Accounting information quality detection method of listed companies based on mobile social network interaction model

Abstract

The existing quality of accounting information detection methods have large errors and cannot meet the needs of listed companies. Therefore, this paper presents a study on the accounting information quality detection methods of listed companies based on the mobile social network interaction model. The interactive model to obtain accounting information of listed companies based on mobile social network, select accounting information quality detection index, construct the accounting information quality detection index system; Determine the weight of accounting information quality detection index by hierarchical analysis method; construct a panel model based on detection index weight to realize the quality detection of accounting information of listed companies. The experimental results show that the accounting information quality detection error of this method is 0.10%–0.31%. It reduces detection error, improves detection accuracy, has good application performance, and is suitable for the promotion and application of accounting information quality detection.

Keywords

Interaction model of mobile social network listed company accounting information quality testing

1. Introduction

In the market economy, many enterprises such as listed companies are the main body of basic economic activities. Among them, listed companies have gradually become the dominant force in the market economy because of their advantages. However, in recent years, there are many cases of accounting information quality problems in listed companies, which highlights the weakness of securities market supervision, and also causes regulators and market investors to pay more attention to accounting information quality problems [1]. Our government supervision department has been encouraging to adopt more scientific and advanced audit methods to audit and identify the financial statements of the audited units. With the advent of the information audit era, more and more technologies and methods can be used. These means can help certified public accountants ensure the audit quality and improve the audit efficiency, and also provide good data support for the development of listed companies.

Enron company of the United States declared bankruptcy, people found that the data of “earnings per share” published by Enron company did not conform to Benford’s law, and the subsequent investigation also proved that the management of Enron company did cheat the financial data of “earnings per share”. After that, many scholars have studied Benford’s law, and the research in the field of financial accounting has proved the applicability of Benford’s law in detecting the quality of accounting information. Therefore, we can use Benford’s law to analyze the quality of accounting information [2]. However, using Benford’s law can only analyze the data that causes the problem, but can not determine the specific time point and location of the data. Therefore, the panel model is introduced into foreign research. If the model fits well, the points with large standardized residuals can be considered as outliers. Then the residual analysis results are combined with the disclosure of major events of listed companies, and the reliability and relevance of accounting information are analyzed combined with cases, so as to measure the quality of accounting information [3].

Information is the basis of statistical research and statistical work, and real and reliable information is the basis of statistical research and statistical work. However, in recent years, the quality of accounting information of listed companies has occurred from time to time, which has exposed the loopholes and weaknesses of market supervision, and also caused the attention of regulatory departments and accounting information users to the quality of accounting information. To test the quality of accounting information, improve the accounting information disclosure system, so as to improve the quality of accounting information is not only the matter of the relevant government departments, but also the responsibility of scholars from all walks of life [4].

It is of great significance to use statistical model to study the quality of accounting information. In terms of applicability, accounting information is based on financial data, while statistics is a subject that uses statistical methods to process and analyze data. Statistical methods are suitable for dealing with the quality of accounting information. From the efficiency point of view, the statistical software based on computer operation improves the analysis efficiency to a new height. Analysts only need to input the code to make the statistical software complete the complex operation tasks, which not only reduces the operation time, but also liberates the labor force. From the perspective of necessity, the advantages of traditional analysis methods and statistical analysis methods complement each other. Combining the high efficiency of statistical analysis methods with the analysis experience of accounting information analysts can not only effectively improve the efficiency of analysis, but also reduce the mistakes in analysis [5].

In recent years, Chinese scholars have conducted a lot of research on the detection of accounting information quality. Reference [4] improved the FLOS comprehensive detection model from the perspective of accrued quality, applies the improved FLOS comprehensive testing model to detect the quality of accounting information; uses the prediction error method and manipulated accruable profit temporary effect test model to maximize the output result of the model. Based on the accounting information reported by Shandong local banks by the People’s Bank of China, through case analysis and empirical research, Reference [5] built an evaluation index system of accounting quality, study the influencing factors of accounting information quality, and put forward policy suggestions on how to improve the quality of accounting information of commercial banks.

However, because the above research results do not consider the impact of the mobile network interaction on the accounting information statistics, the detection indicators are unreasonable, and the error of the detection results is large, which can not meet the needs of today’s society. Therefore, this paper puts forward the accounting information quality detection method of listed companies based on the mobile social network interaction model.

The innovation point of this paper is to build the interactive model of the accounting information of listed companies based on the mobile social network, and collect the accounting information of listed companies; Screen and construct the accounting information quality detection index system according to the characteristics of listed companies and mobile social network; Determine the weight of accounting information quality detection index; construct the panel model based on the weight of detection index, and use residual analysis to realize the quality detection of accounting information of listed companies. The experimental results show that the application of this method to detect the quality of listed company accounting information has small error, high accuracy and practical application value.

2. The quality testing method of accounting information of listed companies

2.1 Access to accounting information of listed companies

In recent years, with the rapid development and increasing popularity of mobile devices and mobile networks, network users are no longer limited by time and place, and can interact with others anytime and anywhere. Social network services begin to transfer from PC to mobile. Based on this, mobile social network has become popular, which has once again changed the interaction habits of network users, and also changed the market. Therefore, this study is based on the mobile social network interaction model to obtain the accounting information of listed companies and prepare for the subsequent quality inspection of accounting information [6].

The interaction model of mobile social network is the extension and development of social network. In essence, it does not break through the interaction mode of social network. There are many similarities between the two, but it is not only the transfer from the traditional PC web page to the mobile app, such as the location-based service function on the mobile terminal. Mobile social app has developed rapidly and has a wide variety, such as WeChat, Momo, Maimai, etc. SNS websites have also launched mobile versions, such as Facebook mobile terminal, Renren mobile terminal, Microblog mobile terminal, etc., obviously, due to the portability of mobile devices, it can better meet the practical needs of social networking of large users [7].

The characteristics of mobile social network interaction model are as follows:

(1)
Immediacy

Immediacy benefits from the development of mobile Internet technology, which is the most prominent feature of mobile social networks. Traditional social networks rely on relatively fixed terminals such as PC, which is not convenient to carry and can not meet the needs of users to access the Internet anytime and anywhere. However, with the development of mobile Internet technology and the fourth generation mobile communication technology, users can break through the limitation of space and time through the fourth generation mobile communication technology of mobile terminal, and surf the Internet anytime and anywhere. Therefore, a large number of traditional social users are transferred to the mobile society.
(2)
Security

Traditional social network account theft is common, complex password is more need to consume memory, retrieve password is more need a variety of procedures. And today’s mobile social account security is very high. Mobile social users need to be bound with mobile phone number when registering mobile social applications, which ensures the security of account and prevents theft [8]. Even if the mobile phone is lost, the account can be retrieved quickly through the SMS verification code received after the replacement of the mobile phone number, which protects the security interests of users to the greatest extent.
(3)
Popularization

Mobile social network account registration is convenient, usually using the mobile phone number to register. Besides password login, you can also login by SMS verification or associated account login. Compared with the traditional social network, this way of using has greatly improved the cumbersome procedures, which makes the mobile social platform not only attract young people who pursue new things to join, but also attract a large number of middle-aged and elderly people to join, so that online social networking can be popularized in the public.
(4)
Collectivization

As mobile social networks become more and more mature, the number of users is also increasing rapidly, showing different needs for social networks. Mobile social users with different interests, hobbies and goals gradually show the characteristics of group, and individuals freely choose to join the group to meet the information needs.

Mobile social network interaction refers to interaction, which refers to the process of interaction or reflection between individual users or user groups. All human social activities are basically based on this kind of interactive behavior. Because social activities are based on the interaction between individuals, individuals and groups, groups and groups, interaction theory emerges as the times require, which has a certain guiding role in the study of social activities. As early as the beginning of last century, the sociologist Simmel put forward the term interaction. He believed that society is the sum of interaction by some people, and interaction behavior is a very common social phenomenon. From the perspective of communication, Wiener believes that interaction is a process of two-way communication, in which the information receiver receives the information from the source, and both sides constantly feedback and modify the information. From the above definition of interaction, interaction is at least involved in the process of communication between the two sides. The development of the Internet fully meets the needs of users’ interaction in the network, from the earliest e-mail to the later information interaction environment such as forum, Microblog, social network and mobile social network. Therefore, this section obtains the accounting information of listed companies through the interactive behavior of mobile social network [9].
2.2 The selection of accounting information quality testing indicators

Based on the above-mentioned accounting information of listed companies, this paper defines the quality of accounting information, and selects the quality detection indicators of accounting information of listed companies:

The most important function of accounting information is to process the relevant data generated by value movement in the process of company’s business activities. According to the prescribed accounting system, regulations, methods and procedures, the accounting department processes these data into information that is easy to be used by the users of accounting information and helps to make decisions. On the one hand, accounting information records the past financial information through financial statements, such as balance sheet, cash flow statement, income statement, etc. On the other hand, through the rational use of accounting information, it can also predict the future economic activities and decide whether to change the current decision [10].

Accounting information is generally presented to investors, creditors or other information users by the internal accounting department of the company in the form of financial statements, financial reports or notes [11]. Therefore, the analysis of the quality of accounting information should start from the financial statements. High quality accounting information is the basic requirement of the accounting information provided by the financial statements of enterprises. It is also the basic feature that the accounting information provided in the financial statements can play a decision-making role for creditors, investors or other information users. These basic characteristics mainly include reliability, relevance, comprehensibility, comparability, substance over form, importance, prudence and timeliness. The primary evaluation standard of accounting information quality is to process the data generated by the company’s business activities and help users make decisions, which requires the synergy of the basic characteristics of accounting information quality at different levels to ensure. Reliability and correlation play an important role. Reliability reflects the authenticity of accounting information, while relevance reflects whether the information provided by the accounting department can help users make decisions from the perspective of whether the information is relevant or not. The providers and users of accounting information have reached a consensus on the most important characteristics of accounting information quality, that is, the evaluation standard of accounting information quality is determined by reliability and relevance. All the information that can record the company’s operation and play a decision-making role must be reliable and relevant [12].

According to the above description, this study selects the listed company accounting information quality detection index system as shown in Table 1.

Table 1
Index system of accounting information quality inspection of listed companies

Classification	Secondary classification	Test index
Reliability	Authenticity	Standardization of basic accounting work
		Stability and reputation of senior management
		Quality and professional level of Accountants
		Availability of enterprise credit
		Change ratio of investment direction of raised funds
	Integrity	The integrity of information disclosure in annual report
		The adequacy of disclosure of changes in accounting estimates
		Guarantee amount ratio
		Detailed and sufficient disclosure of related party transactions
		The integrity of information disclosure of China Daily News
	Comparability	Frequency of accounting policy changes
		Change frequency of accounting estimate
	Prudence	The rationality of the provision for impairment of assets
		The objectivity of the analysis of major investment projects
Relevance	Timeliness	Timely disclosure ratio of annual report
		Timely disclosure of announcements of other major events
		Timely handling of accounting business
		Other information is timely
		Timely disclosure ratio of Interim Report
	Clarity	The message is clear
		Clarity of answers to questions raised by interested parties
	Importance	Reasonable analysis of major financial issues
		Detailed information of important parts
		Strengthen internal audit supervision over important projects

As shown in Table 1, the index system of accounting information quality inspection of listed companies is a complete hierarchical structure: that is, each factor in the upper layer dominates all factors in the next layer, and is also affected by all factors in the next layer.

2.3 Determination of the weight of accounting information quality testing index

Based on the above selected accounting information quality detection indicators of listed companies, the weight of monitoring indicators is calculated by using analytic hierarchy process, which lays a solid foundation for the final accounting information quality detection of listed companies.

Analytic hierarchy process (AHP) is a practical multi criteria method, which represents a complex problem as an ordered hierarchical structure. It sorts the advantages and disadvantages of the decision-making schemes through people’s judgment. This method was put forward by Professor Saty in the early 1970s. It combines quantitative and qualitative analysis, and takes the decision-maker’s experience judgment as the weight and basis to estimate whether the goal can be achieved. Analytic hierarchy process (AHP) is an effective tool in the decision-making of social and economic system. According to the law of thinking and psychology, the decision-making process is stratified and quantified. It is a common system analysis method in system science [13].

The steps of AHP are as follows:

Step 1: To establish a hierarchical structure model to deal with the problem;

Step 2: The judgment matrix of pairwise comparison is constructed;

Step 3: On the consistency test of judgment matrix;

Step 4: Calculating the weight vector.

Due to the limitation of space and the large number of monitoring indicators, this study takes the authenticity index as an example to show the calculation process of index weight.

The judgment matrix of authenticity index is:

$\displaystyle C_{1}=\left[{{\begin{array}[]{ccccc}1&{1/3}&{1/2}&{1/3}&1\\ 3&1&{3/2}&1&2\\ 2&{2/3}&1&{2/3}&2\\ 3&1&{3/2}&1&3\\ 1&{1/3}&{1/2}&{1/3}&1\\ \end{array}}}\right]$ (1)

In Eq. (1), $C_{1}$ represents the code of authenticity monitoring index.

YAAHP 0.5.2 software is used to solve Eq. (1), that is, the maximum value of matrix and its weight vector are $\lambda_{\max}$ and $A$ respectively. Among them, the form of weight vector is column vector, and its value is the weight value corresponding to the authenticity monitoring index.

According to the above method, the weight value of accounting information quality test of listed companies can be obtained, as shown in Table 2.

Table 2

Weight of accounting information quality test of listed companies

Test index	Weight	Test index	Weight
Standardization of basic accounting work	0.0180	The rationality of the provision for impairment of assets	0.0236
Stability and reputation of senior management	0.0586	The objectivity of the analysis of major investment projects	0.0334
Quality and professional level of Accountants	0.0377	Timely disclosure ratio of annual report	0.0334
Availability of enterprise credit	0.0609	Timely disclosure of announcements of other major events	0.0221
Change ratio of investment direction of raised funds	0.0549	Timely handling of accounting business	0.0004
The integrity of information disclosure in annual report	0.0432	Other information is timely	0.0088
The adequacy of disclosure of changes in accounting estimates	0.0522	Timely disclosure ratio of Interim Report	0.1756
Guarantee amount ratio	0.0437	The message is clear	0.1578
Detailed and sufficient disclosure of related party transactions	0.0226	Clarity of answers to questions raised by interested parties	0.1111
The integrity of information disclosure of China Daily News	0.0091	Reasonable analysis of major financial issues	0.0522
Frequency of accounting policy changes	0.0152	Detailed information of important parts	0.0287
Change frequency of accounting estimate	0.0177	Strengthen internal audit supervision over important projects	0.1215

2.4 The realization of accounting information quality inspection

Based on the weight of the detection index determined above, the panel model is constructed and the residual analysis is carried out to realize the detection of accounting information quality [14].

Through the preliminary preparation, we take the operating profit as the explained variable, and the current assets, owner’s equity and operating income as the explanatory variables to establish the model, the specific formula is as follows:

$\displaystyle Y_{it}=\alpha_{i}+\beta_{1}X_{\textit{1it}}+\beta_{2}X_{\textit{% 2it}}+\beta_{3}X_{\textit{3it}}+\mu_{\textit{it}}$ (2)

In Eq. (2), $Y_{\textit{it}}$ represents the operating profit of the $i$ th listed company in year $t$ ; $X_{\textit{1it}}$ represents the current assets of the $i$ th listed company in year $t$ ; $X_{\textit{2it}}$ represents the owner’s equity of the $i$ th listed company in year $t$ ; X3 represents the operating revenue of the $i$ th listed company in year $t$ ; $\alpha_{i}$ represents the intercept items; $\beta_{1}$ , $\beta_{2}$ and $\beta_{3}$ represent the slope coefficients; $u_{\textit{it}}$ represents the random error items.

It should be noted that the selection of current assets, owner’s equity and operating income as the explanatory variables of the model is not only because these three financial data have passed the test of the panel model, but also because these financial data meet the requirements of the model setting in the practical sense, and can play a certain explanatory role [15]. First of all, the purpose of using panel model analysis is to detect whether the quality of accounting information has reliability problems, so as the explanatory variable of the model, the primary attribute is reliability. From this point of view, the three financial data meet the requirements. Current assets include monetary capital, short-term investment, notes receivable, accounts receivable and inventory, and owner’s equity includes paid in capital, capital reserve, surplus reserve and undistributed profit [16]. Operating income includes main business income and other business income [17]. Even if there is a quality problem in one of the three financial data, because it only accounts for a small part of the three financial data, it can greatly reduce the impact in model estimation and analysis. Secondly, current assets reflect the development of the company and the operation ability of the company to a certain extent. The owner’s equity reflects the investor’s ownership of the enterprise’s net assets, and also reflects the financial control ability of the enterprise’s management. The operating income reflects the profitability of the enterprise. These three financial data reflect the company’s financial situation and operating results from different angles, and the model based on them is more comprehensive [18].

Residual analysis of panel model can diagnose the quality of accounting information and locate the problem point [19]. Under the premise of satisfying the significant regression effect of the estimated panel model, the data points with large residuals may have data quality problems [20, 21]. In the residual analysis, the method of standardizing the residual is widely used:

$\displaystyle z_{ij}=\frac{\left|{x_{ij}-\bar{x}_{i}}\right|}{\sigma_{i}}$ (3)

In Eq. (3), $x_{ij}$ is the residual value of $j$ listed companies in the panel model in the $i$ year; $\bar{x}_{i}$ is the average residual value of all listed companies in the model in the $i$ year; $\sigma_{i}$ is the standard deviation of residual value of all listed companies in the model in the $i$ year.

If the $z_{ij}$ value of a sample point is greater than 2, it is considered that the listed company corresponding to the sample point may have the problem of accounting information quality in the current year’s statements. Through the above process, the quality detection of accounting information of listed companies is realized, which provides convenience for statistical research work [22, 23].

3. Experiment and result analysis

In order to verify the application performance difference between the proposed method and the existing methods, the MATLAB software is used to design the experiment.

3.1 Construction of experimental environment

The experimental environment is the basis and premise of its stability, so the primary task is to build the experimental environment. The mobile social network topology is shown in Fig. 1.

Figure 1.

Mobile social network topology.

3.2 Selection of subjects

This study takes agricultural listed companies as the experimental objects. This paper selects the relevant agricultural listed companies in Shanghai Stock Exchange and Shenzhen Stock Exchange as the research object, a total of 20. Because some companies have been losing money in successive years and their financial situation is abnormal, they should be eliminated first. At the same time, in order to facilitate data collection, to meet the needs of research, the new listed companies in recent years are excluded. In this way, the following 10 agricultural listed companies are selected as samples, as shown in Table 3.

Table 3
Experimental subjects

Serial number	Security code	Stock name
1	600108	AXA group
2	600127	Jinjian rice
3	600189	Jilin forest industry
4	600195	China animal husbandry Co., Ltd.
5	600251	Guannong shares
6	600257	Dahu shares
7	600265	Jinggu forestry
8	600354	Dunhuang seed industry
9	600359	New agricultural development
10	600438	Homey

3.3 Experimental results

Based on the above experimental environment and the selected experimental objects, this paper carries out the quality detection experiment of accounting information of listed companies, and shows the application performance of the method through the quality detection error. The error data of accounting information quality detection obtained by experiment is shown in Table 4.

Table 4
Error data table of accounting information quality testing

Serial number	Reference [4] method	Proposed method
1	1.23%	0.15%
2	1.02%	0.10%
3	1.00%	0.22%
4	0.95%	0.12%
5	1.01%	0.31%
6	0.88%	0.30%
7	0.80%	0.24%
8	0.76%	0.21%
9	0.82%	0.15%
10	0.90%	0.29%

As shown in Table 4, under the background of different listed companies, the error range of accounting information quality detection of Reference [4] method is 0.76%–1.23%, and the error range of accounting information quality detection of proposed methods is 0.10%–0.31%. Through the data comparison, we can see that the error of accounting information quality detection is smaller, which fully shows that the application performance of the proposed method is better. This is because this paper collects accounting information based on the mobile social network, which ensures the integrity and accuracy of the basic data, screens the operation and mobile social network characteristics, builds a complete and hierarchical detection index system, laying the foundation for accurate detection, and uses the residual analysis method to obtain the most accurate detection results.

4. Conclusion

In this study, the application of mobile social network interaction model proposed a new method of accounting information quality detection of listed companies, which greatly reduced the error of accounting information quality detection, provided effective help for the development of listed companies and related department statistics, and also provided theoretical reference for the research of accounting information quality detection.

References

Kumar

Bhat

Panda

. Analysis of social network metrics based on the model of random recursive tree. J Interdiscip Math. 2020; 23(1): 237-246.

Abrahams

Sitas

Esler

. Exploring the dynamics of research collaborations by mapping social networks in invasion science. J Environ Manage. 2019; 229: 27-37.

Matheu

Gonzalez-Perez

Poza-Guedes

Alava-Cruz

Luis

Sanchez-Machin

, et al. Letter to the Editor: Support through social networks of e-health in adults with primary immunodeficiencies during COVID-19 pandemic. Telemed e-Health. 2020; 26(12): 87.

Liu

. Establishment of quality evaluation indicator system for accounting information based on the perspective of stakeholders. Stand Sci. 2020; (6): 96-102.

Liu

. Accounting information quality and its supervision of local commercial bank in Shandong province. J Financ Dev Res. 2020; (10): 57-64.

Zhang

Wang

. Integration model of English teaching resources based on artificial intelligence. Int J Contin Eng Edu Life-Long Learn. 2020; 30(4): 398-398.

Canteloup

Puga-Gonzalez

Sueur

Van de Waal

. The effects of data collection and observation methods on uncertainty of social networks in wild primates. Am J Primatol. 2020; 82(7): e23137.

Ahmadian

Afsharchi

Meghdadi

. An effective social recommendation method based on user reputation model and rating profile enhancement. J Inf Sci. 2019; 45(5): 607-642.

Nasiri

Derakhshanfard

. FTR: Features tree based routing in mobile social networks. Wireless Networks. 2020; 26(1): 283-291.

10.

Dhand

Luke

Lang

Tsiaklides

Feske

Lee

. Social networks and risk of delayed hospital arrival after acute stroke. Nat Commun. 2019; 10(1): 191-212.

11.

Kumar

Mukhopadhyay

. Analysis of the quality factor of micro-beam resonators based on heat conduction model with a single delay term. J Therm Stresses. 2019; 42(7-9): 929-942.

12.

Park

Ryu

Yang

. ActiveDBC: Learning knowledge-based information propagation in mobile social networks. Wireless Networks. 2019; 25(4): 1519-1531.

13.

Liu

Zhou

Wang

. Acquisition method of users’ browsing behavior preference based on the fusion of social network link and theme model. J Intell Fuzzy Syst. 2019; 37(5): 1-16.

14.

Nguyen

Xiao

Zhou

. Bias in social interactions and emergence of extremism in complex social networks. Chaos. 2020; 30(10): 103110.

15.

Jokar

Mosleh

. Community detection in social networks based on improved label propagation algorithm and balanced link density. Phys Lett A. 2019; 383(8): 718-727.

16.

Zabielski

Tarapata

Kasprzyk

. Adaptive method of similarity detection of user profiles on online social networks. Bull Mil Univ Technol. 2019; 68(2): 43-57.

17.

Amoruso

Anello

Auletta

Ferraioli

. Contrasting the spread of misinformation in online social networks. J Artif Intell Res. 2020; 69(12): 847-879.

18.

Alksasbeh

Abuhelaleh

Almaiah

AL-Jaafreh

Karaka

. Towards a model of quality features for mobile social networks apps in learning environments: An extended information system success model. Int J Interact Mobile Technol. 2019; 13(5): 75-93.

19.

Meng

. Research on privacy disclosure detection method in social networks based on multi-dimensional deep learning. Comput, Mater Continua. 2019; 61(3): 137-155.

20.

Kong

Zhang

Wan

. A comprehensive decision method of reliability probability distribution model based on the fuzzy support vector machine. J Comput Methods Sci Eng. 2020; 20(2): 575-581.

21.

Zhang

Zhao

Shen

. Failure mechanism of gently inclined bedding slope under hydraulic driving: The case of the Jiangyuan landslide along Heda Highway. J Comput Methods Sci Eng. 2020; 20(2): 383-397.

22.

Huang

. The research progress and prospects of overuse of social network – Based on the I-PACE model perspective. Advn Psychol. 2020; 10(7): 976-988.

23.

Zhan

Kou

Dong

Chiclana

Herrera-Viedma

. Bounded confidence evolution of opinions and actions in social networks. In: IEEE Transactions on Cybernetics; 2021.

Accounting information quality detection method of listed companies based on mobile social network interaction model

Abstract

Keywords

1. Introduction

2. The quality testing method of accounting information of listed companies

2.1 Access to accounting information of listed companies

Table 1 Index system of accounting information quality inspection of listed companies

3.1 Construction of experimental environment

Table 3 Experimental subjects

Table 4 Error data table of accounting information quality testing

References

Table 1
Index system of accounting information quality inspection of listed companies

Table 3
Experimental subjects

Table 4
Error data table of accounting information quality testing