The Earnings Forecast
Accuracy of Financial
Analysts Who are CFA
Charterholders
RICH FORTIN AND STUART MICHELSON
RICH FORTIN
is with New Mexico State
University in Las Cruces,
New Mexico.
STUART MICHELSON
is with Stetson University
in Deland, Florida.
[email protected]
FALL 2006
he central purpose of this article is
to examine the earnings forecast
accuracy of financial analysts who
have earned the right to use the
CFA designation. This research question will
be analyzed in the context of prior research
on earnings forecasts, particularly the past accuracy model of Brown (2001) which was an
extension of the analyst characteristics model
of Clement (1999).
The study of financial analysts’ forecasts of earnings is important since the markets use these forecasts in making assessments
of share price. There is a rich literature on
financial analyst earnings forecasts. For
example, Butler and Saraoglu (1999) examine
the optimism of security analysts’ negative
earnings forecasts. Erwin and Perry (2000)
investigate the effect of foreign diversification on analysts’ prediction errors. Cooper,
Day and Lewis (2001) find that lead analysts
have a greater impact on stock prices than
follower analysts. Sougiannis and Yaekura
(2001) conclude that, on average, analysts’
earnings forecasts convey information about
value beyond that conveyed by current earnings, book values and dividends. Lim (2001)
suggests that positive and predictable bias may
be a rational property of optimal earnings
forecasts. Duru and Reeb (2002) find evidence that suggests that forecasting earnings
becomes more complex as firms become
more geographically diversified. Brown and
Emad (2003) show that analyst-forecast age
T
predicts as well as more complex models
which include analyst characteristics. This
study focuses on the past accuracy model of
Brown (2001), which was patterned on
Sinha, Brown and Das (1997) and Stickel
(1992) and which was an extension of the
analyst characteristics model of Clement
(1999). Brown (2001) found that a simple
model of past accuracy performs as well as a
more complex model based on analyst characteristics and concludes that practitioners’
focus on past accuracy is not misplaced. This
paper extends the Brown (2001) past accuracy model by adding a dummy variable for
financial analysts who have earned the right
to use the CFA designation.
There are two reasons why financial analysts who are CFA charterholders may be
expected to provide more accurate earnings
forecast estimates than their counterparts. First,
earning the right to use the CFA designation
involves the completion of a rigorous, threelevel curriculum over a three-year period (at a
minimum) involving the tools and concepts that
apply to investment valuation and portfolio
management (Level I), a primary focus on asset
valuation (Level II) and, finally, a primary focus
on portfolio management (Level III). There are
also extensive work experience requirements
making the CFA charter arguably one of the
most recognized and respected achievements
within the global investment industry. CFA
charterholders are, thus, among the most technically prepared and qualified in the industry
THE JOURNAL OF INVESTING
19
to make accurate earnings forecasts. Second, all candidates
in the CFA program and CFA charterholders must adhere
to the Association for Investment Management and
Research (AIMR)1 Code of Ethics and Standards of Professional Conduct (1999). These ethical and professional
standards are also part of all three levels of the CFA examination. CFA charterholders are thus subject to a higher
set of standards than others in the investment industry and,
thus, could reasonably be expected to eschew overly optimistic earnings estimates that are not supported by hard
empirical evidence. For instance, The Code of Ethics states
that members of AIMR shall: “Act with integrity, competence, dignity, and in an ethical manner when dealing
with the public, clients, prospects, employers, employees,
and fellow members” and “Use reasonable care and exercise
independent professional judgment.” Many of the Standards
of Professional Conduct also are directly relevant here. For
example, Standard II: B.1 Professional Misconduct states that
“Members shall not engage in any professional conduct involving dishonesty, fraud, deceit, or misrepresentation or commit any act that reflects
adversely on their honesty, trustworthiness, or professional competence.” Standard IV: A.1 Reasonable Basis and Representations indicates that
members shall
“a. Exercise diligence and thoroughness in making
investment recommendations or in taking investment actions. b. Have a reasonable and adequate
basis, supported by appropriate research and investigation, for such recommendations or actions.
c. Make reasonable and diligent efforts to avoid any
material misrepresentation in any research report or
investment recommendation. d. Maintain appropriate records to support the reasonableness of such
recommendations or actions.” Standard IV: A.3
Independence and Objectivity requires that
“Members shall use reasonable care and judgment
to achieve and maintain independence and objectivity in making investment recommendations or
taking investment actions.”
Ultimately, the question of whether CFA charterholders provide more accurate earnings forecasts is an
empirical question and that is the purpose of this article.
20
DATA AND HYPOTHESIS
We used the Thomson Financial First Call I/B/E/S
U.S. Detail History database in this study. This database was
purchased in the Fall of 2003 and the detail file contains
financial analyst annual and quarterly earnings forecasts for
the 19-year period 1984 through 2002. Actual reported
earnings are also included in the “Actuals” file and are entered
in the database on the same basis as analysts’ forecasts which,
by and large, means operating earnings as opposed to net
income. Similar to the existing literature, we obtained the
last forecast made by each analyst during the period. This
study examines analyst forecasts for the coming fiscal year
(Fiscal Year 1 in the database) and coming quarter (Quarter
1 in the database). Thomson First Call also provided a separate file containing the complete first and last names of each
analyst in the I/B/E/S file which was used in conjunction
with the I/B/E/S broker translations file to create a file of
analysts’ last name, first name and firm name for merging
with the CFA charter file described next. The Association
for Investment Management and Research (AIMR) provided a file of CFA charterholders’ last name, first name,
firm name and CFA charter date as of the Fall of 2003. This
file was then merged with the above noted I/B/E/S name
data base to create a file of identified CFA charterholders for
use in the study.2,3 Following Brown (2001), the above
I/B/E/S database is used to construct the dependent forecast accuracy variable, along with a control variable for forecast age and a lagged forecast accuracy variable.
The main hypothesis to be tested in this article is that
professional financial analysts who are CFA charterholders
will provide more accurate earnings forecasts than professional financial analysts who are not CFA charterholders.
METHODOLOGY
The methodology used to test the central hypothesis
noted above will be to follow the past accuracy model used
by Brown (2001). This is essentially a multivariate regression model with a dependent forecast accuracy variable,
along with a control variable for forecast age, and a lagged
forecast accuracy variable [Brown’s (2001) addition to the
Clement (1999) study] and a dummy variable for CFA charterholder versus non CFA charterholder [this study’s contribution]. It is expected that the coefficient on the CFA
charterholder dummy variable will be significantly negative in this model indicating that financial analysts holding
the CFA charter have “tighter” earnings forecasts. It should
THE EARNINGS FORECAST ACCURACY OF FINANCIAL ANALYSTS WHO ARE CFA CHARTERHOLDERS
FALL 2006
be noted that the Brown (2001) paper included both an
analyst characteristics model and the above noted past accuracy model. Since Brown (2001) found that the past accuracy model performed as well as the analyst characteristics
model for both annual and quarterly earnings estimates and
for both estimation and prediction tests, we take the parsimonious approach of extending the simpler prior model.
The precise definitions of the past accuracy model variables
follows, starting with the dependent variable.
Accuracy—The individual analyst forecast error for a given
firm for a given annual period ending date minus the
mean of the forecast errors of all analysts following the
company for that annual period ending date divided by
the mean of the forecast errors of all analysts following the
company for that annual period ending date. Forecast
error is defined as the absolute value of the difference
between the I/B/E/S actual annual earnings and the last
forecast made by the analyst for that year.
Forecast Age—Number of calendar days between the analyst’s last annual forecast and the fiscal year end minus the
average “forecast age” of all analysts following the company that year.
Past Accuracy—Accuracy last year.
CFA Code—1 if the financial analyst is a CFA Charterholder and 0 if the analyst is not a CFA Charterholder.
Our analysis of quarterly data is based on the same
variable definitions as the analysis of annual data except
that the variables are defined relative to the quarter rather
than the year.
EMPIRICAL RESULTS
In order to assess the accuracy of our SAS programming, Exhibits 1 and 2 provide results based on the
Brown (2001) past accuracy model for our time frame of
1986–2002 for our total dataset.4 Our results are quantitatively and qualitatively similar to Brown (2001) even
though a more extended time period is covered. In
Exhibit 1, the summary statistics and Pearson correlations
for the mean adjusted annual variables are quite similar to
Exhibit 1 in Brown (2001).
In the Exhibit 2 regression results, there is a significantly positive relationship between the dependent variable Accuracy and the two independent variables Forecast
Age and Past Accuracy. This is as expected. It should be
noted here that lower levels of Accuracy imply “tighter”
FALL 2006
EXHIBIT 1
Summary Statistics and Correlation
Matrix: All Annual Data, 1986–2002
A. Summary Statistics
Variable *
Mean **
Quartile 1 Median Quartile 3
Accuracy
0
–0.600
–0.170
0.270
Forecast Age
0
–56.5
–20
41.3
Lagged
Accuracy
0
–0.523
–0.108
0.250
B. Pearson Correlations
Accuracy
Forecast
Age
Lagged
Accuracy
Accuracy
Forecast
Age
Lagged
Accuracy
1
0.414 ***
0.093***
1
0.047***
1
Accuracy—The individual analyst forecast error for a given
firm for a given annual period ending date minus the mean
of the forecast errors of all analysts following the company
for that annual period ending date divided by the mean of
the forecast errors of all analysts following the company for
that annual period ending date. Forecast error is defined as
the absolute value of the difference between the I/B/E/S
actual annual earnings and the last forecast made by the analyst for that year.
Forecast Age—Number of calendar days between the analyst’s last annual forecast and the fiscal year end minus the
average “forecast age” of all analysts following the company
that year.
Past Accuracy—Accuracy last year.
(**) Because of the mean adjustment procedure, all means are 0.
(***) Significant at the 1 percent level or better.
Note: N = 331,490
(*)
forecast accuracy as this variable measures the difference
between the individual analyst absolute forecast error and
mean of the absolute forecast errors of all analysts following the firm for a given fiscal year end divided by that
same mean. So, for example, a smaller Forecast Age and
Past Accuracy are associated with smaller (better) current
THE JOURNAL OF INVESTING
21
EXHIBIT 2
EXHIBIT 3
Estimation Results 1986–2002
All Annual and Quarterly Data
Summary Statistics and Correlation
Matrix: CFA-Matched Annual Data 1986–2002
Estimation results for the full sample over the 1986–2002 period
to replicate the past accuracy model of Brown (2001). The
model is: Accuracy = Alpha + Beta 1(Forecast Age) +
Beta2(Lagged Accuracy) + Error Term*
A. Summary Statistics
Data
Period
Forecast
Age
Lagged
Accuracy
Adjusted
R2
Annual
t-Statistic
0.0047
259.99 **
0.0785
46.87 **
0.1766
Quarterly
t-Statistic
0.0036
58.81 **
0.0569
34.11**
0.0125
Mean **
Quartile
1
Median
Quartile
3
Accuracy
0
–0.630
–0.203
0.251
Forecast
Age
0
–55.7
–21.6
38.4
Lagged
Accuracy
0
–0.544
–0.127
0.235
Accuracy Forecast
Age
Lagged
Accuracy
CFA
Code
B. Pearson Correlations
NOTE:
*
For Annual data N=331,490
For Quarterly data N=368,616
Accuracy—The individual analyst forecast error for a given
firm for a given annual period ending date minus the mean of
the forecast errors of all analysts following the company for that
annual period ending date divided by the mean of the forecast
errors of all analysts following the company for that annual
period ending date. Forecast error is defined as the absolute
value of the difference between the I/B/E/S actual annual
earnings and the last forecast made by the analyst for that year.
Forecast Age—Number of calendar days between the analyst’s last annual forecast and the fiscal year end minus the
average “forecast age” of all analysts following the company
that year.
Past Accuracy—Accuracy last year.
(**) Significant at the 1 percent level or better.
Variable *
Accuracy and visa versa for a larger Forecast Age and Past
Accuracy. Both of the independent variable coefficients
and the adjusted R squared are quite similar to the Brown
(2001) results for both annual and quarterly data. It thus
appears that our programming and database construction
are consistent with prior literature.
We did not run our model with the addition of the
CFACODE variable on this data set since not all firm fiscal
year ends have an analyst who is a CFA charterholder. We
examined only those firm fiscal year ends where there are
more than two analysts covering the firm and at least one
of the analysts is a CFA charterholder.5 This is the crux of
the paper and our contribution. The summary statistics
and Pearson correlations for the CFA-matched annual data
file are presented in Exhibit 3. They are quite similar to
those shown in Exhibit 1 for all of the annual data.
The results for the regression models for the CFA22
Accuracy
Forecast Age
Lagged
Accuracy
CFA Code
1
0.438*** 0.103*** –0.034***
1
0.050*** –0.048***
1
–0.009***
1
Accuracy—The individual analyst forecast error for a given
firm for a given annual period ending date minus the mean
of the forecast errors of all analyst’s following the company
for that annual period ending date divided by the mean of
the forecast errors of all analyst’s following the company for
that annual period ending date. Forecast error is defined as
the absolute value of the difference between the I/B/E/S
actual annual earnings and the last forecast made by the analyst for that year.
Forecast Age—Number of calendar days between the analyst’s last annual forecast and the fiscal year end minus the
average “forecast age” of all analysts following the company
that year.
Past Accuracy—Accuracy last year.
CFA Code —1 if the financial analyst is a CFA charterholder and 0 if the analyst is not a CFA charterholder.
(**) Because of the mean adjustment procedure, all means are 0.
(***) Significant at the 1 percent level or better.
Note: N = 159,479
(*)
matched data set are presented in Exhibit 4. We first present the original Brown (2001) model (Panel A) and then
add a CFACODE to the model (Panel B). The results in
Exhibit 4 for the original Brown (2001) past accuracy model
are quantitatively and qualitatively similar to what we have
THE EARNINGS FORECAST ACCURACY OF FINANCIAL ANALYSTS WHO ARE CFA CHARTERHOLDERS
FALL 2006
EXHIBIT 4
Estimation Results; 1986–2002
CFA-Matched Annual and Quarterly Data
A. Model without CFA Code
Estimation results for the CFA-matched sample over the
1986–2002 period to replicate the past accuracy model of Brown
(2001). The model is: Accuracy = Alpha + Beta1(Forecast Age)
+ Beta2(Lagged Accuracy) + Error Term*
Data Period
Forecast
Age
Lagged
Accuracy
Adjusted
R2
Annual
t-Statistic
0.0053
193.56 **
0.0873
36.41 **
0.1989
Quarterly
t-Statistic
0.0048
44.81 **
0.0515
19.17 **
0.0170
B. Model with CFA Code
Estimation results for the CFA-matched sample over the
1986–2002 period. The model is: Accuracy = Alpha +
Beta1(Forecast Age) + Beta2(Lagged Accuracy) + Beta3(CFA
Code) + Error Term*
Data
Period
Forecast
Age
Lagged
Accuracy
CFA
Code
Adjusted
R2
Annual
t-Statistic
0.0053
193.10 **
0.0872
36.38 **
–0.0378
–5.43 **
0.1990
Quarterly
t-Statistic
0.0048
44.83 **
0.0513
19.12 **
–0.0270
–5.06 **
0.0170
Note: For Annual data N = 159,479
For Quarterly data N = 139,057
(*) Accuracy—The individual analyst forecast error for a given
firm for a given annual period ending date minus the mean
of the forecast errors of all analysts following the company for
that annual period ending date divided by the mean of the
forecast errors of all analysts following the company for that
annual period ending date. Forecast error is defined as the
absolute value of the difference between the I/B/E/S actual
annual earnings and the last forecast made by the analyst for
that year.
Forecast Age—Number of calendar days between the analyst’s last annual forecast and the fiscal year end minus the
average “forecast age’’ of all analysts following the company
that year.
Past Accuracy—Accuracy last year.
CFA Code—1 if the financial analyst is a CFA charterholder
and 0 if the analyst is not a CFA charterholder.
(**) Significant at the 1 percent level or better
FALL 2006
presented in Exhibit 2 for both annual and quarterly data.
Both the forecast age and lagged accuracy independent
variables are positive and statistically significant at the one
percent level or better, again showing that lower forecast
age and “tighter” past accuracy are positively related to
“tighter” current forecast accuracy. The adjusted R squares
are 19.89 and 1.70 percent respectively for the annual (quarterly) results. The critical question is what differential impact
CFA charterholders have and the results for this regression
are presented in Panel B of Exhibit 4.6 For both annual and
quarterly data, there is a statistically significant negative
coefficient for the CFACODE dummy variable. This coefficient can be interpreted as follows: Financial analyst’s who
hold the CFA charter provide approximately 3.78 (2.70)
percent “tighter” forecast accuracy than their counterparts
for annual (quarterly) data, respectively. It thus appears that
there is incremental benefit in earnings forecast accuracy
for those analysts holding the CFA charter. These results
should be interpreted with caution, however, since holding
the CFA charter may be a proxy for more fundamental
underlying factors that are driving this relationship.7,8
CONCLUSIONS
Given the extensive rigor of the three-level CFA examination along with the substantial work experience requirements and required adherence to a strict Code of Ethics and
Standards of Professional Conduct, it is hypothesized in this
paper that CFA charterholders will provide more accurate
earnings estimates than other financial analysts not holding
the CFA charter. Our estimation results support this hypothesis. We find that, on average, financial analysts who hold
the CFA charter provide 3.78% (2.70%) “tighter” forecast
accuracy than non CFA charterholders for annual (quarterly) data, respectively, over the 1986 through 2002 time
period. This is important information to the investment
community as investors use financial analyst earnings forecasts in making assessments of correct security valuation.
ENDNOTES
The authors gratefully acknowledge the contribution of
Thomson Financial for providing earnings per share forecast
data, available through the Institutional Brokers Estimate System.
This data has been provided as part of a broad academic program to encourage earnings expectations research.
The authors also thank Lindsay Minnis of the CFA Institute for providing the CFA Charterholders list used in this
study.”
1
AIMR’s name was officially changed to CFA Institute
THE JOURNAL OF INVESTING
23
in June of 2004.
2
Since there were slightly different naming and abbreviation conventions in the two files, we matched-merged based
on the exact last name, exact first name and the first 10 characters of firm name which gave us a final sample of 557 unique
analysts identified. Requiring an exact firm name match provided a sample of 212 matches. There were 8,893(50,507)
observations in the I/B/E/S and CFA files, respectively. This
is a conservative approach to the extent that our sample no
doubt underestimates the true number of financial analysts
holding the CFA charter and this would bias the results against
a significant CFA charterholder effect.
3
Since the AIMR CFA charterholder database also contained the CFA charter date, any analyst estimates in the CFA
grouping made before this date are included in the non CFA
sample.
4
We start with 1986 rather than 1984 to be consistent
with the Brown (2001) starting date.
5
This is consistent with the Brown (2001) approach. We
also followed Brown (2001) in using the last forecast made
before the earnings announcement date without a minimum
number of days requirement and inclusion of team forecasts.
6
We also performed a two sample t-test on both the annual
and quarterly CFA-matched data sets and found significant differences between financial analysts who hold the CFA charter
and financial analysts who do not hold the CFA charter. The
results are as follows:
Mean
(Accuracy)
N
t-stat
Prob
0
0.0127
140183
14.99
0.0001
1
–0.0922
19296
Mean
(Accuracy)
N
t-stat
Prob
0
0.0047
114887
5.31
0.0001
1
–0.0226
24170
Quarterly
CFA Code
There is a straightforward interpretation of these results. For
annual (quarterly) data, analysts holding the CFA charter were
9.22%(2.26%) more accurate than average and analysts not holding
the CFA charter were 1.27%(0.47%) less accurate than average.
7
We don’t have an explanation as to why N is less for
quarterly data than annual data other than there were fewer
financial analysts holding the CFA charter who made next
quarter estimates than who made next fiscal year estimates.
8
We did diagnostic tests for model assumptions and violations do not appear to be driving the results.
24
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THE EARNINGS FORECAST ACCURACY OF FINANCIAL ANALYSTS WHO ARE CFA CHARTERHOLDERS
FALL 2006