CN109509107B - Account data processing method and device, electronic equipment and computer readable medium - Google Patents
Account data processing method and device, electronic equipment and computer readable mediumDownload PDFInfo
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Abstract
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to an account data processing method and apparatus, an electronic device, and a computer-readable medium.
Background
Compared with product accounts such as fund products, asset management products and the like, the insurance account has the following special characteristics:
(1) the insurance account is generally the largest account in the company management account, and the equity assets are generally managed by a plurality of investment managers or even a plurality of departments. Where the risk is more prominent than the performance, it is easier to obscure than other product-class accounts.
(2) In the aspect of accounting valuation, the insurance account and the product closed-type account making are different at the equity investment manager level or the equity sub-asset unit level. Therefore, the insurance account is different from the closely managed product class in analyzing the investment manager level or the sub-asset unit level with a certain degree, and the estimation and the accounting of the insurance account are more difficult.
(3) The insurance account has the characteristics of matching assets and liabilities and the requirement of capital characteristics, and the benefit return requirements of the insurance account on the long-term property, the stability and the safety are different from the prior products, so that the insurance account has great difference.
Various data analysis systems standardized in the market cannot completely cover the system, and cannot meet personalized requirements. Therefore, a new account data processing method, apparatus, electronic device and computer readable medium are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present disclosure provides an account data processing method, an account data processing apparatus, an electronic device, and a computer readable medium, which can implement comprehensive and deep combined risk performance analysis from a risk perspective through a set of complete systems, and provide accurate and timely data analysis support for investment decision makers.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, an account data processing method is provided, which includes: acquiring a risk index of target account data within a preset time range; obtaining benefit indexes of the target account data within a preset time range; attribution analysis is carried out on the target account data according to the risk indexes and the benefit indexes to obtain attribution analysis results; and generating an analysis report of the target account data according to the risk index, the benefit index and the attribution analysis result under a preset condition.
In an exemplary embodiment of the present disclosure, the method further comprises: acquiring a plurality of primary account data through a plurality of data sources; preprocessing the plurality of original account data through a data warehouse to generate the target account data; wherein the primary account data comprises insurance account data.
In an exemplary embodiment of the present disclosure, preprocessing the plurality of primary account data to generate the target account data includes: and preprocessing the plurality of original account data according to the category of the target object to generate the target account data.
In an exemplary embodiment of the present disclosure, the target objects include a first class of target objects, a second class of target objects, and a third class of target objects; preprocessing the plurality of original account data according to the category of the target object to generate the target account data, wherein the preprocessing comprises the following steps: preprocessing all original account data to generate target account data of a first type of target object; preprocessing the original account data corresponding to the second type of target object to generate target account data corresponding to the second type of target object; and preprocessing a plurality of original account data corresponding to a plurality of second-class target objects to generate target account data corresponding to a third-class target object.
In an exemplary embodiment of the present disclosure, performing attribution analysis on the target account data according to the risk indicator and the benefit indicator includes: attribution analysis is carried out on the target account data according to a structured multi-factor risk model, the risk indexes and the benefit indexes; and/or performing attribution analysis on the target account data according to a bond combined performance attribution model, the risk indicator and the benefit indicator.
In an exemplary embodiment of the present disclosure, generating an analysis report of target account data according to the risk indicator, the benefit indicator, and the attribution analysis result under a preset condition includes: and integrating the risk index, the benefit index and the attribution analysis result based on the time sequence of the target account data under a preset condition to generate an analysis report of the target account data.
In an exemplary embodiment of the present disclosure, the preset condition includes: account dimension conditions and/or user hierarchy conditions; integrating the risk index, the benefit index and the attribution analysis result under a preset condition to generate an analysis report of the target account data, wherein the analysis report comprises the following steps: performing different integration on the risk index, the benefit index and the attribution analysis result according to different user hierarchy conditions to generate a plurality of analysis reports of the target account data; and/or performing different integration on the risk index, the benefit index and the attribution analysis result according to different account dimension conditions to generate a plurality of analysis reports of the target account data.
In an exemplary embodiment of the present disclosure, further comprising: and when the risk index is larger than the threshold value, generating alarm information.
According to an aspect of the present disclosure, an account data processing apparatus is provided, the apparatus including: the risk index module is used for acquiring risk indexes of the target account data within a preset time range; the benefit index module is used for acquiring the benefit index of the target account data within a preset time range; the attribution analysis module is used for carrying out attribution analysis on the target account data according to the risk index and the benefit index to obtain an attribution analysis result; and the analysis report module is used for generating an analysis report of the target account data according to the risk index, the benefit index and the attribution analysis result under the preset condition.
In an exemplary embodiment of the present disclosure, further comprising: the target data module is used for acquiring a plurality of original account data through a plurality of data sources; preprocessing the plurality of original account data through a data warehouse to generate the target account data; wherein the primary account data comprises fund account data.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the account data processing method, the account data processing device, the electronic equipment and the computer readable medium, comprehensive and deep combined risk performance analysis from the risk perspective can be realized through a set of complete system, and accurate and timely data analysis support is provided for investment decision makers.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a flow diagram illustrating a method of account data processing in accordance with an exemplary embodiment.
FIG. 2 is a flow chart illustrating a method of account data processing according to another exemplary embodiment.
FIG. 3 is a block diagram illustrating an account data processing apparatus according to an example embodiment.
Fig. 4 is a block diagram illustrating an account data processing apparatus according to another exemplary embodiment.
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 6 is a schematic diagram illustrating a computer-readable storage medium according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
FIG. 1 is a flow diagram illustrating a method of account data processing in accordance with an exemplary embodiment. The accountdata processing method 10 includes at least steps S102 to S108.
As shown in fig. 1, in S102, a risk indicator of the target account data within a predetermined time range is acquired. The target account may be, for example, insurance account data after data processing.
As mentioned above, the insurance account has specificity compared to the general fund product and the resource management product, and in the embodiment of the present disclosure, the risk corresponding to the insurance account may be: equity asset risk. The equity asset risks mainly comprise market risks, position risks, concentration risks, liquidity risks and derivative risks. Wherein, the market risk mainly refers to price risk, and refers to risk that the adverse change of equity price leads to unexpected loss of management assets; the position risk or concentration risk refers to the risk that the equity price fluctuation may be aggravated due to the over-high position or concentration; liquidity risk refers to the risk that securities cannot be rapidly presented at low cost due to insufficient trading volume in the securities market; the derivative risk mainly refers to the risk brought by the trading of the lever characteristic of the stock index futures belt.
In the embodiment of the present disclosure, the risk indicator F corresponding to the insurance account can be comprehensively determined by market risk a, position risk B, concentration risk C, liquidity risk D, derivative risk E, and their corresponding coefficients q1-q5, where the coefficients q1-q5 can be determined according to empirical values, for example, and the specific formula can be:
F＝q1*A+q2*B+q3*C+q4*D+q5*E；
in one embodiment, market risk a may be further analyzed by a market risk indicator as follows:
in the value at risk VaR. The risk value VaR refers to the potential maximum loss to a portfolio that may occur when market risk elements such as stock prices change for a given period of possession and a given confidence level. Methods of calculating VaR may include parametric methods, historical simulations, and monte carlo simulations, with a common method being parametric methods.
The rate of fluctuation. Volatility refers to the standard deviation of return on investment over a past period of time, as reflected by historical data for a past period of time for a target market price for the asset.
Sensitivity index beta coefficient. The sensitivity index beta coefficient represents the price fluctuation condition of individual equity assets relative to the whole market, and is an index for measuring systematic risks.
The level PE/PB is evaluated. For the overall evaluation level of the stocks held by the company, the risk management personnel should perform analysis and monitoring regularly and perform comparative analysis with the evaluation level of the stock market overall, industry division and plate division.
Maximum withdrawal. That is, the maximum value of the rate of return range when the account has performed to the lowest point is obtained by pushing back any historical time point in a certain period.
And (6) testing the pressure. The beta is used for pressure testing, which represents the price fluctuation condition of individual equity assets relative to the whole market, so the pressure testing can be carried out according to the index change and the beta of the combination/ticket, and the influence on the combined asset income under different pressure tests can be analyzed.
In one embodiment, the position risk B can be adjusted monthly on the basis of historical annual references, the position is a combined asset/reference, and upper and lower limits of the position are set at the same time, if the height reference is too high, the position is too high, the risk is increased, and the investment needs to be prompted to pay attention in time.
In one embodiment, concentration risk C may be calculated by the following formula:
different risk levels may also be determined, for example, for different HHI data.
In one embodiment, the flowability risk D may be calculated, for example, by the following equation:
the change factor is the participation rate, namely the change of the stock can be calculated by 5% or 20% on the assumption of the proportion of the market volume in each day.
In one embodiment, derivative risk E may be calculated, for example, by risk exposure, hedge ratio, hedge effectiveness, margin risk, and the like.
In one embodiment, the alert information may be generated, for example, when the risk indicator F is greater than a threshold. And generating alarm information when one or more of market risk A, position risk B, concentration risk C, liquidity risk D and derivative risk E of each sub-index corresponding to the risk index is larger than a corresponding threshold value.
For example, a quota index system can be established according to different sub indexes, and the quota index system comprises an instructional index system, an instructive index system and an observational index system. Selecting a core index to set an instructional risk limit to ensure that the risk of a company is within a tolerance range; selecting a secondary core index as an instructive setting suggestion threshold value, and performing daily monitoring to prompt risks; close attention to the observational systems is required to be quickly located and prompted when large changes occur.
In S104, a benefit indicator of the target account data within a predetermined time range is acquired. In one example of the invention, in the insurance account, the benefit index can be calculated by:
first, calculate the profitability of the insurance account
The rate of return may include a capital weighted rate of return or CWRR and a time weighted rate of return or TWRR. The calculation method is as follows:
interval CWRR ═ period actual revenue amount/period average capital occupancy
Interval TWRR ═ pit is in the interval(1+ daily profitability) -1
Daily rate of return (actual daily amount of return/daily capital occupation)
Second, the yield is compared with the same row
When ranking with products on the market, the yield of the fund needs to be adjusted in cost and bin position. The ranking mode can adopt a mode of establishing analog combination by Monte Carlo analog sampling, and the ranking of the samples is only used as reference.
Thirdly, determining the income index after risk adjustment
By adopting the income indexes after risk adjustment, risk factors can be taken into consideration, and the following indexes can be adopted for calculation:
a sharp ratio, sharp ratio being the ratio of the combined average rate of return to the standard deviation of the risk free rate of return portion to the combined rate of return over an evaluation period:
wherein R isPFor interval combined yield, RFFor interval risk-free profitability, σPThe interval combination fluctuation ratio (standard deviation).
The information ratio. The information ratio is the combined excess yield divided by the combined tracking error. The information rate indicator looks at how much the portfolio can increase in revenue beyond the benchmark when it is at risk of deviating from the index. The specific calculation formula is as follows:
wherein R isP-RBCombining excess profitability, σ (R), for intervalsP-RB) The interval excess yield fluctuation rate is calculated through the daily excess fluctuation rate.
In one embodiment, the risk adjusted revenue indicator may be used as a benefit indicator for the target account data.
In one embodiment, the benefit index of the target account can be further determined comprehensively through the comprehensive ranking of the target account and the risk-adjusted benefit index of the target account obtained in the second step. Specifically, for example, at a certain time, the financial market is more prosperous, and although the revenue index of the target account a is higher, the comprehensive ranking of the target account a on the market is later, and may be, for example, 10% after the market, in which case, the benefit index of the target account a should be "deducted"; conversely, if the revenue target account A has a lower revenue target, but its aggregate ranking on the market is higher, which may be, for example, the top 10% of the market, then the revenue target for target account A should be "awarded". The weight adjustment between the specific ranking and the revenue indicator may be determined based on market or empirical values, and the application is not limited thereto.
In S106, attribution analysis is carried out on the target account data according to the risk indexes and the benefit indexes, and attribution analysis results are obtained.
Attribution analysis is the comparison of account performance and risk according to specified benchmarks and results in different sources and contribution sizes of income and risk. The attribution analysis of the target account data according to the risk indicator and the benefit indicator may specifically be, for example: performing attribution analysis on the target account data according to a structured multifactor risk (Barra) model and the risk indicators and the benefit indicators; and/or performing attribution analysis on the target account data according to a bond combined performance (Brinson) attribution model and the risk index and the benefit index.
In one embodiment, attribution analysis includes a time selection ability analysis, a stock selection ability analysis, a bin selection analysis, and the like. Common analytical models may include the Brinson model and the Barra factor model.
In one embodiment, the Brinson attribution model construction method, as shown in table 1:
| revenue w of combined industry ipi | Reference industry i profit rbi | |
| Combinatorial industries i weight wpi | Q4＝∑wpi×rpi | Q2＝∑wpi×rbi |
| Benchmark industry i weight wbi | Q3＝∑wbi×rpi | Q1＝∑wbi×rbi |
Wherein, wpiThe weight of the industry i in the actual combination is taken up; w is abiThe industry i is weighted in the benchmark set; r ispiThe profitability of the industry i in the actual combination; r isbiThe profitability of industry i in the benchmark set.
After Q1-Q4 are solved, profit decomposition is carried out, and industry allocation profit, individual stock selection profit and interaction profit are solved:
the benchmark combined yield is:
rb＝Q1＝∑wbi×rbi
the industry configuration benefits are:
AR＝Q2-Q1＝∑wpi×rbi-∑wbi×rbi＝∑(wpi-wbi)×rbi
the individual stock selection yield is:
SR＝Q3-Q1＝∑wbi×rpi-∑Wbi×rbi＝∑wbi×(rpi-rbi)
the interactive profit is as follows:
IR＝Q4-Q3-Q2+Q1＝∑(wpi-wbi)×(rpi-rbi)
the total excess yield is:
TR＝rp-rb＝Q4-Q1＝SR+AR+IR
in one embodiment, attribution analysis of account data may also be performed by a modified Brinson attribution model building method:
key parameters are as follows:
the industry configuration represents that under the condition that the industry configuration is over under-matched, the industry benchmark yield is run-win or run-lose XX index full-income, and the contribution brought by the industry configuration is (r)bi-rb)*(wpi-wbi) (ii) a The contribution of the stock selection represents the actual profit rate win or loss industry benchmark index of the company industry, and the contribution brought by the contribution of the stock selection is (r)pi-rbi)*wbi(ii) a The industry index represents the standard profit rate of the industry or the whole profit of theShanghai depth 300 index, and the contribution brought by the industry index is (r)bi-rb)*wbi(ii) a The cross contribution is (r)pi-rbi)*(wpi-wbi)。
Attributing dimensions:
namely, the method is characterized in that the method is attributed to integral combination, and is dug to the industry and then to individual stocks.
Compared with the classic Brinson attribution model, the improved Brinson attribution has more dimensionalities and newly increased industry index contribution; in addition, on the industry configuration contribution, the industry index wins the performance benchmark and the industry configuration is over-matched to show positive contribution.
The improved Brinson attribution differs from the classical Brinson attribution algorithm as follows:
and (3) industrial configuration: classical Brinson attribute rbi*(wpi-wbi) Improved Brinson attribute (r)bi-rb)*(wpi-wbi)；
Industry index: the classic Brinson attribute is none, the improved Brinson attribute is (r)bi-rb)*wbi；
The whole combination is attributed as follows: the classic Brinson attribute is "industry configuration + stocking + cross-contribution ═ Σ WPirPi-∑Wbirbi", improved Brinson attributed to" trade configuration + holdings + trade index + cross-over contribution ═ Σ WPi(rPi-rb)＝∑WPirPi-rb∑WPi”；
To industry and personal stocks: classic Brinson attribute of WPirPi-WbirbiImproved Brinson attribute of WPi(rPi-rb)。
In one embodiment, the Barra attribution model will attribute to stable impact factors such as industry factors, style factors, and the like. The attribution meaning is that the contribution of the performance excess earning rate and the risk fluctuation rate is split into the contribution of each factor; the investment manager determines the factor exposure by judging the future trend of the factor rate of return. The Barra risk attribution model calculates the fluctuation rate through the correlation between the factors, and the factors are considered to have a certain internal relation, the correlation of the factors does not change greatly in a period of time, so the Barra risk attribution model has a certain prospective prediction.
In one embodiment, the Barra model algorithm is as follows:
the combined fluctuation ratio is:
wherein, XkTo combine the exposure to factor k, fkA yield of factor k, umPersonality factor yield, w, for an individual ticket mmThe weight of each strand m in the combination.
The combined fluctuation rate calculated by the above formula has additivity, and the combined fluctuation rate can be split into contributions of various factors to the combined fluctuation rate. The specific fluctuation rate contribution model of the factor k to the combination R is as follows:
likewise, the combined fluctuation rate can also be split into individual strand contribution levels to the combined fluctuation rate. The specific fluctuation rate contribution model of the individual strand M to the combination R is as follows:
wherein, wmWeight of the individual strands m in the combination, XmkIs the exposure of the individual strand m to the kth factor.
In S108, an analysis report of the target account data is generated according to the risk index, the benefit index and the attribution analysis result under a preset condition.
Specifically, the method can comprise the following steps: and integrating the risk index, the benefit index and the attribution analysis result based on the time sequence of the target account data under a preset condition to generate an analysis report of the target account data.
Wherein the preset conditions include: account dimension conditions and/or user hierarchy conditions; integrating the risk index, the benefit index and the attribution analysis result under a preset condition to generate an analysis report of the target account data, wherein the analysis report comprises the following steps: performing different integration on the risk index, the benefit index and the attribution analysis result according to different user hierarchy conditions to generate a plurality of analysis reports of the target account data; and/or performing different integration on the risk index, the benefit index and the attribution analysis result according to different account dimension conditions to generate a plurality of analysis reports of the target account data.
Wherein, the account dimension conditions comprise: overall account level dimension, sub-account, sub-strategy dimension, sub-investment manager dimension, sub-department level dimension, etc.,
the user hierarchy conditions include: risk manager level, investment manager level, and company high management level.
According to the account data processing method, a set of relatively complete analysis system and a set of relatively complete analysis framework are set up, so that comprehensive and deep combined risk performance analysis from the risk perspective is realized, and a strong support is provided for investment decision-making. Risks can be found and positioned in time under a multi-dimension and layer-by-layer drilling risk performance attribution system framework.
According to the account data processing method disclosed by the invention, the data requirements of time nodes such as daily nodes and weekly nodes can be met, and in addition, the data requirements of different dimensions including a sub-combination level, an account level, an investment manager level, a department level and the like can also be met.
Under the requirements of multiple combinations, high timeliness and large data volume, the account data processing method disclosed by the invention can not only meet the requirements from the aspects of content and function, but also greatly improve the working efficiency. In addition, the report data in the account data processing method disclosed by the disclosure can be time-series data, so that different users can know the change condition of each account.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 2 is a schematic diagram illustrating an account data processing method according to another exemplary embodiment. The schematic diagram shown in fig. 2 is a detailed description of "generating target account data" in the present disclosure.
In one embodiment, a plurality of primary account data is obtained via a plurality of data sources; preprocessing the plurality of original account data through a data warehouse to generate the target account data; wherein the primary account data comprises insurance account data. The plurality of data sources may include valuation accounting system data sources, information system data sources, trading system data sources, and the like.
In one embodiment, preprocessing the plurality of raw account data to generate the target account data includes: and preprocessing the plurality of original account data according to the category of the target object to generate the target account data.
The target objects comprise a first type target object, a second type target object and a third type target object; preprocessing the plurality of original account data according to the category of the target object to generate the target account data, wherein the preprocessing comprises the following steps: preprocessing all original account data to generate target account data of a first type of target object; preprocessing the original account data corresponding to the second type of target object to generate target account data corresponding to the second type of target object; and preprocessing a plurality of original account data corresponding to a plurality of second-class target objects to generate target account data corresponding to a third-class target object.
The first type of target object may be, for example, a risk manager, and the risk manager may, for example, need to know information of all accounts, and then preprocess all raw account data to generate target account data for the risk manager.
The second type of target object may be, for example, an investment manager, which may, for example, need an understanding of the risk performance of the own managed account, as well as an analysis of the investment style and risk points of the own account with quantitative data to better make investment decisions. The primary account data of the account corresponding to the investment manager is preprocessed to generate target account data for the investment manager.
The third type of target object may be, for example, company high management, which may require, for example, knowledge of the investment styles and performance risk point related information of all investment managers, and then the corresponding raw account data of different investment managers may be preprocessed to generate target account data for company high management.
The analysis report can detail the conditions of various indexes, various attributions and the like. The requirement for a reporting mechanism is timely and accurate, and it is recommended that a periodic reporting mechanism be formed. The system can periodically send a summary report of the overall performance and market risk of the account to the company high-level administration, so that the company high-level administration can timely grasp the overall situation of the company equity account; detailed reports including performance and market, concentration, liquidity, performance attribution, and risk attribution are periodically made to the investment managers and key performance and risk analyses are added. In addition, the original data can be acquired according to time nodes such as day and week, the analysis report can generate time-sequence data, and a user can know the change condition of each account and pay attention to the accounts with larger changes.
The insurance account is a large account generally in the account managed by the company, and particularly, the risk and the performance are more prominent and are relatively easy to obscure. According to the account data processing method disclosed by the invention, the analysis dimensionality is required to be comprehensive and complete, the analysis dimensionality comprises not only the integral account level dimensionality, but also the sub-account, the sub-strategy, the sub-investment manager and the sub-department level dimensionality, and the mesh structure for performing risk performance evaluation analysis by taking the sub-account as the granularity is really realized. Through the dimensionality of the sub-accounts, the overall performance and risk can be quickly positioned, and particularly which account is more prominent in performance and risk.
The insurance account has higher personalized requirements than the product. The account data processing method disclosed by the invention is subjected to multiple rounds of communication and demonstration with investment. The standardized systems in the market cannot completely cover the system, and cannot meet the personalized requirements. By independently building an analysis platform, the method has great flexibility: the risk performance analysis system can be embodied completely; and secondly, the method can be used for timely processing the changed conditions or more specific accounts in the future, and is very friendly.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
FIG. 3 is a block diagram illustrating an account data processing apparatus according to an example embodiment. The accountdata processing device 30 includes:risk indicator module 302,benefit indicator module 304,attribution analysis module 306, andanalysis reporting module 308.
Therisk indicator module 302 is configured to obtain a risk indicator of the target account data within a predetermined time range; the target account may be, for example, insurance account data after data processing. In the embodiment of the disclosure, the risk index F corresponding to the insurance account can be comprehensively judged through a market risk A, a position risk B, a concentration risk C, a liquidity risk D, a derivative risk E and coefficients q1-q5 corresponding to the derivative risk E.
Thebenefit index module 304 is configured to obtain a benefit index of the target account data within a predetermined time range; the profitability of the insurance account can be calculated first, and then the benefit index of the target account data can be finally determined by comparing with the same industry and determining the risk adjusted profit index.
Theattribution analysis module 306 is configured to perform attribution analysis on the target account data according to the risk indicator and the benefit indicator to obtain an attribution analysis result; attribution analysis is the comparison of account performance and risk according to specified benchmarks and results in different sources and contribution sizes of income and risk. The attribution analysis of the target account data according to the risk indicator and the benefit indicator may specifically be, for example: performing attribution analysis on the target account data according to a structured multifactor risk (Barra) model and the risk indicators and the benefit indicators; and/or performing attribution analysis on the target account data according to a bond combined performance (Brinson) attribution model and the risk index and the benefit index.
Theanalysis report module 308 is configured to generate an analysis report of the target account data according to the risk indicator, the benefit indicator, and the attribution analysis result under a preset condition. Can include the following steps: and integrating the risk index, the benefit index and the attribution analysis result based on the time sequence of the target account data under a preset condition to generate an analysis report of the target account data.
Wherein the preset conditions include: account dimension conditions, user hierarchy conditions; integrating the risk index, the benefit index and the attribution analysis result under a preset condition to generate an analysis report of the target account data, wherein the analysis report comprises the following steps: performing different integration on the risk index, the benefit index and the attribution analysis result according to different user hierarchy conditions to generate a plurality of analysis reports of the target account data; and/or performing different integration on the risk index, the benefit index and the attribution analysis result according to different account dimension conditions to generate a plurality of analysis reports of the target account data.
FIG. 3 is a block diagram illustrating an account data processing apparatus according to an example embodiment. The accountdata processing device 40 includes, on the basis of the account data processing device 30: atarget data module 402.
Thetarget data module 402 is used for acquiring a plurality of original account data through a plurality of data sources; preprocessing the plurality of original account data through a data warehouse to generate the target account data; wherein the primary account data comprises fund account data. The target objects comprise a first type target object, a second type target object and a third type target object; preprocessing the plurality of original account data according to the category of the target object to generate the target account data, wherein the preprocessing comprises the following steps: preprocessing all original account data to generate target account data of a first type of target object; preprocessing the original account data corresponding to the second type of target object to generate target account data corresponding to the second type of target object; and preprocessing a plurality of original account data corresponding to a plurality of second-class target objects to generate target account data corresponding to a third-class target object.
FIG. 4 is a block diagram illustrating an electronic device in accordance with an example embodiment.
Anelectronic device 200 according to this embodiment of the present disclosure is described below with reference to fig. 4. Theelectronic device 200 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, theelectronic device 200 is embodied in the form of a general purpose computing device. The components of theelectronic device 200 may include, but are not limited to: at least oneprocessing unit 210, at least onememory unit 220, abus 230 connecting different system components (including thememory unit 220 and the processing unit 210), adisplay unit 240, and the like.
Wherein the storage unit stores program code executable by theprocessing unit 210 to cause theprocessing unit 210 to perform the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, theprocessing unit 210 may perform the steps as shown in fig. 1.
Thememory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or acache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
Thestorage unit 220 may also include a program/utility 2204 having a set (at least one) ofprogram modules 2205,such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Theelectronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with theelectronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable theelectronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O)interface 250. Also, theelectronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via thenetwork adapter 260. Thenetwork adapter 260 may communicate with other modules of theelectronic device 200 via thebus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with theelectronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present disclosure.
Fig. 5 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the disclosure.
Referring to fig. 5, aprogram product 400 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring a risk index of target account data within a preset time range; obtaining benefit indexes of the target account data within a preset time range; performing attribution analysis on the target account data according to the risk indicator and the benefit indicator; and integrating the risk index, the benefit index and the attribution analysis result under a preset condition to generate an analysis report of the target account data.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (8)
1. An account data processing method, comprising:
acquiring a risk index of target account data within a preset time range, wherein the target account data is insurance account data;
obtaining benefit indexes of the target account data within a preset time range;
performing attribution analysis on the target account data according to the risk indicator and the benefit indicator to obtain an attribution analysis result, wherein the attribution analysis result comprises: attribution analysis is carried out on the target account data according to a structured multi-factor risk model, the risk indexes and the benefit indexes; and/or
Attributing and analyzing the target account data according to an improved bond combination performance attribution model, the risk index and the benefit index; and
generating an analysis report of the target account data according to the risk index, the benefit index and the attribution analysis result under a preset condition, wherein the preset condition comprises the following steps: account dimension conditions and/or user hierarchy conditions;
generating an analysis report of the target account data according to the risk index, the benefit index and the attribution analysis result under the preset condition, wherein the analysis report comprises:
performing different integration on the risk index, the benefit index and the attribution analysis result according to different user hierarchy conditions to generate a plurality of analysis reports of the target account data; and/or
Performing different integration on the risk index, the benefit index and the attribution analysis result according to different account dimension conditions to generate a plurality of analysis reports of the target account data;
when the risk index is larger than a preset threshold value, generating alarm information; and/or
And when one or more of the sub-indexes corresponding to the risk indexes are larger than the corresponding threshold value, generating alarm information.
2. The method of claim 1, wherein the method further comprises:
acquiring a plurality of primary account data through a plurality of data sources; and
preprocessing the plurality of original account data through a data warehouse to generate the target account data;
wherein the primary account data comprises insurance account data.
3. The method of claim 2, wherein pre-processing the plurality of primary account data to generate the target account data comprises:
and preprocessing the plurality of original account data according to the category of the target object to generate the target account data.
4. The method of claim 3, wherein the target objects include a first class of target objects, a second class of target objects, and a third class of target objects;
preprocessing the plurality of original account data according to the category of the target object to generate the target account data, wherein the preprocessing comprises the following steps:
preprocessing all original account data to generate target account data of a first type of target object;
preprocessing the original account data corresponding to the second type of target object to generate target account data corresponding to the second type of target object;
and preprocessing a plurality of original account data corresponding to a plurality of second-class target objects to generate target account data corresponding to a third-class target object.
5. The method of claim 1, wherein generating an analysis report of the target account data according to the risk indicator, the benefit indicator and the attribution analysis result under a preset condition comprises:
and integrating the risk index, the benefit index and the attribution analysis result based on the time sequence of the target account data under a preset condition to generate an analysis report of the target account data.
6. An account data processing apparatus, comprising:
the risk index module is used for acquiring risk indexes of target account data within a preset time range, wherein the target account data are insurance account data;
the benefit index module is used for acquiring the benefit index of the target account data within a preset time range;
the attribution analysis module is used for carrying out attribution analysis on the target account data according to the risk indicator and the benefit indicator to obtain an attribution analysis result, and comprises the following steps: attribution analysis is carried out on the target account data according to a structured multi-factor risk model, the risk indexes and the benefit indexes; and/or performing attribution analysis on the target account data according to an improved bond combined performance attribution model, the risk indicator and the benefit indicator; and
the analysis report module is used for generating an analysis report of the target account data according to the risk index, the benefit index and the attribution analysis result under a preset condition, wherein the preset condition comprises the following steps: account dimension conditions and/or user hierarchy conditions;
the analysis report module is used for performing different integration on the risk index, the benefit index and the attribution analysis result according to different user level conditions to generate a plurality of analysis reports of the target account data; and/or differently integrating the risk index, the benefit index and the attribution analysis result according to different account dimension conditions to generate a plurality of analysis reports of the target account data;
the risk index module is also used for generating alarm information when the risk index is larger than a preset threshold value; and/or generating alarm information when one or more of the sub-indexes corresponding to the risk indexes are larger than the corresponding threshold value.
7. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
8. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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