Analyst Coverage and Earnings Management: Evidence from China

1Zhao-hua Lan, 2Su-sheng Wang, 3Tao Yu, 4Zhen YuShenzhen 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 se-curities analysts’ activity can influence corporate gover-nance . 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 sep-aration of ownership and control, the less demand for an-alysts follow. In the other hand, analysts’ coverage in-crease 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 relation-ship between analyst coverage and earnings manage-ment, Yu (2008) find that firms with more coverage man-age 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 se-lected. Analyst coverage information come from CM-SAR 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 dis-carded by convention. Due to carrying out of new ac-counting 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 con-troller, which is specific and can affect both analyst fol-low and earnings management in China, This paper con-trol it (a dummy variable Control) in all tests. The con-troller information comes from Wind. The value of Con-trol equals 1 when the firm is controlled by state or gov-ernment, 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 institu-tional 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, re-turn 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 VariablesThis table presents the summary statistics for the sample. The sample

consists of all A-shares market in both Shanghai and Shenzhen security ex-change 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 own-ership, which is defined by the average institution holdings of four quarters.

VariablesNumber of

Obser-vation

Mean Median Standard Deviation

Control 3812 0.68 1.00 0.47LnMV 3812 8.08 7.97 0.98ROE 3812 9.51 8.36 11.43

GrowAsset 3812 0.23 0.13 0.71Institution 3812 31.11 28.57 20.49

Table 2 shows the distribution of analysts’ coverage among different industries and years. As shown in the ta-ble, the differences of average coverage among different industries are apparent. Media and cultural industries have the highest average coverage in all 4 years.

Table 2. The Distribution of Analysts’ CoverageIn-code presents different industries. The data of analyst’s coverage

comes from CMSAR database.

In-code

Num-ber of firms

2007Averagecoverage

2008Averagecoverage

2009Averagecoverage

2010Averagecoverage

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 earn-ings management. Besides cash, other major part, ac-counting adjustments sometimes are called accruals. Manager always determine the signs and level of accruals according to their experience and estimation, they manip-ulate 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. Accord-ing 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 Sweeney, 1995) [7][8]. In the first step, by running the following cross-sectional OLS regression of total accru-als (TAs) on changes in sales and fixed assets (FA)

within industries, we can estimate coefficients , ,

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 sec-tional models separately by each industry and get the value of , , and .

Then we use the estimated ; ; and to calcu-late nondiscretionary accruals.

(2)

represents the change in receivables. Thus, dis-cretionary 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 manipu-lations. 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 dis-cretionary accruals are consistent with each other.

Table 3 demonstrates the absolute value of discre-tionary accruals, which means that the more analyst cov-erage, the less discretionary accruals.

Table 3. The Distribution of Discretionary AccrualsAbsDA 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

15-20 263 697.62

>20 809 615.77

III. THE EFFECT OF ANALYST COVERAGE ON EARNINGS MANAGEMENT

In the first part of this section, We use discretionary ac-cruals 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 fi-nancing 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:

(4)

Where analyst coverage (ACi) is the number of ana-lysts 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 rep-resents institutional ownership and Controli is the type of actual controller of firms.

Table 4 show the regression result of analyst cover-age 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 cov-erage, and the more institutional holding, the more cover-age, the better performance, the more coverage, which is consistent with institution and available literature. Also, year and industry effects, GrowAsset and Control is sig-nificant. The adjusted R-squared is 0.5159, and F-statistic is 157.2160 with Prob 0.0000, other statistics is accept-able too, which means the regression model is suitable.

Table 4. The Result of Analyst Coverage on Independent variablesAnalyst 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

LnMV 0.5776 0.0200 28.9118 0.0000

ROE 0.0244 0.0015 16.4343 0.0000

GrowAsset 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

Adjusted R-squared 0.5159 S.D. dependent var 1.3408

S.E. of regression 0.9328 Akaike info criterion 2.7059

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

Prob(F-statistic) 0.0000

We label the residuals from the above regression as “residual coverage” and use it as the main proxy for ana-lyst coverage. It can be considered as a component of an-alyst coverage that is uncorrelated with market value, profit rate of asset, growth rate of assets, external financ-ing activities, or volatility of business.

Then we estimate the effect of analyst coverage on earnings management with the following OLS regres-sion:

(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 loga-rithm 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 ana-lysts’ coverage have restraint effect on managers’ earn-ings management decisions.

Table 5. The Result of Earnings Management on Analyst Coverage is year fixed effects, is industry fixed effects, repre-

sents firm ’s analyst coverage residual in year t. is the type of ac-

tual control of firm i. , , represent the logarithm of market value, the growth rate of assets and the return rate on net assets.

Variable Coeffi-cient

Std. Er-ror

t-Statis-tic 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

YES

LnMV -8.4303 17.2809 -0.4878 0.6257

ROE 2.7999 1.2862 2.1769 0.0296

GrowAsset 334.1501 18.6237 17.9422 0.0000

Institution -0.7027 0.8247 -0.8521 0.3942

Control -59.7162 29.4820 -2.0255 0.0429

R-squared 0.1615 Mean dependent var 691.7734

Adjusted R-squared 0.1555 S.D. dependent var 878.079

6

S.E. ofregression 806.9304 Akaike info criterion 16.2317

Sum squaredresid 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 exter-nal 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 ap-plication. In future, other corporation governance func-tion of analyst coverage, such as reducing tunneling, de-creasing information asymmetry, et al. can be explored.

REFERENCES

[1] Jensen, M.C. and W.H. Meckling, Theory of the firm: Man-agerial behavior, agency costs and ownership structure. Journal of financial economics, 1976. 3(4): pp. 305-360.

[2] Moyer, R.C., R.E. Chatfield and P.M. Sisneros, Security analyst monitoring activity: Agency costs and information demands. Journal of Financial and Quantitative Analysis, 1989. 24(4): pp. 503-512.

[3] Chung, K.H. and H. Jo, The impact of security analysts' monitoring and marketing functions on the market value of firms. Journal of Financial and Quantitative Analysis, 1996: pp. 493-512.

[4] Dyck, A., A. Morse and L. Zingales, Who blows the whis-tle on corporate fraud? The Journal of Finance, 2008. 65(6): pp. 2213-2253.

[5] Yu, F., Analyst coverage and earnings management. Jour-nal of Financial Economics, 2008. 88(2): pp. 245.

[6] Bergstresser, D. and T. Philippon, CEO incentives and earnings management. Journal of Financial Economics, 2006. 80(3): pp. 511-529.

[7] Jones, J., 1991. Earnings management during import relief investigations. Journal of Accounting Research 29, pp. 193–228.

[8] Dechow, P., Sloan, R., Sweeney, A., 1995. Detecting earn-ings management. Accounting Review 70, pp. 193–226.

[9] Warfield, T., Wild, J., Wild, K., 1995. Managerial owner-ship, accounting choices, and informativeness of earnings. Journal of Accounting and Economics 20,pp. 61–92.

[10] Gu, Z., 1999. Heteroscedasticity of accounting accruals and earning management. Ph.D. Dissertation. Tulane Uni-versity,New Orleans, LA.

[11] Klein, A., 2002. Audit committee, board of director char-acteristic, and earnings management. Journal of Account-ing and Economics 33,pp. 375–400.

[12] Bhushan, R., 1989. Firm characteristics and analyst fol-lowing. Journal of Accounting and Economics 121, pp. 255–274.

[13] Dechow, P., Dichev, I., 2002. The quality of accruals and earnings: the role of accrual estimation errors. The Ac-counting Review 77, pp. 35–59.

[14] Kasznik, R., 1999. On the association between voluntary disclosure and earnings management. Journal of Account-ing Research 37, pp. 57–81.