.9)*
C-SPEC
0.93
0.78
0.98
0.79
113.4 *
224.1*
6.30*
(1.00)
(1.00)
(1.00)
(1.00)
(105.2) *
(54.8)*
(23.6)*
B-C
0.83
0.60
0.91
0.64
205.3 *
289.2*
27.6*
(1.00)
(0.67)
(1.00)
(0.68)
(171.2) *
(273.6)*
(16.6)*
(TABLE 3 continued on next page)
37
TABLE 3 (continued)
Raw univariate statistics: Means (medians) by origin, sample period: 1984-2001a
Panel B: Civil Law Origin – French, German, and Scandinavian
French vs.
German
French
German
Scand.
t- stat.
Variablesb
Originf
Origing
Originh
(Z- stat.)e
HORIZ
132.7
139.7
127.4
-10.8 *
(111.0)
(118.0)
(99.0)
(-12.1) *
CHANGE
0.20
0.22
0.21
-9.50 *
(0.00)
(0.00)
(0.00)
(-9.50) *
EXP
0.59
0.60
0.54
-3.64 *
(0.69)
(0.69)
(0.69)
(-1.71)
COMP
27.7
27.6
19.3
0.87
(14.0)
(15.0)
(10.0)
-(5.43) *
SPEC
0.47
0.56
0.52
-42.4 *
(0.40)
(0.57)
(0.50)
(-41.9) *
FREQ
3.04
2.94
3.07
4.79 *
(2.00)
(2.00)
(2.00)
(16.6) *
B-SIZE
0.84
0.86
0.84
-15.6 *
(0.89)
(0.90)
(0.92)
(-3.38) *
B-IND
0.10
0.11
0.17
-3.41 *
(0.08)
(0.08)
(0.11)
(-0.98)
PIN
0.39
0.33
0.38
43.1 *
(0.34)
(0.27)
(0.35)
(44.9) *
POUT
0.28
0.28
0.27
0.07
(0.23)
(0.22)
(0.25)
(4.71) *
C-EXP
0.29
0.36
0.25
-25.7 *
(0.00)
(0.00)
(0.00)
(-25.6) *
C-SPEC
0.77
0.82
0.66
-23.2 *
(1.00)
(1.00)
(0.86)
(-29.4) *
B-C
0.59
0.69
0.42
-40.8 *
(0.59)
(0.94)
(0.29)
(-33.2) *
a
French vs.
Scand.
t- stat.
(Z- stat.)e
5.65*
(5.85)*
-3.25*
(-3.30)*
9.90*
(9.29)*
28.2*
(37.1)*
-16.2*
(-18.8)*
-1.33
(-10.0)*
-0.67
(-2.64)*
-49.6*
(-38.0)*
2.45**
(0.53)
14.9*
(-4.11)*
10.1*
(10.2)*
10.4*
(37.3)*
53.7*
(45.6)*
German
vs. Scand.
t- stat.
(Z- stat.)e
12.9 *
(13.7) *
2.79 *
(2.76) *
12.1 *
(9.84) *
26.8 *
(40.0) *
10.3 *
(13.0) *
-5.09 *
(-20.9) *
-10.2 *
(-0.14)
-63.1 *
(-36.4) *
-27.0 *
(31.7) *
6.61 *
(-8.41) *
27.0 *
(25.4) *
47.3 *
(57.3) *
81.4 *
(74.3) *
Unadjusted raw amounts for mean and median are reported since subtracting annual firm-year means for each
variable produces means equal to zero.
b
Independent variables:
HORIZ
=
The number of calendar days between the forecast issue date and the earnings
announcement date.
CHANGE
=
Dummy variable that takes a value 1 (0 otherwise) when there has been a change in the
assignment of specific analyst k following company j for a particular brokerage in year t.
EXP
=
The natural log of the number of years analyst k has issued forecasts for company j.
COMP
=
The number of companies followed by analyst k in the calendar year in which the
forecast was issued.
SPEC
=
Percentage of companies followed by analyst k with the same I/B/E/S industry code as
company j (1.00= 100%).
FREQ
=
Number of forecasts issued by analyst k for company j in year t.
38
B-SIZE
=
B-IND
=
PIN
=
POUT
=
C-EXP
=
C-SPEC
=
B-C
=
Percentile ranking of the total number of analysts employed by the brokerage house to
which analyst k belongs in the calendar year in which the forecast was issued, relative to
other brokerage houses (1.00= 100%).
Percentage of analyst k’s brokerage house analysts which follows company j’s industry in
the calendar year in which the forecast was issued (1.00= 100%).
Portion of new analysts that come from outside the brokerage house relative to the total
number of analysts who worked for analysts k’s brokerage house during the calendar year
in which the forecast was issued (1.00= 100%).
Portion of analysts who left analyst k’s brokerage house relative to the total number of
analysts who worked for analysts k’s brokerage house during the calendar year in which
the forecast was issued (1.00= 100%).
Dummy variable that takes a value of 1 (0 otherwise) when analyst k has issued forecasts
for more than three years for any company in a country.
Percentage of companies followed by analyst k in the same country where the analyst has
issued forecasts for company j in year t (1.00= 100%).
Percentage of analyst k’s brokerage house analysts which follow company j’s country in
the calendar year in which the forecast was issued (1.00= 100%).
c
Countries in the common law origin are Australia, Canada, Hong Kong, India, Ireland, Malaysia, New Zealand,
Singapore, South Africa, Thailand, United Kingdom, and the United States.
d
Countries in the civil law origin are Argentina, Austria, Belgium, Denmark, Finland, France, Germany, Indonesia,
Italy, Japan, South Korea, Mexico, Netherlands, Norway, Philippines, Portugal, Spain, Sweden, Switzerland,
Taiwan, and Turkey.
e
Amounts shown represent tests of differences in means (medians) between the two groups.
f
Countries in the French origin are Argentina, Belgium, France, Indonesia, Italy, Mexico, Netherlands, Philippines,
Portugal, Spain, and Turkey.
g
Countries in the German origin are Austria, Germany, Japan, South Korea, Switzerland, and Taiwan.
h
Countries in the Scandinavian origin are Denmark, Finland, Norway, and Sweden.
*, ** Significant at p < 0.01, 0.05.
39
TABLE 4
Regression of analyst relative forecast accuracy on analyst- broker-specific characteristics, sample
period: 1984-2001.
U.S. vs
Non-U.S.
Non-U.S Common Non-U.S. Common vs
Common Civil
Common vs Civil Common
Civil
Predicted
Law
Law
United
Law
t-stat.d
t-stat.d
t-stat.d
Variablesa
Sign
Originb Originc States
Origin
H1:
H2:
H3:
Intercept
-0.0019 -0.0030 0.0001 -0.0056
0.30
1.30
-0.55
(-0.91) (-0.99)
(0.04)
(-0.87)
HORIZ
+
CHANGE
-/?
EXP
-
COMP
+
SPEC
-/?
FREQ
-
B-SIZE
-
B-IND
-
PIN
+
POUT
+
C-EXP
-
C-SPEC
-
B-C
-
Chow-F e
R2
Obs.
0.0037 0.0023
(170.2)* (78.4)*
-0.0132 0.0018
(-4.17)* (0.31)
-0.0037 0.0038
(-1.39) (0.73)
0.0002 -0.0001
(2.88)* (-1.13)
-0.0551 0.0076
(-7.86)* (0.72)
-0.0397 -0.0156
(-51.3)* (-13.5)*
-0.220 -0.040
(-18.6)* (-1.87)
-0.0318 0.1760
(-2.16)** (4.60)*
0.0420 0.0367
(4.88)* (3.17)*
0.1385 0.0888
(15.7)* (7.42)*
0.0082 0.0086
(2.92)* (1.55)
-0.0418 -0.0239
(-3.72)* (-2.58)**
-0.0734 -0.0784
(-11.3)* (-8.63)*
0.0038
0.0031
(149.3)* (82.0)*
-0.0227 0.0119
(-6.23)*
(1.86)
0.0011
0.0047
(0.35)
(0.79)
0.0007 -0.0004
(6.18)* (-2.95)*
-0.0620 -0.0260
(-7.39)* (-1.96)**
-0.0535 -0.0356
(-54.5)* (-33.2)*
-0.230
-0.080
(-17.8)* (-2.99)*
-0.0250 -0.0218
(-1.60)
(-0.44)
0.0296
0.0512
(2.76)* (3.50)*
0.1632
0.0920
(14.7)* (6.48)*
0.0076
0.0077
(2.27)** (1.39)
-0.0636 -0.0402
(-2.63)* (-2.98)*
-0.0908 -0.0351
(-10.0)* (-3.44)*
0.1419 0.0811
535,462 138,355
0.1494
390,121
a
36.3*
14.2*
16.2*
-2.29**
-4.70*
1.18
-1.28
-0.54
0.10
2.77*
6.32*
-4.97*
-2.28**
-1.99**
-16.9*
-12.3*
-12.4*
-6.89*
-4.70*
-1.17
-5.07*
-0.06
-3.16*
0.37
-1.19
0.78
50.4*
3.95*
-1.70
0.18
-0.07
-0.02
-0.11
-1.23
-0.48
-1.00
0.46
-4.09*
8.31*
237.6 *
75.5*
76.0*
0.1271
145,341
Independent variables are defined in Table 3 and country classifications are defined in Table 1. White adjusted tstatistics are reported in parentheses.
b
Common law countries are Australia, Canada, Hong Kong, India, Ireland, Malaysia, New Zealand, Singapore,
South Africa, Thailand, United Kingdom, and the United States.
c
Civil law countries are Argentina, Austria, Belgium, Denmark, Finland, France, Germany, Indonesia, Italy, Japan,
South Korea, Mexico, Netherlands, Norway, Philippines, Portugal, Spain, Sweden, Switzerland, Taiwan, and
Turkey.
d
Amounts shown represent tests of differences in coefficient betweens the two groups.
e
The Chow statistics test that the vectors of estimated coefficients are the same for the two groups.
*, ** Significant at p < 0.01, 0.05.
40
TABLE 5
Regression of analyst relative forecast accuracy on analyst- broker-specific characteristics in the Civil
law countries, sample period: 1984-2001.
Variablesa
Intercept
Pred.
Sign
HORIZ
+
CHANGE
-/?
EXP
-
COMP
+
SPEC
-/?
FREQ
-
B-SIZE
-
B-IND
-
PIN
+
POUT
+
C-EXP
-
C-SPEC
-
B-C
-
Chow-F f
R2
Obs
French
Originb
-0.0025
(-0.60)
0.0025
(60.9)*
0.0011
(0.13)
-0.0142
(-1.84)
-0.0004
(-3.31)*
0.0138
(1.01)
-0.0070
(-5.00)*
-0.0011
(-3.40)*
0.1856
(3.19)*
0.0106
(0.73)
0.0877
(4.91)*
0.0078
(0.96)
-0.0319
(-2.69)*
-0.0613
(-4.99)*
German
Originc
-0.0028
(-0.52)
0.0020
(41.6)*
-0.0030
(-0.34)
0.0276
(3.45)*
0.0001
(0.97)
0.0208
(1.05)
-0.0259
(-11.7)*
0.070
(1.74)
0.2544
(3.60)*
0.0955
(4.42)*
0.0811
(4.73)*
0.0097
(1.16)
-0.0011
(-0.06)
-0.1084
(-7.25)*
Scand.
Origind
-0.0085
(-1.02)
0.0025
(24.8)*
0.0304
(1.71)
-0.0026
(-0.16)
0.0007
(1.90)
-0.0679
(-2.42)*
-0.0179
(-4.83)*
-0.020
(0.32)
0.1253
(1.70)
-0.0376
(-1.01)
0.1454
(3.10)*
0.0089
(0.51)
-0.0176
(-0.70)
-0.1100
(-3.10)*
0.0881
70,011
0.0711
52,951
0.0980
15,393
a
French vs
German
t-stat.e
0.05
8.29*
French vs
Scand.
t-stat.e
0.65
German vs
Scand.
t-stat.e
0.58
0.90
-4.28*
-0.33
-1.49
-1.68
-3.76*
-0.63
1.63
-2.61*
-2.77*
-1.40
-0.29
2.62*
7.23*
2.76*
2.59*
-1.85
-3.55*
-1.16
1.10
-0.75
0.64
1.27
3.26*
1.20
3.09*
0.27
-1.15
-1.29
-0.16
-0.02
0.01
-1.40
-0.51
0.53
2.44**
1.30
0.04
76.6*
49.8*
60.8*
Independent variables are defined in Table 3. White adjusted t-statistics are reported in parentheses.
Countries in the French origin are Argentina, Belgium, France, Indonesia, Italy, Mexico, Netherlands, Philippines,
Portugal, Spain, and Turkey.
c
Countries in the German origin are Austria, Germany, Japan, South Korea, Switzerland, and Taiwan.
d
Countries in the Scandinavian origin are Denmark, Finland, Norway, and Sweden.
e
Amounts shown represent tests of differences in coefficients between the two groups.
f
The Chow statistics test that the vectors of estimated coefficients are the same for the two groups.
*, ** Significant at p < 0.01, <0.05.
b
41