CN114022286A - Investment portfolio pressure bearing assessment method, device, equipment and medium - Google Patents

Investment portfolio pressure bearing assessment method, device, equipment and medium Download PDF

Info

Publication number
CN114022286A
CN114022286A CN202111298552.1A CN202111298552A CN114022286A CN 114022286 A CN114022286 A CN 114022286A CN 202111298552 A CN202111298552 A CN 202111298552A CN 114022286 A CN114022286 A CN 114022286A
Authority
CN
China
Prior art keywords
evaluation
target
data
investment
factor
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
CN202111298552.1A
Other languages
Chinese (zh)
Inventor
罗孝培
张战胜
黄美玲
张志辉
王亚鑫
黎松
严凌
郝佳齐
高远
刘丙寅
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.)
Taikang Insurance Group Co Ltd
Taikang Pension Insurance Co Ltd
Original Assignee
Taikang Insurance Group Co Ltd
Taikang Pension Insurance 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 Taikang Insurance Group Co Ltd, Taikang Pension Insurance Co Ltd filed Critical Taikang Insurance Group Co Ltd
Priority to CN202111298552.1A priority Critical patent/CN114022286A/en
Publication of CN114022286A publication Critical patent/CN114022286A/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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or 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
    • G06Q10/063—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

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Accounting & Taxation (AREA)
  • Marketing (AREA)
  • Educational Administration (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Technology Law (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

The embodiment of the application provides a pressure-bearing assessment method, a pressure-bearing assessment device, equipment and a medium for investment portfolio, which are applied to a client side, wherein the method comprises the following steps: acquiring the current estimation data of the investment portfolio triggered by the stress assessment condition and the information data of the investment portfolio in the historical period in response to the triggered stress assessment condition; matching preset evaluation factor configuration information with the evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data; analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor; determining a risk resistance value corresponding to each target evaluation factor based on the asset occupation ratio of the investment target belonging to each target evaluation factor in the valuation data and the corresponding fluctuation value; and outputting the current pressure bearing evaluation information of the investment portfolio based on the risk resistance values respectively corresponding to the target evaluation factors.

Description

Investment portfolio pressure bearing assessment method, device, equipment and medium
Technical Field
The application relates to the technical field of block chains, in particular to a method, a device, equipment and a medium for evaluating the bearing capacity of an investment portfolio.
Background
The enterprise annuity is a supplementary endowment insurance system, which is a supplementary endowment insurance system independently established by enterprises and workers on the basis of taking part in basic endowment insurance legally. When operating the enterprise annuity, the client initiates and the receiver manages the enterprise annuity generally under the supervision of the bank protection supervision. In the actual operation process, besides the consignee and the consignee, the trustee, the administrating person, the account administrator and the like are involved.
The trustee of the enterprise annuity is responsible for making an enterprise annuity fund investment strategy, and the trustee is responsible for distributing enterprise annuity fund property to the administration person according to the instruction of the trustee, accounting and valuation of the enterprise annuity fund, rechecking and examining the fund property net value calculated by the investment manager. In practice, the entrusted person makes an enterprise annuity fund investment strategy, and an investment plan made for one enterprise annuity fund generally comprises a plurality of investment combinations, so that the administrator invests the enterprise annuity fund according to the investment combinations, and the trustee performs accounting and estimation.
In practice, the trustee, as a party to make an investment strategy for an enterprise annuity fund, needs to guarantee the income and low risk of the made investment portfolio. Therefore, the risk bearing capacity (pressure bearing capacity for short) of the investment portfolio can be evaluated, and the pressure bearing capacity for short in the industry refers to the risk bearing capacity of the investment portfolio, and can reflect the high risk that the investment portfolio of the annuity fund can bear. In the related art, the presupposed people generally organize experienced managers to evaluate the pressure bearing capacity of the investment portfolio, but the mode consumes the expenditure of manpower cost, the whole process depends on the subjective experience of people, and the accuracy is low.
Disclosure of Invention
In order to solve the above problems, the present application provides a method, an apparatus, an electronic device, and a medium for evaluating a pressure-bearing capacity of an investment portfolio, and aims to improve an accuracy of evaluating a pressure-bearing capacity of the investment portfolio.
In a first aspect of the embodiments of the present application, a pressure-bearing assessment method for an investment portfolio is provided, which is applied to a client, and the method includes:
acquiring the current estimation data of the investment portfolio triggered by the stress assessment condition and the information data of the investment portfolio in the historical period in response to the triggered stress assessment condition;
matching preset evaluation factor configuration information with the evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data; the configuration information of the assessment factors comprises a plurality of assessment factors, and each assessment factor corresponds to one type of investment targets;
analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor; the fluctuation value is used for representing the fluctuation amplitude of a class of investment targets in the investment process;
determining a risk resistance value corresponding to each target evaluation factor based on the asset occupation ratio of the investment target belonging to each target evaluation factor in the valuation data and the corresponding fluctuation value; the risk-resistance value is used for representing the stability degree of the risk resistance in the investment portfolio of one type of investment target;
and outputting the current pressure bearing evaluation information of the investment portfolio based on the risk resistance values respectively corresponding to the target evaluation factors.
Optionally, the client is deployed to each node in a block chain, and each node in the block chain includes a trusted node, a hosting node, a managed node and an information node; acquiring the current estimation data of the investment portfolio triggered by the pressure estimation condition and the information data of the investment portfolio, comprising:
obtaining the evaluation data from the entrusted node and obtaining the information data from the information node;
outputting the bearing test information of the target annuity plan based on the risk resistance values respectively corresponding to the target evaluation factors, wherein the bearing test information comprises:
and calling a preset first intelligent contract, and outputting pressure-bearing evaluation information aiming at the entrusted node, the hosting node and the cast node respectively based on the risk resistance values corresponding to the target evaluation factors respectively.
Optionally, matching preset evaluation factor configuration information with the evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data, including:
matching the evaluation value data with preset evaluation factor configuration information based on a preset second intelligent contract to obtain a plurality of target evaluation factors matched with the evaluation value data;
analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor, wherein the fluctuation value comprises the following steps:
generating a fluctuation value of the investment target belonging to each target evaluation factor in the information data based on a preset third intelligent contract in response to the running result of the second intelligent contract;
determining a risk resistance value corresponding to each target assessment factor based on the occupation ratio of the assets of the investment target belonging to each target assessment factor in the assessment data and the corresponding fluctuation value, wherein the risk resistance value comprises the following steps:
and responding to the running result of the third intelligent contract, and calculating the asset occupation ratio of the investment target belonging to each target evaluation factor in the evaluation data and the corresponding fluctuation value based on a preset fourth intelligent contract to obtain the risk resistance value corresponding to each target evaluation factor.
Optionally, the method further comprises:
obtaining a grade coefficient corresponding to each of a plurality of preset evaluation grades;
analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor, wherein the fluctuation value comprises the following steps:
extracting sub information data of the investment target belonging to each target evaluation factor in a preset history period from the information data based on each target evaluation factor;
determining fluctuation values of each target evaluation factor respectively aiming at the plurality of preset evaluation levels based on the sub information data corresponding to each target evaluation factor and each level coefficient;
determining a risk resistance value corresponding to each target evaluation factor based on the occupation ratio and the corresponding fluctuation value of the assets belonging to various investment targets in the valuation data, wherein the risk resistance value comprises the following steps:
and determining the risk resistance value of each target evaluation factor respectively aiming at the plurality of preset evaluation grades based on the occupation ratio of the assets belonging to various investment targets in the valuation data and the fluctuation values corresponding to the plurality of preset evaluation grades.
Optionally, determining a fluctuation value of each target evaluation factor respectively for the plurality of preset evaluation levels based on the sub information data corresponding to each target evaluation factor and each of the level coefficients, includes:
determining the fluctuation value of the lowest evaluation grade corresponding to the target evaluation factor based on the fluctuation of the sub information data in each preset time period in a preset history period and the grade coefficient of the lowest evaluation grade;
and determining the fluctuation values corresponding to the other preset evaluation grades based on the fluctuation value of the lowest evaluation grade and the grade coefficients corresponding to the other preset evaluation grades except the lowest evaluation grade.
Optionally, the method further comprises:
displaying the plurality of target evaluation factors, and responding to weight setting operation aiming at the plurality of target evaluation factors to obtain weight values corresponding to the plurality of target evaluation factors;
determining a risk resistance value corresponding to each target evaluation factor based on the occupation ratio and the corresponding fluctuation value of the assets belonging to various investment targets in the valuation data, wherein the risk resistance value comprises the following steps:
and determining the risk resistance value corresponding to each target evaluation factor based on the ratio of the assets of various investment targets belonging to each target evaluation factor in the valuation data, the fluctuation value and the weight value corresponding to each target evaluation factor.
Optionally, outputting pressure bearing assessment information of the investment portfolio based on the risk resistance values respectively corresponding to the plurality of target assessment factors, including:
counting the risk resistance values belonging to the same preset evaluation grade in the risk resistance values respectively corresponding to the target evaluation factors to obtain total risk resistance values respectively corresponding to the preset evaluation grades;
and outputting the pressure-bearing evaluation information of the investment portfolio based on the identification of the investment portfolio and the total wind risk resistance values respectively corresponding to the plurality of preset evaluation grades.
In a second aspect of the embodiments of the present application, there is provided a pressure-bearing assessment apparatus for investment portfolio, applied to a client, the apparatus including:
the data acquisition module is used for responding to the triggered pressure evaluation condition, and acquiring the current evaluation data of the investment portfolio triggered by the pressure evaluation condition and the information data of the investment portfolio in the historical period;
the matching module is used for matching preset evaluation factor configuration information with the evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data; the configuration information of the assessment factors comprises a plurality of assessment factors, and each assessment factor corresponds to one type of investment targets;
the analysis module is used for analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor; the fluctuation value is used for representing the fluctuation amplitude of a class of investment targets in the investment process;
the risk value determining module is used for determining a risk resistance value corresponding to each target evaluation factor based on the asset proportion and the corresponding fluctuation value of the investment target belonging to each target evaluation factor in the valuation data; the risk-resistance value is used for representing the stability degree of the risk resistance in the investment portfolio of one type of investment target;
and the information output module is used for outputting the current pressure-bearing evaluation information of the investment portfolio based on the risk resistance values respectively corresponding to the target evaluation factors.
In a third aspect of the embodiments of the present application, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method for evaluating the pressure bearing of the portfolio according to the first aspect.
In a fourth aspect of the embodiments of the present application, there is provided a computer-readable storage medium storing a computer program for causing a processor to execute the method for evaluating the pressure-bearing capacity of a portfolio according to the first aspect.
By adopting the technical scheme of the embodiment of the application, the method at least has the following advantages:
in the embodiment of the application, the current valuation data of the investment portfolio triggered by the stress assessment condition and the information data of the investment portfolio can be obtained in response to the triggered stress assessment condition; matching preset evaluation factor configuration information with evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data, and analyzing investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor; and determining a risk resistance value corresponding to each target evaluation factor based on the asset occupation ratio and the corresponding fluctuation value of the investment target belonging to each target evaluation factor in the valuation data, determining a risk resistance value corresponding to each target evaluation factor, and then outputting the pressure bearing evaluation information of the investment portfolio based on the risk resistance values respectively corresponding to the plurality of target evaluation factors.
By adopting the technical scheme of the embodiment of the application, a user only needs to upload the configuration information of the assessment factors, when the pressure-bearing assessment is needed to be carried out on the investment portfolio, the pressure-bearing assessment condition of the investment portfolio can be directly triggered, the client can automatically acquire the evaluation value data and the information data, and according to the configuration information of the assessment factors, the evaluation value data and the information data are analyzed according to the target assessment factors in the configuration information of the assessment factors, then the pressure-bearing assessment information of the investment portfolio is obtained, manual operation is not needed to be organized, the judgment is carried out according to experience, and therefore the practicability of the application is improved.
According to the method, the risk resistance value of each type of investment targets (each target evaluation factor) is obtained after the estimation data and the information data are correspondingly processed according to the types of the investment targets. Wherein the valuation data can be the current valuation of the portfolio, and the information data is the basic data obtained during the actual invested investment of the portfolio. When the fluctuation value of the investment targets belonging to each evaluation factor is obtained through the information data, the fluctuation value can reflect the fluctuation range of one type of investment targets in the historical process, and then the current risk resistance value of each type of investment targets in the investment portfolio can be obtained according to the fluctuation range of each type of investment targets in the historical process and the current asset proportion of the same type of investment targets in the current valuation data, the risk resistance value can reflect the stability degree of the one type of investment targets in the investment portfolio against risks, the risk resistance capability of each type of investment targets in the investment portfolio in the investment process can be reflected, and therefore the whole pressure-bearing evaluation information of the investment portfolio can be obtained. Therefore, the risk resistance value determined according to the information data and the estimation data of the investment portfolio can be used for estimating the pressure bearing capacity of the investment portfolio more objectively and accurately.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the description of the embodiments or the related art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a schematic diagram of a communication environment in which a method for evaluating the pressure bearing of an investment portfolio according to an embodiment of the present application is applied;
FIG. 2 is a schematic flow chart illustrating steps of a method for evaluating the bearing pressure of an investment portfolio according to an embodiment of the present application;
FIG. 3 is a communication environment diagram of another pressure-bearing assessment method for investment portfolios according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating steps for determining a fluctuation value of a target evaluation factor according to an embodiment of the present application;
FIG. 5 is a schematic flow chart illustrating a process of determining a fluctuation value corresponding to each target evaluation factor according to an embodiment of the present application;
fig. 6 is a schematic flow chart illustrating the output of pressure-bearing evaluation information according to an embodiment of the present application;
FIG. 7 is a schematic overall flow diagram illustrating a portfolio bearing assessment methodology in accordance with an exemplary embodiment of the present application;
fig. 8 is a schematic diagram of a framework of a pressure-bearing evaluation apparatus for a portfolio according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In view of the problems that in the related art, the pressure bearing capacity of an investment portfolio is evaluated by a manager with an organization experience, the labor cost expenditure is consumed, and the accuracy is low, the application provides a pressure bearing evaluation method of the investment portfolio, and the basic technical concept is as follows: the client side can automatically acquire the current estimated value data and the information data of the investment portfolio in the historical period according to the estimation operation of the user on the investment portfolio, the estimated value data and the information data are analyzed and processed based on the preset estimation factor configuration information, so that the risk resistance values of various investment targets in the investment portfolio are obtained to reflect the fluctuation of the various investment targets in the historical process, and then pressure-bearing estimation information is output according to the risk resistance values to be used by the trustees. The whole process can be automatically completed at the client.
By adopting the method of the embodiment of the application, the client can be used for carrying out pressure-bearing evaluation on various investment portfolios, and the efficiency is high. And because the evaluation is based on the estimated value data and the information data of the historical time period, the objectivity and the accuracy of pressure bearing evaluation are improved.
Referring to fig. 1, a schematic diagram of a communication environment applied by the method for evaluating the pressure bearing of the portfolio provided by the present application is shown, as shown in fig. 1, including a client, wherein the client can operate in a terminal device of a trustee, a hosting party device and an information party device which are linked with the client in a communication manner, wherein the hosting party device can send evaluation data to the client, and the information party device can send information data to the client.
In this embodiment, the user of the trustee may pre-store the evaluation factor configuration information on the client to perform pressure-bearing evaluation on a plurality of investment portfolios, respectively.
Referring to fig. 2, a schematic flow chart illustrating steps of a pressure-bearing assessment method for an investment portfolio according to an embodiment of the present application is shown, and is applied to the client shown in fig. 1, as shown in fig. 2, the method may specifically include the following steps:
step S201: in response to the triggered stress assessment condition, obtaining current valuation data of the investment portfolio triggered by the stress assessment condition and information data of the investment portfolio in a historical period.
In this embodiment, the client may provide a human-computer interface for pressure-bearing assessment for a user, and in the human-computer interface, a list including identifiers of a plurality of investment portfolios may be displayed, and the identifier of each investment portfolios in the list may be selected. The user can select the investment portfolio in the list in the human-computer interaction interface, and therefore pressure bearing evaluation of the selected investment portfolio is triggered.
Accordingly, the triggered stress assessment condition may be: and the user selects and operates the investment portfolio. Further, in response to the triggered stress assessment condition, current assessment data for the selected portfolio and historical portfolio information data are obtained.
The valuation data refers to data obtained by valuation of the asset, the valuation refers to a process of evaluating the current value of the asset, and refers to verifying imported goods which are subject to rate (ad valuem) of the tax and are used as the rate or duty-completing rate (duty-paying value) of the tax and estimating the expected value which can be reached. Valuation data is typically valued by the return of assets, which are referred to herein as investment targets.
The information data can comprise basic data, fund valuation data and the like in a database of a financial data and analysis tool service provider (information node); the basic data can include stock data, stop and reply card data, and company behavior data; whereas the stock book data may include stock currency stock (i.e., stock that is capable of circulation in the stock exchange market, distinguishable from restricted stock) data; the stop and reply data can comprise data of stop stock and reply stock; the company behavioral data may include allocation data for company stock bonuses; the fund valuation data may include data on equity and equity share of the exponential fund, and the value of the assets and liabilities of the exponential fund may be calculated and evaluated according to the fair price.
In the embodiment of the application, the information data in the preset historical time period in the historical process can be obtained. For example, information data within three years before the current time may be obtained.
In an embodiment, after the valuation data is obtained, the valuation data can be processed, and specifically, information such as asset scale, unit net worth, details of taken-position products and the like of each combination can be analyzed, so that the format of the valuation data is consistent with a preset format, and pressure-bearing evaluation of the valuation data is facilitated at a later stage.
Step S202: and matching preset evaluation factor configuration information with the evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data.
The configuration information of the assessment factors comprises a plurality of preset assessment factors, and each preset assessment factor corresponds to one type of investment targets.
In this embodiment, the configuration information of the evaluation factors may include a plurality of preset evaluation factors, and each preset evaluation factor corresponds to one type of investment targets. Where, as noted above, an investment target may be understood to be an investment asset, where in an annuity program, an investment asset may include stocks, bonds, etc., and in one possible embodiment, the investment target may be categorized into equity categories, solid income categories, etc.
In this embodiment, a user may store evaluation factor configuration information in the client in advance, and in practice, the evaluation factor configuration information may be configured to the client after the user sets the evaluation factor configuration information according to actual pressure-bearing evaluation requirements, so that the user may selectively perform pressure-bearing evaluation on assets in the investment portfolio according to different granularities, for example, the finer the classification of investment targets, the smaller the analysis granularity of the pressure-bearing evaluation.
Referring to table 1-1, table 1-1 exemplarily shows a preset evaluation factor included in evaluation factor configuration information.
TABLE 1-1 evaluation factor configuration information Table
Figure BDA0003337553520000081
Figure BDA0003337553520000091
As shown in Table 1-1, 10 predetermined valuation factors are included, each of which corresponds to a class of investment targets, e.g., the valuation factor "equity assets" corresponds to an investment target of equity assets class.
In this embodiment, after the estimation data is obtained, each investment target included in the estimation data may be matched with each preset estimation factor in the estimation factor configuration information, and a target estimation factor corresponding to each investment target in the current estimation data among the plurality of preset estimation factors is determined. And the determined target evaluation factors have corresponding investment targets in the estimation data.
When the matching is performed, each preset evaluation factor in the evaluation factor configuration information can be traversed, whether an investment target corresponding to the traversed preset evaluation factor exists in the estimation data or not is determined, if yes, the preset evaluation factor is used as a target evaluation factor, and if not, the following preset evaluation factors are traversed.
For example, taking table 1-1 as an example, if the equity asset class is included in the preset evaluation factor, the equity asset may be used as the target evaluation factor if the investment asset of the equity asset class is included in the valuation data.
Therefore, after the evaluation factor configuration information is matched with the estimation data, the evaluation factor configuration information can be adapted to various investment targets included in the current estimation data, so that the pressure-bearing evaluation of the current investment portfolio can be more accurately carried out.
Step S203: analyzing the data of the investment targets belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor.
Wherein the fluctuation value is used for representing the fluctuation range of one type of investment target in the investment process.
In this embodiment, the information data includes basic data and valuation data of each asset, and also includes data such as a deal amount and an earning rate of each asset in a history period process. After the target evaluation factors are obtained, the information data of the investment targets (assets) belonging to each target evaluation factor in the information data can be determined, and then the fluctuation of the exchange amount, the profitability and the like of the investment targets belonging to the target evaluation factors in the historical period can be obtained, so that the fluctuation value corresponding to each target evaluation factor can be obtained, the fluctuation value can represent the fluctuation range of the exchange amount, the profitability and the like of the assets belonging to the target evaluation factors in the historical period, namely the fluctuation degree can be understood, and the risk resistance condition of the assets belonging to the target evaluation factors in the historical period can be reflected.
In practice, the greater the fluctuation value, the lower the stability of a class of assets that can be characterized as belonging to the target valuation factor in the historical period, the weaker its ability to fight risks.
In one embodiment, the fluctuation value corresponding to each target evaluation factor can be obtained according to the data of the investment targets belonging to the target evaluation factors within one month with the maximum drop amplitude such as the exchange amount and the profitability in the historical period, so that the fluctuation value can be obtained based on the data with the maximum drop amplitude, generally speaking, the drop amplitude maximally reflects the lower limit of the countermeasure risk of the investment targets, and the pressure-bearing evaluation is performed based on the lower limit, so that the pressure-bearing evaluation of the investment portfolio can be more accurately reflected.
Step S204: and determining a risk resistance value corresponding to each target assessment factor based on the asset occupation ratio of the investment target belonging to each target assessment factor in the assessment data and the corresponding fluctuation value.
Wherein the risk-resistance value is used to characterize the degree of risk of a class of investment targets at the current confrontation.
In this embodiment, since the valuation data is the current valuation of the investment portfolio, and the information data is the basic data and the valuation data of the investment portfolio in the historical period, after the fluctuation value corresponding to each target valuation factor is obtained according to the information data, the fluctuation value can reflect the risk resistance of the assets belonging to the target valuation factor in the historical period, so that the degree of the assets in the valuation data at the current confrontation risk can be predicted according to the fluctuation value.
In practice, for each type of investment target in the portfolio, at different times, the administrator may adjust the investment proportion of each type of investment target, and thus the asset proportion of the investment target is different. In this embodiment, for the current pressure-bearing evaluation of the investment portfolio, the asset proportion of the investment targets belonging to the target evaluation factor in the valuation data may be determined first, and then the corresponding risk-resisting value of the investment targets belonging to the target evaluation factor may be determined according to the asset proportion and the corresponding fluctuation value.
Specifically, the asset proportion of the investment target belonging to each target valuation factor in the valuation data can be determined, which can reflect the proportion of a class of investment targets in the portfolio, for example, the portfolio investment amount is 100 ten thousand, the total investment amount of assets of the equity asset class is 50 ten thousand, and the asset proportion of the equity asset is 50%. It will be appreciated that the asset proportion may reflect the specific gravity of each type of investment target in the portfolio.
After the asset proportion of each type of investment target is obtained, the current risk resistance value of the type of investment target can be obtained according to the asset proportion of the type of investment target and the corresponding fluctuation value. Specifically, the product of the asset proportion and the corresponding fluctuation value can be used as the current risk resistance value of the investment target; since the fluctuation value represents the fluctuation range of the intersection amount, the profitability and the like of the assets belonging to the same category in the historical period, the fluctuation range can be understood as the fluctuation of the profit, and therefore, the risk resistance value determined in the embodiment can reflect the current fluctuation range of the investment targets, namely the current degree of the risk resistance.
For example, if the ratio of the equity assets is 0.5, the fluctuation value of the equity assets obtained from the information data is 0.8, and the risk resistance value is 0.4. The asset ratio of stock index futures is 0.3, the fluctuation value of equity assets obtained according to the information data is 0.9, and the risk resistance value obtained is 0.27; thus, the risk-resistant value of the stock-index futures is less than the risk-resistant value of the equity assets, characterizing the equity assets' poor stability against risk in the portfolio.
As can be seen from the above example, the larger the share of assets and the larger the fluctuation value of a class of investment targets, the less stable it is against risks. The greater the risk resistance value corresponding to the target evaluation factor is, the weaker the stability degree of the countermeasure risk in the whole investment portfolio, which represents a class of investment targets belonging to the target evaluation factor, is, that is, the larger the fluctuation is, the weaker the stability of the countermeasure risk is.
Step S205: and outputting the current pressure bearing evaluation information of the investment portfolio based on the risk resistance values respectively corresponding to the target evaluation factors.
In this embodiment, after the risk resistance value corresponding to each target evaluation factor is obtained, the current risk resistance degree of each type of investment target can be obtained, the risk resistance value is obtained according to the valuation data, and the valuation data is obtained by valuating the value of the asset, so that the pressure bearing evaluation can be performed according to the risk resistance value corresponding to each target evaluation factor.
In practice, pressure-bearing evaluation information including the risk resistance values corresponding to the target evaluation factors can be generated, and then the pressure-bearing evaluation information is output and displayed on a front-end page of the client for a user to view and analyze.
Because each target evaluation factor corresponds to one type of investment targets in the valuation data, the pressure-bearing evaluation information can reflect the risk-resisting value of each type of investment targets in the current valuation data in the whole investment portfolio under the current asset proportion, namely the contribution of each type of investment targets in the risk resistance can be obtained, and further the current pressure-bearing capacity of the investment portfolio can be obtained.
By adopting the technical scheme of the embodiment of the application, a user only needs to upload the configuration information of the assessment factors, when the pressure-bearing assessment is needed to be carried out on the investment portfolio, the pressure-bearing assessment condition of the investment portfolio can be directly triggered, the client can automatically acquire the evaluation value data and the information data, and according to the configuration information of the assessment factors, the evaluation value data and the information data are analyzed according to the target assessment factors in the configuration information of the assessment factors, then the pressure-bearing assessment information of the investment portfolio is obtained, manual operation is not needed to be organized, the judgment is carried out according to experience, and therefore the practicability of the application is improved.
According to the method, the risk resistance value of each type of investment targets (each target evaluation factor) is obtained after the estimation data and the information data are correspondingly processed according to the types of the investment targets. Wherein the valuation data can be the current valuation of the portfolio, and the information data is the basic data obtained during the actual invested investment of the portfolio. When the fluctuation value of the investment targets belonging to each evaluation factor is obtained through the information data, the fluctuation value can reflect the fluctuation range of one type of investment targets in the historical process, and further the current risk resistance value of each type of investment targets in the investment portfolio can be obtained according to the fluctuation range of each type of investment targets in the historical process and the current asset proportion of the same type of investment targets in the current valuation data, the risk resistance value can reflect the stability degree of the one type of investment targets in the investment portfolio against risks, the risk resistance capacity of each type of investment targets in the investment portfolio in the investment process can be reflected, and therefore the whole pressure-bearing evaluation information of the investment portfolio can be obtained. Therefore, the risk resistance value determined according to the information data and the estimation data of the investment portfolio can be used for estimating the pressure bearing capacity of the investment portfolio more objectively and accurately.
On the other hand, by automatically carrying out pressure-bearing evaluation on the investment portfolio, the agent/client, the receiver and the like can master the influence of market risk events and the like on indexes such as income and the like of the investment portfolio in real time according to the pressure-bearing evaluation result, provide important basis for coping with risks and adjust the ratio of assets and investment targets in the investment portfolio in time, thereby improving the management capability of annual fund investment risks and assisting the development progress of the annual fund industry.
In an embodiment, in order to realize the full-flow of pressure-bearing assessment of the investment portfolio and improve the assessment efficiency, a block chain can be constructed, a client is deployed to each node of the block chain, and the pressure-bearing assessment is performed by utilizing the characteristics of high efficiency and high confidentiality of the block chain.
Referring to fig. 3, there is shown a schematic diagram of another implementation environment of the portfolio pressure-bearing assessment method of the present application, and as shown in fig. 3, clients may be deployed into each node in a blockchain. The pressure bearing evaluation of the present application is described with reference to fig. 3.
Specifically, the trustee, the administrator and the information party can construct a block chain, and each node in the block chain can include: a trusted node, a managed node and an information node. And each node is provided with a client, so that all parties participating in the annuity can utilize the clients to realize data uploading and downloading in the block chain and pressure-bearing evaluation of investment portfolio.
Accordingly, in acquiring the current estimation data of the investment portfolio triggered by the stress assessment condition and the information data of the investment portfolio, the estimation data can be acquired from the entrusted node and the information data can be acquired from the information node.
In practice, in order to enable authorized users to perform pressure bearing assessment, the pressure bearing assessment authority of the user of each node can be preset, and the user with the pressure bearing assessment authority can trigger the pressure assessment condition. Specifically, the authority can be written into the intelligent contract, when the user of each node triggers pressure evaluation, the authority of the user can be verified based on the intelligent contract, after the verification is passed, the pressure evaluation condition is triggered again, and then evaluation data is obtained from the entrusted node and information data is obtained from the information node.
The entrusted node can upload the estimation data to the block chain periodically, and the information node can upload the information data to the block chain periodically, so that when the pressure evaluation condition is triggered, the estimation data closest to the current time can be acquired from the block chain, and the information data in the historical period can be acquired.
And then, after the risk resistance values corresponding to the target evaluation factors are obtained, a preset first intelligent contract can be called, and pressure-bearing evaluation information aiming at the entrusted node, the hosting node and the cast-in node is output based on the risk resistance values corresponding to the target evaluation factors.
In this embodiment, when outputting the pressure-bearing evaluation information, the pressure-bearing evaluation information is generally checked by a node having a pressure-bearing evaluation authority, so that a first intelligent contract can be uploaded in a block chain in advance, the first intelligent contract can be a contract customized by each party participating in annuity, the contract can specify an authority node for reading the pressure-bearing evaluation information, and then, after obtaining risk resistance values corresponding to a plurality of target evaluation factors respectively, pressure-bearing evaluation information for a trusted node, a managed node, and a managed node can be generated based on the first intelligent contract, and then, when the trusted node, the managed node, and the managed node need to obtain the industry evaluation information, the corresponding pressure-bearing evaluation information can be sent to the corresponding node.
In yet another embodiment, in the blockchain, intelligent contracts may be invoked for various steps of pressure bearing evaluation.
The intelligent contract is understood to be a computer readable code solidified into a blockchain, when the intelligent contract is generated, when a trigger event which accords with the intelligent contract is detected by the blockchain, the running of the intelligent contract can be automatically triggered, and therefore the execution of the corresponding event is carried out.
Specifically, in step S202, when preset evaluation factor configuration information is matched with the evaluation value data to obtain a plurality of target evaluation factors matched with the evaluation value data, the evaluation value data may be matched with the preset evaluation factor configuration information based on a preset second intelligent contract to obtain a plurality of target evaluation factors matched with the evaluation value data.
In this embodiment, the second intelligent contract may be generated by the client on the blockchain according to the uploaded evaluation factor configuration information, the second intelligent contract may be started after the evaluation data is detected and obtained, and when the second intelligent contract runs, the evaluation data and the preset evaluation factor configuration information may be matched to obtain a plurality of target evaluation factors.
The details of the specific matching process are described in the above embodiments, and are not described herein again.
In step S203, when analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor, the fluctuation value of the investment target belonging to each target evaluation factor in the information data may be generated based on a preset third intelligent contract in response to the operation result of the second intelligent contract.
In this embodiment, the operation result of the second intelligent contract is a plurality of target evaluation factors obtained after matching the evaluation value data with the preset evaluation factor configuration information; after obtaining a plurality of target evaluation factors, a third intelligent contract may be started, and when the third intelligent contract runs, the third intelligent contract may analyze the data of the investment target belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor.
Specifically, the process of obtaining the fluctuation value may be as described in the foregoing embodiments, and is not described herein again.
In step S204, when the risk resistance value corresponding to each target evaluation factor is determined based on the proportion of the assets of the investment target belonging to each target evaluation factor in the evaluation data and the corresponding fluctuation value, the proportion of the assets of the investment target belonging to each target evaluation factor in the evaluation data and the corresponding fluctuation value may be calculated based on a preset fourth intelligent contract in response to the operation result of the third intelligent contract, so as to obtain the risk resistance value corresponding to each target evaluation factor.
In this embodiment, the operation result of the third intelligent contract is the fluctuation value corresponding to each target evaluation factor, when the fluctuation value corresponding to each target evaluation factor is obtained, the fourth intelligent contract and the jade amount may be started, and when the fourth intelligent contract is operated, the asset occupation ratio of the investment target belonging to each target evaluation factor in the evaluation data and the corresponding fluctuation value may be calculated to obtain the risk resistance value corresponding to each target evaluation factor.
By adopting the technical scheme of the embodiment, on one hand, as the method is applied to the block chain, the pressure-bearing evaluation can be automatically carried out on the investment portfolio in the block chain, the evaluation value data and the information data uploaded by each node are stored in the block chain, the safety of the data can be enhanced, and the efficiency of the pressure-bearing evaluation can be improved as the whole process is carried out in the block chain. On the other hand, in the process of pressure-bearing evaluation, various intelligent contracts are used, so that the flow pressure-bearing evaluation can be performed by using the characteristics of the intelligent contracts, the irreparable modification of the pressure-bearing evaluation is ensured, and the accuracy of the pressure-bearing evaluation is ensured.
Next, a specific process of the pressure bearing evaluation method of the present application will be described with reference to the implementation environments shown in fig. 2 and 3.
First, an implementation of how to obtain a fluctuation value corresponding to each target evaluation factor will be described.
In an embodiment, since there are multiple target evaluation factors, in practice, it is necessary to determine the fluctuation value corresponding to each target evaluation factor, and therefore, the fluctuation value corresponding to each target evaluation factor may be sequentially determined according to a certain order. Referring to fig. 4, a flowchart of the steps for determining the fluctuation value corresponding to each target evaluation factor is shown, and as shown in fig. 4, the method specifically includes the following steps:
step S401, traversing the multiple target evaluation factors.
Step S402, for the currently traversed target evaluation factor, based on the target evaluation factor, extracting the sub information data corresponding to the investment target belonging to the target evaluation factor from the information data, and based on the sub information data, determining the fluctuation value corresponding to the target evaluation factor.
And step S403, repeating the steps until all the target evaluation factors are traversed.
In this embodiment, when determining the fluctuation value corresponding to the target evaluation factor, the fluctuation value corresponding to each target evaluation factor may be sequentially determined, where the sub-information data may refer to information data corresponding to investment targets belonging to the target evaluation factor, in practice, one target evaluation factor corresponds to one type of investment targets, and each type of investment targets includes a plurality of assets, and therefore, the sub-information data may include information data of a plurality of investment targets belonging to the same type.
When the fluctuation value is determined, the fluctuation value of each investment target can be determined according to the information data of the plurality of investment targets belonging to the same category, and then the fluctuation value corresponding to the target evaluation factor is determined according to the fluctuation values of the plurality of investment targets belonging to the same category. For example, for the equity asset, including 3 investment targets, the fluctuation values of the 3 investment targets are 0.5, 0.6, and 0.4, respectively, the average or mean square error of the fluctuation values of the 3 investment targets can be determined as the fluctuation value corresponding to the target evaluation factor of the equity asset.
After the fluctuation value corresponding to the currently traversed target evaluation factor is determined, the next target evaluation factor can be traversed, so that the fluctuation value corresponding to the next target evaluation factor is obtained, and the fluctuation values corresponding to all the target evaluation factors can be obtained by repeating the above processes.
Referring to fig. 5, a schematic flow chart illustrating a process of determining a fluctuation value corresponding to each target evaluation factor is shown, as shown in fig. 5, this step may be completed by a third intelligent contract, specifically, the third intelligent contract may read a plurality of target evaluation factors obtained when the second intelligent contract is run, and further, traverse the target evaluation factors, and for the currently traversed evaluation factor, corresponding processing may be performed according to the category of the target evaluation factor, for example, specifically, the category belongs to a equity category, a debt category, and an pension category.
For example, if the target evaluation factor is of interest type, the fluctuation value may be determined according to the sub information data belonging to the target evaluation factor, and if the target evaluation factor is of upper-certified comprehensive fingers, stock futures, large, medium and small stock fingers, etc., the sub information data within one month in which the corresponding index falls the maximum within three years may be analyzed according to the sub information data belonging to the target evaluation factor to obtain the fluctuation value. Specifically, the mean square error and the like of the rise-fall index within one month with the largest fall can be processed to obtain the fluctuation value. For other types of target evaluation factors, the fluctuation value may be determined with reference to the flow chart shown in fig. 5.
As shown in fig. 5, in one embodiment, for each target evaluation factor, the corresponding risk-resistance value may be evaluated from a plurality of evaluation levels to obtain different degrees of risk-resistance values for each type of investment target. Specifically, a grade coefficient corresponding to each of a plurality of preset evaluation grades may be obtained, where the grade coefficient may refer to a preset coefficient, and the grade coefficient of each preset evaluation grade may represent a specific gravity of the preset evaluation grade in the whole evaluation system.
In this embodiment, the preset evaluation level may be preset by a user, and the preset evaluation level may reflect an expectation on the risk resistance of the investment portfolio; for example, the preset assessment ratings may include a mild rating, a moderate rating, and a severe rating, the mild rating characterizing the user's optimistic expectations of the portfolio's ability to resist the risk, and the severe rating characterizing the user's lowest expectations of the portfolio's ability to resist the risk.
Correspondingly, when analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor, extracting sub-information data of the investment target belonging to the target evaluation factor in a preset history period from the information data based on each target evaluation factor; and determining the fluctuation value of each target evaluation factor respectively aiming at the plurality of preset evaluation grades based on the sub information data corresponding to each target evaluation factor and each grade coefficient.
As described above, the sub information data is information data corresponding to the investment target belonging to the target evaluation factor, and when there are a plurality of preset evaluation levels, for each target evaluation factor, the corresponding fluctuation value can be determined from the plurality of preset evaluation levels, respectively. That is, each target evaluation factor may determine a plurality of fluctuation values corresponding to the plurality of preset evaluation levels, respectively, and different fluctuation values correspond to different preset evaluation levels.
The fluctuation value corresponding to each preset evaluation grade is determined according to the sub-information data corresponding to the target evaluation factor and the grade coefficient corresponding to the preset evaluation grade.
Since one target evaluation factor corresponds to fluctuation values of a plurality of preset evaluation levels, when determining a risk resistance value corresponding to each target evaluation factor, a risk resistance value corresponding to the plurality of preset evaluation levels for each target evaluation factor may be determined based on a proportion of assets belonging to various types of investment targets in the evaluation value data to fluctuation values corresponding to the plurality of preset evaluation levels.
Correspondingly, each target evaluation factor can determine a plurality of fluctuation values respectively corresponding to a plurality of preset evaluation grades, each target evaluation factor can also determine a plurality of risk resistance values respectively corresponding to a plurality of preset evaluation grades, and different risk resistance values correspond to different preset evaluation grades.
By adopting the technical scheme of the embodiment, each target evaluation factor corresponds to a plurality of preset evaluation grades, so that different risk resistance values can be determined from the dimensionalities of the plurality of preset evaluation grades for each target evaluation factor, pressure bearing evaluation can be performed based on the expectation of a user, and the flexibility of pressure bearing evaluation is improved.
In an embodiment, when determining the fluctuation values of each target evaluation factor respectively for the plurality of preset evaluation levels based on the sub information data corresponding to each target evaluation factor and each of the level coefficients, the fluctuation value corresponding to the lowest level of the preset evaluation levels may be determined first, and then the fluctuation values corresponding to the remaining preset evaluation levels may be determined according to the fluctuation value corresponding to the lowest level of the preset evaluation levels.
Specifically, first, based on the fluctuation of the sub information data in each preset time period within the preset history period and the grade coefficient of the lowest evaluation grade, the fluctuation value of the lowest evaluation grade corresponding to the target evaluation factor is determined.
The fluctuation of the sub information data in each preset period of the preset history period may refer to: in the case where the fluctuation of the sub information data in the preset time period is larger in the drop amplitude, for each target evaluation factor, when determining the fluctuation values of the target evaluation factor corresponding to the plurality of preset evaluation levels, the fluctuation value of the lowest evaluation level may be determined first, where the fluctuation value of the lowest evaluation level may be determined based on the fluctuation of the sub information data in each preset time period in the preset history period, and in this case, the level coefficient of the lowest evaluation level may be set to 1.
As shown in fig. 5, the preset evaluation level includes a light level, a middle level and a heavy level, and the fluctuation value corresponding to the light level may be determined first.
And then, based on the fluctuation value of the lowest evaluation grade and grade coefficients corresponding to other preset evaluation grades except the lowest evaluation grade, determining the fluctuation values corresponding to the other preset evaluation grades.
After the fluctuation value of the lowest evaluation level is obtained, when the fluctuation values corresponding to the other preset evaluation levels are determined, the product of the fluctuation value of the lowest evaluation level and the corresponding level coefficient may be used as the fluctuation value corresponding to the other preset evaluation levels.
As shown in fig. 5, after obtaining the fluctuation value corresponding to the light level, the fluctuation value of the medium level may be a product of the fluctuation value corresponding to the light level and a level coefficient of 1.2.
Correspondingly, when each target evaluation factor has a risk resistance value corresponding to a plurality of preset evaluation levels, when outputting the pressure-bearing evaluation information, the risk resistance values belonging to the same preset evaluation level among the risk resistance values corresponding to the plurality of target evaluation factors respectively may be counted to obtain total risk resistance values corresponding to the plurality of preset evaluation levels respectively, and then the pressure-bearing evaluation information of the investment portfolio is output based on the identification of the investment portfolio and the total risk resistance values corresponding to the plurality of preset evaluation levels respectively.
In this embodiment, each target evaluation factor has a plurality of risk resistance values, and different risk resistance values correspond to different preset evaluation levels, so that the risk resistance values of the plurality of target evaluation factors can be subjected to statistical processing from the dimension of the preset evaluation level when pressure-bearing evaluation information is output. Specifically, the risk resistance values belonging to the same preset evaluation level in the multiple target evaluation factors may be counted to obtain a total risk resistance value of the preset evaluation level, so that the total risk resistance value corresponding to one preset evaluation level is the risk value of all the target evaluation factors at the level.
Illustratively, the target evaluation factors include 10, and the preset evaluation grades include a mild grade, a moderate grade and a severe grade; the total risk resistance value for the mild grade is the sum of the risk resistance values for the 10 target assessment factors at the mild grade.
By adopting the embodiment, the risk resistance of the investment portfolio can be evaluated from the dimensionality of the evaluation grade, so that the flexibility of pressure bearing evaluation is improved, and the diversified requirements of users on the pressure bearing evaluation are met.
Wherein, each target evaluation factor corresponds to one type of investment target, and the stability of the counter risk of different types of investment targets is different. Therefore, in a further embodiment, a weight value may also be preset for each target evaluation factor to adjust the risk-resisting value corresponding to each target evaluation factor.
Specifically, a plurality of target evaluation factors may be displayed, and in response to a weight setting operation for the plurality of target evaluation factors, weight values corresponding to the plurality of target evaluation factors may be acquired.
Then, when determining the risk resistance value corresponding to each target evaluation factor, the risk resistance value corresponding to each target evaluation factor may be determined based on the proportion of the assets of each investment target belonging to each target evaluation factor in the valuation data, the fluctuation value, and the weight value corresponding to each target evaluation factor.
In this embodiment, each target evaluation factor corresponds to a weight value, and the weight value may reflect an evaluation proportion occupied by the target evaluation factor in pressure-bearing evaluation, wherein the weight value may be adjusted according to an actual demand. Then, when determining the risk-resistant value, the risk-resistant value may be determined according to the occupation ratio, the fluctuation value, and the corresponding weight value of the assets of the various investment targets belonging to each target evaluation factor.
Of course, in the case of multiple preset evaluation levels and multiple risk resistance values corresponding to each target evaluation factor, when determining each risk resistance value of each target evaluation factor, the determination may be performed according to the weight value corresponding to the target evaluation factor.
Referring to fig. 6, which is a schematic flow chart illustrating the output of the pressure bearing evaluation information, as shown in fig. 6, for each target evaluation factor, a risk resistance value of a mild grade, a risk resistance value of a moderate grade, and a risk resistance value of a severe grade may be determined according to a weight corresponding to the target evaluation factor, an asset proportion of an investment target corresponding to the target evaluation factor in the evaluation data, and a fluctuation value of a mild grade, a fluctuation value of a moderate grade, and a fluctuation value of a severe grade corresponding to the target evaluation factor, respectively.
And then, counting the wind risk resistance values of the mild grades of all the target evaluation factors to obtain the total wind risk resistance value of the investment portfolio at the mild grade, and so on to obtain the total wind risk resistance value of the investment portfolio at the moderate grade and the severe grade.
Referring to fig. 7, an overall flow diagram of a pressure-bearing assessment method for an investment portfolio is exemplarily shown, and as shown in fig. 7, a specific flow for implementing pressure-bearing assessment in a blockchain is described, which specifically includes the following flows:
and S1, contract design. First, after building the blockchain, a plurality of intelligent contracts as shown in tables 1-2 below may be created.
TABLE 1-2 Intelligent contract detail sheet
Figure BDA0003337553520000191
Figure BDA0003337553520000201
S2: and uploading the evaluation data to a block chain by the host, converting the format of the evaluation data through an intelligent contract C001, and storing the evaluation data in a database.
Then, based on the intelligent contract C002, information such as scale, profit, index, and the like in the estimation data is automatically calculated. Wherein, the uplink content of the evaluation data is as the interface shown in the following tables 1-3:
s3: and operating the intelligent contract C004 to generate the target evaluation factor according to the combination scale, the income, the index information and the preset evaluation factor configuration in the evaluation data.
1-3 uplink content detail table for evaluation data
S4: the information provider uploads the information data to the block chain and, together with the information data contract C003,
serial number Name of field Type (B) Length of Description of the invention Remarks for note Whether or not it is necessary to
1 name Character(s) 120 Name of combination Is that
2 code Character(s) 30 Combined number Is that
3 dataDate Date Date of data Is that
4 netAssetVal Numerical value 24,2 Equity of property Is that
5 unitVal Numerical value 8,6 Net unit value Is that
6 yieldDay Numerical value 32,16 Rate of daily gain Is that
Storing the information data into the information database.
Specifically, the information data is classified into a product class and an information class. Wherein, the product category comprises bond information, stock information, pension information and fund product information, and the uplink content is the interface shown in the following tables 1-4; the information class comprises upper certificate comprehensive fingers, stock fingers, large, medium and small disc stock fingers, stock industry fluctuation range information, bond earning rate information, bond longevity, pension unit net value and fund unit net value, and the uplink content is an interface shown in the following tables 1-5:
TABLE 1-4 UpLink content List for product classes in the information data
Figure BDA0003337553520000211
Tables 1-5 information data-related uplink content detail tables
S5: operating the intelligent contract C006 to automatically calculate the evaluation factors of each target such as mild degree, moderate degree and severe degree
Serial number Name of field Type (B) Length of Description of the invention Remarks for note Whether or not it is necessary to
1 name Character(s) 120 Index name Is that
2 code Character(s) 30 Index code Is that
3 datadate Date Date of data Is that
4 indexValue Numerical value 24,6 Index value Is that
The fluctuation value of (2). If the fluctuation values of the mild, moderate, and severe target evaluation factors can be allowed to be adjusted, the fluctuation values of the mild, moderate, and severe target evaluation factors can be calculated according to the intelligent contract C007. Wherein, the agent/consignee can adjust the fluctuation value according to the self condition, and after finishing the uplink, the uplink content is the interface shown in the following tables 1-6:
tables 1-6 details of the adjustment of the fluctuation values
S6: the agent/the entree are respectively configured according to the combined evaluation information and the pressure test factor configuration
Serial number Name of field Type (B) Length of Description of the invention Remarks for note Whether or not it is necessary to
1 name Character(s) 120 Factor name Is that
2 code Character(s) 30 Factor code Is that
3 mild Numerical value 5,2 Mild degree of Is that
4 moderate Numerical value 5,2 Of moderate degree Is that
5 severe Numerical value 5,2 Severe degree Is that
Each evaluation factor is weighted and linked to form a smart contract C005, and the link content is shown in the following tables 1-7.
S7: and the block chain allows an intelligent contract C008 to automatically calculate the asset proportion corresponding to each evaluation factor according to the estimation data of the investment portfolio and the configuration of the stress test factor.
Tables 1-7 detailed tables of weights of evaluation factors
S8: calculating according to the weight of the evaluation factor, the fluctuation value of the evaluation factor and the asset proportion of the evaluation factor
Serial number Name of field Type (B) Length of Description of the invention Remarks for note Whether or not it is necessary to
1 name Character(s) 120 Factor name Is that
2 code Character(s) 30 Factor code Is that
3 weight Numerical value 5,2 Weight of Is that
Mild, moderate, severe risk resistance values for each evaluation factor. The calculation formula of the mild, moderate and severe contributions of each factor is as follows:
mild risk-resistance value of current evaluation factor (current factor weight) asset-to-mild impact
Current factor moderate risk resistance value (current factor weight) asset to moderate fluctuation value (current factor weight) asset to severe fluctuation value
Wherein, the combined pressure test result (pressure bearing evaluation information) calculation formula:
mild ═ sum (mild risk resistance value for all assessment factors)
Moderate ═ sum (risk resistance value of all assessment factors moderate)
Severe ═ sum (risk resistance value for all assessment factors severe)
S9: and operating the intelligent contract C009, and outputting the respective corresponding combined stress test results to the trustee, the trustee and the administrative staff according to the calculated mild, moderate and severe risk resistance values of the evaluation factors.
Based on the same inventive concept, in an embodiment, there is further provided a pressure-bearing assessment apparatus for an investment portfolio, and as shown in fig. 8, there is shown a block diagram of the pressure-bearing assessment apparatus for an investment portfolio, where the apparatus is applied to a client, and the apparatus may specifically include the following modules:
a data obtaining module 801, configured to, in response to a triggered stress assessment condition, obtain current assessment data of an investment portfolio triggered by the triggered stress assessment condition and information data of the investment portfolio in a historical period;
a matching module 802, configured to match preset evaluation factor configuration information with the evaluation data to obtain multiple target evaluation factors matched with the evaluation data; the configuration information of the assessment factors comprises a plurality of assessment factors, and each assessment factor corresponds to one type of investment targets;
the analysis module 803 is configured to analyze the data of the investment targets belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor; the fluctuation value is used for representing the fluctuation amplitude of a class of investment targets in the investment process;
a risk value determining module 804, configured to determine a risk-resisting value corresponding to each target evaluation factor based on the asset proportion and the corresponding fluctuation value of the investment target belonging to each target evaluation factor in the valuation data; the risk-resistance value is used for representing the stability degree of the risk resistance in the investment portfolio of one type of investment target;
and an information output module 805, configured to output the current pressure-bearing assessment information of the investment portfolio based on the risk resistance values corresponding to the multiple target assessment factors, respectively.
Optionally, the client is deployed to each node in a block chain, and each node in the block chain includes a trusted node, a hosting node, a managed node and an information node; the data obtaining module 801 is specifically configured to obtain the evaluation data from the entrusted node and obtain the information data from the information node;
the information output module 805 is specifically configured to invoke a preset first intelligent contract, and output pressure-bearing evaluation information for the entrusted node, the hosting node, and the hosting node based on the risk-resistance values corresponding to the target evaluation factors, respectively.
Optionally, the matching module 802 is specifically configured to match the evaluation value data with preset evaluation factor configuration information based on a preset second intelligent contract, so as to obtain a plurality of target evaluation factors matched with the evaluation value data;
the analysis module 803 is specifically configured to generate a fluctuation value of the investment target belonging to each target evaluation factor in the information data based on a preset third intelligent contract in response to the operation result of the second intelligent contract;
the risk value determining module 804 is specifically configured to, in response to an operation result of the third intelligent contract, calculate, based on a preset fourth intelligent contract, an asset occupation ratio of the investment target belonging to each target evaluation factor in the evaluation value data and a corresponding fluctuation value, and obtain a risk resistance value corresponding to each target evaluation factor.
Optionally, the apparatus may further include the following modules:
the grade coefficient obtaining module is used for obtaining grade coefficients corresponding to a plurality of preset evaluation grades respectively;
the analysis module 803 specifically includes the following units:
the data extraction unit is used for extracting sub information data of the investment target belonging to each target evaluation factor in a preset history period from the information data based on each target evaluation factor;
the fluctuation value determining unit is used for determining the fluctuation value of each target evaluation factor aiming at the plurality of preset evaluation grades respectively based on the sub information data corresponding to each target evaluation factor and each grade coefficient;
the risk value determining module 804 is specifically configured to determine, based on the percentage of assets belonging to each type of investment target in the valuation data and fluctuation values corresponding to the plurality of preset evaluation levels, a risk resistance value of each target evaluation factor for each of the plurality of preset evaluation levels.
Optionally, the fluctuation value determination unit may specifically include the following sub-units:
a first determining subunit, configured to determine a fluctuation value of a lowest evaluation level corresponding to the target evaluation factor based on fluctuations of the sub information data in each preset time period within a preset history period and a level coefficient of the lowest evaluation level;
and the second determining subunit is used for determining the fluctuation values corresponding to the other preset evaluation grades based on the fluctuation value of the lowest evaluation grade and the grade coefficients corresponding to the other preset evaluation grades except the lowest evaluation grade.
Optionally, the apparatus may further include the following modules:
an evaluation factor display module, configured to display the plurality of target evaluation factors, and obtain weight values corresponding to the plurality of target evaluation factors in response to a weight setting operation for the plurality of target evaluation factors;
the risk value determining module 804 is specifically configured to determine a risk resistance value corresponding to each target evaluation factor based on the ratio of the assets of each investment target belonging to each target evaluation factor in the evaluation data, the fluctuation value, and the weight value corresponding to each target evaluation factor.
Optionally, the information output module 805 may specifically include the following units:
the statistical unit is used for counting the risk resistance values belonging to the same preset evaluation grade in the risk resistance values respectively corresponding to the target evaluation factors to obtain total risk resistance values respectively corresponding to the preset evaluation grades;
and the information output unit is used for outputting the pressure-bearing assessment information of the investment portfolio based on the identification of the investment portfolio and the total wind risk resistance values respectively corresponding to the plurality of preset assessment grades.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method, the device, the equipment and the medium for evaluating the pressure bearing of the investment portfolio provided by the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A pressure-bearing assessment method for an investment portfolio is applied to a client side, and comprises the following steps:
acquiring the current estimation data of the investment portfolio triggered by the stress assessment condition and the information data of the investment portfolio in the historical period in response to the triggered stress assessment condition;
matching preset evaluation factor configuration information with the evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data; the configuration information of the assessment factors comprises a plurality of assessment factors, and each assessment factor corresponds to one type of investment targets;
analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor; the fluctuation value is used for representing the fluctuation amplitude of a class of investment targets in the investment process;
determining a risk resistance value corresponding to each target evaluation factor based on the asset occupation ratio of the investment target belonging to each target evaluation factor in the valuation data and the corresponding fluctuation value; the risk-resistance value is used for representing the stability degree of the risk resistance in the investment portfolio of one type of investment target;
and outputting the current pressure bearing evaluation information of the investment portfolio based on the risk resistance values respectively corresponding to the target evaluation factors.
2. The method of claim 1, wherein the client is deployed to each node in a blockchain, and each node in the blockchain comprises a trusted node, a managed node, a cast node, and an informational node; acquiring the current estimation data of the investment portfolio triggered by the pressure estimation condition and the information data of the investment portfolio, comprising:
obtaining the evaluation data from the entrusted node and obtaining the information data from the information node;
outputting the bearing test information of the target annuity plan based on the risk resistance values respectively corresponding to the target evaluation factors, wherein the bearing test information comprises:
and calling a preset first intelligent contract, and outputting pressure-bearing evaluation information aiming at the entrusted node, the hosting node and the cast node respectively based on the risk resistance values corresponding to the target evaluation factors respectively.
3. The method of claim 2, wherein matching pre-determined evaluation factor configuration information with the evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data comprises:
matching the evaluation value data with preset evaluation factor configuration information based on a preset second intelligent contract to obtain a plurality of target evaluation factors matched with the evaluation value data;
analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor, wherein the fluctuation value comprises the following steps:
generating a fluctuation value of the investment target belonging to each target evaluation factor in the information data based on a preset third intelligent contract in response to the running result of the second intelligent contract;
determining a risk resistance value corresponding to each target assessment factor based on the occupation ratio of the assets of the investment target belonging to each target assessment factor in the assessment data and the corresponding fluctuation value, wherein the risk resistance value comprises the following steps:
and responding to the running result of the third intelligent contract, and calculating the asset occupation ratio of the investment target belonging to each target evaluation factor in the evaluation data and the corresponding fluctuation value based on a preset fourth intelligent contract to obtain the risk resistance value corresponding to each target evaluation factor.
4. The method according to any one of claims 1-3, further comprising:
obtaining a grade coefficient corresponding to each of a plurality of preset evaluation grades;
analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor, wherein the fluctuation value comprises the following steps:
extracting sub information data of the investment target belonging to each target evaluation factor in a preset history period from the information data based on each target evaluation factor;
determining fluctuation values of each target evaluation factor respectively aiming at the plurality of preset evaluation levels based on the sub information data corresponding to each target evaluation factor and each level coefficient;
determining a risk resistance value corresponding to each target evaluation factor based on the occupation ratio and the corresponding fluctuation value of the assets belonging to various investment targets in the valuation data, wherein the risk resistance value comprises the following steps:
and determining the risk resistance value of each target evaluation factor respectively aiming at the plurality of preset evaluation grades based on the occupation ratio of the assets belonging to various investment targets in the valuation data and the fluctuation values corresponding to the plurality of preset evaluation grades.
5. The method of claim 4, wherein determining the fluctuation value of each target evaluation factor respectively for the plurality of preset evaluation levels based on the sub information data corresponding to each target evaluation factor and the respective level coefficients comprises:
determining the fluctuation value of the lowest evaluation grade corresponding to the target evaluation factor based on the fluctuation of the sub information data in each preset time period in a preset history period and the grade coefficient of the lowest evaluation grade;
and determining the fluctuation values corresponding to the other preset evaluation grades based on the fluctuation value of the lowest evaluation grade and the grade coefficients corresponding to the other preset evaluation grades except the lowest evaluation grade.
6. The method according to any one of claims 1-3, further comprising:
displaying the plurality of target evaluation factors, and responding to weight setting operation aiming at the plurality of target evaluation factors to obtain weight values corresponding to the plurality of target evaluation factors;
determining a risk resistance value corresponding to each target evaluation factor based on the occupation ratio and the corresponding fluctuation value of the assets belonging to various investment targets in the valuation data, wherein the risk resistance value comprises the following steps:
and determining the risk resistance value corresponding to each target evaluation factor based on the ratio of the assets of various investment targets belonging to each target evaluation factor in the valuation data, the fluctuation value and the weight value corresponding to each target evaluation factor.
7. The method according to claim 4, wherein outputting the pressure bearing assessment information of the investment portfolio based on the risk resistance values respectively corresponding to the plurality of target assessment factors comprises:
counting the risk resistance values belonging to the same preset evaluation grade in the risk resistance values respectively corresponding to the target evaluation factors to obtain total risk resistance values respectively corresponding to the preset evaluation grades;
and outputting the pressure-bearing evaluation information of the investment portfolio based on the identification of the investment portfolio and the total wind risk resistance values respectively corresponding to the plurality of preset evaluation grades.
8. A pressure-bearing assessment device for investment portfolio, which is applied to client side, the device comprises:
the data acquisition module is used for responding to the triggered pressure evaluation condition, and acquiring the current evaluation data of the investment portfolio triggered by the pressure evaluation condition and the information data of the investment portfolio in the historical period;
the matching module is used for matching preset evaluation factor configuration information with the evaluation data to obtain a plurality of target evaluation factors matched with the evaluation data; the configuration information of the assessment factors comprises a plurality of assessment factors, and each assessment factor corresponds to one type of investment targets;
the analysis module is used for analyzing the investment target data belonging to each target evaluation factor in the information data to obtain a fluctuation value corresponding to each target evaluation factor; the fluctuation value is used for representing the fluctuation amplitude of a class of investment targets in the investment process;
the risk value determining module is used for determining a risk resistance value corresponding to each target evaluation factor based on the asset proportion and the corresponding fluctuation value of the investment target belonging to each target evaluation factor in the valuation data; the risk-resistance value is used for representing the stability degree of the risk resistance in the investment portfolio of one type of investment target;
and the information output module is used for outputting the current pressure-bearing evaluation information of the investment portfolio based on the risk resistance values respectively corresponding to the target evaluation factors.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executed implements the method of pressure bearing assessment of a portfolio according to any one of claims 1-7.
10. A computer-readable storage medium storing a computer program for causing a processor to execute the method of evaluating the pressure-bearing capacity of a portfolio according to any one of claims 1-7.
CN202111298552.1A 2021-11-04 2021-11-04 Investment portfolio pressure bearing assessment method, device, equipment and medium Pending CN114022286A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111298552.1A CN114022286A (en) 2021-11-04 2021-11-04 Investment portfolio pressure bearing assessment method, device, equipment and medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111298552.1A CN114022286A (en) 2021-11-04 2021-11-04 Investment portfolio pressure bearing assessment method, device, equipment and medium

Publications (1)

Publication Number Publication Date
CN114022286A true CN114022286A (en) 2022-02-08

Family

ID=80060565

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111298552.1A Pending CN114022286A (en) 2021-11-04 2021-11-04 Investment portfolio pressure bearing assessment method, device, equipment and medium

Country Status (1)

Country Link
CN (1) CN114022286A (en)

Similar Documents

Publication Publication Date Title
Bonica Ideology and interests in the political marketplace
Petersen et al. How do firms implement impairment tests of goodwill?
Brockett et al. Financial intermediary versus production approach to efficiency of marketing distribution systems and organizational structure of insurance companies
Mallick et al. Sources of financing, profitability and productivity: First evidence from matched firms
Barbero et al. The effect of broadband on European Union trade: A regional spatial approach
Pratama et al. Sharia Firm Value: The Role of Enterprise Risk Management Disclosure, Intellectual Capital Disclosure, and Intellectual Capital
Statistics Socio-economic indexes for areas (SEIFA)
CN110796539A (en) Credit investigation evaluation method and device
Mirzaei et al. Real effect of bank efficiency: Evidence from disaggregated manufacturing sectors
Jalali Naini et al. A two‐stage DEA to analyze the effect of entrance deregulation on Iranian insurers: A robust approach
Zhang et al. Fairness of ratemaking for catastrophe insurance: Lessons from machine learning
CN113450158A (en) Bank activity information pushing method and device
Abdurakhmonov et al. Perceptions of political Self‐Dealing? An empirical investigation of market returns surrounding the disclosure of politician stock purchases
CN112633709A (en) Enterprise credit investigation evaluation method and device
Dzikevičius et al. Establishing a set of macroeconomic factors explaining variation over time of performance in business sectors
Schmid et al. A structured comparison of the corporate information security maturity level
Ghosal Impact of uncertainty and sunk costs on firm survival and industry dynamics
US20110213731A1 (en) Techniques for identifying high-risk portfolio with automated commercial real estate stress testing
Alotaibi The Effect of Accounting Disclosure for Sustainable Development on the Quality of Financial Report and the Extent of Its Reflection on the Company Value
Brunner et al. Hold-up in multiple banking: Evidence from SME lending
CN114022286A (en) Investment portfolio pressure bearing assessment method, device, equipment and medium
Desai et al. On the county-level credit outcome beta
Niknya et al. Financial distress prediction of Tehran Stock Exchange companies using support vector machine
Arshad et al. Determinants of CSR Disclosure in the Swedish Setting-Effect of firm/industry charecteistics along with media exposure on CSR disclosure practices
Vynaryk et al. Effects of the QMS ISO 9000 certification on Russian manufacturing companies

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