CN112884301A - Method, equipment and computer storage medium for enterprise risk analysis - Google Patents

Method, equipment and computer storage medium for enterprise risk analysis Download PDF

Info

Publication number
CN112884301A
CN112884301A CN202110135131.0A CN202110135131A CN112884301A CN 112884301 A CN112884301 A CN 112884301A CN 202110135131 A CN202110135131 A CN 202110135131A CN 112884301 A CN112884301 A CN 112884301A
Authority
CN
China
Prior art keywords
enterprise
capital
debt
evaluation
parameters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110135131.0A
Other languages
Chinese (zh)
Inventor
黄海阳
吴中山
黎维春
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Dimension Data Technology Co Ltd
Original Assignee
Shenzhen Dimension Data Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Dimension Data Technology Co Ltd filed Critical Shenzhen Dimension Data Technology Co Ltd
Priority to CN202110135131.0A priority Critical patent/CN112884301A/en
Publication of CN112884301A publication Critical patent/CN112884301A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application discloses a method, equipment and a computer storage medium for enterprise risk analysis, which relate to the field of risk evaluation, and the method comprises the steps of obtaining operation data of a plurality of enterprises; obtaining investment efficiency parameters, liability capital parameters and comprehensive evaluation parameters of the enterprises according to the operation data of each enterprise; inputting the investment efficiency parameter of each enterprise into a preset first residual error measurement model to obtain the capital investment efficiency of the enterprise; inputting the debt capital parameters of each enterprise into a preset second residual measurement model to obtain the debt use efficiency of the enterprise; inputting the comprehensive evaluation parameters, the capital investment efficiency and the debt use efficiency of all the enterprises into an enterprise risk comprehensive evaluation model to obtain enterprise risk rating information of the enterprises; the evaluation result is more comprehensive and reliable, and the accuracy of enterprise risk assessment is improved.

Description

Method, equipment and computer storage medium for enterprise risk analysis
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for enterprise risk analysis, and a computer storage medium.
Background
With the development of economy, more and more factors influencing the development of enterprises are generated, so that investors or economic development decision makers cannot quickly identify the operation risk of the enterprises, and the survival and the long-term development of the enterprises are influenced for a long time. Thus, enterprises typically analyze business data to measure the inefficient investment level of the enterprise to maintain the value of the enterprise. And the acquisition of the non-efficiency investment degree is usually measured by enterprise investment efficiency obtained by a residual error measurement (Richardson) model. However, the enterprise investment efficiency obtained only through the residual error measurement model cannot accurately reflect the enterprise capital operation capability and the risk control capability, and the accuracy of enterprise risk assessment is not high.
Disclosure of Invention
The present application is directed to solving at least one of the problems in the prior art. Therefore, the application provides a method, equipment and computer storage medium for enterprise risk analysis, and accuracy of enterprise risk assessment can be improved.
A method for enterprise risk analysis according to an embodiment of the first aspect of the application includes:
acquiring the operation data of a plurality of enterprises;
obtaining investment efficiency parameters, liability capital parameters and comprehensive evaluation parameters of the enterprises according to the operation data of each enterprise;
inputting the investment efficiency parameter of each enterprise into a preset first residual error measurement model to obtain the capital investment efficiency of the enterprise;
inputting the debt capital parameters of each enterprise into a preset second residual measurement model to obtain the debt use efficiency of the enterprise;
and inputting the comprehensive evaluation parameters, the capital investment efficiency and the debt use efficiency of a plurality of enterprises into a preset enterprise risk comprehensive evaluation model to obtain enterprise risk rating information of each enterprise.
According to the above embodiments of the present application, at least the following advantages are provided: respectively obtaining the capital investment efficiency and the debt use efficiency of each enterprise through the first residual error measurement model and the second residual error measurement model; the capital investment efficiency and the debt use efficiency are added into the enterprise risk comprehensive evaluation model, so that the enterprise capital operation capacity and the risk control capacity can be better embodied, the enterprise risk rating information is more comprehensive and reliable, and the accuracy of enterprise risk evaluation is improved.
According to the method for enterprise risk analysis in some embodiments of the first aspect of the present application, the investment efficiency parameters include year virtual variables, industry virtual variables, first enterprise investment capital of the year to be evaluated and second enterprise investment capital of the last year, asset liability rate, enterprise cash holding level, total enterprise assets, enterprise establishment years and equity return rate;
inputting the investment efficiency parameter of each enterprise into a preset first residual error metric model to obtain the capital investment efficiency of the enterprise, wherein the method comprises the following steps:
inputting other parameters of the investment efficiency parameters of each enterprise except the first enterprise investment capital into a preset first residual error measurement model as independent variables to obtain enterprise investment capital predicted values of the enterprises;
obtaining a first residual term of each enterprise according to the enterprise investment capital predicted value and the first enterprise investment capital of the enterprise;
setting the first residual term for each of the businesses to the capital investment efficiency for the corresponding business.
Therefore, by inputting the year virtual variable, the industry virtual variable, and the like as dependent variables into the first residual error metric model, the influence of the special year or the industry on the investment efficiency of the enterprise investment capital can be eliminated, so that the capital investment efficiency of the enterprise obtained by the first residual error metric model is more accurate.
According to the method for enterprise risk analysis in some embodiments of the first aspect of the present application, the debt capital parameters include year virtual variable, industry virtual variable, enterprise debt capital of the year to be evaluated and second enterprise investment capital of the last year, enterprise cash holding level, enterprise establishment age, total enterprise assets, asset return rate, asset liability rate and enterprise market value index;
inputting the debt capital parameters of each enterprise into a preset second residual measurement model to obtain the debt use efficiency of the enterprise, wherein the debt use efficiency comprises the following steps:
inputting other parameters of the debt capital parameters of each enterprise except the debt capital of the enterprise into a preset second residual error measurement model as independent variables to obtain a predicted value of the debt capital of the enterprise;
obtaining a second residual item of each enterprise according to the enterprise debt capital forecast value and the enterprise debt capital of the enterprise;
setting the second residual term of each of the enterprises as the debt usage efficiency of the corresponding enterprise.
Therefore, by inputting the year virtual variable, the industry virtual variable and the like as dependent variables into the second residual error measurement model, the influence of the special year or the industry on the debt use efficiency of the enterprise investment capital can be eliminated, and the debt use efficiency of the enterprise obtained through the second residual error measurement model is more accurate.
According to the method for enterprise risk analysis in some embodiments of the first aspect of the present application, the comprehensive evaluation parameters include flowing asset-to-liability proportion, asset-liability rate, flowing asset turnover rate, total equity rate, talent reserve rate, and practitioner growth rate;
the step of inputting the comprehensive evaluation parameters, the capital investment efficiency and the debt use efficiency of the plurality of enterprises into a preset enterprise risk comprehensive evaluation model to obtain enterprise risk rating information of the enterprises includes:
respectively carrying out standardization processing on the comprehensive evaluation parameters, the capital investment efficiency and the debt use efficiency of each enterprise to obtain a comprehensive evaluation standard parameter set of each enterprise;
weighting each element in the comprehensive evaluation standard parameter set of each enterprise to obtain a comprehensive score of the enterprise;
and matching the comprehensive score of each enterprise with preset risk parameters to obtain enterprise risk rating information of the enterprise.
Therefore, more accurate enterprise risk rating information can be obtained by performing standardization processing on the comprehensive evaluation parameters and then performing weight processing.
According to the method for enterprise risk analysis in some embodiments of the first aspect of the present application, the normalizing the composite evaluation parameters, the capital investment efficiency, and the debt usage efficiency of each enterprise to obtain a composite evaluation criterion parameter set of each enterprise includes:
respectively acquiring each evaluation item in the comprehensive evaluation parameters of each enterprise, the standard deviation and the average value of the capital investment efficiency and the debt use efficiency;
respectively acquiring the difference value between each evaluation item in the comprehensive evaluation parameters of each enterprise, the capital investment efficiency and the debt use efficiency and the corresponding average value;
and respectively obtaining the ratio of each difference value of each enterprise to the corresponding standard deviation to obtain the comprehensive evaluation standard parameter set of the enterprise.
Therefore, by processing the raw data values of the same evaluation item of all enterprises and the mean and variance of the corresponding same evaluation item, the difference of the relative average level of the data of the same evaluation item of each enterprise in the same evaluation item can be obtained.
According to the method for enterprise risk analysis in some embodiments of the first aspect of the present application, weighting each element in the set of comprehensive evaluation criterion parameters of each enterprise to obtain a comprehensive score of the enterprise, includes:
performing factor load rotation processing on each element of the comprehensive evaluation standard parameter set of each enterprise through a factor analysis method to obtain an evaluation common factor set of each enterprise and a variance contribution rate corresponding to each evaluation common factor in the evaluation common factor set;
acquiring the ratio of each variance contribution rate of each enterprise to the total variance contribution rate of the enterprise, and setting the ratio as the weight of the evaluation common factor corresponding to the variance contribution rate of the enterprise;
and obtaining the comprehensive score of the enterprise according to each evaluation common factor in the evaluation common factor set of each enterprise and the corresponding weight.
According to the method for enterprise risk analysis in some embodiments of the first aspect of the present application, the weighting processing is performed on the comprehensive evaluation criterion parameter set of each enterprise to obtain a comprehensive score of the enterprise, further comprising;
ranking each evaluation common factor in the evaluation common factor set of each enterprise according to the corresponding variance contribution rate;
sequentially accumulating the variance contribution rate corresponding to each evaluation common factor in each evaluation common factor set to obtain an accumulated variance value of the evaluation common factor set;
comparing each accumulated variance value with a preset contribution value to obtain comparison data;
and deleting the evaluation common factors which are not subjected to accumulation processing in the corresponding evaluation common factor set according to each piece of comparison data.
An apparatus for enterprise risk analysis according to some embodiments of the second aspect of the present application, comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions for execution by the at least one processor to cause the at least one processor, when executing the instructions, to implement a method of enterprise risk analysis as in any one of the first aspects.
A computer-readable storage medium according to some embodiments of the second aspect of the present application, having stored thereon computer-executable instructions for causing a computer to perform a method of enterprise risk analysis as in any one of the first aspects.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart of a method for enterprise risk analysis according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of capital investment efficiency of a method of enterprise risk analysis of an embodiment of the present application;
FIG. 3 is a flow chart illustrating the efficiency of liability usage of a method of enterprise risk analysis according to an embodiment of the present application;
FIG. 4 is a schematic flow chart diagram illustrating an enterprise risk rating of a method of enterprise risk analysis in an embodiment of the present application;
fig. 5 is a flowchart illustrating a weight process of a method for enterprise risk analysis according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
The methods, apparatus, and computer storage media for enterprise risk analysis of the present application are described below with reference to fig. 1-5.
According to an embodiment of the first aspect of the present application, as shown in fig. 1, a method for enterprise risk analysis includes:
and S100, acquiring the operation data of a plurality of enterprises.
And S200, obtaining investment efficiency parameters, debt capital parameters and comprehensive evaluation parameters of the enterprise according to each operation data.
It should be noted that the evaluation parameters of the investment efficiency parameters include year virtual variables, industry virtual variables, enterprise investment capital, enterprise debt capital, asset liability rate, enterprise cash holding level, enterprise total assets, enterprise establishment years and equity return rate.
It should be noted that the evaluation parameters of the debt capital parameters include year virtual variables, industry virtual variables, enterprise debt capital, enterprise investment capital, enterprise cash holding level, enterprise established years, enterprise total assets, asset return rate, asset liability rate, and enterprise market value indexes.
And step S300, inputting the investment efficiency parameters of each enterprise into a preset first residual error measurement model to obtain the capital investment efficiency of the enterprise.
And S400, inputting the debt capital parameters of each enterprise into a preset second residual error measurement model to obtain the debt use efficiency of the enterprise.
It should be noted that the first residual measurement model and the second residual measurement model may be set as Richardson investment efficiency regression models.
And S500, inputting the comprehensive evaluation parameters, capital investment efficiency and debt use efficiency of a plurality of enterprises into a preset enterprise risk comprehensive evaluation model to obtain enterprise risk rating information of each enterprise.
It should be noted that the year virtual variable and the industry virtual variable are related to the industry of the enterprise to be evaluated and the year to be evaluated; the method is respectively used for reducing enterprise data evaluation abnormity caused by year specificity and enterprise data evaluation abnormity caused by industry specificity. See in particular the application of the existing Richardson investment efficiency regression model. The year virtual variable and the industry virtual variable can be set as required, and therefore, detailed description is omitted here.
It should be noted that the enterprise risk comprehensive evaluation model is used for obtaining the enterprise comprehensive capacity (i.e. enterprise risk classification information) according to the capital use efficiency (including capital investment efficiency and debt use efficiency), the debt paying risk (e.g. the proportion of mobile assets to debt assets, the rate of assets to debt, etc.), the capital turnover capacity (e.g. the turnover rate of mobile assets, the turnover rate of total assets, etc.), the profitability (e.g. the total equity rate), other non-financial indexes (e.g. talent reserve rate, the growth rate of workers), etc. of the year to be evaluated. Wherein the balance is the ratio of the total balance to the total balance; the turnover rate of the mobile assets is the ratio of the business income of the main business to the mobile assets; the total asset turnover rate is the ratio of the main business income to the total asset amount; the total equity rate is the ratio of business surplus to total amount of assets; the talent reserve rate is the ratio of the remuneration of the laborers to the revenue of the main business; the increase rate of the workers is the ratio of the number of workers in the year to the number of workers in the last year. The business surplus, the total amount of assets, the mobile assets, the number of workers and the like are all business data of corresponding enterprises.
Therefore, the capital investment efficiency and the debt use efficiency of each enterprise are respectively obtained through the first residual error measurement model and the second residual error measurement model; the capital investment efficiency and the debt use efficiency are added into the enterprise risk comprehensive evaluation model, so that the enterprise capital operation capacity and the risk control capacity can be better embodied, the enterprise risk rating information is more comprehensive and reliable, and the accuracy of enterprise risk evaluation is improved.
It is understood that the investment efficiency parameters include year virtual variables, industry virtual variables, first business investment capital for the year being evaluated and second business investment capital for the last year, asset liability rates, business cash-holding levels, total business assets, business form years, and equity return rates. As shown in fig. 2, step S300 includes:
and S310, respectively inputting other parameters of the investment efficiency parameters of each enterprise except the first enterprise investment capital into a preset first residual error measurement model as independent variables to obtain enterprise investment capital predicted values of the enterprises.
And S320, obtaining a first residual error item of each enterprise according to the enterprise investment capital predicted value and the first enterprise investment capital of each enterprise.
It should be noted that the first residual term is the difference between the predicted value of the investment capital of the enterprise and the actual investment capital of the enterprise.
And step S330, setting the first residual item of each enterprise as the capital investment efficiency of the corresponding enterprise.
It should be noted that, when the first residual term is greater than 0, it indicates excessive investment of the enterprise, and when the first residual term is less than 0, it indicates insufficient investment of the enterprise, and the value size of the first residual term indicates the degree of excessive investment or insufficient investment, and thus, it is used to indicate the capital investment efficiency.
Therefore, by inputting the year virtual variable, the industry virtual variable, and the like as dependent variables into the first residual error metric model, the influence of the special year or the industry on the investment efficiency of the enterprise investment capital can be eliminated, so that the capital investment efficiency of the enterprise obtained by the first residual error metric model is more accurate.
It is understood that the debt capital parameters include year virtual variables, industry virtual variables, corporate debt capital for the year being assessed and second corporate investment capital for the previous year, corporate cash holding levels, corporate established years, total corporate assets, return on assets, rate of liability, and corporate market value indicators. As shown in fig. 3, step S400 includes:
and S410, respectively inputting other parameters of the debt capital parameters of each enterprise except the debt capital of the enterprise into a preset second residual error measurement model by using the parameters as independent variables to obtain the predicted value of the debt capital of the enterprise.
And step S420, obtaining a second residual item of the enterprise according to the enterprise debt capital forecast value and the enterprise debt capital of each enterprise.
It should be noted that the second residual term is the difference between the predicted value of the business liability capital and the actual business liability capital.
And step S430, setting the second residual error item of each enterprise as the debt use efficiency of the corresponding enterprise.
It should be noted that, when the second residual term is greater than 0, it indicates that the business is over-invested in the debt capital, and when the second residual term is less than 0, it indicates that the business is under-invested in the debt capital, and the size of the value of the first residual term indicates the degree of over-investing in the debt capital or under-investing in the debt capital, and thus is used to indicate the efficiency of the use of the debt.
Therefore, by inputting the year virtual variable, the industry virtual variable and the like as dependent variables into the second residual error measurement model, the influence of the special year or the industry on the debt use efficiency of the enterprise investment capital can be eliminated, and the debt use efficiency of the enterprise obtained through the second residual error measurement model is more accurate.
It is understood that the comprehensive evaluation parameters include the flowing asset to liability ratio, the asset liability ratio, the flowing asset turnover rate, the total equity rate, the talent reserve rate and the practitioner growth rate; as shown in fig. 4, step S500 includes:
step S510, respectively carrying out standardization processing on the comprehensive evaluation parameters, capital investment efficiency and debt use efficiency of each enterprise to obtain a comprehensive evaluation standard parameter set of each enterprise.
And S520, performing weight processing on each element in the comprehensive evaluation standard parameter set of each enterprise to obtain a comprehensive score of the enterprise.
It should be noted that each element represents a value corresponding to an evaluation item of the enterprise in the enterprise risk comprehensive evaluation model.
And step S530, matching the comprehensive score of each enterprise with preset risk parameters to obtain enterprise risk rating information of the enterprise.
It should be noted that the enterprise can be classified into four categories, i.e., high-quality business, general risk enterprise, light risk enterprise, and serious risk enterprise, according to actual needs, and each category is set up as a partition. At this time, the comprehensive score of each enterprise may be matched with the scoring area to obtain risk assessment information (i.e., enterprise risk classification information) of the enterprise.
Therefore, more accurate enterprise risk rating information can be obtained by performing standardization processing on the comprehensive evaluation parameters and then performing weight processing.
It is understood that step S510 includes the following sub-steps:
first, the standard deviation and the mean of each evaluation item, capital investment efficiency and debt use efficiency in the comprehensive evaluation parameters of each enterprise are respectively obtained.
It should be noted that the evaluation item corresponds to the flowing asset-to-liability proportion, the asset-liability rate, the flowing asset turnover rate, the total equity rate, the talent reserve rate and the practitioner growth rate in the comprehensive evaluation parameters.
In the case of an evaluation item, since there are a plurality of companies, the average value and the standard deviation of the value of the same evaluation item of the plurality of companies can be obtained by performing the average value and the standard deviation of the value of the corresponding evaluation item.
And secondly, respectively acquiring the difference value between the value of the same evaluation item in the comprehensive evaluation parameters of each enterprise, the capital investment efficiency and the debt use efficiency and the corresponding average value.
And finally, respectively obtaining the ratio of each difference value of each enterprise to the corresponding standard deviation to obtain a comprehensive evaluation standard parameter set of the enterprise.
Therefore, by processing the raw data values of the same evaluation item of all enterprises and the mean and variance of the corresponding same evaluation item, the difference of the relative average level of the data of the same evaluation item of each enterprise in the same evaluation item can be obtained.
It is understood that, as shown in fig. 5, step S520 includes:
and step S521, performing factor load rotation processing on each element of the comprehensive evaluation standard parameter set of each enterprise through a factor analysis method to obtain an evaluation common factor set of each enterprise and a variance contribution rate corresponding to each evaluation common factor in the evaluation common factor set.
Step S522, a ratio of each variance contribution rate of each enterprise to the total variance contribution rate of the enterprise is obtained, and the ratio is set as a weight of an evaluation common factor corresponding to the variance contribution rate of the enterprise.
Step S523, obtaining a comprehensive score of each enterprise according to each evaluation common factor and the corresponding weight in the evaluation common factor set of each enterprise.
It is understood that the following four sub-steps are also included between step S521 and step S522;
firstly, each evaluation common factor in the evaluation common factor set of each enterprise is sorted according to the corresponding variance contribution rate.
Note that the variance contribution rate indicates the influence of the evaluation common factor on the scoring result. Therefore, the ordering can be from large to small according to the variance contribution rate.
And then, accumulating the variance contribution rate corresponding to each evaluation common factor in each evaluation common factor set in sequence to obtain an accumulated variance value of the evaluation common factor set.
It should be noted that the evaluation common factor with smaller influence (i.e. smaller variance contribution rate) can be removed; thereby improving the accuracy of the evaluation.
Then, each accumulated variance value is compared with a preset contribution value to obtain comparison data.
It should be noted that, when the accumulated variance value reaches the contribution value, the evaluation common factor indicating that the accumulation calculation has been performed may be used for evaluation, and the evaluation common factor that is not subjected to the accumulation operation may not affect the rating result and may be deleted.
And finally, deleting the evaluation common factors which are not accumulated in the corresponding evaluation common factor set according to each comparison data.
It should be noted that the contribution value may be set according to an actual application scenario, wherein in an actual application, a better rating result may be obtained by selecting 75% to 85% of the contribution value. The evaluation common factor set after sorting is combined into { F by taking the contribution value as 85%1,F2.......FnThe corresponding variance contribution rates are respectively k1,k2...kn. Then when k is1+k2+.....km≥85％(m<n) is k1+k2+.....kmThe corresponding evaluation common factor is effective, and in this case, F in the evaluation common factor set is required to be usedm+1.......FnAnd (5) deleting.
An apparatus for enterprise risk analysis according to some embodiments of the second aspect of the present application, comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions for execution by the at least one processor to cause the at least one processor, when executing the instructions, to implement a method of enterprise risk analysis as in any one of the first aspects.
A computer-readable storage medium according to an embodiment of the third aspect of the present application stores computer-executable instructions for causing a computer to perform the method of enterprise risk analysis of any one of the first aspects.
It should be noted that the term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer.
It should be noted that all or some of the steps of the methods disclosed above can be implemented as software, firmware, hardware and suitable combinations thereof, as would be understood by one of ordinary skill in the art.
The method for enterprise risk analysis according to the embodiment of the present application is described in detail in a specific embodiment with reference to fig. 1 to 5. It is to be understood that the following description is illustrative only and is not intended to be in any way limiting.
Referring to step S100 in fig. 1, business data of a plurality of enterprises are obtained, wherein the business data of each enterprise includes the number of persons working in the last year, and the Debt capital Debt of the enterprise to be evaluated for the year ttFirst enterprise investment capital InvesttAnd a second business investment capital Invest of t-1 of the last yeart-1The ratio of assets and liabilities Leveraget-1Enterprise Cash holding level Casht-1Enterprise capital Sizet-1Enterprise establishment Aget-1Stock right return rate Stock Returnst-1Asset rate of return, ROAt-1The balance of the mobile assets, the total amount of the debt, the total amount of the assets, the income of the main business, the mobile assets, the business surplus, the remuneration of the laborers, the number of the employees in the year and the market value of the enterprise.
In this case, referring to step S200, the investment efficiency parameter, the debt capital parameter, and the comprehensive evaluation parameter of each enterprise can be obtained.
Wherein, the parameters of the investment efficiency parameters are as follows:
year virtual variable sigma Yeast Indicator, Industry virtual variable sigma Industry Indicator, first enterprise investment capital InvesttAnd second Enterprise investment capital Investt-1The ratio of assets and liabilities Leveraget-1Enterprise Cash holding level Casht-1Enterprise capital Sizet-1Enterprise establishment Aget-1Stock return ratest-1。
Wherein the debt capital parameters are as follows:
year virtual variable sigma Yeast Indicator, Industry virtual variable sigma Industry Indicator, and Enterprise Debt capital DebttAnd second Enterprise investment capital Investt-1Enterprise Cash holding level Casht-1Enterprise establishment Aget-1Enterprise capital Sizet-1Asset rate of return, ROAt-1The ratio of assets and liabilities Leveraget-1And enterprise market value index Tobin _ Qt-1(wherein Tobin _ Q)t-1As a ratio of enterprise market value to total assets).
Wherein, the parameters of the comprehensive evaluation parameters are as follows:
mobile asset to liability ratio X3(i.e., total volume of flowing assets/liabilities), rate of liabilities X4(i.e., total liability/total assets), liquidity turnover X5(i.e., main business revenue/liquidity), total asset turnover rate X6(i.e., business revenue/total assets), Total equity Rate X7(i.e., business surplus/total assets), talent reserve rate X8(i.e., worker reward/operating charge) and practitioner growth rate X9(i.e. theThe number of workers in the year/the number of workers in the year).
Further, referring to step S300, the capital investment efficiency X of each enterprise is obtained1。
Specifically, referring to steps S310 to S330, the parameters are input into the first residual error metric model as follows:
Invest′t＝β+β1Investt-1+β2Leveraget-1+β3Casht-1+β4Aget-1+β5Sizet-1+β6Stock Returnst-1+∑Year Indicator+∑Industry Indicator
wherein t represents the year to be evaluated, and t-1 represents the previous year of the year to be evaluated.
At the moment, the enterprise investment capital prediction value Invest 'of the year to be evaluated is obtained't. At this time, the capital investment efficiency per business is X1＝Invest′t-Investt。
Further, referring to step S400, the debt usage efficiency X of each enterprise is obtained2。
Specifically, referring to steps S410 to S430, the parameters are input into the second residual error metric model as follows:
Debt′t＝α+α1Investt-1+α2Casht-1+α3Aget-1+α4Sizet-1+α5ROAt-1+α6Leveraget-1+α7Tobin_Qt-1+∑Year Indicator+∑Industry Indicator2
wherein, Tobin _ Qt-1Is the ratio of the enterprise market value of the last year (t-1) to the total amount of assets of the last year
At this time, the enterprise Debt capital forecast value Debt of each enterprise of the year to be evaluated is obtained'tAt this time, the efficiency of use of the debt of the enterprise X2＝Debt′t-Debtt。
At this time, X for each business1～X9Respectively corresponding to 9 of the enterprise risk comprehensive evaluation modelsThe values of the terms are evaluated.
Further, referring to step S500, enterprise risk rating information of each enterprise is obtained.
Specifically, referring to step S510, a comprehensive evaluation criterion parameter set of each enterprise is obtained.
Specifically, referring to the first sub-step of step S510, the average of each evaluation item, capital investment efficiency, and debt usage efficiency of each enterprise is obtained
Figure BDA0002926509970000101
And variance S1......S9Wherein, in
Figure BDA0002926509970000102
S1For the purpose of example only,
Figure BDA0002926509970000103
for integrating X of multiple enterprises1Is subjected to mean value processing to obtain S1For integrating X of multiple enterprises1And obtaining the variance.
At this time, a comprehensive evaluation criterion parameter set of each enterprise may be obtained, where the value of each element in each comprehensive evaluation criterion parameter set is:
Figure BDA0002926509970000111
specifically, referring to step S520, a composite score of each enterprise is obtained.
Specifically, referring to the substep of step S520, a set of ranked evaluation factors { F } for each enterprise is obtained1,F2.......Fn}; wherein
Figure BDA0002926509970000112
The corresponding variance contribution rates are respectively k1,k2...kn。
Specifically, the cumulative variance value is obtained to be 85%, i.e., k1+k2+.....km≥85％(m<n), wherein m is a number, and the evaluation factors after the number m in the evaluation common factor set are removed; obtaining an updated evaluation factor set { F1,F2.......Fm}。
At this time, the composite score for each business is as follows:
F＝(k1×F1+....+km×Fm)/(k1+....+km)
further, referring to step 530, enterprise risk rating information for each enterprise is obtained.
Specifically, the enterprises are classified into four types, namely high-quality business, general risk enterprise, mild risk enterprise, severe risk enterprise and the like, wherein the inter-score of the high-quality business is less than or equal to V1(ii) a The score of the general risk enterprise is (V)1，V2](ii) a The score of the light risk enterprise is (V)2，V3](ii) a The score between major risk enterprises is greater than V3。
At this time, the enterprise classification to which the F of each enterprise belongs between scoring areas is obtained, so as to obtain the conclusion that the enterprise is a high-quality business or a general risk enterprise or a light risk enterprise or a severe risk enterprise, that is, the enterprise risk rating information.
In the description herein, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.

Claims (9)

1. A method of enterprise risk analysis, the method comprising:
acquiring the operation data of a plurality of enterprises;
obtaining investment efficiency parameters, liability capital parameters and comprehensive evaluation parameters of the enterprises according to the operation data of each enterprise;
inputting the investment efficiency parameter of each enterprise into a preset first residual error measurement model to obtain the capital investment efficiency of the enterprise;
inputting the debt capital parameters of each enterprise into a preset second residual measurement model to obtain the debt use efficiency of the enterprise;
and inputting the comprehensive evaluation parameters, the capital investment efficiency and the debt use efficiency of a plurality of enterprises into a preset enterprise risk comprehensive evaluation model to obtain enterprise risk rating information of each enterprise.
2. The method of enterprise risk analysis of claim 1,
the investment efficiency parameters comprise year virtual variables, industry virtual variables, first enterprise investment capital of a year to be evaluated, second enterprise investment capital of a previous year, asset liability rate, enterprise cash holding level, total enterprise assets, enterprise establishment years and equity return rate;
inputting the investment efficiency parameter of each enterprise into a preset first residual error metric model to obtain the capital investment efficiency of the enterprise, wherein the method comprises the following steps:
inputting other parameters of the investment efficiency parameters of each enterprise except the first enterprise investment capital into a preset first residual error measurement model as independent variables to obtain enterprise investment capital predicted values of the enterprises;
obtaining a first residual term of each enterprise according to the enterprise investment capital predicted value and the first enterprise investment capital of the enterprise;
setting the first residual term for each of the businesses to the capital investment efficiency for the corresponding business.
3. The method of enterprise risk analysis of claim 1,
the debt capital parameters comprise year virtual variables, industry virtual variables, enterprise debt capital of the year to be evaluated, second enterprise investment capital of the previous year, enterprise cash holding level, enterprise establishment age, enterprise total assets, asset return rate, asset liability rate and enterprise market value indexes;
inputting the debt capital parameters of each enterprise into a preset second residual measurement model to obtain the debt use efficiency of the enterprise, wherein the debt use efficiency comprises the following steps:
inputting other parameters of the debt capital parameters of each enterprise except the debt capital of the enterprise into a preset second residual error measurement model as independent variables to obtain a predicted value of the debt capital of the enterprise;
obtaining a second residual item of each enterprise according to the enterprise debt capital forecast value and the enterprise debt capital of the enterprise;
setting the second residual term of each of the enterprises as the debt usage efficiency of the corresponding enterprise.
4. The method of enterprise risk analysis of claim 1,
the comprehensive evaluation parameters comprise the proportion of the mobile assets to the liabilities, the rate of the assets to the liabilities, the turnover rate of the mobile assets, the total turnover rate of the assets, the total equity rate, the reserve rate of talents and the growth rate of employees;
the step of inputting the comprehensive evaluation parameters, the capital investment efficiency and the debt use efficiency of the plurality of enterprises into a preset enterprise risk comprehensive evaluation model to obtain enterprise risk rating information of the enterprises includes:
respectively carrying out standardization processing on the comprehensive evaluation parameters, the capital investment efficiency and the debt use efficiency of each enterprise to obtain a comprehensive evaluation standard parameter set of each enterprise;
weighting each element in the comprehensive evaluation standard parameter set of each enterprise to obtain a comprehensive score of the enterprise;
and matching the comprehensive score of each enterprise with preset risk parameters to obtain enterprise risk rating information of the enterprise.
5. The method of enterprise risk analysis of claim 4,
the step of standardizing the comprehensive evaluation parameters, the capital investment efficiency and the debt use efficiency of each enterprise to obtain a comprehensive evaluation standard parameter set of each enterprise comprises:
respectively acquiring each evaluation item in the comprehensive evaluation parameters of each enterprise, the standard deviation and the average value of the capital investment efficiency and the debt use efficiency;
respectively acquiring the difference value between each evaluation item in the comprehensive evaluation parameters of each enterprise, the capital investment efficiency and the debt use efficiency and the corresponding average value;
and respectively obtaining the ratio of each difference value of each enterprise to the corresponding standard deviation to obtain the comprehensive evaluation standard parameter set of the enterprise.
6. The method of enterprise risk analysis of claim 4,
weighting each element in the comprehensive evaluation standard parameter set of each enterprise to obtain a comprehensive score of the enterprise, wherein the weighting comprises the following steps:
performing factor load rotation processing on each element of the comprehensive evaluation standard parameter set of each enterprise through a factor analysis method to obtain an evaluation common factor set of each enterprise and a variance contribution rate corresponding to each evaluation common factor in the evaluation common factor set;
acquiring the ratio of each variance contribution rate of each enterprise to the total variance contribution rate of the enterprise, and setting the ratio as the weight of the evaluation common factor corresponding to the variance contribution rate of the enterprise;
and obtaining the comprehensive score of the enterprise according to each evaluation common factor in the evaluation common factor set of each enterprise and the corresponding weight.
7. The method of enterprise risk analysis of claim 6,
carrying out weight processing on the comprehensive evaluation standard parameter set of each enterprise to obtain a comprehensive score of the enterprise, and further comprising;
ranking each evaluation common factor in the evaluation common factor set of each enterprise according to the corresponding variance contribution rate;
sequentially accumulating the variance contribution rate corresponding to each evaluation common factor in each evaluation common factor set to obtain an accumulated variance value of the evaluation common factor set;
comparing each accumulated variance value with a preset contribution value to obtain comparison data;
and deleting the evaluation common factors which are not subjected to accumulation processing in the corresponding evaluation common factor set according to each piece of comparison data.
8. An apparatus for enterprise risk analysis, comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions for execution by the at least one processor to cause the at least one processor, when executing the instructions, to implement a method of enterprise risk analysis as claimed in any one of claims 1 to 7.
9. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of enterprise risk analysis of any of claims 1-7.
CN202110135131.0A 2021-02-01 2021-02-01 Method, equipment and computer storage medium for enterprise risk analysis Pending CN112884301A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110135131.0A CN112884301A (en) 2021-02-01 2021-02-01 Method, equipment and computer storage medium for enterprise risk analysis

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110135131.0A CN112884301A (en) 2021-02-01 2021-02-01 Method, equipment and computer storage medium for enterprise risk analysis

Publications (1)

Publication Number Publication Date
CN112884301A true CN112884301A (en) 2021-06-01

Family

ID=76052250

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110135131.0A Pending CN112884301A (en) 2021-02-01 2021-02-01 Method, equipment and computer storage medium for enterprise risk analysis

Country Status (1)

Country Link
CN (1) CN112884301A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116468271A (en) * 2023-04-17 2023-07-21 北京融信数联科技有限公司 Enterprise risk analysis method, system and medium based on big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116468271A (en) * 2023-04-17 2023-07-21 北京融信数联科技有限公司 Enterprise risk analysis method, system and medium based on big data
CN116468271B (en) * 2023-04-17 2024-02-27 北京融信数联科技有限公司 Enterprise risk analysis method, system and medium based on big data

Similar Documents

Publication Publication Date Title
Antunes et al. Firm default probabilities revisited
KR100914307B1 (en) System of constructing credit risk model, method of managing credit risk using credit risk model construction and Recording medium thereof
JP2003316950A (en) System and method for credit evaluation
CN112598500A (en) Credit processing method and system for non-limit client
CN112053233A (en) Dynamic small and medium enterprise credit scoring method and system based on GRA
CN110930248A (en) Credit risk prediction model construction method and system, storage medium and electronic equipment
JP6251383B2 (en) Calculating the probability of a defaulting company
JP2001125962A (en) Support system for management consulting and decision making in management
CN111079941A (en) Credit information system combining expert experience model and supervised machine learning algorithm
CN116385057A (en) Building engineering material provider selection evaluation method based on multidimensional feature analysis
CN111626855A (en) Bond credit interest difference prediction method and system
CN112037006A (en) Credit risk identification method and device for small and micro enterprises
Wei [Retracted] A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management
CN112884301A (en) Method, equipment and computer storage medium for enterprise risk analysis
CN117252677A (en) Credit line determination method and device, electronic equipment and storage medium
CN115829722A (en) Training method of credit risk scoring model and credit risk scoring method
US20210201403A1 (en) System and method for reconciliation of electronic data processes
JP2004185551A (en) Management quality control system, management quality control method, management quality control program and medium
CN107977804B (en) Guarantee warehouse business risk assessment method
JACKOVÁ THE PROCESS OF FINANCIAL PERFORMANCE EVALUATING OF THE COMPANY.
Hargreaves Machine learning application to identify good credit customers
CN116992265B (en) Carbon emission estimation method, apparatus, device, and storage medium
CN112926816B (en) Vendor evaluation method, device, computer device and storage medium
Mileris Macroeconomic Business Cycle Indicators of Credit Risk Increase in Commercial Banks.
AU2016200314A1 (en) Performance analytics based on high performance indices

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20210601