The Relevance of Audit Quality, Debt Financing and Earnings

Management

HUI JING

Lyceum of the Philippines University Manila Campus,

Manila 1002,

PHILIPPINES

Abstract: - The relevance of audit quality, DF, and EM is a topic of common concern at home and abroad. The

research first analyzes the DF mode, EM motivation, and financial audit mechanism. Then the modified Jones

model is applied to EM. Two regression models are constructed by introducing control variables and adjustment

variables. According to the empirical results of 11835 observed sample values, there are differences in the degree

of earnings management among A-share companies. The average accrued profit is 0.063, the maximum value is

3.960, the minimum value is 0, and the standard deviation is 0.091. The situation of different listed companies

getting debt financing increments varies greatly, with an average value of 0.095 and a standard deviation of

0.214. The average asset-liability ratio is 0.429, and the average enterprise size is 22.215. Correlation analysis

shows that there is a positive correlation between bank loan increment, total debt increment, commercial credit

increment, and earnings management behavior, while there is a negative correlation between audit quality and

earnings management. The regression analysis results show that there is a positive relationship between the total

increase in corporate debt, the increase in commercial credit, the increase in bank loans, and the degree of

earnings management, while there is a negative correlation between audit quality and the degree of earnings

management.

Key-Words: - Debt financing; Earnings management; Financial audit; Modified Jones model; Regression model

Received: March 29, 2023. Revised: September 18, 2023. Accepted: September 24, 2023. Published: October 6, 2023.

1 Introduction

The audit work is mainly to review the financial

report of the enterprise and give relevant suggestions.

The audit quality (AQ) is closely related to the

accountants' workability, professional quality, the

size of the accounting firm, and the risk of

undertaking audit work, [1]. Because the earnings

management (EM) behavior of enterprise

management has always existed, it is a "legal" tax

reduction behavior. If the accountant cannot reveal

the financial problems in the audit work, the

high-quality audit work cannot be guaranteed, [2].

There is rich research on EM at home and abroad,

mainly focusing on two aspects. EM mainly

represents the management of businesses. The

financial report and EM behavior of the enterprise are

the active management behavior of businesses.

Businesses often use two management techniques for

EM: accrual items and real operating earnings, [3]. At

this stage, most scholars focus on the impact of DF on

EM, but there is no clear classification of debt

increment and increment methods, [4]. To investigate

the effects of DF on EM, this study introduces

financial audits. Based on the analysis of the

correlation among the three, the regression analysis

model of debt financing, EM, and financial audit is

established to improve the EM of enterprises.

2 Related Works and Research

Hypothesis

2.1 Related Work

EM is usually used to operate the profit balance point

by enterprise management, which has a two-way

promoting and inhibiting effect. In, [5], the authors

collected listed companies in Vietnam from 2007 to

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2016 as data samples to discuss the impact of CEO

rights on EM. The results of regression analysis

showed that CEO rights had a positive impact on EM.

In enterprises with high foreign shareholding, this

effect was more significant. In other words, the

potential dual role of foreign investors should be

considered by policymakers with oversight

responsibilities. Taking the directors of Indonesian

manufacturing companies as a sample, [6], discussed

the impact of bonus motivation, political motivation,

debt contract motivation, and tax motivation on EM.

The results of multiple regression analysis showed

that these four motivations had an impact on EM

practice. The highest motivation of directors for EM

was political cost motivation. The second was bonus

incentive, tax incentive, and debt contract incentive,

which provided evidence for the potential motivation

of directors to encourage EM practice. In, [7], the

authors studied the relationship between EM and the

culture of time-honored enterprises. The results

showed that the accrued EM and actual EM of

time-honored enterprises were much lower than

those of other enterprises. The transition between

corporate EM and culture came from cultural

infiltration with senior management. The association

between trusted brands and EM was significantly

moderated by property rights and incentive

compatibility. In, [8], the authors analyzed the impact

of accrual earnings management (AEM), the

adoption of international financial reporting

standards (IFRS), and the stock market integration of

Latin American Integrated Market (MILA)

companies. A multi-level mixed model was used for

robustness analysis. The findings demonstrated that

businesses adopted AEM as a premeditated tactic to

erode corporate governance. Agency costs were

reduced through the introduction of MILA and the

adoption of IFRS. In, [9], the authors used EM to

measure the mentality of enterprise managers. The

discretionary accruals represented the EM of

enterprises. The results showed that EM had a

negative correlation with the probability and

frequency of stock repurchase. The scale of EM

could be used as a reliable indicator of company

valuation. In, [10], the authors used the panel data set

of 250 companies in the Baltic Sea from 2012 to

2016 to analyze the relationship between EM and

reporting complexity. The findings revealed that EM

was completed about 6-11% of the company year.

EM and reporting complexity were positively

correlated, although this association was only seen in

enterprises with higher liquidity.

It is noteworthy that AQ is also closely related to

EM. Generally, AQ can effectively promote the real

EM of enterprises. In, [11], the authors studied

whether audit companies would hire former Public

Company Accounting Oversight Board (PCAOB)

employees based on negative PCAOB inspection

reports and whether such employment would reduce

future inspection defects and improve audit quality.

The findings revealed a strong correlation between

the amounts of deficiencies listed in prior inspection

reports and the number of PCAOB staff working for

large audit firms. However, there was a bad

correlation between the number of former PCAOB

personnel employed and the number of problems in

the company's subsequent inspection reports. It

indicated that the former PCAOB personnel had

higher audit professional quality, which improved the

AQ. In, [12], the authors used reputation theory to

predict the potential personal costs of auditors when

performing their professional obligations. The

experiment found that auditors with negative

reputations faced more stringent choices when

predicting budget overruns. The auditor's perceived

reputation directly affected AQ. In, [13], the authors

discussed the impact of external auditors' regular

interaction with some parties in their work, such as

accounting firms, audit team members, and

customers on audit quality. The AQ was translated

into improved financial reports, which depended on

the stakeholders of audited financial statements to

make wise business decisions. The results verified

the essential function of auditors' view on the fair

treatment of partners in AQ. In, [14], the authors

studied the impact of geographic distance between

audit partners and customers on audit quality. The

research uses modeling distance to match partners

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and customers. The result showed that the

geographical distance between partners and potential

customers was an important matching criterion. If

other qualities of a partner are more significant, they

will also be weighed again. In addition, when the

partner is farther away from the customer, the AQ

will be reduced.

To sum up, there are a large number of research

data on debt financing (DF) and earning management

at home and abroad, mainly including EM

motivation, DF scale, and DF sources. However, the

analysis of DF and EM on the quality of financial

audits is relatively small. The study takes the quality

of financial audit as an adjusting variable to explore

the impact of DF and EM, aiming to provide

effective ideas for EM of enterprises.

The advantage of this study is that it takes the

quality of financial audit as a moderating variable to

explore the impact between debt Financing and

earnings management. In the existing literature, there

is relatively little research on the relationship

between debt Financing and earnings management in

terms of financial audit quality. This study fills this

research gap and provides a new perspective for

further revealing the relationship between debt

Financing and earnings management. At the same

time, the use of the modified Jones model to measure

the degree of earnings management provides more

realistic and accurate results, providing a more

reliable data source for research. In summary, this

study is innovative and practical.

2.2 Research Hypothesis

As for the relationship between DF and EM, through

the analysis of principal-agent theory and contract

theory, the relationship between the increment and

source of debt and EM is obtained. In contract theory,

restrictive treaties and debt contracts influence each

other. In contract theory, restrictive treaties and debt

contracts influence each other. With the increase of

corporate debt, in order not to violate the contract,

managers must make the enterprise realize well

before creditors through EM. When creditors

increase their investment, they will also add the

treaty in the contract to restrict the operators. Once a

breach of contract is discovered by creditors, it may

bring great pressure on the operation of the enterprise,

[15]. It will also promote the business operators to

achieve non-default through the adjustment of

earnings information. In the principal-agent theory,

from the perspective of financing, the relationship

between the two parties is the relationship between

the creditor and the debtor. The entrusting party

hopes to obtain a stable income after lending their

funds. The operator hopes to use the funds in

high-risk and high-return projects. There will also be

many problems between the two parties, [16].

Therefore, hypothesis 1 is put forward, that is, the

EM behavior of enterprises is positively correlated

with the increase of DF.

In view of the relationship between DF and EM,

domestic enterprises often obtain financing through

bank loans. Before the bank loans, they will know

the actual financial information, debt repayment

ability, and profitability of the enterprise through the

audit of the bank's financial experts and regulatory

authorities in advance, [17]. The ability of enterprises

to obtain bank loans increases with the addition of

earnings information. To obtain lower-cost loans,

enterprises are more active in EM. Therefore,

hypothesis 2 is proposed, that is, the degree of EM of

enterprises with more bank loans will also increase.

From the perspective of signal transmission theory,

enterprise investors often audit through a

high-quality third-party institution to strengthen the

transparency of the financial information of the

enterprise, deliver relatively reliable investment

information to the majority of investors, and improve

the trust between investors and enterprises. The

supplier determines whether to continue to cooperate

with the enterprise and provide the source of goods

through the financial information and operating

ability of the enterprise. Therefore, enterprises pay

great attention to the credit of enterprises in the

operation. This also urges operators to establish good

financial information through EM to ensure

cooperation between upstream and downstream

enterprises. Therefore, hypothesis 3 is put forward,

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that is, business credit loans of enterprises increase

with the deepening of EM, [18]. Although China's

bond market is still at the initial stage, there are still a

small number of enterprises with strong operating

ability and good performance that use bond

financing.

The higher the audit quality, the lower the impact on

debt financing and earnings management

The higher the

audit quality, the

lower the impact

on bank loans,

commercial use

and earnings

management

Audit quality has

little impact on

corporate bond

financing and

earnings

management

H2 H3

Three hypotheses

Audit quality Debt financing

Earnings

management

Fig. 1: Three hypotheses

After financing through the bond market, these

enterprises will disclose their financing information

to make it more transparent. Investors and financial

analysts can better supervise, and also connect with

the interests of enterprises. To establish a good

corporate image and win the trust of investors in the

capital market, such enterprises do not need the

operation of EM. Therefore, hypothesis 4 is put

forward, that is, the increase in corporate bond

financing has little impact on EM.

In view of the relationship among debt financing,

EM, and audit quality, listed bond financing

enterprises are subject to strict supervision by

relevant departments, and the company's financial

management is more rigorous. Enterprises have good

operating ability, and EM operations are also few.

Therefore, bond financing enterprises have little

impact on EM, [19]. Therefore, three hypotheses are

proposed. Firstly, the higher the AQ is, the lower the

impact on DF and EM. Secondly, the stronger the

AQ is, the lower the impact on bank loans,

commercial use, and EM. Thirdly, the AQ has no

effect on corporate bond financing and EM. In Figure

1, hypotheses are displayed.

3 Multiple Regression Model of Debt

Financing, EM, and Audit Quality

3.1 Definition and Calculation of Variables

At present, the models commonly used in

quantitative EM are the time series model and the

Jones model. Due to the systematic limitation of the

time series model on time and EM, the development

status of China's capital market cannot be analyzed

using this model. Jones model and modified Jones

model, which are developing faster at home and

abroad, are more in line with domestic EM. These

two models are relatively more realistic and accurate

in representing the EM of enterprises and are more

suitable for verifying empirical assumptions. Both of

these models represent the degree of EM of the

enterprise with controllable accrued profits. Firstly,

the non-controllable accruals model is obtained by

model fitting. Then the controllable accruals are

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obtained through the difference between the total

accruals and the non-controllable accruals. In view of

the fact that the Jones model sets revenue as

non-controllable accrued profit, this study selects the

improved Jones model to replace the degree of EM

to prevent the calculation error of EM, [20].

Combined with the research purpose,

absEM

refers

to the absolute value of the controllable accrued

profits. The calculation formula of the model is

shown in Formula (1).

, , ,

,..

1 2 3 .

, 1 , 1 , 1 , 1

,. . .

1 2 3

, 1 , 1 , 1 , 1

, 1 ,

, 1 , 1

* * *

i t i t i t

it i t i t it

i t i t i t i t

it i t i t i t

i t i t i t i t

i t i t

TAC NE OCF

TAC REV PPE

A A A A

NAC REV REC PPE

A A A A

TAC NAC

DAC AA

   

  

   











   





  









(1)

Formula (1),

,it

and

,it

TAC

refer to the net

profit and accrued profit of the enterprise

at the

end of the accounting year

,it

NAC

,it

DAC

and

,it

OCF

represent the non-controllable accrued profit,

controllable accrued profit, and net cash flow of

operating activities of the enterprise

at the end of

the accounting year

. The total assets of the

enterprise

in time

1t

are expressed in

,1it

A

.it

REV

refers to the growth of the current operating

income, which is the difference between the current

period and the previous period in the main operating

income.

.it

PPE

refers to the original value of fixed

assets in the current period. The increase in accounts

receivable in the current period compared with the

previous period is indicated by

.it

REC

,it

DAC

the controllable accrued profits in the

period of

the enterprise

after the total assets correction.

absEM

can also refer to the degree of EM. The

enterprise's EM level increases with the addition of

the absolute value of controllable accruals.

The multiple regression model is a widely used

statistical analysis method in fields such as finance

and economics. Its basic idea is to use multiple

independent variables (or predictive factors) to

predict the dependent variable (or outcome variable).

In the research, a multiple regression model is used

to study the impact of debt Financing, EM, and audit

quality on corporate earnings. In the study, we

construct a multiple regression model with earnings

as a dependent variable and debt Financing, EM, and

audit quality as independent variables. By fitting the

sample data, the impact coefficients of each variable

on corporate earnings can be obtained, and it is also

possible to observe whether there is a significant

correlation between them. By analyzing the results of

the regression equation, conclusions and experiences

can be drawn, which can provide decision-making

suggestions for enterprise decision-makers.

It should be noted that multiple regression models

can only predict the correlation between the

dependent variable and the independent variable, and

cannot explain causal relationships. Therefore, in

practical applications, it is necessary to carefully

analyze the regression results and pay attention to

controlling for other possible influencing factors. For

the "multiple regression model of debt Financing,

EM and audit quality", the definition and calculation

of variables are also very critical. In this model, debt

Financing, EM, and audit quality are all regarded as

independent variables, while corporate earnings are

regarded as dependent variables. Among them, debt

Financing refers to enterprises obtaining funds by

issuing bonds to creditors or loans to banks and other

financial institutions. The debt financing ratio refers

to the proportion of total liabilities of an enterprise in

total assets. The calculation method is debt Financing

ratio=total liabilities/total assets; The size of an

enterprise's business scale is generally measured by

total assets. There is a certain relationship between

EM and corporate earnings, and generally speaking,

larger enterprises have relatively higher levels of

earnings. The calculation method for EM is total

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assets; Audit quality refers to the quality of an

auditor's audit of a company's financial statements.

Generally, factors such as the size of the auditing

firm, audit fees, auditor qualifications, and

experience are used to reflect audit quality. Among

them, audit fees are considered an important

indicator reflecting audit quality. The calculation

methods of audit quality variables can be selected

and compared based on actual situations; Corporate

surplus refers to the sum of a company's net profit

and accrued profit. In a multiple regression model,

corporate earnings are used as the dependent variable,

and the relationship between other independent

variables and corporate earnings needs to be obtained

through regression analysis. After determining the

definitions and calculation methods of each variable,

multiple regression models can be used to study their

interactions. Through multiple regression analysis,

the degree to which each independent variable affects

corporate earnings can be determined.

The explanatory variables of the study are DF

methods, including corporate bond increment,

commercial credit increment, and bank loan

increment of listed companies. Enterprise DF

accounts are shown in Table 1.

Table 1. Corporate DF subjects

Non-current liabilities

Belong to DF

account

Does not belong to debt

financing

Long-term loan

Bonds payable

Including:

preferred stock

Perpetual bond

Long-term payables

Long-term payroll payable

Estimated liabilities

Deferred income

Deferred Tax Liability

Other non-current liabilities

Current liabilities

Belong to DF

account

Does not belong to DF

account

Short-term borrowings

Trading financial assets

Financial liabilities measured at fair value with changes included in

current profit and loss

Derivative financial liabilities

Notes payable

Advance receipts

Employee compensation payable

Taxes payable

Other payables

Including:

Interest payable

Dividends payable

Liabilities held for sale

Non-current liabilities due within one year

Other current liabilities

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DF is mainly concentrated on long-term loans,

bonds payable, long-term accounts payable, and

short-term loans and notes payable. According to

previous studies, the calculation method of total debt

increment

is the ratio of the difference between

the total debt at the end of the period and the

beginning of the period and the total assets at the end

of the period. The calculation formula of corporate

debt increment

CBI

is the ratio of the difference

between the ending value and the beginning value of

bonds payable and the total value of assets at the end

of the period. When calculating the commercial

credit increment

, the first step is to get the

difference between the sum and difference of the

closing value of the advance receipts, notes payable,

and accounts payable, and then calculate the ratio of

the difference to the total value of the closing assets,

which is the commercial increment. When

calculating the increment

BLI

of bank loans, the

first step is to calculate the difference between the

sum of the ending value of short-term and short-term

loans and the sum of the initial value of long-term

and short-term loans, and then divide the difference

by the total amount of assets at the end of the period

to obtain the increment of bank loans (Table 2).

Table 2. Definition and calculation method of variables

Variable

classification

Variable

Symbolic

representation

Corresponding calculation formula

Control variable

Industry virtualization

ndI

Industry dummy variable

Annual virtual

earY

Annual dummy variable

Situation of the Board of

Supervisors

pvboS ard

Number of members of the Board of Supervisors

Independence of the Board of

Directors

ndI

Percentage of independent directors on the Board

of Directors

Tobin Q value

obQT

Ratio of stock market value to total assets at the

end of the period

Proportion of fixed assets

Ratio of fixed assets to total assets at the end of

the period

Net cash flow from operating

activities

OCF

Ratio of net cash flow from operating activities to

total assets at the end of the period

Return on total assets

ROA

Proportion of net profit to total assets at the end

of the period

Company size

Size

Natural logarithm of total assets at the end of the

period

Asset-liability ratio

evL

Ratio of total liabilities to total assets at the end of

the period

Adjusting

variables

Audit quality

The value of audit passed by the top ten

accounting firms is 1, and the value of failed audit

is 0

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Unqualified comments with emphasis

Unable to express opinions

Reserved opinions

Negative opinion

Standard

audit

opinion

Fig. 2: Audit opinion type

The definitions and related explanations of the

control variables and adjustment variables selected in

the study are shown in Table 1. The asset-liability

ratio is used to evaluate the state of the financial

leverage of an enterprise, which can reflect the

financial risk to a certain extent. In the stage of debt

financing, if the creditors have a significant increase

in liabilities or a gradual decline in operating

conditions, the company will encounter financial risk

problems, which will lead to EM motivation. The AQ

is determined based on the audit opinions. For the

adjustment variable, the value audited by the top ten

accounting firms is 1, otherwise, it is set to 0. The

types of audit opinions are shown in Figure 2.

From Figure 2, the main classification nodes of

AQ are different types of audit reports. The two basic

categories are non-standard audit opinions and

standard audit opinions. The standard audit opinion

refers to the unqualified audit opinion. Non-standard

audit opinions include unqualified audit opinions

with emphasis on matters, qualified opinions,

negative opinions, and opinions that cannot be

expressed. In addition, in terms of company size, the

company size can influence the EM through both

income and cost. When the scale of the company is

small, the degree of EM is relatively high, because

the regulations and management of the enterprise

need to be further improved. On the contrary, the EM

is relatively low. The return on total assets can not

only reflect the profitability of the enterprise but also

display the relevant performance of the enterprise

management. The net cash flow from operating

activities reflects whether the enterprise has

financing needs. When the enterprise has more cash

flow, the financing demand will be reduced, and the

probability of normal operation of the company is

high. On the contrary, enterprises will have a strong

demand for financing, which will urge managers to

conduct EM. In the case of imperfect new accounting

standards and related systems, the proportion of fixed

assets may urge the enterprise managers to conduct

EM. When the Tobin Q value is greater than 1, the

enterprise will not make additional investments. On

the contrary, enterprises will increase corresponding

investments. In this state, it may urge the enterprise

managers to conduct EM. The higher the

independence of the board of directors is, the

stronger the corresponding role of supervision and

governance. It can reduce the EM behavior of

enterprises. The board of supervisors has the

responsibility of restriction and supervision within

the enterprise, which can ensure the maximization of

shareholders' rights and interests and standardize the

operation of the company. The larger the size of the

board of supervisors is, the stronger the

corresponding supervision ability of the enterprise,

thus reducing the degree of EM of the enterprise. For

the annual dummy variable, the years from 2014 to

2018 are set as 1, and the remaining years are 0.

According to the industry classification standard of

the China Securities Regulatory Commission in 2013,

the manufacturing industry and non-manufacturing

industry are set as Level 2 and Level 1 respectively.

The meaning of the explanatory variable and

explained variable is displayed in Table 3.

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Table 3. Definition and calculation method of

variables

Variable

type

Variable

name

Variable

code

Variable calculation

method

Interpreted

variable

Accrued

profit

(operable

part)

absEM

Calculation according

to Jones model

Explanatory

variable

Increase in

debt

Quotient value

between the amount of

debt increase and the

total amount of assets

at the end of the

accounting year

Increase in

corporate

bonds

CBI

Quotient value

between the increased

amount of corporate

bonds and the total

amount of assets at the

end of the accounting

year

Increase in

business

credit

Quotient value

between the increased

amount of commercial

credit and the total

amount of assets at the

end of the accounting

year

Increase in

bank loans

BLI

The quotient between

the increased amount

of bank loans and the

total amount of assets

at the end of the

accounting year

From Table 3, the research mainly uses four

variables, namely, the increase in debt, the increase

in corporate bonds, the increase in commercial credit,

and the increase in bank loans, to explain the accrued

profits (the operational part).

3.2 Sample Selection and Regression Model

The research selects an A-share company as the

research object. The selected time period is from

2014 to 2018. The sample of companies with a lack

of data and an asset-liability ratio greater than 1 are

eliminated. At the same time, the sample is processed

by Winsorize. Finally, 2839 listed companies with

11834 observation samples in five years are selected.

The annual distribution of sample data is shown in

Figure 1. From Figure 1, in five years, some

enterprise data fail to meet the requirements and are

excluded from the sample. The most excluded data is

in 2015, up to 653. The least excluded year is 2018,

with 128. On the whole, the annual distribution of

samples is reasonable and uniform. The financial

data is from the Guotai'an database (Figure 3).

2199 2186 2270 2468 2711

18.58 18.47 19.18

20.86

22.91

500

1000

1500

2000

2500

3000

2014 2015 2016 2017 2018

Number of samples

Number of

samples Percentage /%

Percentage /%

Fig. 3: Distribution of selected samples

To verify the impact of DF methods and DF on EM,

regression model 1 is created to test. The calculation

method is Formula (2).

, 1 0 1 , 2 , 3 ,

4 , 5 . 6 , 7 , 8 ,

9 , , , ,

i t i t i t i t

i t i t i t i t i t

i t i t i t i t

absEM Debt Size ROA

OCF Lev AS TobQ Ind

SpvBoard IND Year

   

    



    

    

  



(2)

Formula (2),

,it



refers to the random error term.



is a constant term.

aa

refers to the

parameters of corresponding variables.

aa

refers to four explanatory variables. A regression

model 2 is established to verify the impact of AQ on

the correlation between DF and EM. The calculation

formula is shown in Formula (3).

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, 1 0 1 , 2 ,

3 , 4 , 5 . 6 ,

7 , 8 , 9 ,

10 , , 11 , , , ,

i t i t i t

i t i t i t i t

i t i t i t

i t i t i t i t i t i t

absEM Debt Size

ROA OCF Lev AS

TobQ Ind SpvBoard

Debt AQ AQ IND Year

  

   

  

  

   

  

   



(3)

4 Analysis of Empirical Results

4.1 Descriptive Statistics

Figure 2 displays descriptive statistical analysis

results, with a total of 11835 observed sample values.

The average value of controllable accrued profit is

0.063. The maximum value is 3.960, and the

minimum value is 0, which indicates that there is a

large gap in the EM of a few enterprises. The

standard deviation of this indicator is relatively small,

with a value of 0.091, which indicates that the gap of

EM degree of most A-share enterprises is relatively

small. In terms of explanatory variables, the

minimum, maximum, average, and standard

deviation of total debt increment is -0.28, 1.317,

0.095, and 0.214 respectively. It means that there are

certain differences between different listed

companies in obtaining DF increments, and a few

companies have debt increments. This data reflects

the correlation between EM and debt increment to

some extent. For the three ways of debt financing,

the difference between the minimum and maximum

value of commercial credit increment and bank loan

increment is the most obvious, and the average is

smaller of the three. The minimum gap and average

corporate debt increment are small, which indicates

that corporate bond financing is rare and the amount

of financing is small. In terms of control variables,

the average, median, maximum, and minimum

asset-liability ratios are 0.429, 0.419, 0.898, and

0.053 respectively. This shows that the asset-liability

ratio of most enterprises is at a relatively low level,

and the enterprises have very serious insolvency, or

the funds are not fully utilized. The average, median,

maximum, and minimum company sizes are 22.215,

0.419, 0.898, and 0.053 respectively, which shows

that there is a large gap between the size of different

listed companies, and the size of listed companies is

relatively large. The average, median, maximum, and

minimum return on total assets is 0.042, 0.037, 0.205,

and -0.144 respectively. The average, median,

maximum, and minimum of net cash flow from

operating activities are 0.041, 0.041, 0.234, and

-0.169 respectively, which indicates that the overall

cash level of listed companies is poor and has urgent

financing needs. For the adjustment variable, the

average value of AQ is 0.631, which shows that

63.1% of the sample listed companies have been

audited by the top ten domestic accounting firms

(Figure 4).

4.2 Correlation Analysis

The correlation between numerous variables is

investigated in this study. Table 4 (Appendix)

displays the particular outcomes.

From Table 4 (Appendix), the correlation

coefficient between control variables and explanatory

factors is less than 0.40, which indicates that there is

only a weak correlation between them.

Therefore, the analysis of model regression does

not have a multicollinearity issue. The relationship

between the three variables of bank loan increment,

enterprise total debt increment, and business credit

increment and EM behavior is a positive correlation.

The correlation coefficient between AQ and EM is

below 0, which demonstrates that the relationship

between them is negative.

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0.091 0.103 0.082 0.032 0.214

0.483

0.209

1.276

0.052 0.069 0.167

2.161

0.053

1.035

0.0

0.5

1.0

1.5

2.0

2.5

absEM BLI CI CBI DI AQ lev Size ROA OCF AS TobQ Ind Spvboard

Standard deviation

Average

Standard deviation

-5

absEM BLI CI CBI DI AQ lev Size ROA OCF AS TobQ Ind Spvboard

Minimum value

Median

Maximum

(a) Average and standard deviation results

(b) Median and maximum results

Minimum value / Median

Maximum

Fig. 4: Descriptive statistical analysis results

4.3 Regression Analysis

The model variables have a positive relationship with

the total increment of corporate debt, EM behavior,

commercial credit increment, and bank loan

increment. The EM has a negative correlation with

AQ. The absolute value between the control variable

and the explanatory variable of the regression model

is less than 0.4, so the model does not have a very

obvious linear problem. The required variables are

reasonable. *, **, and *** represent significant levels

at 1%, 5%, and 10% respectively, and the data in “()”

represent t values. The relationship between DF, DF

methods, and EM is shown in Table 5. From model 1,

the EM and the increase in corporate debt have a

significant positive relationship, with a regression

coefficient of 0.073. The degree and motivation of

EM increase with the growth of debt financing. The

1% significance level confirms the validity of initial

hypothesis 1. The degree of EM has a positive

relationship with bank loans, which is consistent with

hypothesis 2. The EM and the regression coefficient

of corporate debt increment are not significant,

which verifies hypothesis 4. The regression

coefficient between EM and corporate business

credit is 0.146, which verifies hypothesis 3. The

degree of EM increases with the improvement of

business credit. Enterprises with better business

credit have more customer control advantages. If the

repayment ability of the enterprise is weakened, the

normal operation of the enterprise will be seriously

affected, because there is no necessary raw material

supply. There is a significant positive correlation

between the asset-liability ratio and EM at 1%. An

increase in the debt ratio of enterprises will add to

the financial risk of enterprises. The regression

coefficients of the enterprise size and the return on

total assets are negative and positive respectively,

and show obvious correlation at the level of 1%.

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Therefore, enterprises can provide corporate profits

by processing financial information related to the

return on total assets, thus increasing the degree of

EM.

Table 5. The relationship between DF, DF methods, and EM in model

absEM（1）

absEM（2）

absEM（3）

absEM（4）

BLI

0.106***

(12.66)

0.146***

(14.25)

CBI

0.019

(0.65)

0.073***

(18.26)

evL

0.058***

(11.36)

0.056***

(10.88)

0.069***

(13.45)

0.048***

(9.28)

Size

-0.004***

(-4.04)

-0.003***

(-3.63)

-0.003***

(-3.70)

-0.004***

(-4.32)

ROA

0.139***

(7.20)

0.146***

(7.6)

0.174***

(9.06)

0.115***

(5.97)

OCF

-0.195***

(-13.83)

-0.248***

(-18.16)

-0.239***

(-17.38)

-0.205***

(-14.96)

-0.024***

(-4.50)

-0.013***

(-2.37)

-0.023***

(-4.25)

-0.015***

(-2.79)

obQT

0.005***

(10.95)

0.005***

(11.32)

0.005***

(11.08)

0.005***

(10.73)

ndI

0.015

(1.00)

0.020

(1.32)

0.016

(1.03)

0.018

(1.21)

pvboS ard

-0.001

(-1.09)

-0.001

(-1.10)

-0.002**

(-1.97)

-0.000

(-0.40)

-cons

0.107***

(5.46)

0.095***

(4.87)

0.101***

(5.13)

0.109***

(5.60)

11834

ndustryFEI

YES

earY FE

YES

110.28

115.38

91.65

130.84

0.0767

0.0800

0.0645

0.0899

The relationship between EM, AQ, and DF

method increment and DF increment in model 2 are

shown in Table 6. Under the influence of audit

quality, the impact of DF increment on EM changes.

The regression coefficient between the interaction of

AQ and total DF increment and the degree of EM is

-0.051, which shows a significant negative

correlation at the level of 1%, that is, the higher the

audit quality, the less the impact on DF and EM.

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Table 6. The relationship between EM, AQ, and DF method increment and DF increment in model 2

absEM（1）

absEM（2）

absEM（3）

absEM（4）

BLI

0.166***

(12.82)

0.205***

(12.98)

CBI

0.010

(1.04)

0.104***

(16.93)

-0.006***

(-3.54)

-0.005***

(-3.01)

-0.008***

(-4.79)

-0.004***

(-2.06)

*BLI AQ

-0.098***

(-6.05)

*CI AQ

-0.099***

(-6.90)

*CBI AQ

-0.091

(-0.70)

*DI AQ

-0.051***

(-6.60)

evL

0.059***

(11.41)

0.056***

(10.96)

0.068***

(13.38)

0.049***

(9.41)

Size

-0.003***

(-3.52)

-0.003***

(-3.21)

-0.003***

(-3.19)

-0.003***

(-3.87)

ROA

0.140***

(7.26)

0.149***

(7.80)

0.175***

(9.14)

0.117***

(6.11)

OCF

-0.193***

(-13.69)

-0.247***

(-18.14)

-0.237***

(-17.27)

-0.203***

(-14.89)

-0.024***

(-4.45)

-0.012***

(-2.30)

-0.023***

(-4.19)

-0.014***

(-2.71)

obQT

0.005***

(11.18)

0.006***

(11.52)

0.005***

(11.23)

0.005***

(11.02)

ndI

0.014

(0.92)

0.020

(1.31)

0.016

(1.04)

0.017

(1.13)

pvboS ard

-0.001

(-1.15)

-0.001

(-1.15)

-0.002**

(-2.04)

-0.000

(-0.42)

-cons

0.100***

(5.15)

0.090***

(4.61)

0.096***

(4.89)

0.102***

(5.27)

11834

ndustryFEI

YES

earY FE

YES

96.53

99.33

77.71

113.99

0.0816

0.0838

0.0666

0.0950

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The regression coefficient between AQ and bank

loan increment and EM is -0.098. The regression

coefficient between the interaction of AQ and

business credit and EM degree is -0.099, and both of

them show a significant negative correlation at the

level of 1%. The higher the audit quality, the lower

the impact on bank loans, commercial increment, and

EM. The regression coefficient of AQ and corporate

bonds to EM is -0.091, but it is not significant. In

regard to corporate bond financing and EM, AQ has

no impact, which is consistent with the three

presumptions mentioned above. Therefore, AQ can

greatly reduce the positive effect of bank loans and

commercial credit increment on EM, but it cannot

affect the regulatory effect of corporate debt

increment on EM.

This study uses a large number of data and

analytical methods to explore the relationship

between earnings management behavior and debt

Financing. The empirical results indicate that there is

a certain correlation between these two factors. At

the same time, the empirical results also indicate the

impact of the interaction between control variables

on this relationship. From the results of descriptive

statistical analysis, it can be seen that the degree of

earnings management of most enterprises is

relatively stable, and different listed enterprises have

certain differences in the amount of debt financing

increment. On this basis, through correlation analysis

and regression analysis, the following results are

obtained: First, the degree of earnings management is

positively related to debt Financing and the

increment of debt financing, while the degree of

earnings management is negatively related to audit

quality. This can be understood as that when facing

financing needs, enterprises will affect the increase

of debt Financing by adjusting earnings management

behavior in order to maintain profitability; Secondly,

from the results of regression analysis, it can be seen

that when interaction terms appear in the control

variables, audit quality has a moderating effect on the

relationship between bank loans, commercial credit

increment, and earnings management. This indicates

that in reality, more consideration needs to be given

to the impact of the interactions between different

variables on the results. In practical applications,

these results may have significant implications for

businesses and policymakers. For example, in the

case of high audit quality, to reduce the impact of

debt financing, enterprises can actively adjust

earnings management behavior to reduce financing

costs. At the government level, relevant policies can

be formulated to guide enterprises to achieve more

stable financial operations. In conclusion, this study

provides a method to explore the relationship

between earnings management and debt financing

behavior and suggests further exploring other

possible factors in future research to better

understand the interaction of these two factors.

Through empirical research on the data of A-share

listed companies, this study systematically analyzes

the relationship between debt financing increment

and earnings management and considers the impact

of multiple regulatory factors on the relationship,

including audit quality, debt Financing methods, etc.

The research results have high credibility and

explanatory power and have important reference

value for an in-depth understanding of corporate

financial management and its influencing factors. In

addition, in terms of research methods, the study

adopted a multiple regression analysis method to

consider multiple factors, effectively avoiding the

impact of a single factor, and more comprehensively

and objectively presenting the relationship between

debt Financing and earnings management. Therefore,

the advantage of this study is that it comprehensively

considers the impact of multiple factors on the

relationship between debt financing increment and

earnings management, and the results are highly

reliable and explanatory, which has enlightenment

and reference value for the practice and theoretical

research of enterprise financial management.

4.4 Robustness Test

In the robustness test, two kinds of test methods are

used to test the robustness of model parameters and

the regression function. Table 7 displays the results

of the model parameters' robustness test.

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Table 7. Robustness test

Sample

size

Change of

parameters

Variation range of variable parameters

Confidence

interval

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Standard

deviation

0.055

0.056

0.055

0.044

0.049

0.055

0.054

0.053

0.052

0.051

[0.047,0.055]

0.048

0.055

0.053

0.054

0.051

0.045

0.051

0.056

0.055

0.052

110

0.049

0.055

0.051

0.053

0.055

0.047

0.052

0.055

0.056

Table 8. Results of the robustness test of EM, DF, and DF methods

absEM（1）

absEM（2）

absEM（3）

absEM（4）

BLI

0.122***

(14.48)

0.194***

(18.81)

CBI

0.002

(1.02)

0.092***

(22.82)

evL

0.051***

(9.90)

0.046***

(8.96)

0.063***

(12.26)

0.037***

(7.17)

Size

-0.003***

(-3.28)

-0.003***

(-2.82)

-0.003***

(-2.88)

-0.003***

(-3.67)

ROA

0.132***

(6.81)

0.135***

(7.02)

0.173***

(8.92)

0.098***

(5.11)

OCF

-0.199***

(-14.00)

-0.261***

(-19.08)

-0.250***

(-17.98)

-0.207***

(-15.08)

-0.030***

(-5.65)

-0.016***

(-2.87)

-0.029***

(-5.35)

-0.019***

(-3.54)

obQT

0.006***

(12.00)

0.006***

(12.50)

0.006***

(12.13)

0.006***

(11.76)

ndI

0.005

(0.31)

0.011

(0.72)

0.005

(0.34)

0.008

(0.55)

pvboS ard

-0.001

(-0.81)

-0.001

(-0.66)

-0.002**

(-1.81)

-0.000

(-0.15)

-cons

0.099***

(5.02)

0.084***

(4.32)

0.92***

(4.65)

0.102***

(5.25)

11834

ndustryFEI

YES

earY FE

YES

122.70

139.89

98.15

159.83

dj.R-SquareA

0.0847

0.0955

0.0688

0.1078

The sample size changes along the scale of 50, 80,

and 110 in the test. The parameters vary from 0.0 to

0.9. With the change of parameters and sample size,

the confidence interval of the model is always within

the range of 0.047 to 0.055. The model itself has the

advantages of parameter operation robustness and

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performance. It can be inferred that even when

encountering small range parameter changes or

sample size changes in practical applications, the

model is likely to still maintain reliable performance.

Therefore, when using this model, it is more

reassuring to adjust parameters or change the scale of

data collection without worrying about the potential

impact on the performance of the model. This also

provides greater flexibility and security guarantees

for the practical application of the model. At the

same time, the application value of this model has

also been effectively validated in areas involving

high reliability or stability.

To ensure the stability of the proposed model, the

extended Jones model is used to verify the

practicability of the modified Jones model. The

robustness test results of EM, DF, and DF methods

are shown in Table 8. The commercial credit

increment, bank loan, total asset financing, and EM

still show a significant positive correlation, below

1%. There is no obvious correlation between the

increase in corporate debt and the degree of EM. The

robustness tests of EM, audit quality, DF, and DF

methods show that AQ is a good regulatory variable,

and significantly weakens the positive effect of the

increment of industrial credit, bank loan, and total

DF on EM. The AQ cannot well adjust the

relationship between the increase of corporate debt

and the degree of EM. This is consistent with the

hypothesis proposed in the study and the regression

results mentioned above. Therefore, the proposed

model is stable.

It can be considered that the modified Jones

model is reliable, and has certain practicability in

studying the relationship between earnings

management, debt financing, and debt financing. In

addition, audit quality also plays a certain role in

regulating this relationship. When enterprises

conduct earnings management, business credit

increment, bank loan increment, or total debt

financing increment, audit quality can play a role in

limiting and controlling, thus improving the

robustness and sustainability of enterprises.

Therefore, when conducting earnings management

and debt Financing, enterprises should improve audit

quality as much as possible, adopt reasonable and

stable financing methods, and avoid negative impacts

on the financial stability of enterprises. In conclusion,

the modified Jones model is reliable and practical in

studying the relationship between earnings

management, debt Financing, and debt Financing.

Audit quality can be used as a regulating variable to

control the earnings management behavior and debt

financing of enterprises. When conducting earnings

management and debt financing, enterprises should

pay attention to avoiding excessive borrowing and

earnings management, and adopt reasonable and

stable financing methods to ensure financial stability

and sustainable development.

5 Conclusion and Inspiration

Through theoretical analysis of the relationship

between AQ, DF, and EM, this paper puts forward

the interaction between different DF methods and

EM of enterprises, and the role of AQ in their impact.

The empirical analysis results show that more DF

enterprises, whether through bank loans or

commercial credit financing enterprises use the most,

trigger corporate managers to manage corporate

earnings, increase their advantages, and then make

corporate DF higher. Bond financing has little impact

on EM. AQ can reduce the impact of bank loans and

commercial credit DF on EM, but it has little impact

on bond financing. For the company, when faced

with operational and financial difficulties, the

company's operators should properly adopt EM

measures to avoid financial risks and default. At the

same time, they can also obtain more financing to

improve the production efficiency of enterprises.

However, earnings information should be used

reasonably. The long-term operation of the enterprise

should pay more attention. To support the enterprise's

successful development, the management capability

of the business is enhanced, making investors more

confident. For creditors, before and after investment,

creditors should reduce their dependence on the

evaluation of enterprise earnings information,

objectively and comprehensively understand the

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enterprise's operating ability and loan repayment

ability, and carefully examine the specific financial

information and profitability of the enterprise. In the

operation, it is also necessary to supervise, and

improve the debt contract, reduce the operation of

enterprises through surplus information, maintain the

operation of enterprises, understand the direction of

financing funds, ensure the safety of funds, and

reduce risks. For the market supervision department,

many listed companies in China have EM. It is

mainly caused by the information asymmetry

between the debtor and the debtor. Most financing

entities, including bank loans, only pay attention to

the financial information of the enterprise and rarely

know the specific scale and operating capacity of the

enterprise, which also increases the risk of financing.

Relevant departments should effectively monitor the

financial information of enterprises and the capital

trend of debt financing, and disclose major events of

the company.

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69-105.

[20] Bronson S N, Masli A, Schroeder J H.

Releasing Earnings when the Audit Is Less

Complete: Implications for Audit Quality and

the Auditor/Client Relationship, Accounting

Horizons, Vol.35, No.2, 2021, pp. 27-55.

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2023.20.191

Hui Jing

E-ISSN: 2224-2899

2222

Volume 20, 2023

APPENDIX

Table 4. Variable Correlation Analysis

absE

BLI

CBI

Lev

Size

ROA

OCF

TobQ

Ind

Spvb

oard

absE

1.000

0.074

***

0.096*

0.008

0.13

4***

-0.04

2***

0.07

8***

-0.04

2**

0.032

***

-0.12

7***

-0.15

6***

0.054

7***

0.024

-0.04

4***

BLI

0.173

***

1.000

0.196*

-0.01

0.62

3***

0.012

0.18

2***

0.101

***

-0.00

5***

-0.25

6***

-0.09

7***

-0.08

7***

0.008

-0.02

8***

0.151

***

0.323

***

1.000

0.024

***

0.63

8***

0.021

***

0.13

8***

0.097

***

0.126

0.027

***

-0.16

8***

-0.03

2***

-0.00

-0.05

7***

CBI

0.015

0.006

0.042*

1.000

0.13

8***

0.006

0.14

2***

0.168

***

-0.02

1***

-0.02

5***

-0.03

5***

-0.13

4***

-0.00

0.036

***

0.217

***

0.716

***

0.719*

0.176

***

1.00

0.018

***

0.24

4***

0.147

***

0.098

***

-0.16

9**

-0.20

3***

-0.07

8***

0.008

-0.06

7***

-0.05

6***

0.011

0.007*

-0.01

0.00

1.000

0.02

1**

-0.08

3***

0.035

***

0.052

***

0.013

-0.03

4***

0.001

0.015

Lev

0.065

***

0.487

***

0.156*

*****

0.083

***

0.21

7***

0.023

1.00

0.534

***

-0.41

5**

-0.16

8***

0.024

-0.62

7***

-0.01

0.232

***

Size

-0.05

4***

0.101

***

0.094*

0.087

***

0.13

4***

0.108

***

0.52

4***

1.000

-0.06

8***

0.043

***

0.009

-0.70

6***

-0.02

5***

0.297

***

ROA

0.013

-0.00

0.086

0.025

***

0.06

7***

0.042

***

-0.3

84**

-0.03

2**

1.000

0.387

***

-0.13

4***

0.364

***

-0.02

7***

-0.12

5***

OCF

-0.17

5***

-0.24

8***

0.013*

-0.05

6***

-0.1

51**

0.048

***

-0.1

75**

0.045

***

1.001

1.000

0.026

0.098

***

-0.02

5***

0.024

***

-0.12

6***

-0.08

8***

-0.154

***

-0.07

6***

-0.1

61**

0.017

0.07

0.086

***

0.386

***

0.263

***

1.000

-0.12

8***

-0.05

8***

0.157

***

Tob

0.103

***

-0.06

4***

-0.048

***

-0.05

2***

-0.0

54**

-0.02

7**

-0.4

61**

-0.58

8**

0.256

***

0.078

***

-0.16

7**

1.000

0.049

***

-0.29

4***

Ind

0.023

0.011

-0.014

-0.00

0.00

0.005

-0.0

17*

0.001

-0.01

7***

-0.02

4**

-0.05

6**

0.072

***

1.000

-0.08

5***

Spvb

oard

-0.05

1***

-0.04

2***

-0.053

***

-0.01

-0.0

0.017

0.22

6***

0.331

***

-0.09

4***

0.026

***

0.195

***

-0.21

4***

-0.09

7***

1.000

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The author contributed to the present research at all

stages from the formulation of the problem to the final

findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The author has no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2023.20.191

Hui Jing

E-ISSN: 2224-2899

2223

Volume 20, 2023