JOURNAL OF ECONOMICS AND FINANCE • Volume 31 • Number 1 • Spring 2007
87
THE IMPACT OF REGULATION FAIR DISCLOSURE
ON EARNINGS MANAGEMENT AND ANALYST
FORECAST BIAS
By Seung-Woog (Austin) Kwag and Kenneth Small∗
Abstract
Regulation Fair Disclosure (FD) has changed the information transfer process in the US
securities market. We examine the impact that regulation FD has had on earnings management
and analyst forecast bias. First, we examine the accuracy of financial analysts’ earnings forecasts
in the post-FD period. We find that analysts have become less accurate in forecasting earnings in
the post-FD period and tend to overestimate earnings more relative to the pre-FD period. Second,
we examine the level of earnings management after the passage of regulation FD and we find that
the level of earnings management did not change after the implementation of regulation FD. (JEL
G10, G28)
Introduction
In October of 2000, the Securities Exchange Commission’s (SEC) Regulation Fair Disclosure
(FD) took effect. The SEC adopted regulation FD to combat selective disclosure, which occurs
when corporations release material non-public information to selected stakeholders, such as
financial analysts or institutional investors, and leaves other investors in the dark. It was suggested
that this practice undermined the integrity of and decreased investor confidence in the US
securities markets. The SEC was also concerned with insider trading and misappropriation that
arises in connection with selective disclosure.
To comply with regulation FD, corporations must make intentional and inadvertent
disclosures of material information public by filing the information with the SEC, or by another
method intended to reach the public on a broad and non-exclusionary basis – e.g., a press release.
Although a majority of market participants still expects the regulation’s positive effect – e.g.,
reduction in private information flow by leveling the information playing field, dissenting
concerns – e.g., reduction in the quantity and quality of financial disclosures – do still exist.1 In
sum, the expected impact of regulation FD on the securities industry is mixed.
Proponents of regulation FD argue that it improves analyst forecast accuracy and reduces
information asymmetry between managers and analysts, and between analysts and investors.
Heflin et al. (2003) list three possible factors that may prevent information deterioration in the
post-FD period: 1) Post-FD public information released directly from firms may fully substitute
for private information that analysts were privy to in the pre-FD period; 2) Firms are likely to
reduce the quality and quantity of information in the post-FD period; 3) Whether regulation FD
has an impact on the financial markets is an empirical question in a situation where regulation FD
does not effectively inhibit selective disclosure to financial analysts. Heflin et al. (2003) find
supporting evidence that regulation FD improves informational efficiency of stock prices prior to
earnings announcements and that firms voluntarily, and more frequently, disclose forward-looking
earnings-related information. Therefore, it can be hypothesized that regulation FD partially
alleviates forecast errors.
∗
Seung-Woog (Austin) Kwag, Assistant Professor of Finance, Department of Business Administration, Utah State
University, 3510 Old Main Hill, Logan, UT, austin.kwag@usu.edu; Kenneth Small, Assistant Professor, Department of
Finance, Loyola College in Maryland, Baltimore, MD, ksmall@loyola.edu.
1
Unger, L. S. Special Study: Regulation Fair Disclosure Revisited, U.S. Securities and Exchange Commission, 2001.
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88
The SEC has signaled a strong interest in enforcing regulation FD by bringing enforcement
actions against firms that are not following the standard. Raytheon, Motorola, Siebel Systems, and
Secure Computing were among the first firms to receive enforcement actions. The cases of
Raytheon and Motorola involved efforts to induce analysts to lower their published earnings
estimates to be consistent with the company’s earnings expectations. Siebel Systems violated
regulation FD by disclosing the company’s improved outlook to a selected group of brokerage
investors. Secure Computing failed to disseminate material nonpublic information that it had
disclosed selectively. Disciplinary actions have been taken in all of these cases.
Many academicians and practitioners have begun to study the current and future implications
of regulation FD on the securities industry. Proponents of regulation FD argue that it has leveled
the informational playing field for all investors and fulfilled its promise of fair and equal
disclosure of corporate information to the public. According to a survey, about 90% of top
executives surveyed say that regulation FD should continue and has provided at least the pre-FD
level of fairness to the stakeholders – analysts and investors.2 On the other hand, opponents of
regulation FD claim that it has had a chilling effect on public dissemination of information. A
survey conducted by the Association for Investment Management and Research (AIMR)3 shows
that a majority of investment analysts and portfolio managers feel that many types of information
(e.g., earnings guidance, forward-looking information, facts about internal operations, facts about
costs and pricing, or facts about sales volume) are less available since the enactment of regulation
FD.4
This paper provides empirical evidence of the impact of regulation FD on the investment
community by investigating earnings management and analyst forecast bias (forecast accuracy and
the direction of any bias) before and after the passage of regulation FD. We present evidence that
the accuracy of financial analyst’s earnings estimates have decreased since the introduction of
regulation FD. Not only has the inaccuracy of these estimates increased, but also earnings
estimates tend to be overestimated in the post-FD period. However, our results suggest that
regulation FD has had no significant impact on earnings management. In the following sections,
we discuss related academic research, develop hypotheses, and analyze empirical results.
Research Review
Many studies present evidence that managers smooth their firm’s earnings to meet analysts’
forecasts [Brown (2001); Degeorge et al. (1999)]. Firms could manage earnings to create good
news at the time of an earnings announcement or could manage earnings to meet expectations.
Historically, to avoid earnings disappointment, firms manage earnings expectations through
conference calls and preannouncements of earnings [Tasker (1998); and Soffer et al. (2000)].
Brown and Higgins (2001) present a comparative international study on earnings management.
They find that U.S. firms tend to have twice as many small positive forecast errors (good news)
than small negative (bad news) forecast errors. This ratio is significantly smaller for the non-U.S.
firms. The percentage of extremely negative forecast errors is also smaller for the U.S. firms than
for the non-U.S. firms. Degeorge et al. (1999) document that the distribution of forecast errors is
skewed, with a smaller mass of forecast errors to the left of zero relative to the right. They also
present striking evidence that zero forecast errors are the most frequent in the distribution.
Many studies report that analyst forecast bias is, on average, optimistic. Abarbanell (1991)
studies analyst optimism for the period of 1981 to 1984, and finds that the mean forecast error is
significantly negative (i.e., optimistic) in each year. He also presents evidence that the optimistic
forecasts are more frequent than the pessimism forecasts in each year during the four-year period.
2
Heffes, Ellen M. “Regulation fair disclosure (FD) and beyond,” Financial Executive 18 (2002), 52-54.
AIMR changed its name to the CFA Institute in 2004.
4
CPA Journal. “Research Studies Show Differing Views on Regulation FD,” The CPA Journal 71 (2001), p.10.
3
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89
According to Francis and Philbrick (1993), management relation biases analysts’ earnings forecast
toward optimism. La Porta (1996) forms ten portfolios on the basis of analysts’ earnings growth
forecasts and reports that the forecast errors tend to be optimistic (i.e., actual earnings are
algebraically smaller than analysts’ forecasts) for almost all portfolios. Other extant studies also
document consistent findings. Among others are Fried and Givoly (1982) and O’Brien (1988).
Heflin et al. (2003) find that the informational efficiency of stock prices improves after the
implementation of FD and there is no significant change in analyst forecast accuracy and
dispersion. They report that absolute cumulative abnormal returns prior to the quarterly earnings
announcement are significantly smaller for the post-FD period than for the pre-FD, suggesting
earnings information is conveyed to investors more efficiently after FD. Controlling for non-FD
determinants of analyst forecast accuracy and dispersion, they also find that FD does not impair
forecast accuracy and dispersion. In contrast to Heflin et al.’s findings, Irani and Karamanou
(2003) provide evidence that FD has a negative impact on the informativeness of the firm’s
information environment and information asymmetry. They document that in both the univariate
and multivariate frameworks, the number of analysts following a firm significantly decreases and
that analyst forecast dispersion significantly increases after FD.
In this work we attempt to reconcile differences between the results presented in previous
research. We examine the direction and magnitude of financial analysts’ earnings forecasts
around the passage of regulation FD as well as the level of earnings management around the
passage of regulation-FD.
Hypothesis Development
Regulation FD prohibits selective disclosure of material non-public information. The passage
of regulation FD could have decreased firm specific information signals available to analysts (i.e.,
pre-announcement guidance), and the bias in forecast errors could have increased after the passage
of the act. However, because of possible strengthening of private information channels, forecast
errors may have remained unchanged in the post-FD period. This leads to the first hypothesis:
H1A: Analyst forecast errors have increased in the post-FD period relative to the pre-FD
period.
Given a possible reduction in the accuracy of the analysts forecast predictions, a firm’s
propensity to manage earnings to meet the analyst’s “target” in the post-FD period may have also
decreased. If analyst accuracy decreases in the post-FD period, the “costs” associated with missing
the consensus estimates should also decrease. In addition, if forecasts are less accurate, the ability
of firm’s to meet the more inaccurate forecasts may be reduced. This leads to the second
hypothesis:
H2A: Earnings management has increased in the post-FD period relative to the pre-FD
period.
Note that we state both hypotheses in the form of the alternatives, and in the next section we
discuss the data and research design that we employ to test the two central hypotheses.
Data and Research Design
We collect analyst consensus forecast data compiled by the I/B/E/S, accounting information
from COMPUSTAT, and monthly and daily price data from the Center for Research on Security
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90
Prices (CRSP) database. We are interested in the pre-FD period, which includes 1999 and 2000,
and the post-FD period, which includes 2001 and 20025. The initial /B/E/S sample contains 1268
firm-year observations. The sample size is reduced to 1099 firm-year observations after merging
the databases together.
To gauge the level of earnings management, we employ an extension of the cross-sectional
Jones model used in Teoh, Welch, and Wong (TWW, 1998). Following TWW, we use
discretionary current accruals (DCA) as a proxy for earnings management because managers have
greater flexibility and control over current accruals versus long-term accruals. The following
specifies the TWW model:
CAit = ∆[ AR (2) + INV (3) + OCA(68)] − ∆[ AP (70) + TP (71) + OCL(72)]
(1)
CAit
∆Sales it
1
= α0 (
) + α1 (
) + ε it
TAi ,t −1
TAi ,t −1
TAi ,t −1
(2)
NDCAit = αˆ 0 (
DCAit =
∆Sales it − ∆TRit
1
) + αˆ 1 (
)
TAi ,t −1
TAi ,t −1
(3)
CAit
− NDCAit ,
TAi ,t −1
(4)
CAit is the current accruals for firm i in year t; AR is accounts receivables; INV is
inventory; OCA is other current assets; AP is accounts payable; TP is tax payable; OCL is
other current liabilities; ∆Sales it is the change in sales for firm i from year t-1 to year t; TAt ,t −1
where
is the total assets for firm i in year t-1;
αˆ 0 , αˆ 1
are the OLS estimates of
nondiscretionary current accruals for firm i in year t; and
accruals for firm i in year t. The negative
management, while the positive
α 0 , α 1 ; NDCAit
is the
DCAit is the discretionary current
DCAit indicates the income-decreasing earnings
DCAit suggests the income-increasing earnings management. To
quantify the earnings management regardless of its impact on earnings, we use the absolute value
of DCAit ( | DCA | it ) as the operational measure of earnings management. A higher |DCA|
indicates a higher degree of earnings management. We form five earnings management portfolios
from the least managed (or unmanaged; DCA_P1) to the most managed (DCA_P5).
Annual forecast errors are computed for pre-FD years (1999, 2000) and post-FD years (2001,
2002) as:
FE it =
5
Ait − Fit
Pi ,t −1
The use of the differing pre-FD periods does significantly impact the results of the research.
(5)
JOURNAL OF ECONOMICS AND FINANCE • Volume 31 • Number 1 • Spring 2007
Where
91
FE it = the analyst forecast error at the earnings announcement of year t for firm i deflated
by the stock price one month prior to the earnings announcement;
year t for firm i;
Ait = the reported earnings of
Fit = the analyst consensus forecast of year t for firm i; and Pi ,t −1
= the
stock price one month prior to year t for firm i.
Absolute forecast error (absFE) indicates the absolute value of FE and is used to measure
analyst forecast accuracy. We form five behavioral portfolios from the most overestimated
(FE_P1) to the least overestimated (FE_P5), on the basis of FE. We also create five accuracy
portfolios from the most accurate (absFE_P1) to the least accurate (absFE_P5) on the basis of
absFE.
Empirical Results
Univariate Analysis
To evaluate the impact that FD has had on earnings management and analyst forecast errors,
we first examine the level of discretionary accruals (|DCA|), the level of forecast errors, and the
absolute value of forecast errors before and after FD. This analysis is presented in Figure 1.
Figure 1: Means of |DCA|, FE, and absFE
0.08
|DCA| / FE/ absFE
0.06
0.04
0.02
Pre-FD
Post-FD
0
-0.02
-0.04
-0.06
|DCA|
FE
absFE
Pre-FD
0.0672
-0.0187
0.0295
Post-FD
0.0655
-0.0354
0.0437
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JOURNAL OF ECONOMICS AND FINANCE • Volume 31 • Number 1 • Spring 2007
Figure 2A: |DCA| Portfolio Means of Forecast Error (FE)
0.0000
-0.0100
-0.0200
FE
FE Pre-FD
FE Post-FD
-0.0300
-0.0400
-0.0500
-0.0600
FE Pre-FD
FE Post-FD
DCA_P1
DCA_P5
-0.0175
-0.0314
-0.0216
-0.0567
|DCA| Portfolio
Figure 2B: |DCA| Portfolio Means of Absolute Forecast Error (absFE)
0.0700
0.0600
0.0500
absFE
0.0400
absFE Pre-FD
absFE Post-FD
0.0300
0.0200
0.0100
0.0000
absFE Pre-FD
absFE Post-FD
DCA_P1
DCA_P5
0.0257
0.0354
0.0332
0.0648
|DCA| Portfolio
As can be seen, the |DCA|s slightly decrease from the pre-FD to the post-FD period. The
average |DCA| in the pre-FD period is 0.0672 and the average in the post-period is 0.0655. Both
JOURNAL OF ECONOMICS AND FINANCE • Volume 31 • Number 1 • Spring 2007
93
parametric (Fisher's least-significant-difference; LSD) and non-parametric (Scheffé's) tests show
that the post-FD |DCA| is statistically the same as the pre-FD |DCA|. The results of the univariate
analysis indicate that the level of earning management did not change after the FD took effect,
which rejects our original conjecture. Although the information playing field has been leveled,
firm’s propensity to engage in earnings management has not changed.
The level (FE) and absolute value (absFE) of forecast errors before and after the introduction
of FD indicate that the magnitude of forecast errors has decreased from -0.0187 to -0.0354 since
the introduction of FD. The average absolute value of forecast errors has increased from 0.0295 to
0.0437 around the introduction of FD. LSD and Scheffé's tests exhibit significant mean differences
between pre- and post-FD FE and absFE. These results together indicate that financial analysts
have become less accurate in the post FD period. Further, they provide evidence that the
information flow from firm to analysts in the pre-earnings announcement period has been
effectively reduced in the post-FD period. The benefit of a level information playing field has been
balanced by the costs associated with decreased analyst accuracy.
Next, we examine the level and absolute value of forecast errors before and after the
introduction of FD with respect to the level of earnings management (|DCA|). Figure 2.A contains
the univariate specification of pre and post values of FE classified into two extreme portfolios
based on the level of |DCA|, and Figure 2.B contains the specification of pre and post absFE
classified into the same two extreme portfolios. |DCA| is ranked from its lowest level, DCA_P1,
which includes firms with the lowest level of earnings management, to its largest level, DCA_P5,
which includes firms with the largest level of managed earnings.
The results in Figure 2.A indicate that for both the least-managed (DCA_P1) and mostmanaged (DCA_P5) portfolios, the magnitude of forecast errors has decreased since the
introduction of FD from -0.0175 to -0.0314 and from -0.0216 to -0.0567, respectively. But, notice
that the decrease in forecast error is greater for DCA_P5 than for DCA_P1. LSD and Scheffé's
tests present significant mean differences between pre-FD and post-FD forecast error for both
portfolios. The average absolute value of forecast errors has increased around the introduction of
FD for both |DCA| portfolios – from 0.0257 to 0.0354 and from 0.0332 to 0.0648, respectively.
These results confirm the evidence provided in Figure 1 that analyst accuracy has decreased and
information flow has decreased from management to analysts in the post-FD period. There is also
evidence that higher earnings management results in lower accuracy. In particular, the mean
(absolute) forecast error is smaller (greater) for DCA_P5 than for DCA_P1 regardless of the
introduction of FD.
Categorical Data Analysis: 2 × 5 Contingency Table
Using a categorical data analysis, we examine the impact of FD on earnings management,
forecast accuracy, and the direction of forecast errors. The treatment variable (FD) has two levels
before and after the implementation of FD, while the response variables have five levels which
range from P1 to P5. P1 through P5 represents five portfolios of the response variable (i.e.,
earnings management, forecast accuracy, and the direction of forecast errors) ranked from highest
level (P5) to its lowest level (P1). As a result, the relationships between the treatment variable and
the response variables are summarized in three 2 × 5 contingency tables (Table 1). Our interest
lies in determining whether there is an association between the row variable and the column
variable.
Panel A of Table 1 contains the contingency analysis of |DCA|. The Mantel-Haenszel ChiSquare test is used to examine changes in DCA around the introduction of FD. The value of the
statistic is 0.7654 (p-value = 0.3817), which indicates no significant change in DCA at the
conventional significance levels post-FD relative to pre-FD. This is consistent with the results in
Figure 1. Panel B of Table 1 contains the contingency analysis of forecast errors (FE). The
Mantel-Haenszel Chi-Square test statistic value of 9.7478 (p-value = .0018) indicates that
financial analysts’ forecast errors have significantly decreased over the pre-FD to post-FD period.
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JOURNAL OF ECONOMICS AND FINANCE • Volume 31 • Number 1 • Spring 2007
Table 1: 2 × 5 Contingency Table Analysis: Treatment = Regulation Fair Disclosure (FD)
Row Levels
(Treatment)
Pre-FD
Post-FD
Total
Column (Response) Levels
P2
P3
P4
n13
n14
n12
%n12
%n13
%n14
n22
n23
n24
%n22
%n23
%n24
N+2
N+3
N+4
P1
n11
%n11
n21
%n21
N+1
Total
P5
n15
%n15
n25
%n25
N+5
N1+
N2+
N
Panel A. 2 × 5 Contingency Table of |DCA|
Frequency
Row Percentage
Pre-FD
Post-FD
Total
DCA_P1
DCA_P2
DCA_P3
DCA_P4
DCA_P5
Total
112
18.76
90
17.93
202
102
17.09
106
21.12
208
135
22.61
112
22.31
247
120
20.10
97
19.32
217
128
21.44
97
19.32
225
1099
Total
597
502
Mantel-Haenszel Chi-Square ( χ MH ) = 0.7654 (p-value=0.3817)
2
Panel B. 2 × 5 Contingency Table of FE
Frequency
Row Percentage
Pre-FD
Post-FD
Total
FE_P1
FE_P2
FE_P3
FE_P4
FE_P5
102
17.09
117
23.31
219
120
20.10
100
19.92
220
115
19.26
105
20.92
220
122
20.44
98
19.52
220
138
23.12
82
16.33
220
1099
Total
Mantel-Haenszel Chi-Square
χ
2
MH
597
502
=9.7478 (p-value=0.0018)
Panel C. 2 × 5 Contingency Table of absFE
Frequency
Row Percentage
Pre-FD
Post-FD
Total
|FE|_P1
|FE|_P2
|FE|_P3
|FE|_P4
|FE|_P5
129
21.61
90
17.93
219
129
21.61
91
18.13
220
132
22.11
88
17.53
220
112
18.76
108
21.51
220
95
15.91
125
24.90
220
Mantel-Haenszel Chi-Square
χ
2
MH
597
502
1099
=10.4487 (p-value=0.0012)
Notes: This table summarizes the association between the treatment (row) variable and the response variable (column)
variable. The treatment variable is the regulation fair disclosure (FD), while the response variable is one of three variables
of interest – earnings management, analyst optimism, and forecast accuracy. For earnings management, P1 (P5) indicates
the least (most) managed portfolio – denoted DCA_P1 (DCA_P5) in Panel A. For analyst overestimation, P1 (denoted
FE_P1) indicates the most overestimated (or least pessimistic) portfolio, while P5 (denoted FE_P5) represents the least
overestimated (or most pessimistic) portfolio. For forecast accuracy, P1 (P5) denotes the most (least) accurate portfolio –
denoted |FE|_P1 (|FE|_P5). nij is the number of observations in the cell i, j where i is the ith row and j is the jth column; Ni+
is the number of observations in the ith row; N+j is the number of observations in the jth column; % n1j (% n2j) is n1j (n2j)
divided by N1+ (N2+); and N is the total number of observations in the sample. The null hypothesis is that the treatment has
no impact on the response variable – i.e., there is no difference between the response variable before and after FD.
JOURNAL OF ECONOMICS AND FINANCE • Volume 31 • Number 1 • Spring 2007
95
Panel C of Table 1 contains the contingency analysis of absolute value of financial analysts’
forecast errors (absFE). Like with the previous analysis of FE the Mantel-Haenszel Chi-Square
test statistic value of 10.4487 (p-value = .0012) indicates that absFE has significantly increased
over the pre-FD to post-FD period. This analysis supports the results from the graphical analysis
and our earlier univariate results. In the next section, we extend the analysis to a multivariate
framework to control of the influence that book-to-market, firm size, analyst coverage, forecast
dispersion might have on the pre-post-FD levels of analyst optimism and forecast accuracy.
However, we note that the univariate analysis does not control for other factors that may also have
influenced the incidence of earnings management around the FD introduction date. This issue is
addressed in a multivariate specification that is discussed in the next section.
Multivariate Empirical Models
To further examine the relationships between forecast accuracy and earnings management
around the introduction of FD, we examine two regression models. In each regression
specification we control for firm size (logarithm of market value of equity; logSize), book-tomarket ratio (BtoM), analyst coverage (NUMEST), and forecast dispersion (STD):6
FE it = β 0 + β 1 FDit + β 2 | DCA | it + β 3 FDit * | DCA | it + β 4 log Sizeit + β 5 BtoM it
+ β 6 NUMESTit + β 7 STDit + u it
(6)
absFE it = β 0′ + β 1′FDit + β 2′ | DCA |it + β 3′ FDit * | DCA | it + β 4′ log Sizeit
+ β 5′ BtoM it + β 6′ NUMESTit + β 7′ STDit + vit
(7)
| DCA | it = the earnings management score [i.e., the absolute value of the discretionary
current accrual] for firm i in year t; FDit is a dummy variable for firm i that takes the value of 0
in the pre-FD period and 1 in the post-FD period and FE it is the analyst forecast error at the
where
earnings announcement of year t for firm i deflated by the stock price one month prior to year t;
absFE it is the absolute value of FE it ; logSizeit is the logarithm of the market value of equity
for firm i at the earnings announcement of year t;
the market value of equity for firm i in year t;
for firm i in year t and
BtoM it is the book value of equity divided by
NUMESTit is the number of analysts’ forecasts
STDit is the standard deviation among individual analyst’s forecasts of
earnings.
The results of the multiple regressions are presented in Table 2. Panel A contains the results
from the first regression specification [i.e., Equation (6)]. The binary variable ( β1 ) in this
specification is interpreted as the mean change in forecast errors from the pre-FD period to the
post-FD period, while controlling for |DCA|, logSize, BtoM, NUMEST, and STD. The value of
β1 is negative and significant, indicating that the level of analyst overestimation of earnings
increased from the pre-period to the post-period. The effects of firm size, analyst coverage, and
forecast dispersion on analyst optimism are all significant at the 1% level. Evidence suggests that
analyst overestimation decreases for larger and less-covered firms with lower dispersion in
6
The control variables are used or suggested for use in Gleason and Lee (2003) and Fama and French (1995).
96
JOURNAL OF ECONOMICS AND FINANCE • Volume 31 • Number 1 • Spring 2007
earnings forecasts. Notice that both
β2
and
β3
are not statistically significant, implying that
earnings management is not a significant factor affecting the level of analyst overestimation.
Table 2: Regression Results
Panel A. Regression of FE on FD, |DCA|, logSize, BtoM, NUMEST, and STD
Coefficient
β0
β1
β2
β3
β4
β5
β6
β7
Estimate
Pr > |t|
-0.1382
<.0001a
-0.0109
0.0051a
-0.0014
0.9713
-0.0001
0.9975
0.0135
<.0001a
-0.0002
0.1348
-0.0024
0.0047a
-0.0599
<.0001a
Adjusted R-square
0.1462
Number of observations
1,099
Panel B. Regression of absFE on FD, |DCA|, logSize, BtoM, NUMEST, and STD
Coefficient
β 0′
β1′
β 2′
β 3′
β 4′
β 5′
β 6′
β 7′
Estimate
Pr > |t|
0.1763
<.0001a
0.0094
0.0132b
0.0025
0.9487
0.0713
0.1480
-0.0151
<.0001a
0.0004
0.3750
-0.0026
0.0014a
0.0613
<.0001a
Adjusted R-square
0.1783
Number of observations
1,099
Notes: This table presents the coefficient estimates of the regression equations (6) and (7) and their p-values. Significance
levels denoted as follows: a (1%), b (5%), and c (10%).
The second specification [i.e., Equation (7)] captures the change in the absolute value of
forecast errors, while controlling for |DCA|, logSize, BtoM, NUMEST, and STD. The coefficient
estimate on β 1′ is 0.0094 and significant at the 5% level. This positive and significant coefficient
estimate indicates that analyst forecast accuracy deteriorated after the introduction of FD. This
JOURNAL OF ECONOMICS AND FINANCE • Volume 31 • Number 1 • Spring 2007
97
supports our conjecture that the reduction in information flow from firm to financial analyst has
impaired their ability to forecast earnings. Again size, analyst coverage, and forecast dispersion
effects are significant at the 1% level. Firms with large size, high analyst coverage, and low
forecast dispersion tend to receive more accurate forecasts than firms with small size, low analyst
coverage, and high forecast dispersion.
As in Panel A, notice that both β 2′ and β 3′ are not significant. This evidence similarly
suggests that earnings management is not a significant factor affecting the level of analyst forecast
accuracy. All told, the multiple regression analysis confirms the results of the univariate analysis.
Specifically, we reject the hypothesis that managers have a reduced incentive to manage earnings
after the enactment of FD and the hypothesis that analyst accuracy has not changed in the post-FD
period relative to the pre-FD period. Instead, we find that earnings management has not changed
and that analysts have become less accurate since the introduction of FD.
Conclusions
Has regulation FD impaired the ability of financial analysts to make accurate earnings
forecasts and reduced the need for earning management? In our univariate specification, we
present evidence that analyst forecast accuracy has deteriorated in the post-FD period, while the
magnitude of earnings management has not been significantly reduced. Specifically, we find that
analysts tend, on average, to overestimate earnings more in the post-FD period. After controlling
for potential confounding factors, our multiple regression specifications also confirm the results of
the univariate analysis.
In particular, our results highlight the impact that regulation FD has had on the ability of
financial analysts to make accurate forecasts. Our findings open the door for future research in this
area. For example, has the market’s reaction to missed earnings changed in the post-FD period? If
financial analysts’ ability to correctly forecast earnings have deteriorated in the post-FD period,
has this created a phantom hit-or-miss opportunity for firms? In addition, what is the best
information dissemination method for firms to use in the post-FD period? In particular, how has
the market reacted to firms that have chosen not to provide earnings guidance? Each of these
questions should be addressed to determine the impact that regulation-FD has had on the
information dissemination process in the U.S.
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