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
24 Journal of Finance and Economics
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),
Journal of Finance and Economics 25
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
26 Journal of Finance and Economics
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.
Journal of Finance and Economics 27
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].
28 Journal of Finance and Economics
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.
Journal of Finance and Economics 29
Table 9. The Multivariate Regression Results of Model (6)
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
30 Journal of Finance and Economics
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.
Table 10. The Multivariate Regression Results of Model (6)
Variable all enterprises state-owned enterprises private enterprises
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
Variable all enterprises state-owned enterprises private enterprises
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
Journal of Finance and Economics 31
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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