Analyst Coverage and Earnings Management: Evidence from China
1
Zhao-hua Lan, 2Su-sheng Wang, 3Tao Yu, 4Zhen Yu
Shenzhen Graduate School,Harbin Institute of Technology,Shenzhen, China
(1rambo_hua@yahoo.com.cn,2wangsusheng@gmail.com,3332032515@qq.com,4516184207@qq.com)
Abstract - Besides information function, security analysts may play external supervision role in corporate governance which has been verified in developed market.
Whether it is popular in China is unresolved. Nowadays,
security analysts have become more and more important as
intermediary in Chinese flourishing capital market. This
paper study the relationship of analyst coverage and earnings management, trying to find direct evidence that security analysts’ coverage have restraint effect on managers’
earnings management decisions. The empirical result supports the opinion, and this paper contributes to the corporation governance theory and application.
Keywords - security analyst, analyst coverage, earnings
management, corporate governance
I. INTRODUCTION
Jensen and Meckling (1976)[1] first point out that the
securities analysts’ activity can influence corporate
governance . Moyer, Chatfield and Sisneros (1989) [2]
make an empirical test about it based on Jensen and
Meckling (1976). The result indicates that the higher
degree of separation of ownership and control, the less
demand for analysts follow. In the other hand, analysts’
coverage increase the firm’s value (Tobin’s Q) (Chung
and Jo ,1996)[3].
Besides these indirect evidence, Dyck, Morse and
Zingales (2008) and Yu (2008) found direct evidence
about analysts’ influence on corporate governance [4][5].
Analysts play an important role in discovering company's
financial fraud, especially like Compaq, Motorola (Dyck
, Morse and Zingales, 2008)[4]. By studying the
relationship between analyst coverage and earnings
management, Yu (2008) find that firms with more
coverage manage their earnings less in developed capital
market [5]. But whether it is true or not in China
(flourishing capital market) is unresolved.
II. DATA AND METHODOLOGY
2.1. Sample Selection
Firstly, all A-share public firms which listed before 2007
in Shanghai and Shenzhen security exchange are
selected. Analyst coverage information come from
CMSAR database and accounting variables come from
Wind. Second, firms with missing values for sales, total
assets, net income before extraordinary items, cash flow
from operations, market value less than 10 million RMB
are deleted, Also, firms from the financial industry are
discarded by convention. Due to carrying out of new
accounting principle in 1/1/2007, the sample starts from
1/1/2007, to 31/12/2010. The sample consists of 953
firms.
To account for the effect of the type of actual
controller, which is specific and can affect both analyst
follow and earnings management in China, This paper
control it (a dummy variable Control) in all tests. The
controller information comes from Wind. The value of
Control equals 1 when the firm is controlled by state or
government, others equals 0. Other variables, such as
market value, return on net asset (ROE), the growth rate
of asset, institution ownership, also affect both analysts’
follow and earnings management. Furthermore, this
paper uses the average value of tradable shares at the
beginning and the end of the year. For each firm, the
average institutional holdings of four quarters are
adopted.
Table1 shows the summary statistics at the firm level.
The mean of Control is 0.68, indicating that more than
half sample is state-owned which is in line with reality in
China. The mean logarithm of market value is 8.08,
return on net asset is 9.51, growth rate of assets is 0.23
and institutional ownership is 31.11%, demonstrating the
high growth of capital market in China.
Table 1. Summary of Variables
This table presents the summary statistics for the sample. The sample
consists of all A-shares market in both Shanghai and Shenzhen security
exchange from 2007 to 2010. Control is a dummy variable, which indicates
the actual controller of a firm. LnMV is the logarithm of market value.
Market value equals price multiply by the tradable stock. ROE is the return
rate of net assets, calculated by return scaled by net assets. GrowAsset
represents the growth rate of assets yearly. Institution is the value of
institutional ownership, which is defined by the average institution holdings
of four quarters.
Number of
Standard
Variables
Mean
Median
Observation
Deviation
Control
3812
0.68
1.00
0.47
LnMV
3812
8.08
7.97
0.98
ROE
3812
9.51
8.36
11.43
GrowAsset
3812
0.23
0.13
0.71
Institution
3812
31.11
28.57
20.49
Table 2 shows the distribution of analysts’ coverage
among different industries and years. As shown in the
table, the differences of average coverage among
different industries are apparent. Media and cultural
industries have the highest average coverage in all 4
years.
Sweeney, 1995) [7][8]. In the first step, by running the
following cross-sectional OLS regression of total accruals (TAs) on changes in sales and fixed assets (FA) within industries, we can estimate coefficients , , and .
Table 2. The Distribution of Analysts’ Coverage
In-code presents different industries. The data of analyst’s coverage
comes from CMSAR database.
Num2007
2008
2009
2010
ber of
Average
Average
Average
Average
In-code
firms
coverage
coverage
coverage
coverage
A
17
5.00
11.24
12.06
17.94
B
25
6.52
25.56
21.20
33.28
C-C0
41
6.37
19.44
20.10
26.22
C-C1
37
2.76
6.43
6.68
7.59
C-C2
4
1.50
2.00
7.75
8.75
C-C3
18
6.06
17.39
13.11
11.44
C-C4
92
3.55
9.91
9.88
10.52
C-C5
37
4.19
9.68
8.97
12.51
C-C6
84
7.08
20.06
16.43
25.73
C-C7
144
6.38
17.78
16.83
19.94
C-C8
70
5.91
15.37
14.56
19.13
C-C99
9
6.56
13.67
11.11
13.11
D
57
3.58
8.98
10.82
13.35
E
24
2.83
8.42
8.96
14.71
F
51
7.20
18.47
17.51
22.20
G
47
7.04
17.96
18.43
19.13
H
74
3.99
11.00
12.80
16.07
J
58
4.05
12.38
11.98
14.72
K
22
7.73
22.27
18.68
25.55
L
6
12.00
30.83
36.17
39.83
M
36
1.64
4.64
4.17
5.83
Min
4
1.50
2.00
4.17
5.83
Max
144
12.00
30.83
36.17
39.83
2.2. Estimation of Earnings Management
DA (Discretionary Accruals) can be used to present
earnings management. Besides cash, other major part,
accounting adjustments sometimes are called accruals.
Manager always determine the signs and level of accruals
according to their experience and estimation, they
manipulate accruals easily. But earnings manipulation is
just only one part of accruals. For some specific goal, it
is needful and suitable to adjust some accrual on a
regular basis. Thus, nondiscretionary accruals (NDAs)
together with discretionary accruals (DAs) make up total
accruals (TAs). A variety of papers use discretionary
accruals (DAs) as the proxy for earnings management,
such as Bergstresser and Philippon(2006) [6].
There are several models to calculate DAs. According to our sample firms and scope, the modified version
of the Jones model would be a good model to estimate
the firms’ DAs (Jones, 1991; Dechow, Sloan, and
(1)
Where i indexes firms, t indexes time,
equals
net income minus cash flow from operations;
is
changes in sales revenues; and
, fixed asset;
, legged total asset. All variables used here
are scaled by legged total assets. We estimate the cross
sectional models separately by each industry and get the
value of , , and .
Then we use the estimated ; ; and to calculate nondiscretionary accruals.
(2)
represents the change in receivables. Thus,
discretionary accruals can be derived as
(3)
Because all the variables are scaled by total assets at
the beginning of each period, the magnitude of a firm’s
discretionary accruals is demonstrated as a percentage of
the assets of the firm.
The magnitude of a firm’s discretionary accruals is
indicated as a percentage of the lagged assets of the firm.
Positive DAs suggests income-increasing manipulations,
while negative DAs indicates income decreasing
manipulations. Managers have incentives to manage
earnings not only upward, but also downward. In good
years, they could want to hide some earnings for future
reporting use, while, in bad years, they could take a bath
(e.g., overstate bad assets or take a large restructuring
charge) to make future earnings targets easier to meet.
Because we are interested in manipulations in both
directions, we use the absolute value of discretionary
accruals, that is also used in several recent studies (e.g.,
Warfield, Wild, and Wild, 1995; Gu, 1999; Klein, 2002;
Bergstresser and Philippon, 2006)[9][10][11][6].
In addition, we split the sample according to the sign
of discretionary accruals for all the tests. Doing so allows
us to check whether the patterns of effects on signed
discretionary accruals are consistent with each other.
Table 3 demonstrates the absolute value of
discretionary accruals, which means that the more analyst
coverage, the less discretionary accruals.
Table 3. The Distribution of Discretionary Accruals
AbsDA is the absolute value of discretionary accruals which is computed
through the OLS regression model above.
Analyst Coverage
Number of Firms
AbsDA
0
812
677.20
1-5
1156
744.27
6-10
474
709.94
10-15
298
700.10
GrowAsset
15-20
263
697.62
>20
809
615.77
0.0604
0.0215
2.8047
0.0051
Institution
0.0149
0.0010
15.6569
0.0000
Control
-0.0628
0.0341
-1.8433
0.0654
R-squared
0.5192
Mean dependent var
1.8053
III. THE EFFECT OF ANALYST COVERAGE ON
EARNINGS MANAGEMENT
Adjusted R-squared
0.5159
S.D. dependent var
1.3408
S.E. of regression
0.9328
Akaike info criterion
2.7059
In the first part of this section, We use discretionary accruals as a proxy for earnings management and start the
analysis with OLS regressions.
Analyst coverage is associated with many factors,
such as firm size, past performance, growth, external
financing activities, and volatility of business (Bhushan,
1989; Dechow and Dichev, 2002; Kasznik,
1999)[12][13][14].Some of those factors could also affect firms’ earnings management. To control for those
factors, we first run the following regression:
Sum squared resid
3293.7170
Schwarz criterion
2.7501
Log likelihood
-5130.4560
Hannan-Quinn criter.
2.7216
F-statistic
157.2160
Durbin-Watson stat
1.7824
(4)
Where analyst coverage (ACi) is the number of analysts who made forecasts about firm’s earnings in any
given year. αt are year fixed effects. γin is the industry
fixed effect. ei is an error term. lnMVi is the logarithm of
market value. ROEi represents the profit rate of net asset.
GrowAsseti are the growth rate of assets. Institutioni represents institutional ownership and Controli is the type of
actual controller of firms.
Table 4 show the regression result of analyst coverage on independent variables. It can be indicated that
LnMV, Institution, ROE is significant at 0.1% level,
means that the larger market value, the more analyst coverage, and the more institutional holding, the more coverage, the better performance, the more coverage, which
is consistent with institution and available literature. Also,
year and industry effects, GrowAsset and Control is significant. The adjusted R-squared is 0.5159, and F-statistic
is 157.2160 with Prob 0.0000, other statistics is acceptable too, which means the regression model is suitable.
Table 4. The Result of Analyst Coverage on Independent variables
Analyst coverage (AC) is the number of analysts who made forecasts
about firm’s earnings in any given year. LnMV is the logarithm of market
value. ROE represents the profit rate of net asset. GrowAsset is the growth
rate of assets. Institution represents institutional ownership and Control is
the type of actual controller of firms.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-4.3141
0.1804
-23.9184
0.0000
D1
-0.1925
0.0471
-4.0857
0.0000
D2
0.2406
0.0465
5.1798
0.0000
D3
0.2684
0.0436
6.1570
0.0000
YES
Prob(F-statistic)
0.0000
We label the residuals from the above regression as
“residual coverage” and use it as the main proxy for analyst coverage. It can be considered as a component of
analyst coverage that is uncorrelated with market value,
profit rate of asset, growth rate of assets, external financing activities, or volatility of business.
Then we estimate the effect of analyst coverage on
earnings management with the following OLS regression:
(5)
Where
is year fixed effects,
is industry fixed
effects,
represents firm ’s analyst coverage
residual in year t.
is the type of actual control of
firm i.
,
,
represent the logarithm of market value, the growth rate of assets and the
return rate on net assets.
From the table 5, the AcResidual is significant at 5%
level, while F-statistic is 26.9882 significantly, and other
statistics are acceptable, which infer that security analysts’ coverage have restraint effect on managers’ earnings management decisions.
Table 5. The Result of Earnings Management on Analyst Coverage
is year fixed effects, is industry fixed effects,
represents firm ’s analyst coverage residual in year t.
is the type of
actual control of firm i.
,
,
represent the logarithm of market value, the growth rate of assets and the return rate on net
assets.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
718.8726
156.0222
4.6075
0.0000
D1
28.6345
40.7607
0.7025
0.4824
D2
22.4723
40.1838
0.5592
0.5760
D3
88.8899
37.7125
2.3570
0.0185
AcResidual
-35.9370
14.0603
-2.5559
0.0106
LnMV
-8.4303
17.2809
-0.4878
0.6257
YES
ROE
2.7999
1.2862
2.1769
0.0296
GrowAsset
334.1501
18.6237
17.9422
0.0000
LnMV
0.5776
0.0200
28.9118
0.0000
Institution
-0.7027
0.8247
-0.8521
0.3942
ROE
0.0244
0.0015
16.4343
0.0000
Control
-59.7162
29.4820
-2.0255
0.0429
R-squared
0.1615
Mean dependent var
691.7734
Adjusted Rsquared
0.1555
S.D. dependent var
878.0796
S.E. of
regression
806.9304
Akaike info criterion
16.2317
Sum squared
resid
2.46E+09
Schwarz criterion
16.2776
Log likelihood
-30909
Hannan-Quinn criter.
16.2480
F-statistic
26.9882
Durbin-Watson stat
2.0097
Prob(F-statistic)
0.0000
IV. CONCLUSION
Nowadays, security analysts have become more and
more important as intermediary in Chinese flourishing
capital market. Furthermore, security analysts play external supervision role in corporate governance, indicating
that more analyst coverage, less earnings coverage. The
result is consistent with developed market. This paper
contributes to the corporation governance theory and
application. In future, other corporation governance function of analyst coverage, such as reducing tunneling, decreasing information asymmetry, et al. can be explored.
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