Earnings Management, Analyst Forecasts and Credit Rating

Journal of Finance and Economics, 2020, Vol. 8, No. 1, 21-32

Available online at http://pubs.sciepub.com/jfe/8/1/4

Published by Science and Education Publishing

DOI:10.12691/jfe-8-1-4

Earnings Management, Analyst Forecasts and

Credit Rating of Corporate Bond: Empirical

Evidences from Chinese Listed Companies

Zi-jian Huang1, Hui Huang2,*, Yuan-yuan Song3, Ting-yan Feng2

1School of economics,Yunnan University, Kunming, P. R. China 2School of accountancy, Chongqing Technology and Business University, Chongqing, P. R. China 3The Engineering & Technical College, Chengdu University of Technology, Sichuan, P. R. China

*Corresponding author: [email protected]

Received December 27, 2019; Revised February 06, 2020; Accepted February 23, 2020

Abstract Bond credit rating is a comprehensive evaluation by credit rating agencies on the credit records,

financial status and operating results of bond issuing companies. Because of information asymmetry, bond credit

rating is influenced by information disclosed by companies through earnings management and information

forecasted by analysts as an independent third party. Based on 311 samples of Chinese listed companies which

issued bonds for the first time from 2011 to 2017, this paper studied the relationships among company earnings

management, analyst forecasts and credit rating of corporate bonds. Our empirical results show that, in order to

improve the bond credit rating, company managers do have some earnings management behaviors before bond

issuing, and the extent of earnings management is positively correlated with its bond rating. We also find that the

forecasted corporate rating by analysts is positively correlated with its bond rating, and analyst forecasts cannot

restrain the impact of earnings management on bond rating and does not exclude the collusion with corporate

management. Earnings management and analysts forecast and have certain mutual promotion effect on bond

credit rating. The conclusions of this paper are conducive to information regulates and stakeholder decisions.

Keywords: earnings management, analyst forecasts, bond credit rating, Chinese listed companies

Cite This Article: Zi-jian Huang, Hui Huang, Yuan-yuan Song, and Ting-yan Feng, “Earnings Management,

Analyst Forecasts and Credit Rating of Corporate Bond: Empirical Evidences from Chinese Listed Companies.”

Journal of Finance and Economics, vol. 8, no. 1 (2020): 21-32. doi: 10.12691/jfe-8-1-4.

1. Introduction

Information asymmetry and accounting uncertainty are

objective existence in capital market, where requires and

impels rational companies to manage earnings. When a

company plan to issue its bonds, company managers,

perhaps, have strong motivation of earnings management

for higher bond credit ranking. However, whether the

credit rating agencies can identify the earnings management

of enterprises, the conclusions drawn by the theoretical

circle are not the same. Some scholars have found that

enterprises can obtain high bond credit rating through

earnings management, however, some scholars point out

that credit rating agencies understand the enterprise's

earnings management behavior, and give the bond credit

rating downgrading penalty.

When companies disclose information about their

bonds, securities analysts also verify that information and

provide their predictions. As independent professionals

who use information to analysis and do research, security

analysts play an important role in regulating manager’s

behaviors in corporate, protecting minority shareholders,

improving investment efficiency and alleviating information

asymmetry. But can securities analysts effectively regulate

the behavior of corporate managers? Some scholars think

that Chinese listed companies will conduct corresponding

earnings management behavior in order to cater to

analysts’ earnings forecast, while others find that analysts’

forecasts can effectively reduce the degree of earnings

management of listed companies through empirical tests.

Thus it can be seen, whether there is any earnings

management behavior before the bond issue, whether

corporate earnings management will improve its bond

rating, whether the bond rating results predicted by

analysts are accurate, and whether the predicted bond

rating modifies the impact of earnings management on

bond ratings? Such issues require further study.

Based on the bond rating data of Chinese listed

companies from 2011 to 2017, this paper conducted

empirical analyses on these problems. Research results

show that companies do have earnings management

behaviors before bond issuing, and the extent of it

is positively correlated with its bond rating. What’s

more, analyst forecasts can weaken the impact of

earnings management on the rating, because bond rating

agencies make comprehensive analyses and judgement

22 Journal of Finance and Economics

according to various relevant information of the target

company.

The contribution of this paper is that, we study bond

credit rating by the perspective of internal earnings

management behaviors, and by the perspective of external

analyst forecasts, as well as by the perspective of their

synthetical action mechanism respectively. Considering

alleviating the influence of information asymmetry, internal

governance is linked to external governance in our

research. Nowadays, the predication from analysts is

increasingly accurate, so analyst forecasts has been widely

used, as well as been paid more attention, in corporate

strategic decisions and national economic policies.

2. Literature Review

The purpose of this paper is to study the relationships

among earnings management, analyst forecasts and corporate

bond rating. We summarize the existing research literatures

in three categories: (1) earnings management and bond

rating; (2) analyst forecasts and bond rating; (3) earnings

management and analyst forecasts.

First, related researches on earnings management and

bond credit rating. Kisgen [1] believed that credit rating

agencies can measure company’s quality accurately by

using the public information and their survey information.

Ayers [2] further argued that credit rating agencies know

clearly the earnings management behaviors in listed

companies, and in return, give them downgrade punishment

in credit rating and rating adjustments. Besides, Caton et

al. [3] pointed out that the listed companies have large

amount of earnings management and manipulation before

their bonds issuing, whether the first-time or once more,

and the bond credit rating is significantly positive in

correlation with earnings management behaviors in

China’s bond market [4,5], except the highly indebted

companies [6].

Second, related researches on analyst forecasts and

bond credit rating. Bradshaw [7] made a comparison on

interpretation ability of analyst ratings among various

income model, and found that the bond rating is

significantly negative with the fade-rate residual income

model and not significantly positive with the sustainable

residual income model, while significantly positive with

the PEG and LTG models. The interpretation ability of

LTG model has increased significantly compared to that

of PEG, even the coefficient of LTG is significantly

stronger than PEG when PEG and LTG are checked at the

same time. Chan and Hameed [8] found that analysts who

could forecast accurate ratings can indeed predicate a

more profitable rating, when accuracy ratings are

measured as the absolute value of the difference between

actual EPS and predicted EPS. Zhang [9] tested the

relationship between bond rating and predicted earnings in

Chinese companies from the perspective of PE model, and

found that the higher the predicted earning, the more

likely analysts give a high bond rating.

Third, related researches on earnings management and

analyst forecasts. Burgstahler and Dichev [10] believed

that executives of American listed companies have the

motivation and behaviors to avoid loss or prevent profit

fell through earnings management. Burgstahler and Eames

[11,12] found that the closer to the threshold of the

analyst’s earnings prediction window, the more significant

the phenomenon which listed companies achieve analyst’s

performance expectations through earnings management.

Zheng and Cai [13] also showed that company managers

have obvious earning management behaviors because of

loss or poor profit, and this would increase the predicting

difficulty for the analysts, and therefore, lower the

accuracy of the analyst prediction. In addition, Wang [14]

found that there exists the avoidance of unexpected

negative earnings in China capital market, and Wei et al.

[15] found that some Chinese listed companies have

earnings management behaviors to cater for analysts’

profit forecasts.

Through the review of the previous literature, it can be

found that there is still some controversy in the theoretical

circle about the relationship between earnings management

and bond rating. Some scholars have pointed out that

enterprise may improve enterprise profitability through

earnings management, to improve the rating of corporate

bonds, but some scholars believe that when the earnings

management behavior of enterprises is too high, not only

will not play a role in promoting the rating of bonds, but

will reduce the rating they receive, because the credit

rating agencies clearly understand the earnings management

of listed companies, and in the credit rating and credit

rating adjustment to its downgrade penalty. In addition,

domestic and foreign scholars have respectively studied

the relationship between internal earnings management

and external analysts' forecasts and bond ratings, but the

effect of the combination of internal manipulation and

external supervision on bond ratings has been seldom

studied, this paper will specifically study whether there is

earnings management behavior and its impact on bond

rating, the impact of analysts' forecast on bond rating and

the combined impact of earnings management and

analysts' forecast on bond rating. Of course, credit ratings

have very important influence on bond prices in bond

secondary market [16].

3. Research Hypotheses

3.1. The Impact of Earnings Management on

Bond Rating

As an important external financing way, bond financing

requires credit rating before the bond is publicly issued.

From the perspective of bond issuing, bond rating is not

only related to the smooth issuance of bonds but also

affects the coupon rate and issuance cost of it. For listed

companies, it is very important to obtain a favorable initial

bond rating; while, for credit rating agencies, profitability

is the focus basis of rating. From the principal-agent

theory, the interests of the principal and that of agent is

not completely consistent. The principal pursuits the

maximum shareholder wealth under the minimum agency

cost, while agents pay more attention to personal income,

On-the-job consumptions and leisure time. Thus, considering

the asymmetric information between the Internal managers

and the external information users, the managers may

conduct earnings management to improve their profitability

before their bond issuing [17]. For the enterprises with

Journal of Finance and Economics 23

better performance, they think that the bond rating should

be higher, so it seems unnecessary to carry out excessive

earnings management. To test the existence of earnings

management before the company’s initial issuance of

bonds, and the earnings management level of companies

with different performance, this paper proposes the

following hypothesis:

Hypothesis 1a: the listed companies have earnings

management behaviors before the initial issuance of

bonds.

If such behaviors exist, then what is the relationship

between bond rating and earnings management? Some

existing studies point out that earnings management is

positively correlated with bond rating. While other

scholars pointed out that the rating agencies can identify

the company’s earnings management behaviors, and

consequently, give rating adjustment to it. And thus,

analyst forecasts have an inhibitory effect on earnings

management. Although the theory circle is still holding

different opinions on the relationship between earnings

management and bond rating, the standpoint of positive

relevant is more convincing. In order to test the

relationship between the both on the condition of

excluding the impact of corporate performance, this paper

assigns the bond rating into different gradation by scored,

and puts forward the following hypothesis:

Hypothesis 1b: when the other conditions are

unchanged, earnings management of the listed companies

is positively correlated with their bond rating.

3.2. The Impact of Analyst Forecasts on Bond

Rating

Before rating a company, a crucial step for analysts is

evaluation of the target company. For appraising a

company, analysts will make predictions on the profitability

of the company based on the information of past stock

price and company current information. For example, the

company’s financial statement, competitors, field investigation,

industry information and macroeconomic situations. This

prediction is the key independent variable for evaluation

and rating. Obviously, what we can be sure about the

analyst evaluation is the “input variable” (forecast earnings)

and the “output variable” (valuation and rating), but how

the analyst goes from “input” to “output” is still a “black

box”.

Some foreign scholars point out that analyst forecasts

are important bases for the evaluation of corporate rating,

and is positively correlated with the rating [18,19]. PE

model which is believed more accurately by most scholars

is often used to test this relationship in analysts’ reports.

Besides, we can know from the existing researches that

results of analyst forecast are strong supports for investors

in selecting and evaluating the target company. According

to the signal transmission theory, the market will react to

the signals sent by company managers and analyst. Then,

what and how analysts predict the role of signaling? In

order to test the relationship between analyst forecasts and

bond rating, we propose the following hypothesis:

Hypothesis 2: when the other conditions are

unchanged, the higher the analyst forecasts is, the higher

the bond rating will be.

3.3. The Combined Impact of Earnings

Management and Analyst Forecasts on

Bond Rating

From the external supervision theory and the efficient

market hypothesis, the external third party can play a pre-

supervision role in earnings management, but cannot stop

it. Furthermore, the respond speed of related information

and its authenticity varies, depending on the effectiveness

of the market. According to the above statement we know

that, from the internal perspective of the listed company,

in order to get a higher rating, they may refer to earnings

management. That means, earnings management in listed

companies can influence its bond rating. What’s more,

before giving the final rating, analysts will make earnings

forecasts from the perspective of external supervision, and

the higher the earnings forecast, the higher the bond rating

will be.

Then, what does the combination of internal manipulation

and external oversight do to bond rating? Can analyst

forecasts, as information from an external third party,

affect the internal earnings manipulation? Are analysts

predicting results independent and impartial or are they

the result of collusion with management? Because there

are many imperfections in Chinese bond market, the

relevant information is lagging and opaque, and intermediaries

such as securities companies are often inextricably linked

with bond issuing companies, securities analysts cannot

achieve absolute fairness, on the contrary, they are more

likely to collude with companies. To test these

relationships, we propose the following hypothesis:

Hypothesis 3: the combined effect of earnings

management and analyst forecasts can enhance their

separate impact on bond rating.

Figure 1. The Logic Diagram of the Hypothesis

Model (6)

Management

Motivation

Analyst

The third party

Earnings

Management

Analyst

Forecasts

(Disclosure)

Information

(Research)

Rating

Results

Model (5)

Hypothesis 1

Hypothesis 2

Model (7) Hypothesis 3

Supervision

Conspiracy


4. Research Design

4.1. Sample Selection and Data Source

This paper takes the listed companies in China that

issued bonds for the first time in 2013-2016 and thereafter

as the initial samples. The financial data observation

period is 2011-2017. The financial data, enterprise

attributes and analyst forecast data used in this paper are

all from the CSMAR (China Stock Market & Accounting

Research) Database, and the bond rating records are from

the WIND Economic database.

All the rating data come from domestic rating agencies

in WIND database. We thinkinternational rating agencies,

such as Standard & Poor’s and Moody’s, have formed

their own set of standards, but they are not necessarily

suitable for China. For example, Huaneng Power

International Inc was rated BBB by S&P in 2006, while

Chinese CCXI were rated AAA, which is a big gap.The

purpose of credit rating industry is to regulate corporate

financing and market development internally, and to

provide principal protection for domestic companies

externally,which is also the concept and practice of many

countries, such as Japan, India, Russia and so on.

In addition, the financial and insurance industry need to

be eliminated because of particularity of accounting

system and financial characteristics; the extreme data may

disrupt the normal relationship among the data, all the

poor operating results of ST and *ST companies need to

be excluded (ST is Special treatment, a company that has

lost money for two and three consecutive years is called

ST and *ST, whose stock transactions have some policy

restrictions). Therefore, we finally obtain 331 companies

as the whole sample, including 170 state-owned enterprises

and 161 private enterprises.

4.2. Variable Design

4.2.1. Dependent Variable

This paper takes the bond rating of listed companies as

the dependent variable. There are four basic methods of

bond rating in the market: factor analysis method,

multivariate discriminant model method, multiple regression

method and analytic hierarchy process. Among them,

factor analysis is mainly based on factor score, which is

too subjective, and is also difficult to guarantee the

fairness and accuracy of rating. The analytic hierarchy

process is mainly based on weights to determine the

rating, so its classification is relatively rough.This paper

uses assigning score method to refine each bond rating,

suchasthefollowing:

The bond rating is divided into bond project rating (PR)

and corporate entity rating (CR). Corporate overall rating

is the overall credit evaluation of the issuing enterprises

themselves, which can be regarded as the judgment of the

comprehensive solvency of various kinds of debts, while

bond project rating is the judgment of the security of

a specific debt. And then, scoring method is used to

quantify the rating (referred to the existing researches).

The ratings of AAA, AA, A, BBB, BB, B, CCC, CC and

C were assigned 100, 95, 90, 85, 80, 75, 70, 65, 60,

respectively. Where there is “+” or “-”, we will plus 2

points or minus 2 points on the assigned score.

4.2.2. Independent Variable

This paper uses the accrual earnings management to

measure the independent variable (earnings management).

Many researches [20,21] show that the modified Jones

model is an ideal method to measure accrual earnings

management. Therefore, in this paper, we also use the

modified Jones model to measure the accrual earnings

management. The total accruals are divided into controllable

and uncontrollable accruals. In this paper, total accruals

are defined as the difference between net profit and net

operating cash flow, that is:

, , ,i t i t i tTA NI CFO (1)

TAi,t represents the total accrued profit of enterprise i in

year t, NIi,t represents the net profit of that year, and CFOi,t

represents the net cash flow of operating activities.

We use the Raman and Shahrur modified Jones model

[22] to measure the uncontrollable accrualsNDAi,t:

, , ,0 1 2

, 1 , 1 , 1

,3 4 , 5 ,

, 1

1i t i t i t

i t i t i t

i ti t i t

i t

NDA REV REC

A A A

PPEROA BM

A

(2)

Among them, Ai,t-1 represents the total assets from the

previous year, ΔREVi,t represents sales revenue increase,

ΔRECi,t accounts receivable increase, PPEi,trepresents the

net value of fixed assets, ROAi,t represents return on

assets. BMi,t equals to the market share of tradable shares

at the end of the year plus the value of non-tradable shares

calculated on net assets plus total liabilities, and then

divide total assets. The value of non-tradable shares

calculated on net assets equals to the number of non-

tradable shares multiply company’s net assets. a0, al, a2, a3,

a4 and a5 from (3) are obtained by the annual regression of

all listed companies in China. Controllable accruals (4) are

equal to total accruals minus uncontrollable accruals.

, , ,0 1 2 3

, 1 , , 1 , 1

4 , 5 ,,

1i t i t i t

i t i t i t i t

i t i ti t

TA REV PPE

TA A A A

ROA BM

(3)

, ,

,, 1 , 1

i t i ti t

i t i t

TA NDADA

TA A

(4)

Referring to the existing studies, this paper downloads

the earnings forecast per share (Feps) of each company

from the CSMAR economic and financial research

database, and use it to represent the analyst forecasts. The

mean value of each analyst forecast is taken as the year’s

indicator.

4.2.3. Control Variables

Based on previous studies by domestic and foreign

scholars, this paper selected corporate size (Size), capital

structure (Lev), the revenue growth rate (Growth), return

on asset (ROA), operating cash flow ratio (Cashflow),


long-term liabilities (LD) and analyst forecasts error rate

(Accuracy) as control variables.

The impact of control variables on ratings is expected

to be as follows: (1) the logarithm of the total assets of the

company is used to indicate the size of the company. It’s

expected that the larger corporate size is, the higher the

bond rating will be. (2) Asset-liability ratio is used to

measure the capital structure of a company. Previous

studies have shown that a company with a high liability-

asset ratio is more likely to violate its debt contract and is

expected to get a lower bond rating. (3) The revenue

growth rate is used to measure the growth of the company.

The higher the revenue growth rate is, the better the

growth of the company will be. However, the company is

also likely to conduct positive earnings management.

Therefore, it’s double edged. (4) Return on asset is used to

measure the profitability of a company. The higher the

expected profitability of a company, the higher its bond

rating will be. (5) Operating cash flow ratio is used to

measure the proportion of operating cash flow to total

assets. (6) Long-term debt ratio is used to measure the

proportion of long-term liabilities to total liabilities. the

higher the ratio is, the greater the probability of default is.

(7) Analyst forecasts error rate is used to measure the

accuracy of analyst forecasts.

4.3. Model Building

In order to test the influence of earnings management

on bond rating, this paper constructs models (5). Since the

rating agencies are mainly based on the company’s

performance over the past year, the independent variable

uses data lagging one year.

i,t i,t 0 1 , 1 1 , 1

2 , 1 3 , 1 4 , 1

5 , 1 6 , 1 ,

i t i t

i t i t i t

i t i t i t

CR PR DAs Size

Growth LD ROA

Cashflow Lev

(5)

In model (5), β0 is constant, εi,t is the residual. β1 is

earnings management coefficient, which is expected to be

positive. The greater its value is, the greater the influence

of analyst forecasts on corporate rating and debt rating.

According to Standard & Poor’s rating index, it’s

expected that control variables of Size, Growth, ROA,

Cashflow, LD are positive correlation with the corporate

rating and the debt rating, but Lev is contrary to ratings.

In order to test the influence of analyst forecasts on

bond rating, this paper constructs models (6) to test the

influence of analyst forecasts on corporate rating and debt

rating, respectively. In the model (6), the analyst forecast

(Feps) is the value of the rating year, and the analyst

forecast accuracy variable (Accuracy) is added, which is

also an important factor for rating agencies to consider.

i,t i,t

0 1 , 1 , 1 2 , 1

3 , 1 4 , 1 5 , 1

6 , 1 7 , 1 ,

i t i t i t

i t i t i t

i t i t i t

CR PR

Feps Size Growth

LD ROA Cashflow

Lev Accuracy

(6)

In model (6), β1 is analyst forecasts coefficient, which

is expected to be positive. It shows that the rating agencies

will take full account of the analyst’s forecast information.

Other control variables have the same meaning and

function as the model (5).

This paper constructs models (7) to test the combined

influence of earnings management and analyst forecast on

bond rating. We introduced the cross-multiplication value

of earnings management and analyst forecasts, and tested

the comprehensive influence of analyst forecasts and

earnings management on corporate rating and debt rating

respectively.

i,t i,t 0 1 , 1 2 ,

3 , 1 , 1 , 1 2 , 1

3 , 1 4 , 1 5 , 1

6 , 1 7 , 1 ,

i t i t

i t i t i t i t

i t i t i t

i t i t i t

CR PR DA Feps

DA Feps Size Growth

LD ROA Cashflow

Lev Accuracy

(7)

Table 1. Variable Definition and Computing Method

Types of

variable

Variable

symbol Meaning Calculation rules or interpretation

Dependent

variable

PR Bond project

rating

AAA、AA、A、BBB、BB、B、CCC、CC and C are assigned 100、95、90、85、80

、75、70、65、60 respectively, which with“+”“-”, plus 2 points and minus 2 points.

CR Corporate entity

rating

AAA、AA、A、BBB、BB、B、CCC、CC and C are assigned 100、95、90、85、80

、75、70、65、60 respectively, which with “+” or “-”, plus 2 points and minus 2 points.

Independent

variable

DA Earnings management The extent of a company managing its earnings, or abnormal accrued earnings, is based on

data from the year prior to the first rating

Feps Analyst forecasts Equal to the analyst forecasts for EPS

Controlled

variable

Size Company size The natural logarithm of the total market value of a company

Lev Capital structure Use the asset-liability ratio as the proxy variable

Growth Growth On behalf of the company's growth index, equals to the growth ratio of the operating

revenue

ROA Return on asset To measure the profitability of enterprises, is equal to the average annual profit divided by

the total assets

Cashflow Operating cash flow ratio Equal to the total operating cash flows accounted for the proportion of total assets

LD Long-term debt ratio Equal to long- term debt divided by totalliabilities

Accuracy Analyst forecasts error ratio Equal to the analyst forecasts minus the value of actual EPS, and divide the actual EPS


Table 2. Comparison of Annual Earnings Indicators Near the First Bond Rating

Variable 2 years before rating 1 year before rating the rating year 1 year after rating 2 years after rating

Mean median Mean median Mean median Mean median Mean median

ROA 0.0562 0.0471 0.0519 0.0413 0.0364 0.0302 0.0243 0.0248 0.0254 0.0239

ROE 0.1180 0.1058 0.1093 0.0992 0.0785 0.0777 0.0622 0.0650 0.0267 0.0583

IG -0.3048 0.1570 0.1859 0.0718 -0.069 -0.083 -0.5888 -0.0338 -0.7241 0.0042

Table 3. The Descriptive Statistics of Variables

Statistic CR PR DA Feps Size Lev Growth ROA LD Cashflow Accuracy

Mean 95.968 96.069 0.107 0.624 23.186 0.564 0.145 0.036 0.374 0.029 1.243

Median 96.124 96.035 0.112 0.602 24.025 0.588 0.133 0.077 0.395 0.045 1.057

SD 2.171 2.129 0.363 0.518 1.278 0.151 0.449 0.208 0.176 0.065 5.269

Maximum 100.000 100.000 5.567 3.210 28.405 0.872 7.043 0.138 0.811 0.203 67.096

Minimum 92.000 92.000 0.000 -0.080 20.478 0.203 -0.844 0.000 0.000 -0.184 -13.803

Obs. 331 331 331 331 331 331 331 331 331 331 331

In the model (7), β1and β2 are coefficient of

earnings management and analyst forecasts. β3 is the

cross-multiplication coefficient of earnings management

and analyst forecasts. Given β3 positive, it means analyst

forecasts and earnings management together can strengthen

their separate influence on bond rating. if its negative, that

means analyst forecasts can, to some extent, restrain the

influence of earnings management on bond rating.

5. Empirical Analysis

5.1. Descriptive Statistics of Variable

Referencing to related research (Liu, 2014), this paper

first makes a preliminary judgment of the existence of

earnings management by analyzing the changes of

earnings before and after the credit rating. Table 2 shows

the changes of the profitability indicators of the sample

near their first rating. To analyzing the profitability of the

listed companies before and after the rating, we use return

on asset (ROA), return on equity (ROE) and operating

profit growth ratio (IG) as the main indicators. From the

mean and median value of each indicator, we find that

ROA goes down gradually from 2 years before the rating

to 1 year after the rating, and in the second year after the

rating, the average ROA climbs back up slightly. The

mean and median value of ROE keep going down from 2

years before the rating to 2 years after the rating.

Specially, for IG, the mean value of it peaks in the year

before rating, while in the year of rating, that means the

year after the rating, IG value falls to negative. All these

statistics show that the listed companies have earnings

management behaviors before the first bond rating.

Table 3 gives the descriptive statistical results of all

samples. As can be seen from Table 3, the mean value of

corporate rating and debt rating is 95.968 and 96.069

respectively, which is between AAA and AA. This

indicates that the bond issuer has a high overall credit

rating. In addition, the debt rating is slightly higher than

the corporate rating, indicating that Chinese listed

companies has a high requirement for issuing bonds. The

mean absolute value of earnings management is 0.107,

which is totally different from zero. The maximum value

of the analyst forecasts is 3.210, the minimum value

is -0.080, so the average vale is 0.624. There are both

positive and negative values, but the positive forecast is

significantly higher than the negative value. The average

liability-asset ratio is 0.564, with maximum and minimum

were 0.872 and 0.203 respectively. This indicates that the

liability level of the existing sample is above the average,

and they face high financial risks. The standard deviation

of the growth index is 0.449, indicating that there is a

certain difference in the growth stage of the companies,

and thus, may have negative effect on rating’s

expectations. The average operating cash flow ratio is

0.029, which is expected to have a positive impact on the

rating. Analysts are forecasting an average margin of error

of 1.243, and standard deviation of 5.269, indicating that

there is a certain differencebetween the analyst forecasts

and the real value. So, it’s expected that analyst forecasts

have a certain impact on the rating.

5.2. Correlation Analysis of Variables

Table 4 shows the result of the correlation coefficient of

variables. From the correlation coefficient matrix, we can

see that the long-term liability ratio and the operating cash

flow ratio is significantly positively correlated, and the

relationships between company’s growth and operating

revenue growth ratio or the net operating cash flow ratio

are significantly negatively correlated. Asset-liability ratio

and net operating cash flow are negatively correlated with

long-term liabilities ratio significantly. The size of the

company is negatively correlated with the long-term

liability ratio, and is positively correlated with the

asset-liability ratio. Analyst forecasts is significantly

positively correlated with enterprise size, while analyst

forecasts error rate is negatively correlated with the net

operating cash flow ratio.

5.3. Comparison Analysis on Earnings

Management

In order to verify the hypothesis 1, this paper adopts the

comparative analysis method of earnings management.

The first is to judge whether a company has earnings

management behavior before issuing bonds by comparing

the degree of earnings management before and after

issuing bonds, next is to judge whether a company

with higher bond rating will have more earnings

management.


Table 4. Correlation Coefficient Matrix

Cashflow LD Growth Lev Size DA ROA Feps Accuracy

Cashflow 1.0000

LD 0.1583*** 1.0000

Growth -0.1350** -0.0334 1.0000

Lev -0.2484*** -0.2964*** 0.0204 1.0000

Size 0.0119 -0.1230** -0.0052 0.4837*** 1.0000

DA -0.1668*** -0.0353 0.0384 0.0088 0.0996* 1.0000

ROA 0.0173 -0.0120 -0.0030 0.0256 -0.0014 -0.0976* 1.0000

Feps -0.0046 -0.0698 0.0554 -0.0472 0.1422*** 0.0354 0.5058*** 1.0000

Accuracy -0.0926* -0.0012 -0.0797 0.0136 -0.0169 -0.0138 -0.0005 -0.0202 1.0000

Note: Value in table is Pearson correlation coefficient;***, **and * represent significant at 1%, 5% and 10% level respectively (double-tailed T-test).

Table 5. Earnings Management Before and After Rating

(DA) 1 year before rating The rating year 1 year after rating

Mean 0.10653 0.06252 0.05581

SD 0.36342 0.24572 0.22528

N 331 331 331

Table 6. Paired Difference Test on Earnings Management

Paired sample difference 95% confidence interval

df t Sig. lower limit upper limit

(1 year before rating) vs. (the rating

year) 0.0073 0.0838 330 2.341 0.020

(1 year before rating) vs. (1 year after

rating) 0.0035 0.0874 330 8.557 0.000

Table 7. The Relationship between Bond Rating and Earnings Managements

Variable CR (92-95 points) CR (95-100 points) PR (92-95 points) PR (95-100 points)

DA 0.066297 0.052109 0.068343 0.054434

ROA 0.0374 0.0485 0.0382 0.0488

Cashflow 0.0285 0.0297 0.0287 0.0307

N 224 107 213 118

5.3.1. Comparison of Earnings Management before

and after Rating

Earnings management is calculated from the model

(1) - (4). In order to test hypothesis la, we classify the

absolute value of earnings management as earnings

management before rating, earnings management in the

rating year, and earnings management after the rating

year, and then, compare the mean value of them.

From Table 5, we can find that the extent of earnings

management steps down gradually from the year before

the rating to the year after the rating. A paired sample test

is made on earnings management in the year before rating

and in the rating year, and the test results in Table 6 show

that there is a significant difference of the earnings

management in the 95% confidence interval, indicating

that reduction of earnings management in the rating year.

What’s more, as profit indicators in Table 2 have pointed

out that the ROA and ROE is on the decline, and IG peaks

before the rating year and then goes to negative.

Therefore, it further illustrates the fact that the listed

companies have earnings management behaviors before

first rating, and hypothesis 1a is true.

5.3.2. Comparison of Earnings Management in Different Rating

Then, what are the differences in earnings management

between companies with different rating? We divide the

whole sample into two categories depending on the rating

score: 92-95 (including) points and 95-100 points (All the

company’s bond rating in the sample is at grade A and

above). For corporate entity rating, we have 224 samples

in category 92-95 points, and 107 in category 95-100

points. For bond project rating, we have 213 samples in

92-95 points category, and 118 samples in 95-100 points

category. We can find the relationships between bond

rating and earnings management for different groups from

Table 7. There is the same rule whether it’s corporate

rating and debt rating, that is, companies with higher

ratings have lower earnings management. Thus,it seems

that hypothesis 1b could not be verified and is incorrect.

This result is identical to the research conclusions of Yang

[23], who argue that rating agencies have the ability of

discriminate to identify earnings management behavior of

listed companies. But we don’t think so, because the

company get a high credit rating mainly due to their good

profitability and solvency. Many studies [24,25,26] show

that companies with better performance do not need

excessive earnings management, the sectional statistics of

ROA and Cashflow in Table 7 also explain this. If we

want to test the relationship between bond rating and

earnings management, we must exclude the influence of

enterprise performance itself, and it’s necessary to carry

out multiple regression analysis [27].


5.4. Multivariate Linear Regression Analysis

To further test the hypothesis1b, this paper carries out

multiple regression analysis on model (5), which includes

conventional parameters of bond rating agencies as

control variables. At the same time, in order to test

hypothesis 2 and 3, Multivariate regression analysis of the

model (6) and (7) is also needed. We checked the relevant

statistical variables of the three models, and find that:

(1) the maximum variance inflation factors (VIF) of

explanatory and control variables is 1.58, which is far less

than the standard value 10, so we believe that there isn’t

multicollinearity in this model (The VIF of variables in

model (7) are as follows: DA, 1.05; Feps, 1.06; DA*Feps,

1.19; Size,1.43; Growth, 1.04; LD, 1.12; ROA, 1.00;

Cashflow, 1.18; Lev, 1.58; Accuracy, 1.03). (2) the

Durbin-Watson statistic (D.W) is around 2.13, which

shows that there is no autocorrelation. In other words, the

sample and explanatory variables of this paper are

consistent with the basic assumptions of linear regression.

5.4.1. Empirical Analysis of Hypothesis 1b

In order to reflect the influence of property rights, this

paper make multivariate regression analysis based on the

samples of state-owned enterprises, non-state-owned

enterprises and all enterprises respectively, and the

regression results of model (5) are shown in Table 8. (1)

The adjusted goodness of fit R2 can be accepted, and the F

statistic is significant at the 1% significance level, which

indicates that the model is effective. (2) In full sample

regression, whether interpreted variable is CR or PR, the

partial regression coefficient of DA is positive, and DA is

significant at the 10% level for PR. Corporate ratings

may focus on the company’s overall financial situation,

including the company's early-stage basis, while bond

ratings focus more on the company’s performance in the

past year, and management is more motivated to carry out

earnings management performance. The empirical results

basically confirm the hypothesis 1b. (3) What’s more, for

the sample of state-owned enterprises, the symbols and

significance of DA are basically the same as that of the

total sample. For the sample of private enterprises, CR and

PR is significant positive correlation with DA at the 10%

level, and the coefficient is larger than the sample of state-

owned enterprises, which indicate private enterprises are

more likely to conduct earnings management in order to

get a better bond rating.

In addition, the empirical results of multiple regression

in Table 8 overturn the results ofsegmental comparison in

Table 7, because the results in Table 8 consider the

company’s own profitability and solvency, which is an

important and direct reference for rating agencies. So, the

significance of some control variables is also worth

analyzing in Table 8, and it can verify the accuracy of

bond rating by bond evaluation agencies.

(1) Size has a significant positive impact on both CR

and PR, which indicates that the larger the size of the

listed company, the higher the bond rating it obtains, and

this is in line with the expectation.

(2) Cashflow and ROA are significantly positively

correlated with the CR and PR, which is in line with the

expectation. According to the significance level, this

feature is more obvious in state-owned enterprises.

(3) Lev has a significant negative impact on the CR and

PR, which is in line with the expectation. According to the

significance level, this feature is more obvious in private

enterprises.

(4) LD has a significant positive impact on both the CR

and PR, which indicates that the more long-term liabilities

an enterprise obtains, the better its external credit will be,

and the higher its bond rating will be. This feature is more

obvious in state-owned enterprises.

(5) Growth is significantly negatively correlated with

the CR at the level of 5% in private enterprises. Growth is

characterized by the change rate of main business income,

which can be understood as income volatility, representing

a certain risk, so it is negatively correlated with bond

evaluation, especially in private enterprises.

Table 8. The Multivariate Regression Results of Model (5)

Variable

all enterprises state-owned enterprises private enterprises

CR PR CR PR CR PR

DA 0.425 (1.12) 0.439* (1.72) 0.332 (0.73) 0.346* (1.66) 0.356* (1.80) 0.369* (1.74)

Size 1.085*** (15.63) 1.175*** (15.95) 1.332*** (18.23) 1.282*** (19.12) 0.921*** (10.87) 0.852*** (9.56)

Growth -0.594* (-1.46) -0.217 (-1.04) -0.245 (-0.98) -0.226 (-1.02) -0.682** (-1.97) -0.414 (-1.23)

LD 1.345*** (3.13) 1.306*** (3.57) 0.557** (2.19) 0.662** (2.22) 1.149* (1.76) 0.804 (1.27)

ROA 0.226*** (3.56) 0.188** (5.12) 0.154** (2.61) 0.156*** (5.51) 2.594** (2.49) 2.556** (2.54)

Cashflow 4.232*** (3.21) 4.020*** (3.09) 4.635*** (2.70) 4.024** (2.00) 2.049 (1.27) 2.914* (1.94)

Lev -0.232* (-1.85) -1.020*** (-2.98) -0.452 (-0.95) -0.064** (-2.01) -0.156* (-1.79) -1.527*** (-3.27)

Obs. 331 331 170 170 161 161

F-sta. 47.38*** 49.01*** 28.45*** 29.21*** 18.02*** 17.85***

Adj-R2 0.601 0.647 0.524 0.519 0.428 0.416

Note: the value in parentheses is t value; ***, **and * represent significant at 1%, 5% and 10% level respectively; the following is the same.



Variable all enterprises state-owned enterprises private enterprises

CR PR CR PR CR PR

Feps 0.432**(1.83) 0.397**(2.50) 0.4584**(2.00) 0.3848**(1.71) 0.6055***(2.81) 0.6820***(3.27)

Size 1.184***(18.08) 1.160***(17.93) 1.214***(12.13) 1.182***(12.06) 0.858***(8.85) 0.787***(8.38)

Growth -0.291**(-1.60) -0.223*(-1.24) -0.197(-0.87) -0.226(-1.02) -0.682**(-1.97) -0.414(-1.23)

LD 1.337***(2.82) 1.236***(2.64) 0.808(1.19) 0.807(1.22) 1.149*(1.76) 0.804(1.27)

ROA 0.226(0.26) 0.198*(1.28) 0.169(0.61) 0.156*(1.51) 2.594**(2.49) 2.556**(2.54)

Cashflow 4.232***(3.21) 4.020***(3.09) 5.596***(2.70) 4.064**(2.00) 2.049(1.30) 2.954*(1.94)

Lev -0.334*(-1.79) -0.914**(-1.98) -0.452**(-1.95) -0.123(-0.71) -0.174*(-1.77) -1.027***(-4.01)

Accuracy 0.00076(0.05) -0.00032(-0.02) 0.0468(1.12) 0.0402(0.98) -0.0085(-0.59) -0.0075(-0.54)

Obs. 331 331 170 170 161 161

F-sta. 53.30*** 51.86*** 26.14*** 24.75*** 17.18*** 16.45***

Adj-R2 0.526 0.519 0.5101 0.4959 0.415 0.403

5.4.2. Empirical Analysis of Hypothesis 2

The regression results of model (6) are shown in

Table 9. (1) In full sample regression, whether interpreted

variable is CR or PR, the partial regression coefficient of

analyst forecast is positive, and significant at the 5% level.

The results indicate that analyst forecast has a positive

effect on both CR and PR. As an independent third-party

organization, information released by analyst is often

considered objective, and the rating agencies will make

full use of the information. That is, the higher the

analyst forecasts are, the higher the bond rating of the

listed company, Hypothesis 2 is validated. (2) Whether

interpreted variable is CR or PR, analyst forecasts

coefficient is positive and significant at the 5% level in the

sample of state-owned enterprises. Inthesampleof private

enterprise, analyst forecasts coefficient is alsopositive and

significant at the 1% level, and the coefficient is larger

than the sample of state-owned enterprises. Namely,

analyst forecasts have a bigger impact on bond rating

in private enterprises. This may be the reason that the

information asymmetry of private enterprises is more

serious, and rating agencies prefer to get information from

analysts. (3) There is no significant positive or negative

correlation with the Accuracy, indicating that the accuracy

of analysts’ past forecasts has not been considered by

rating agencies, which also had proved by Yao [28]. The

symbols and significant of other control variables are

basically the same as those of model (5).

5.4.3. Empirical Analysis on Hypothesis 3

Table 10 shows the multiple regression results of

hypothesis 3. The relevant statistics indicate that the

model (7) is effective.

(1) The coefficient of DA is positive in the whole

sample, which is like hypothesis 1b, shows that rating

agencies only use company performance information and

can’t identify earnings management behavior. Analyst

forecasts are still significantly positive, which implies

analyst forecasts have greater influence on bond

rating. The cross-multiplication coefficient of earnings

management and analyst forecasts is positive but not

significant, which shows to some extent that hypothesis 3

exists and is probably correct.

(2) In state-owned enterprises, DA is significantly

positively correlated with the CR, and analyst forecasts

is significantly positively correlated with the PR.

Cross-multiplication coefficient is positive, especially

significant positive for CR. In private enterprises, the

analyst forecasts coefficient is significantly positive,

and the cross-multiplication coefficient is significantly

positive, indicating that the combined effect of analyst

forecasts and earnings management in private enterprises

strengthens the impact of each on the bond rating.

hypothesis 3 was confirmed.

(3) From the control variables, ROA and Cashflow are

significant. It is easy to see that the rating agencies prefer

cash flow to net profit in the state-owned enterprises and

just reversed in the private enterprises. Of course, earnings

management of profits is much more convenient than cash

flow [29].

Understandably, the earnings management of enterprise

management is to get a better bond rating, but whether the

analyst as an independent third party can objectively

analyze and expose the earnings management behavior of

enterprise management? So, By analyzing the influencing

factors of earnings management, we can find some important

information to explain the hypothesis 3. Therefore, taking

earnings management (DA) as dependent variable in

model (6), the regression results are shown in the Table

11. Earnings management is significantly positively

correlated with analyst forecasts, whether in total sample

or subsample. Since the analyst’s forecast is based on the

company’s profitability, solvency, operation and growth

in the previous year, it should reflect the real situation

without earnings management.

To explain the empirical results, on the one hand, the

results of analysts forecast may become the goal of the

company, which will urge company management to carry

out earnings management by various ways to achieve the

results of analyst forecast; on the other hand, it does not

exclude the collusion between analysts and the

management, that is to say, the results of analyst forecast

are obtained through consultation with the company

management, so the analysts do not undertake evaluation

function as an independent and impartial third party.

The Table 11 also shows what kind of companies prefer

earnings management, from which we can find the

ways of earnings management. Growth is significantly

positively correlated with earnings management, while

Lev and Cashflow are just reversed. So high income

growth gives earnings management opportunities, but debt

rate and cash flow cannot be manipulated. ROA is

negatively correlated with earnings management but not


significant, which shows that companies with strong

profitability do not need excessive earnings management,

and the motivation of profit manipulation is weak, which

is consistent with the previous analysis.

5.5. Robustness Test

Dueto the dependent variable (bond rating) is a discrete

variable, and by referring to the existing literature, this

paper uses the ordered logit regression method to test the

robustness of three assumptions in this paper.

To be specific, since all the samples are rated with A,

AA, AAA, or add “+”, “-” to them, we will divide A, AA,

AAA rating samples into 1, 2, 3 groups, and then quantify

the rating level to 1, 2, 3. In corporate entity rating (CR),

we have 7 samples in the first group, 236 samples in the

second group, and 88 samples in the third group. For bond

project rating (PR), we have 6 samples in the first group,

238 samples in the second group, and 87 samples in the

third group.

Table 12 shows the robustness test results (Table 12

does not list control variables, and the coefficients,

symbols, and significance of the control variables

are consistent with those above). No matter it’s in the

whole sample or the sample of state-owned enterprises

and private enterprises, no matter it’s model (5) or

(6), earnings management and analyst forecasts are

significantly positive correlated with CR and PR, analyst

forecasts can enhance their separate impact on bond

rating.



CR PR CR PR CR PR

DA 0.21(1.05) 0.373*(1.71) 0.230*(1.90) 0.304(1.51) 0.334(1.63) 0.233*(1.76)

Feps 0.339*(1.66) 0.340*(1.69) 0.155(0.51) 0.149*(1.50) 0.255***(3.35) 0.241***(3.81)

DA*Feps 1.162(0.79) 0.729(0.50) 0.885*(1.57) 0.213(1.24) 0.341*(1.81) 0.462*(1.94)

Size 1.196***(18.16) 1.171***(17.98) 1.239***(12.20) 1.202***(12.03) 0.833***(8.55) 0.761***(8.08)

Growth -0.28(-1.53) -0.215(-1.19) -0.147(-0.65) -0.186(-0.84) -0.644*(-1.87) -0.376(-1.13)

LD 1.344***(2.84) 1.242***(2.65) 0.856(1.27) 0.843(1.27) 1.229*(1.90) 0.886(1.42)

ROA 0.027*(1.67) 0.038**(1.85) 0.067(0.60) 0.055*(1.69) 2.689***(2.60) 2.655***(2.65)

Cashflow 3.931***(2.95) 3.722***(2.82) 5.436***(2.63) 3.935**(1.94) 1.427(0.88) 2.336(1.50)

Lev -0.137(-1.12) -0.847**(-1.72) -0.325*(-1.88) -0.098(-0.59) -0.161*(-1.85) -1.874***(-3.63)

Accuracy 0.00038(0.02) -0.00075(-0.05) 0.0531(1.27) 0.045(1.10) -0.0078(-0.55) -0.0068(-0.49)

Obs. 331 331 170 170 161 161

F-sta. 41.81*** 40.60*** 21.10*** 19.65*** 14.07*** 13.58***

Adj-R2 0.5267 0.5192 0.517 0.4983 0.4236 0.4145

Table 11. Influence Factor of Earnings Management

Variable DA (all enterprises) DA (state-owned

enterprises)

DA (private

enterprises)

Feps 0.0382***

(9.09)

0.0398***

(9.23)

0.0325***

(4.11)

Size 0.0024

(1.23)

0.0019

(0.93)

0.0006

(0.15)

Growth 0.0354***

(7.52)

0.0330***

(7.96)

0.0554***

(4.36)

LD -0.0614

(-1.44)

-0.0721

(-0.10)

-0.0541

(-1.01)

ROA -0.0012

(-0.46)

-0.0009

(-0.43)

-0.0429

(-1.11)

Cashflow -1.1096***

(-31.56)

-1.0209***

(-25.19)

-1.2062***

(-20.61)

Lev -0.1173***

(-6.89)

-0.1199***

(-6.35)

-0.0980***

(-3.32)

Obs. 331 170 161

F-sta. 181.27*** 136.60*** 71.60***

Adj-R2 0.7927 0.8489 0.7554

Table 12. Robustness Test of Hypotheses 1-3


CR PR CR PR CR PR

H1

DA 0.4249** (2.42) 0.4189** (2.32) 0.4321* (1.73) 0.4469* (1.66) 0.3247* (1.80) 0.3096* (1.74)

LR-sta. 279.59*** 168.40*** 86.54*** 268.84*** 162.48*** 79.08***

Pseudo-R2 0.3174 0.3437 0.2596 0.3041 0.3333 0.2392

H2

Feps 0.3191** (2.03) 0.3254** (2.06) 0.0796* (1.85) 0.3555** (2.04) 0.5181** (1.95) 0.5157** (1.97)

LR-sta. 147.42*** 79.78*** 48.03*** 148.57*** 80.09*** 46.38***

Pseudo-R2 0.3299 0.3210 0.2928 0.3395 0.3336 0.2896

H3

DA 0.1868 (0.71) 0.2596* (1.68) 0.4755 (1.38) 0.5062 (1.20) 0.3516* (1.82) 0.3527* (1.81)

Feps 0.4249** (2.08) 0.4224** (2.05) 0.3261* (1.70) 0.3581* (1.71) 0.5011** (2.54) 0.5108** (2.26)

DA*Feps 1.1391 (0.81) 1.0508* (1.74) 0.5148** (2.11) 0.3432 (1.18) 0.0596* (1.61) 0.0502* (1.92)

LR-sta. 148.16*** 82.02*** 52.44*** 149.14*** 81.21*** 49.47***

Pseudo-R2 0.3316 0.33 0.3196 0.3408 0.3383 0.3089


There is no theoretical endogeneity in the empirical

research. Firstly, the company’s related variables lag one

year (the annual report was published before April next

year), and the analyst forecast is based on the financial

data of the previous year of the company. Secondly, the

rating agencies have multiple ratings for the same bond,

and we use the last rating result, which is given based on

company’s financial data from the previous year.

6. Conclusions and Suggestions

6.1. Research Conclusions

This paper studies the relationship between earnings

management, analyst forecasts and corporate bond credit

rating, and the empirical results show that: (1) Before the

initial issuance of bonds, listed companies have serious

earnings management behavior to obtain higher bond

credit rating. Although companies with strong profitability

have a lower degree of earnings management, excluding

the profitability factors, the higher the degree of earnings

management, the higher the bond rating, the bond

rating agencies cannot effectively identify the company’s

earnings management behavior. (2) The information of

analyst forecast is an important reference for bond rating

agencies. The higher the analysts forecast, the higher the

bond rating. Moreover, the impact of analysts forecast on

the corporate entity rating (CR) is greater than that of

bond project rating (PR), and the impact on private

enterprises is greater than that of state-owned enterprises.

Bond rating agencies prefer profit information of state-

owned enterprises and cash flow information of private

enterprises. (3) Analysts forecast have a strong positive

correlation with earnings management, which cannot

represent the fairness of external third parties to a certain

extent, and does not exclude the collusion with corporate

management. Earnings management and analysts forecast

and have certain mutual promotion effect on bond credit

rating.

6.2. Policy Suggestions

Bond investors should have their own independent

judgment, not blindly follow the conclusions of securities

analysts, which cannot really play the role of fairness and

supervision as a third party, and have a certain degree of

collusion with the management of enterprises. Forecast

information isn’t a personal view of analysts because of its

strong influence on bond investors and rating agencies.

Governmental subdivision and industry organizations

must enact regulations to curb inappropriate and incorrect

opinions on bond transaction. Analysts and their

organizations that seriously mislead investors need to be

severely punished including fines and cancellation of

qualifications. If an analyst is found to have conspired

with the company management, such conduct constitutes a

crime and is subject to criminal law sanctions.

Bond rating agencies should not only have certain

professional qualifications, but also fulfill their rating

tasks with due diligence. Especially, they should not

conspire with bond issuing companies. The biggest

scandal of credit rating industry in 2018 was the suspected

high-priced sales credit rating by Dagong Global Credit

Rating (During the period from November 2017 to March 2018,

Dagong Global Credit Rating directly demanded consulting fees

while providing credit rating services for the company. For

example, after two working days Xinguang Company paid a high

consulting fee, Xinguang Debt rating has been quickly adjusted

from AA to AA+), which had a serious warning, rectifying

within a time limit, and suspending the business related to

the debt financing instrument market for one year.

National Association of Financial Market Institutional

Investors (NAFMII) has also announced that it will accept

S&P Credit Rating (China) and enter the interbank bond

market for the registration of bond rating business.

Fortunately, National Development and Reform

Commission (NDRC) has carried out the credit evaluation

of the principal underwriters of corporate bonds and credit

rating agencies in 2019. The evaluation indexes are

divided into credit behavior index, business ability index,

and expert & institution evaluation index. The evaluation

results will be published on the “Credit China” website

and included in the credit files of enterprise bond

intermediaries. According to the evaluation results, the

main underwriters and rating agencies are classified and

managed, and the punishment measures of trustworthiness

incentive and dishonesty are implemented.

Of course, it is also necessary to establish a corporate

credit evaluation system, improve accounting standards

and corporate governance mechanism, which are foundation

for containing corporate profit manipulation [30,31].

Acknowledgements

The author would like to thank the instructions of

Professor De-ren XIE (Tsinghua University) and Professor

Zhao-guo ZHANG (Huazhong University of Science

and Technology). This work is supported by the

National Social Science Foundation (No. 13BJY018)

and Enterprise Management Research Center of CTBU

(No. QYGLTD201802).

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Earnings Management, Analyst Forecasts and Credit Rating