CN112101770B - Audit quality model generation method and device and audit quality prediction method - Google Patents
Audit quality model generation method and device and audit quality prediction method Download PDFInfo
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Abstract
The invention provides an audit quality model generation method, an audit quality model generation device and an audit quality prediction method, wherein the method comprises the following steps: obtaining audit quality index sample data; calculating the maneuverability accrued profit corresponding to the audit quality index sample data; generating a linear regression model, taking each audit quality index in the audit quality index sample data as an independent variable, taking the steerable accrued profit as a dependent variable, and carrying out linear regression analysis on the independent variable and the dependent variable to obtain a regression equation; and carrying out significance test on the regression equation, and removing independent variables with significance test values P larger than a preset threshold value, so as to obtain an audit quality model. The method, the device and the audit quality prediction method can solve the problems that the audit quality indexes related to the audit quality cannot be obtained and the prediction of the annual audit quality in the future cannot be realized because the influence of all the audit quality indexes on the audit quality is not unified in academia.
Description
Technical Field
The invention relates to the technical field of information, in particular to an audit quality model generation method and device and an audit quality prediction method.
Background
High-quality audit is a core part of a well-functioning capital market, however, the academic circles of influence results of all audit quality indexes on the audit quality are not unified at present, audit quality indexes related to the audit quality cannot be obtained, and prediction of the audit quality of the future year cannot be realized.
Therefore, providing an audit quality model generating method, an audit quality model generating device and an audit quality predicting method is a problem to be solved urgently by those skilled in the art.
Disclosure of Invention
The invention aims to solve the technical problems that aiming at the defects of the prior art, an audit quality model generating method, an audit quality model generating device and an audit quality predicting method are provided, so that the problems that the academic circles of the influence results of all audit quality indexes on the audit quality are not unified, the audit quality indexes related to the audit quality cannot be obtained, and the prediction of the audit quality of the future year cannot be realized are solved.
In a first aspect, an embodiment of the present invention provides a method for generating an audit quality model, including:
obtaining audit quality index sample data;
calculating the maneuverability accrued profit corresponding to the audit quality index sample data;
generating a linear regression model, taking each audit quality index in the audit quality index sample data as an independent variable, taking the steerable accrued profit as a dependent variable, and carrying out linear regression analysis on the independent variable and the dependent variable to obtain a regression equation;
and carrying out significance test on the regression equation, and removing independent variables with significance test values P larger than a preset threshold value, so as to obtain an audit quality model.
Preferably, the obtaining audit quality index sample data includes:
obtaining audit quality statistical data of a preset region or country;
screening the audit quality statistical data, and eliminating incomplete data in the audit quality statistical data;
and performing correlation analysis on each audit quality index in the screened audit quality statistics data, and removing audit quality indexes which have correlation with other audit quality indexes in the audit quality statistics data, so as to obtain the audit quality index sample data.
Preferably, the calculating the steerable accrued profit corresponding to the audit quality index sample data includes:
and calculating the maneuverability accrued profit corresponding to the audit quality index sample data by adopting a Jones model based on section correction.
Preferably, the audit quality indicator sample data includes a reputation indicator.
Preferably, the reputation index comprises an interest index of the public for an audited company and an interest index of the public for an audited company; the public interest index of the audited company is the public search index of the audited company in a given time period; the public interest index of the audited company is the public search index of the audited company in a given time period;
the linear regression model conforms to the following formula:
DAABS it ＝b+β 1 IOT it +β 2 IOTA it +β 3 TE it +β 4 SAT it +β 5 AF it +β 6 SC it +β 7 NAF it +β 8 EXP it +β 9 TA it +β 10 LR it +β 11 SR it +β 12 ROA it +β 13 GR it +β 14 PRO it +β 15 AGE it +β 16 NLD it +∈
wherein DAABS is the absolute value of the profit due to the operability of the audited company;
it is the data of the ith company in the t year;
b is a constant term;
beta is the coefficient of each audit quality index;
IOT is an index of public interest to audited companies;
IOTA is an index of public interest to auditing companies;
TE is the number of audit years of the audited company and the auditing company;
SAT is the number of audit years of signature auditors of audited companies and auditing companies;
AF is audit expense paid to an audited company by the audited company;
SC is whether the audit company is a preset four accounting firm, if so, the SC is 1, otherwise, the SC is 0;
NAF is non-audit expense paid to the audit company by the audit company;
EXP is whether an audit company is an expert in the industry, if so, the EXP is 1, otherwise, the EXP is 0;
TA is the total assets of the audited company;
LR is the flow rate of the audited company;
SR is the repayment capability ratio of the audited company;
ROA is the asset return rate of the audited company;
GR is the rate of revenue increase for the audited company;
PRO is whether the audited company is profitable or not, if yes, the PRO is 1, otherwise, the PRO is 0;
AGE is the number of years established by the audited company;
NLD is the change of long-term liabilities of audited companies;
e is error;
and performing linear regression analysis on the independent variable and the dependent variable to obtain a regression equation, wherein the method comprises the following steps:
inputting the independent variable and the dependent variable into a linear regression model corresponding to the formula for training so as to obtain a parameter value of a target parameter in the linear regression model, thereby obtaining the regression equation.
Preferably, the performing the saliency test on the regression equation, removing the independent variable with the saliency test value P being greater than the preset threshold value, includes:
setting the significance level alpha as 95%, and obtaining a significance test value P of each independent variable in the regression equation;
if the p value is less than 0.05, the argument is retained;
if the p value is greater than 0.05, rejecting the independent variable until the independent variable with the p value greater than 0.05 does not exist in the regression equation, thereby obtaining a final audit quality model.
In a second aspect, an embodiment of the present invention provides an audit quality model generating apparatus, including:
the acquisition module is used for acquiring audit quality index sample data;
the calculation module is connected with the acquisition module and used for calculating the maneuverability accrued profit corresponding to the audit quality index sample data;
the regression module is connected with the acquisition module and the calculation module and is used for generating a linear regression model, taking each audit quality index in the audit quality index sample data as an independent variable and taking the steerable accrued profit as a dependent variable, and carrying out linear regression analysis on the independent variable and the dependent variable to obtain a regression equation;
the generation module is connected with the regression module and is used for carrying out significance test on the regression equation and eliminating independent variables with significance test values P larger than a preset threshold value so as to obtain an audit quality model.
Preferably, the acquiring module specifically includes:
the acquiring unit is used for acquiring audit quality statistical data of a preset region or country;
the data screening unit is used for screening the audit quality statistical data and eliminating incomplete data in the audit quality statistical data;
and the analysis unit is used for carrying out correlation analysis on each audit quality index in the audit quality statistical data after screening, and eliminating audit quality indexes which have correlation with other audit quality indexes in the audit quality statistical data so as to obtain the audit quality index sample data.
Preferably, the reputation index comprises an interest index of the public for an audited company and an interest index of the public for an audited company; the public interest index of the audited company is the public search index of the audited company in a given time period; the public interest index of the audited company is the public search index of the audited company in a given time period;
the linear regression model conforms to the following formula:
DAABS it ＝b+β 1 IOT it +β 2 IOTA it +β 3 TE it +β 4 SAT it +β 5 AF it +β 6 SC it +β 7 NAF it +β 8 EXP it +β 9 TA it +β 10 LR it +β 11 SR it +β 12 ROA it +β 13 GR it +β 14 PRO it +β 15 AGE it +β 16 NLD it +∈
wherein DAABS is the absolute value of the profit due to the operability of the audited company;
it is the data of the ith company in the t year;
b is a constant term;
beta is the coefficient of each audit quality index;
IOT is an index of public interest to audited companies;
IOTA is an index of public interest to auditing companies;
TE is the number of audit years of the audited company and the auditing company;
SAT is the number of audit years of signature auditors of audited companies and auditing companies;
AF is audit expense paid to an audited company by the audited company;
SC is whether the audit company is a preset four accounting firm, if so, the SC is 1, otherwise, the SC is 0;
NAF is non-audit expense paid to the audit company by the audit company;
EXP is whether an audit company is an expert in the industry, if so, the EXP is 1, otherwise, the EXP is 0;
TA is the total assets of the audited company;
LR is the flow rate of the audited company;
SR is the repayment capability ratio of the audited company;
ROA is the asset return rate of the audited company;
GR is the rate of revenue increase for the audited company;
PRO is whether the audited company is profitable or not, if yes, the PRO is 1, otherwise, the PRO is 0;
AGE is the number of years established by the audited company;
NLD is the change of long-term liabilities of audited companies;
e is error;
the regression module is specifically configured to input the independent variable and the dependent variable into a linear regression model corresponding to the formula for training, so as to obtain a parameter value of a target parameter in the linear regression model, thereby obtaining the regression equation.
In a third aspect, an embodiment of the present invention provides an audit quality prediction method, including:
obtaining audit quality index data of the year to be predicted;
inputting the audit quality index data of the year to be predicted into an audit quality model generated by the audit quality model generation method in the first aspect so as to predict the audit quality of the year to be predicted.
According to the audit quality model generation method, the audit quality model generation device and the audit quality prediction method, linear regression analysis is carried out on each audit quality index and the steerable accrued profit in the audit quality index sample data by means of big data analysis to obtain a regression equation, meanwhile, saliency test is carried out on the regression equation, and independent variables with the saliency test value P being greater than a preset threshold are removed, so that a final audit quality model is obtained, and the audit quality index related to the audit quality is obtained. In addition, the qualitative concept of the audit quality can be intuitively displayed through the calculated numerical value by the audit quality model, so that the audit quality is visualized and quantized. And the subsequent audit quality can be predicted after the generated audit quality model is brought into the related audit quality index. By comparing the information risk with the audit report, the information risk of the public and market caused by information asymmetry is reduced, and the information risk of the financial statement user is reduced to a socially acceptable level, so that the problems that the academic world is not unified due to the influence of each audit quality index on the audit quality, the audit quality index related to the audit quality cannot be obtained and the prediction of the audit quality of the future year cannot be realized in the prior art are solved.
Drawings
Fig. 1: a flowchart of an audit quality model generating method in embodiment 1 of the present invention;
fig. 2: the regression analysis experiment result;
fig. 3: the invention relates to a structure diagram of an audit quality model generating device in an embodiment 2;
fig. 4: a flowchart of an audit quality prediction method in embodiment 3 of the present invention is provided.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
Example 1:
the embodiment provides an audit quality model generating method, as shown in fig. 1, including:
and step S102, obtaining audit quality index sample data.
Optionally, obtaining audit quality indicator sample data may include:
obtaining audit quality statistical data of a preset region or country;
screening the audit quality statistical data, and eliminating incomplete data in the audit quality statistical data;
and performing correlation analysis on each audit quality index in the screened audit quality statistics data, and removing audit quality indexes which have correlation with other audit quality indexes in the audit quality statistics data, so as to obtain audit quality index sample data.
In this embodiment, the generation of the audit quality model relies on big data analysis, and the data can be based on all the companies listed in the uk (including scotland, england, wilms and north irish) 1591, the companies in the financial industry and the companies with data missing due to different self industry and supervision characteristics are removed, and finally a sample of 732 companies is formed, and the total of 2928 companies is 55632 data.
In this embodiment, in order to ensure reasonable design of the audit quality model and selection of variables, correlation analysis is performed on each audit quality index in the audit quality statistics data, whether correlation exists or not is judged through correlation analysis between every two indexes, if no correlation exists, the audit quality index is reserved, and if correlation exists, the audit quality index highly correlated with the audit quality index is removed.
Optionally, the audit quality indicator sample data may include a reputation indicator. Reputation indexes comprise an interest index of the public to an audited company and an interest index of the public to the audited company; the public interest index of the audited company is the public search index of the audited company in a given time period; the public interest index for an audited company is the public search index for the audited company over a given period of time.
In the course of research and practice of the prior art, the inventors found that: high quality auditing is a core part of a well-functioning capital market, and the existing research on auditing quality related factors often ignores the influence of external visual angles such as capital markets, audit report users and the like on auditing quality, and the inventor finds that the reputation of an auditing company and an audited company also affects auditing quality. For example, if a company is in a rise or maturity stage and has a high reputation for the audited company, it is easier for the public to be drawn to any information available to the company, including previous audit reports. The more attention a company attracts, the more likely the public will find that the company has problems, and conversely if a company is unwelcome, the more secure the counterfeit data and financial conditions will be.
In this embodiment, the reputation index may include a public interest index for the audited company and a public interest index for the audited company, since the reputation of the company cannot be directly observed and is difficult to define from the company's perspective. Thus, the public's interest in the company over time is selected to reflect the reputation of the company. In particular, the public interest index for the audited company is a public search index for the audited company over the world or within a given period of time in a preset region or country, i.e., an index of search interest with respect to an online site displayed in a given region and time. The larger the search index, the larger the interest index representing the public to the audited company, the search index or the interest index can range from 0 to 100, and the interest index size is determined through a preset search frequency peak value. For example, if the searching frequency peak value is 10000, the total searching frequency of a certain audited company on each well-known website (such as google and hundred degrees) in a certain year is greater than or equal to 10000, the interest index is 100, if the searching frequency is less than 100, i.e. the searching frequency is less than 1% of the searching frequency peak value, the corresponding interest index is 0, and the reputation of the audited company is contained in the formula of the regression model as a dependent variable. Similarly, the public interest index for the audited company is a public search index for the audited company over the globe or within a given time period in a predetermined region or country, which may include a public search index for the audited company and/or a public search index for auditors in the audited company.
In this embodiment, the audit quality index in the audit quality index sample data may be the following 16 indexes: public interest index for audited companies; public interest index to auditing companies; audited companies and audit years of audit companies; the number of audit years of signature auditors of audited companies and audit companies; the auditing cost paid to the auditing company by the auditing company; whether the audit company is a preset four accounting firm or not, if so, the audit company is 1, otherwise, the audit company is 0; non-audit fees paid by the audit company to the audit company; whether the auditing company is an expert in the industry or not, if so, the auditing company is 1, otherwise, the auditing company is 0; total assets of the audited company; flow rate of audited company; the rate of repayment capacity of the audited company; asset return rate of audited company; the rate of revenue increase for the audited company; whether the audited company is profitable or not, if yes, the audited company is 1, otherwise, the audited company is 0; years established by the audited company; changes in long-term liabilities of audited companies; by carrying out correlation analysis on the 16 indexes, the fact that no correlation exists among the indexes is confirmed, and therefore audit quality index data without correlation is used as audit quality index sample data.
In this embodiment, by introducing the reputation index into the sample data of the audit quality index, the influence of the reputation index of the audit company and the audited company on the audit quality is comprehensively considered, and because the reputation is important soft competitiveness of the enterprise, both the enterprise and the audit company should keep good reputation and be responsible for the public and the market, and the trust of the public and the market for the enterprise and the audit company should not be consumed, thereby being beneficial to better judging the public trust of the audit company by the public and the market.
Step S104, calculating the maneuverability accrued profit corresponding to the audit quality index sample data.
Optionally, calculating the steerable accrued profit corresponding to the audit quality index sample data includes:
and calculating the maneuverability accrued profit corresponding to the auditing quality index sample data by adopting a Jones model based on section correction.
In this embodiment, to visualize and quantify the audit quality, the impact of reputation factors on the audit quality may be verified using the steerable accrued profit as a reflective index of the audit quality. Specifically, the steerable accrued profit can be metered based on the cross-section corrected jones model.
And S106, generating a linear regression model, taking each audit quality index in the audit quality index sample data as an independent variable, taking the steerable accrued profit as a dependent variable, and carrying out linear regression analysis on the independent variable and the dependent variable to obtain a regression equation.
In this embodiment, assuming that there is no correlation between the 16 audit quality indexes after analysis, the 16 audit quality indexes are taken as independent variables, the steerable accrued profit is taken as a dependent variable, and regression analysis is performed on the steerable accrued profit and the 16 independent variables, and the linear regression model conforms to the following formula:
DAABS it ＝b+β 1 IOT it +β 2 IOTA it +β 3 TE it +β 4 SAT it +β 5 AF it +β 6 SC it +β 7 NAF it +β 8 EXP it +β 9 TA it +β 10 LR it +β 11 SR it +β 12 ROA it +β 13 GR it +β 14 PRO it +β 15 AGE it +β 16 NLD it +∈
wherein DAABS is the absolute value of the profit due to the operability of the audited company;
it is the data of the ith company in the t year;
b is a constant term;
beta is the coefficient of each audit quality index;
IOT is an index of public interest to audited companies;
IOTA is an index of public interest to auditing companies;
TE is the number of audit years of the audited company and the auditing company;
SAT is the number of audit years of signature auditors of audited companies and auditing companies;
AF is audit expense paid to an audited company by the audited company;
SC is whether the audit company is a preset four accounting firm, if so, the SC is 1, otherwise, the SC is 0. Among them, four major accountant offices refer to general Hua Yongdao (PWC), bi Mawei (KPMG), deluxe (DDT), and An Yong (EY);
NAF is non-audit expense paid to the audit company by the audit company;
EXP is whether an audit company is an expert in the industry, if so, the EXP is 1, otherwise, the EXP is 0;
TA is the total assets of the audited company;
LR is the flow rate of the audited company;
SR is the repayment capability ratio of the audited company;
ROA is the asset return rate of the audited company;
GR is the rate of revenue increase for the audited company;
PRO is whether the audited company is profitable or not, if yes, the PRO is 1, otherwise, the PRO is 0;
AGE is the number of years established by the audited company;
NLD is the change of long-term liabilities of audited companies;
epsilon is the error.
Optionally, performing linear regression analysis on the independent variable and the dependent variable to obtain a regression equation may include:
inputting the independent variable and the dependent variable into a linear regression model corresponding to the formula for training so as to obtain the parameter value of the target parameter in the linear regression model, thereby obtaining a regression equation.
In this embodiment, after regression analysis is performed on the steerable accrued profit and the 16 independent variables, a regression equation is obtained, and then the coefficient, the constant term and the error value in the linear regression model of each audit quality index are obtained.
And S108, performing significance test on the regression equation, and removing independent variables with significance test values P larger than a preset threshold value, so as to obtain an audit quality model.
Optionally, performing a saliency test on the regression equation, and removing the argument with the saliency test value P being greater than the preset threshold value may include:
setting the significance level alpha as 95%, and obtaining the significance test value P of each independent variable in the regression equation;
if the p value is less than 0.05, the argument is retained;
if the p value is greater than 0.05, rejecting the independent variable until the independent variable with the p value greater than 0.05 does not exist in the regression equation, thereby obtaining the final audit quality model.
In this embodiment, after obtaining the regression equation, in order to further verify whether a linear relationship exists between each variable and the dependent variable, and in order to further optimize the model, the independent variables in the model are variables having significant effects on the dependent variable, the regression equation is subjected to a significant test, the independent variables having insignificant effects in the regression equation are removed, and under the condition that the significant level α=95%, the significant test value P of each variable is obtained through the significant test, if the P value is smaller than 0.05, it is indicated that the relationship between the independent variable and the dependent variable is large, that is, the significance is large, and the independent variable is reserved. If the p value is greater than 0.05, the relevance between the independent variable and the dependent variable is small or irrelevant, namely the significance is low, the independent variable has no statistical significance on the dependent variable, the independent variable is deleted, the corresponding regression equation is re-solved, and the significance test is performed on the regression equation until the independent variable with low significance does not exist in the regression equation, so that the final audit quality model is obtained. In the implementation process, the SPSS software can be used for calculating the P value, and the P value is used for detecting the significance of the independent variable in the regression equation.
In this embodiment, the relationship between the respective variables and the audit quality can be determined by the coefficient of the obtained regression equation, and if the coefficient is greater than 0, the relationship is positive between the independent variable and the audit quality, and if the coefficient is less than 0, the relationship is negative between the independent variable and the audit quality.
In this embodiment, the final regression equation obtained through the saliency test includes less than or equal to 16 independent variables, and the obtained regression equation may be different according to the data conditions of the actual area. Through experimental analysis, reputation indexes have influence on audit quality: the reputation of the audited company is inversely related to the steerable accrued profit, i.e. based on reputation theory, the public's attention will help to restrict manager's opportunistic behaviour, the public acting as audit report user, playing an important role in the audit process and improving the quality of the audit. However, the reputation of the audited company is positively correlated with the manageability accrued profit, although the public may learn about the audited company in a variety of ways, such as online searching, accessing a corporate network, reading comments from others, and so forth. But the public has less knowledge and skill about the auditing expertise than the audited company, in which case the public would prefer to trust the auditor.
In a specific embodiment, 732 companies are involved in the uk marketing company data, and data of 2928 years are taken as audit quality index sample data to carry out regression analysis, and when the significance level alpha is set to be 95%, the regression analysis result is shown in fig. 2. Regression results showed that the interest index IOT for the audited company correlated significantly inversely with the steerable accrued profit over time. This result is consistent with the assumption that the higher the quality of audit of the publicly-focused marketable companies. The higher the public attention, the higher the cost of the opportunistic behaviour of the manager and the greater the difficulty of implementation. At the same time, more public attention is being drawn to, typically, emerging or successful companies. These companies are likely to maintain good reputation for further development. They are therefore willing to guarantee good surplus quality and audit quality.
There is no significant evidence for the interest index IOTA of the auditing company that could improve its relationship to the manageability accrued profit. But the coefficients of the index are positive, this result may indicate that the public is more inclined to trust the auditors with which they are familiar, and therefore that these auditors have more opportunity to assist the client in forging financial data. There is no evidence supporting the relationship between the period of the audit company and the period of the senior auditor and the manageability accrued profit. In terms of audit costs, audit costs are significantly positively correlated with the manageability accrued profit. This result demonstrates that economic dependencies can have an impact on auditors, and that high audit costs can compromise auditors' independence.
In terms of whether the audit company is a four-major accounting firm SC, the profit due to operability is inversely related and the coefficient is large, which proves that the audit quality of a "four-major" accounting firm is better because their income is independent of a particular customer from a quasi-lease perspective. Non-audit service fees are inversely related to the manageability accrued profit. The results indicate that non-audit services help constrain manager opportunistic behavior. Although non-audit services increase the economic link between auditors and clients, audit quality is not compromised on the premise that auditors adhere to audit independence and audit guidelines, which is in line with accounting ethics. Most audit offices at present can investigate the background and scope of non-audit services in order to avoid damaging audit independence. To the expertise of auditors, industry professionals will greatly help improve audit quality and constrain manageability accrued profits.
The total asset TA of the audited company is significantly positively correlated with the manageability accrued profit, indicating that large business is complex and there may be more ways to manage the profit. And the management is relatively elusive, thus increasing the auditing difficulty. The virtual variable PRO is inversely related to the steerable profit-by-profit, but fails the significance test. But this still shows that it is unlikely that a growing enterprise will consciously manage revenue. The increase in revenue is inversely related to the manageability accrued profit but does not meet the significance test. For this reason, for fast growing companies, the management and internal control regimes lag behind the growth of business. Thus, companies in the growing phase have more opportunities to manage revenue.
Fig. 2 shows only regression analysis results at a significance level α of 95%, and different confidence intervals also differ in the results of the judgment, and it can be seen from fig. 2 that five indexes, IOT, AF, NAF, EXP and TA, have significant effects on the manageability accrued profit.
In the embodiment, the obtained audit quality model can be used for carrying relevant indexes based on the management conditions of the subsequent years, predicting the audit quality conditions of the subsequent years, comparing with an audit report, reducing the information risks caused by information asymmetry of the public and the market, and reducing the information risks of the financial statement users to a socially acceptable level. In addition, the audit quality model can be applied to other countries and regions, and the calculation of the audit quality of the countries and regions is realized.
According to the audit quality model generation method provided by the embodiment, by means of big data analysis, linear regression analysis is carried out on each audit quality index and the steerable accrued profit in the audit quality index sample data to obtain a regression equation, meanwhile, the regression equation is subjected to significance test, and independent variables with significance test values P larger than a preset threshold are removed, so that a final audit quality model is obtained, and the audit quality index related to the audit quality is obtained. In addition, the qualitative concept of the audit quality can be intuitively displayed through the calculated numerical value by the audit quality model, so that the audit quality is visualized and quantized. And the subsequent audit quality can be predicted after the generated audit quality model is brought into the related audit quality index. By comparing the information risk with the audit report, the information risk of the public and market caused by information asymmetry is reduced, and the information risk of the financial statement user is reduced to a socially acceptable level, so that the problems that the academic world is not unified due to the influence of each audit quality index on the audit quality, the audit quality index related to the audit quality cannot be obtained and the prediction of the audit quality of the future year cannot be realized in the prior art are solved.
Example 2:
as shown in fig. 3, the present embodiment provides an audit quality model generating device, including:
an obtaining module 202, configured to obtain audit quality index sample data;
the calculation module 204 is connected with the acquisition module 202 and is used for calculating the maneuverability accrued profit corresponding to the audit quality index sample data;
the regression module 206 is connected with the acquisition module 202 and the calculation module 204, and is used for generating a linear regression model, taking each audit quality index in the audit quality index sample data as an independent variable, taking the steerable accrued profit as a dependent variable, and carrying out linear regression analysis on the independent variable and the dependent variable to obtain a regression equation;
the generating module 208 is connected to the regression module 206, and is configured to perform a significance test on the regression equation, and reject the independent variables with the significance test value P being greater than the preset threshold, so as to obtain an audit quality model.
Optionally, the acquiring module 202 specifically may include:
the acquiring unit is used for acquiring audit quality statistical data of a preset region or country;
the data screening unit is used for screening the audit quality statistical data and eliminating incomplete data in the audit quality statistical data;
and the analysis unit is used for carrying out correlation analysis on each audit quality index in the screened audit quality statistical data, and eliminating audit quality indexes which have correlation with other audit quality indexes in the audit quality statistical data so as to obtain audit quality index sample data.
Optionally, the reputation index comprises an interest index of the public for an audited company and an interest index of the public for an audited company; the public interest index of the audited company is the public search index of the audited company in a given time period; the public interest index of the audited company is the public search index of the audited company in a given time period;
the linear regression model conforms to the following formula:
DAABS it ＝b+β 1 IOT it +β 2 IOTA it +β 3 TE it +β 4 SAT it +β 5 AF it +β 6 SC it +β 7 NAF it +β 8 EXP it + 9 TA it +β 10 LR it +β 11 SR it +β 12 ROA it +β 13 GR it +β 14 PRO it +β 15 AGE it +β 16 NLD it +∈
wherein DAABS is the absolute value of the profit due to the operability of the audited company;
it is the data of the ith company in the t year;
b is a constant term;
beta is the coefficient of each audit quality index;
IOT is an index of public interest to audited companies;
IOTA is an index of public interest to auditing companies;
TE is the number of audit years of the audited company and the auditing company;
SAT is the number of audit years of signature auditors of audited companies and auditing companies;
AF is audit expense paid to an audited company by the audited company;
SC is whether the audit company is a preset four accounting firm, if so, the SC is 1, otherwise, the SC is 0;
NAF is non-audit expense paid to the audit company by the audit company;
EXP is whether an audit company is an expert in the industry, if so, the EXP is 1, otherwise, the EXP is 0;
TA is the total assets of the audited company;
LR is the flow rate of the audited company;
SR is the repayment capability ratio of the audited company;
ROA is the asset return rate of the audited company;
GR is the rate of revenue increase for the audited company;
PRO is whether the audited company is profitable or not, if yes, the PRO is 1, otherwise, the PRO is 0;
AGE is the number of years established by the audited company;
NLD is the change of long-term liabilities of audited companies;
e is error;
the regression module 206 is specifically configured to input the independent variable and the dependent variable into a linear regression model corresponding to the above formula for training, so as to obtain a parameter value of the target parameter in the linear regression model, thereby obtaining a regression equation.
Example 3:
as shown in fig. 4, the present embodiment provides an audit quality prediction method, including:
obtaining audit quality index data of the year to be predicted;
the audit quality index data of the year to be predicted is input into the audit quality model generated by the audit quality model generation method described in embodiment 1 to predict the audit quality of the year to be predicted.
According to the audit quality model generating device and the audit quality predicting method provided by the embodiments 2 to 3, by means of big data analysis, linear regression analysis is carried out on each audit quality index and the manageability accrued profit in the audit quality index sample data to obtain a regression equation, meanwhile, the regression equation is subjected to significance test, and independent variables with the significance test value P larger than a preset threshold are removed, so that a final audit quality model is obtained, and the audit quality index related to the audit quality is obtained. In addition, the qualitative concept of the audit quality can be intuitively displayed through the calculated numerical value by the audit quality model, so that the audit quality is visualized and quantized. And the subsequent audit quality can be predicted after the generated audit quality model is brought into the related audit quality index. By comparing the information risk with the audit report, the information risk of the public and market caused by information asymmetry is reduced, and the information risk of the financial statement user is reduced to a socially acceptable level, so that the problems that the academic world is not unified due to the influence of each audit quality index on the audit quality, the audit quality index related to the audit quality cannot be obtained and the prediction of the audit quality of the future year cannot be realized in the prior art are solved.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present invention, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.
Claims (7)
1. An audit quality model generation method, comprising:
obtaining audit quality index sample data; the audit quality index sample data comprises reputation indexes; the reputation index comprises an interest index of the public to an audited company and an interest index of the public to the audited company; the public interest index of the audited company is the public search index of the audited company in a given time period; the public interest index of the audited company is the public search index of the audited company in a given time period;
calculating the maneuverability accrued profit corresponding to the audit quality index sample data;
generating a linear regression model, taking each audit quality index in the audit quality index sample data as an independent variable, taking the steerable accrued profit as a dependent variable, and carrying out linear regression analysis on the independent variable and the dependent variable to obtain a regression equation;
performing significance test on the regression equation, and removing independent variables with significance test values P being larger than a preset threshold value, so as to obtain an audit quality model;
the linear regression model conforms to the following formula:
DAABS it ＝b+β 1 IOT it +β 2 IOTA it +β 3 TE it +β 4 SAT it +β 5 AF it +β 6 SC it +β 7 NAF it +β 8 EXP it +β 9 TA it +β 10 LR it +β 11 SR it +β 12 ROA it +β 13 GR it +β 14 PRO it +β 15 AGE it +β 16 NLD it +∈
wherein DAABS is the absolute value of the profit due to the operability of the audited company;
it is the data of the ith company in the t year;
b is a constant term;
beta is the coefficient of each audit quality index;
IOT is an index of public interest to audited companies;
IOTA is an index of public interest to auditing companies;
TE is the number of audit years of the audited company and the auditing company;
SAT is the number of audit years of signature auditors of audited companies and auditing companies;
AF is audit expense paid to an audited company by the audited company;
SC is whether the audit company is a preset four accounting firm, if so, the SC is 1, otherwise, the SC is 0;
NAF is non-audit expense paid to the audit company by the audit company;
EXP is whether an audit company is an expert in the industry, if so, the EXP is 1, otherwise, the EXP is 0;
TA is the total assets of the audited company;
LR is the flow rate of the audited company;
SR is the repayment capability ratio of the audited company;
ROA is the asset return rate of the audited company;
GR is the rate of revenue increase for the audited company;
PRO is whether the audited company is profitable or not, if yes, the PRO is 1, otherwise, the PRO is 0;
AGE is the number of years established by the audited company;
NLD is the change of long-term liabilities of audited companies;
e is error;
and performing linear regression analysis on the independent variable and the dependent variable to obtain a regression equation, wherein the method comprises the following steps:
inputting the independent variable and the dependent variable into a linear regression model corresponding to the formula for training so as to obtain a parameter value of a target parameter in the linear regression model, thereby obtaining the regression equation.
2. The method for generating an audit quality model according to claim 1 wherein said obtaining audit quality index sample data includes:
obtaining audit quality statistical data of a preset region or country;
screening the audit quality statistical data, and eliminating incomplete data in the audit quality statistical data;
and performing correlation analysis on each audit quality index in the screened audit quality statistics data, and removing audit quality indexes which have correlation with other audit quality indexes in the audit quality statistics data, so as to obtain the audit quality index sample data.
3. The method for generating an audit quality model according to claim 1, wherein said calculating the manipulable accrued profit corresponding to the audit quality index sample data includes:
and calculating the maneuverability accrued profit corresponding to the audit quality index sample data by adopting a Jones model based on section correction.
4. The method for generating an audit quality model according to claim 1, wherein said performing a saliency test on the regression equation, eliminating the independent variables whose saliency test values P are greater than a preset threshold value, includes:
setting the significance level alpha as 95%, and obtaining a significance test value P of each independent variable in the regression equation;
if the P value is less than 0.05, the independent variable is reserved;
if the P value is larger than 0.05, rejecting the independent variable until the independent variable with the P value larger than 0.05 does not exist in the regression equation, so that a final audit quality model is obtained.
5. An audit quality model generating device, comprising:
the acquisition module is used for acquiring audit quality index sample data; the audit quality index sample data comprises reputation indexes; the reputation index comprises an interest index of the public to an audited company and an interest index of the public to the audited company; the public interest index of the audited company is the public search index of the audited company in a given time period; the public interest index of the audited company is the public search index of the audited company in a given time period;
the calculation module is connected with the acquisition module and used for calculating the maneuverability accrued profit corresponding to the audit quality index sample data;
the regression module is connected with the acquisition module and the calculation module and is used for generating a linear regression model, taking each audit quality index in the audit quality index sample data as an independent variable and taking the steerable accrued profit as a dependent variable, and carrying out linear regression analysis on the independent variable and the dependent variable to obtain a regression equation;
the generation module is connected with the regression module and is used for carrying out significance test on the regression equation and removing independent variables with significance test values P larger than a preset threshold value so as to obtain an audit quality model;
the linear regression model conforms to the following formula:
DAABS it ＝b+β 1 IOT it +β 2 IOTA it +β 3 TE it +β 4 SAT it +β 5 AF it +β 6 SC it +β 7 NAF it +β 8 EXP it +β 9 TA it +β 10 LR it +β 11 SR it +β 12 ROA it +β 13 GR it +β 14 PRO it +β 15 AGE it +β 16 NLD it +∈
wherein DAABS is the absolute value of the profit due to the operability of the audited company;
it is the data of the ith company in the t year;
b is a constant term;
beta is the coefficient of each audit quality index;
IOT is an index of public interest to audited companies;
IOTA is an index of public interest to auditing companies;
TE is the number of audit years of the audited company and the auditing company;
SAT is the number of audit years of signature auditors of audited companies and auditing companies;
AF is audit expense paid to an audited company by the audited company;
SC is whether the audit company is a preset four accounting firm, if so, the SC is 1, otherwise, the SC is 0;
NAF is non-audit expense paid to the audit company by the audit company;
EXP is whether an audit company is an expert in the industry, if so, the EXP is 1, otherwise, the EXP is 0;
TA is the total assets of the audited company;
LR is the flow rate of the audited company;
SR is the repayment capability ratio of the audited company;
ROA is the asset return rate of the audited company;
GR is the rate of revenue increase for the audited company;
PRO is whether the audited company is profitable or not, if yes, the PRO is 1, otherwise, the PRO is 0;
AGE is the number of years established by the audited company;
NLD is the change of long-term liabilities of audited companies;
e is error;
the regression module is specifically configured to input the independent variable and the dependent variable into a linear regression model corresponding to the formula for training, so as to obtain a parameter value of a target parameter in the linear regression model, thereby obtaining the regression equation.
6. The audit quality model generating device according to claim 5, wherein the obtaining module specifically includes:
the acquiring unit is used for acquiring audit quality statistical data of a preset region or country;
the data screening unit is used for screening the audit quality statistical data and eliminating incomplete data in the audit quality statistical data;
and the analysis unit is used for carrying out correlation analysis on each audit quality index in the audit quality statistical data after screening, and eliminating audit quality indexes which have correlation with other audit quality indexes in the audit quality statistical data so as to obtain the audit quality index sample data.
7. An audit quality prediction method, comprising:
obtaining audit quality index data of the year to be predicted;
inputting the audit quality index data of the year to be predicted into an audit quality model generated by the audit quality model generation method according to any one of claims 1-4 so as to predict the audit quality of the year to be predicted.
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