CN115689764A

CN115689764A - Method and device for processing data to be evaluated, electronic equipment and medium

Info

Publication number: CN115689764A
Application number: CN202210401042.0A
Authority: CN
Inventors: 张家玮; 于淼; 邓琳
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2022-04-14
Filing date: 2022-04-14
Publication date: 2023-02-03

Abstract

The disclosure provides a method for processing data to be evaluated, which can be applied to the financial field or other fields. The method comprises the following steps: extracting estimation parameters from data to be estimated, wherein the estimation parameters comprise time factors of the data to be estimated; determining a standard value parameter expression by using a loss function; substituting the estimation parameters into the standard value parameter expression to calculate to obtain standard value parameters of the data to be estimated, wherein the standard value parameters are positive values and are right deviation distribution; and inputting the standard value parameters into a bond valuation system to obtain valuation of the data to be valued. The disclosure also provides a processing device, equipment, storage medium and program product of the data to be evaluated.

Description

Method and device for processing data to be evaluated, electronic equipment and medium

Technical Field

The present disclosure relates to the field of finance, and more particularly, to a method, apparatus, device, medium, and program product for processing data to be evaluated.

Background

In the course of a commercial bank conducting financial transactions, it is necessary to make valuations for some non-standardized bond assets (non-standard assets) of non-marketing companies. In the file "notice about problems related to the investment operation of the standardized commercial banking business" of the bank insurance premiums 2013, the non-standard assets are defined as: refers to the property of bonds that are not traded in the interbank securities exchange market and includes, but is not limited to, credit assets, trust loans, commitment bonds, acceptance drafts, credits, accounts receivable, various receiving (or receiving) equity, equity type financing with buy-back terms, etc. Thus, commercial banks need to make advance valuations of these assets in order to guard against risk.

In the process of implementing the concept of the present disclosure, the inventor finds that the current bond valuation system of the commercial bank lacks a data processing device, and cannot preprocess data to be valued, so that the application range of the bond valuation system is relatively small.

Disclosure of Invention

In view of the above, the present disclosure provides a method, apparatus, device, medium, and program product for processing data to be evaluated.

According to a first aspect of the present disclosure, there is provided a method for processing data to be evaluated, which is applied to a bond evaluation system, the method including: extracting an estimation parameter from data to be estimated, wherein the estimation parameter comprises a time factor of the data to be estimated; determining a standard value parameter expression by using a loss function; substituting the valuation parameters into the standard value parameter expression to calculate to obtain standard value parameters of the data to be valuationd, wherein the standard value parameters are positive values and are distributed in a right deviation mode; and inputting the standard value parameters into a bond valuation system to obtain the valuation of the data to be valued.

According to an embodiment of the present disclosure, the step of determining the standard value parameter expression using the loss function includes: determining the risk type of the data to be evaluated; and determining a standard value parameter expression for the risk type using a loss function.

According to an embodiment of the present disclosure, the step of determining the standard value parameter expression of the risk type using a loss function includes: obtaining historical evaluation data of the risk types, wherein the historical evaluation data comprise historical values of standard value parameters of the risk types; constructing an initial standard value parameter expression containing parameters; substituting the historical estimation data and the initial standard value parameter expression into a loss function; solving the minimum value of the loss function to obtain the parameter value of the parameter; and determining a standard value parameter expression for the risk type based on the parameter values.

According to an embodiment of the present disclosure, the step of constructing an initial standard value parameter expression including parameters includes: constructing an initial standard value parameter expression comprising parameters based on public factors, wherein the public factors comprise a real estate starting area, a domestic total power generation amount, a domestic coal yield and an international crude oil yield, and the initial standard value parameter expression is as follows:

Lamda＝A*T+B*REAL+C*COAL+D*OIL+E*EL

in the formula, lamda is an initial standard value parameter, T is a time factor of historical estimation data, REAL is the REAL estate starting area, EL is the total domestic power generation amount, COAL is the domestic COAL yield, OIL is the international crude OIL yield, and A, B, C, D and E are parameters.

According to an embodiment of the present disclosure, the risk types include low risk, medium risk, and high risk.

According to an embodiment of the present disclosure, the step of obtaining the estimation of the data to be estimated includes: extracting default probability of the data to be evaluated from the data to be evaluated; calculating to obtain the actual default probability of the data to be evaluated based on the default probability; calculating to obtain the neutral default probability of the data to be evaluated by utilizing the actual default probability and the standard value parameter; and calculating to obtain an estimated value of the data to be estimated based on the neutral default probability of the data to be estimated.

A second aspect of the present disclosure provides a data processing apparatus applied to a bond valuation system, including: the acquisition module is used for extracting an estimation parameter from the data to be estimated, wherein the estimation parameter comprises a time factor of the data to be estimated; the first calculation module is used for determining a standard value parameter expression by using a loss function; the second calculation module is used for substituting the valuation parameters into the standard value parameter expression to calculate standard value parameters of the data to be valuationd, wherein the standard value parameters are positive values and right deviation distribution; and the valuation module is used for inputting the standard value parameters into a bond valuation system and calculating the valuation of the data to be valued.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-described method.

A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described method.

A fifth aspect of the disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above method.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, taken in conjunction with the accompanying drawings of which:

fig. 1 schematically illustrates an application scenario diagram of a processing method, apparatus, device, medium, and program product of data to be evaluated according to an embodiment of the present disclosure;

fig. 2 schematically shows a flow chart of a processing method of data to be evaluated according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow chart for determining a standard value parameter expression using a loss function according to an embodiment of the disclosure;

FIG. 4 schematically illustrates a processing system diagram of data to be evaluated according to an embodiment of the disclosure;

fig. 5 is a block diagram schematically showing a configuration of a processing apparatus of data to be evaluated according to an embodiment of the present disclosure; and

fig. 6 schematically shows a block diagram of an electronic device adapted to implement a method of processing data to be evaluated according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).

When the commercial bank evaluates the data of the non-listed companies at present, the evaluation of the data by the bond evaluation system is invalid because the technical scheme of the bond evaluation system of the listed companies is still adopted. By analyzing the bond valuation system, the reason of system failure is found that the data of non-listed companies lack standard value parameters representing the asset value of the companies, so that the condition of system failure occurs when the bond valuation system is used for valuation. The standard value parameter is a parameter for representing the asset value of a company, for a listed company, the standard value parameter is a sharp ratio, for a non-listed company, due to the lack of the standard value parameter, the data to be evaluated needs to be processed, so that the data can be evaluated by using the existing bond evaluation system, and the application range of the bond evaluation system is expanded.

In view of the above problem, an embodiment of the present disclosure provides a method for processing data to be evaluated, which is applied to a bond evaluation system, and the method includes: extracting estimation parameters from data to be estimated, wherein the estimation parameters comprise time factors of the data to be estimated; determining a standard value parameter expression by using a loss function; substituting the estimation parameters into the standard value parameter expression to calculate to obtain standard value parameters of the data to be estimated, wherein the standard value parameters are positive values and are right deviation distribution; and inputting the standard value parameters into a bond valuation system to obtain the valuation of the data to be valued.

It should be noted that the method and apparatus for determining in the present disclosure may be used for processing data to be evaluated in the financial field, and may also be used for processing data to be evaluated in any field other than the financial field.

Fig. 1 schematically shows an application scenario diagram of a processing method, apparatus, device, medium, and program product of data to be evaluated according to an embodiment of the present disclosure.

As shown in fig. 1, an application scenario 100 according to this embodiment may include

terminal devices

101, 102, 103, a network 104 and a server 105. Network 104 is the medium used to provide communication links between

terminal devices

101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use

terminal devices

101, 102, 103 to interact with a server 105 over a network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The backend management server may analyze and process the received data such as the user request, and feed back a processing result (for example, a web page, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the processing method of the data to be evaluated provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the processing device for data to be evaluated provided by the embodiment of the present disclosure may be generally disposed in the server 105. The processing method of the data to be evaluated provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 105 and is capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Correspondingly, the processing device for data to be evaluated provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster that is different from the server 105 and is capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The processing method of the data to be evaluated of the disclosed embodiment will be described in detail below with fig. 2 to 3 based on the scenario described in fig. 1.

Fig. 2 schematically shows a flowchart of a processing method of data to be evaluated according to an embodiment of the present disclosure.

As shown in fig. 2, the processing method of data to be evaluated of this embodiment includes operations S210 to S240.

In operation S210, an estimation parameter is extracted from data to be estimated, and the estimation parameter includes a time factor of the data to be estimated. The estimation parameter includes a parameter of the data to be estimated, for example, a time factor T of the data to be estimated, and the specific parameter selection may be selected according to an actual situation, which is not limited in the embodiment of the present disclosure.

It should be noted that the data to be evaluated may be bond data, stock data, or the like, and all the data to be evaluated may be data, which is not limited in this embodiment of the disclosure.

In operation S220, a standard value parameter expression is determined using the loss function.

According to the embodiment of the present disclosure, the risk types include low risk, medium risk, and high risk, and the specific risk type division standard may be determined according to an actual situation, which is not limited by the embodiment of the present disclosure.

FIG. 3 schematically illustrates a flow chart for determining a standard value parameter expression using a loss function according to an embodiment of the disclosure.

As shown in fig. 3, the step of determining the expression of the standard value parameter using the loss function of this embodiment includes operations S310 to S350.

In operation S310, historical evaluation data of the risk type is obtained, wherein the historical evaluation data includes a historical value Lamda _ h of a standard value parameter of the risk type.

In operation S320, an initial standard value parameter expression including parameters is constructed.

According to an embodiment of the present disclosure, the step of constructing an initial standard value parameter expression including parameters includes: based on a public factor, constructing an initial standard value parameter expression comprising parameters, wherein the public factor is an objective factor reflecting the social and economic degrees, the public factor comprises the real estate starting area, the domestic total power generation, the domestic coal yield and the international crude oil yield, and the initial standard value parameter expression is as follows:

Lamda＝A*T+B*REAL+C*COAL+D*OIL+E*EL

in the formula, lamda is an initial standard value parameter, T is a time factor of historical estimation data, REAL is the REAL estate starting area, EL is the total domestic power generation amount, COAL is the domestic COAL yield, OIL is the international crude OIL yield, and A, B, C, D and E are parameters. The value of the initial standard value parameter is a positive value and is distributed in a right deviation mode, and therefore the technical problem that systematic deviation occurs in a bond valuation system is solved.

Because the values of the parameters A, B, C, D and E of the initial standard value parameter expression corresponding to the data of the same risk type are the same, for example, all the data to be evaluated of the low risk type have the same values of A, B, C, D and E, the values of the parameters A, B, C, D and E of the initial standard value parameter expression can be determined according to the historical evaluation data of the same risk rating

In operation S330, the historical estimation data and the initial standard value parameter expression are substituted into a loss function.

In operation S340, the loss function is minimized to obtain a parameter value of the parameter.

In operation S350, a standard value parameter expression for the risk type is determined based on the parameter value.

For example, taking the data to be evaluated as the non-standard bond data as an example, for the data to be evaluated of a specific risk type, assuming that there are n customers under the specific risk type, the loss function is:

wherein the content of the first and second substances,

in the formula, EIS _T = r1-r0, which means the credit interest difference of bonds with time limit T, the unit of T is year, which means there is credit risk on marketThe bond yield r1 and the risk-free rate, in the embodiment of the present disclosure, the risk-free rate is the immediate yield r0 of the chinese debt. LGD is default loss rate, which in the disclosed embodiment is the default risk exposure constant of the un-marketed company, LGD0 is the default loss rate when warranted, which in the disclosed embodiment can be set to 0.9; when no insurance is paid, the default loss rate is LGD1, which may be set to 0.1 in the embodiment of the present disclosure, and may be specifically adjusted according to actual situations, which is not limited in the embodiment of the present disclosure. N is not a distribution function of normal distribution, and the form of the distribution function of the normal distribution is as follows:

CEDF _T the cumulative actual default probability for the n customers over the T period.

At this time, substituting the historical estimation data and the initial standard value parameter expression into a loss function, establishing a function with arguments of A, B, C, D and E, and then solving the minimum value of the loss function to obtain parameters A, B, C, D and E under the corresponding internal rating. The precondition for obtaining the minimum value of the loss function is that the values of bond duration, yield, power generation amount, area and the like are all larger than 0, namely T, REAL, CIAL, OIL and EL cannot be negative numbers. There are five parameters, a, B, C, D, E, and each parameter is solved.

Taking the solution of A as an example, assume that there are n customers under the rating

From Lamda _ h = A × T + B × REAL + C × COAL + D × OIL + E × EL

Then

Then

From the above formula can be obtained

The sign of (a) depends only on the numerator, since the denominator is fixed to be a positive number, and from the preconditions,

must be positive, so that the point of 0 is the minimum point of L, and the corresponding a of this point is the required parameter a.

Let the denominator of the above equation be 0, then there is the equation:

in the same way, the same operations are performed on the parameters BCDE, respectively, as the following equation:

the formulas 1 to 5 form five equation sets, 5 unknowns are shared, and the parameters A, B, C, D and E can be solved by combining the five equation sets, so that the standard value parameter expression of the non-standard bond data is determined based on the parameter values of the parameters A, B, C, D and E.

In operation S230, the estimation parameters are substituted into the standard value parameter expression, and the standard value parameters of the data to be estimated are calculated, including substituting the time factors of the data to be estimated into the standard value parameter expression to obtain the standard value parameters. The standard value parameters are positive values and are distributed in a right-handed manner, so that the problem of failure of the bond valuation system caused by overlarge value range of the standard value parameters when the bond valuation system is used for valuing non-standard bonds is solved, and the application range of the bond valuation system is expanded.

In operation S240, the standard value parameter is input to a bond valuation system to obtain an valuation of the data to be valued.

According to an embodiment of the present disclosure, the step of obtaining the estimation value of the data to be estimated includes: extracting default probability of the data to be evaluated from the data to be evaluated; calculating to obtain the actual default probability of the data to be evaluated based on the default probability; calculating to obtain the neutral default probability of the data to be evaluated by utilizing the actual default probability and the standard value parameter; and calculating to obtain an estimated value of the data to be estimated based on the neutral default probability of the data to be estimated.

According to an embodiment of the present disclosure, in the step of extracting a default probability of the data to be evaluated from the data to be evaluated, the default probability includes: an upper bound violation probability, a lower bound violation probability, and a mean violation probability.

According to an embodiment of the present disclosure, the estimating of the data to be estimated includes: an upper bound violation probability estimate, a lower bound violation probability estimate, and a mean violation probability estimate.

Illustratively, the bond valuation system obtains the valuation of the data to be valued by using a fixed income asset present value method, taking the data to be valued as non-standard bond data as an example, the fixed income asset present value method involves the following parameters:

PV ₀ : indicating the present value of the asset at time 0,namely the evaluation of the bond, unit: and (5) Yuan.

CF _i : indicating the ith cash flow generated by the asset.

CQDF _i : representing the cumulative risk neutral default probability from time 0 to the time period during which the ith cash flow was generated.

DF _i : the risk-free discount factor of the ith cash flow is represented by the following calculation formula:

DF _i ＝e ^-r0，t*t

CEDF _T : the actual default probability of the zero-information bond with the term T, EDF represents the marginal default probability, and the following relationship exists between the marginal default probability and the marginal default probability:

CEDF _T ＝1-(1-EDF) ^T

wherein: r is a radical of hydrogen _0，t The term represents the medium debt earning rate at the moment of 0, with the term of t/YD years. YD represents the actual days of the rest year of the year, the head and the tail of the calculation are not calculated, and the unit is day. The rest years are the time intervals from the first rest day to the same month and same day corresponding to the next year, and the rest years are similar. t represents the time when the ith cash flow occurs.

The specific fixed revenue asset present value method comprises the following steps:

1. based on the result of the internal rating, obtaining a third-gear default probability, which is an interval, an upper limit and a lower limit, the upper limit and the lower limit of the default probability are obtained, and the average value of the upper limit and the lower limit is EDFA.

2. From the formula CEDF _T ＝(1-EDF _i ) ^T Can obtain CEDF _i The present embodiment defaults EDF in each period _i And the user can adjust the formula according to the actual situation as long as the formula is met. Similar processing can be performed on the third gear actual default probability, so that:

CEDF _{T_p} ＝(1-EDF _p ) ^T

the same can be obtained:

CEDF _{T_d} ＝(1-EDF _d ) ^T

CEDF _{T_a} ＝(1-EDF _a ) ^T 。

3. by the formula

Obtaining risk neutral default probability CQDF of each stage _i . The different risk neutral default probabilities of the third gear can be obtained according to the different actual default probabilities of the third gear: CQDFp, CQDFa, CQDFd.

4. Obtaining discount factor DF of each stage from Chinese debt and national debt immediate earning rate _i 。

5. By the formula

And obtaining a final bond evaluation result of the system, and finally obtaining an upper default probability evaluation value, a lower default probability evaluation value and an average default probability evaluation value by the difference of three categories of CQDFp, CQDFa and CQDFd.

FIG. 4 schematically shows a processing system for data to be evaluated according to an embodiment of the disclosure.

As shown in fig. 4, the data storage module is configured to store all data, including data to be evaluated and historical data, the physical entity may be a database running on a hard disk, and the data storage structure is a two-dimensional table.

After a user selects data to be evaluated, a standard value parameter calculation module extracts data in the evaluation data from a data storage module, taking the data to be evaluated as non-standard bond data as an example, the standard value parameter calculation module acquires the non-listed customer internal rating information historical data corresponding to the non-standard bond to be evaluated, non-listed company default risk exposure constants (LGD 0 and LGD 1), non-listed company zero-information bond interest rate (r 1), non-listed company zero-information bond actual default rate (CEDF), time limit (T) corresponding to the zero-information bond, medium-bond national bond on-demand yield (r 0), REAL estate exploitation area (REAL), domestic total power generation (EL), domestic COAL yield (COAL) and international crude OIL yield (OIL) from the data storage module. And then calculating to obtain a standard value parameter value of the non-standard bond to be evaluated, and inputting the standard value parameter value into an evaluation module.

The evaluation module obtains standard value parameter values of the non-standard bonds to be evaluated from the standard value parameter calculation module, and obtains the internal rating of the bond issuing main body to be evaluated, default probability corresponding to the internal rating, the immediate earning rate (r 0) of the Chinese bonds and cash flow CF of each period of the bonds from the data storage module _i And estimating the bond to be estimated based on the data to obtain the estimation of the non-standard bond and transmitting the estimation to the user.

By the method for processing the data to be evaluated, the standard value parameters of the data to be evaluated can be determined by using the loss function, the application range of the bond evaluation system is expanded, and the bond evaluation system can effectively evaluate various types of data without increasing the system overhead remarkably.

Based on the evaluation method of the non-standard bond, the disclosure also provides a processing device of data to be evaluated, which is applied to a bond evaluation system. The apparatus will be described in detail below with reference to fig. 5.

Fig. 5 schematically shows a block diagram of a processing apparatus of data to be evaluated according to an embodiment of the present disclosure.

As shown in fig. 5, the apparatus 500 for processing data to be evaluated of this embodiment includes an obtaining module 510, a first calculating module 520, a second calculating module 530, and an evaluating module 540.

The obtaining module 510 is configured to extract an estimation parameter from the data to be estimated, where the estimation parameter includes a time factor of the data to be estimated. In an embodiment, the obtaining module 510 may be configured to perform the operation S210 described above, which is not described herein again.

The first calculation module 520 is used to determine the standard value parameter expression using a loss function. In an embodiment, the first calculating module 520 may be configured to perform the operation S220 described above, which is not described herein again.

The second calculating module 530 is configured to substitute the estimation parameter into the standard value parameter expression, and calculate a standard value parameter of the data to be estimated, where the standard value parameter is a positive value and is a right-biased distribution. In an embodiment, the second calculating module 530 may be configured to perform the operation S230 described above, and is not described herein again.

The valuation module 540 is configured to input the standard value parameter into a bond valuation system to obtain an valuation of the data to be valued. In an embodiment, the evaluation module 540 may be configured to perform the operation S240 described above, and is not described herein again.

According to the embodiment of the present disclosure, any plurality of the obtaining module 510, the first calculating module 520, the second calculating module 530 and the estimating module 540 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 510, the first calculating module 520, the second calculating module 530 and the estimating module 540 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware and firmware, or implemented by a suitable combination of any several of them. Alternatively, at least one of the obtaining module 510, the first calculating module 520, the second calculating module 530 and the estimating module 540 may be at least partially implemented as a computer program module, which when executed may perform the respective functions.

As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.

In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or RAM 603. Note that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

Electronic device 600 may also include input/output (I/O) interface 605, input/output (I/O) interface 605 also connected to bus 604, according to an embodiment of the disclosure. The electronic device 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that the computer program read out therefrom is mounted in the storage section 608 as necessary.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer 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. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 602 and/or RAM 603 described above and/or one or more memories other than the ROM 602 and RAM 603.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. The program code is for causing a computer system to carry out the methods of the embodiments of the disclosure when the computer program product is run on the computer system.

The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 601. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of signals over a network medium, downloaded and installed via the communication section 609, and/or installed from a removable medium 611. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices 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 external computing devices (e.g., through the internet using an internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the disclosure, and these alternatives and modifications are intended to fall within the scope of the disclosure.

Claims

1. A processing method of data to be evaluated is applied to a bond evaluation system, and is characterized by comprising the following steps:

extracting estimation parameters from data to be estimated, wherein the estimation parameters comprise time factors of the data to be estimated;

determining a standard value parameter expression by using a loss function;

substituting the valuation parameters into the standard value parameter expression to calculate to obtain standard value parameters of the data to be valuationd, wherein the standard value parameters are positive values and are distributed in a right deviation mode; and

and inputting the standard value parameters into a bond valuation system to obtain the valuation of the data to be valued.

2. The method of claim 1, wherein the step of determining the standard-value parameter expression using the loss function comprises:

determining the risk type of the data to be evaluated; and

and determining a standard value parameter expression of the risk type by using a loss function.

3. The method of claim 2, wherein the step of determining the standard cost parameter expression for the risk type using a loss function comprises:

acquiring historical evaluation data of the risk types, wherein the historical evaluation data comprises historical values of standard value parameters of the risk types;

constructing an initial standard value parameter expression containing parameters;

substituting the historical estimation data and the initial standard value parameter expression into a loss function;

solving the minimum value of the loss function to obtain the parameter value of the parameter; and

based on the parameter values, a standard value parameter expression for the risk type is determined.

4. The method of claim 3, wherein the step of constructing an initial standard value parameter expression containing parameters comprises: constructing an initial standard value parameter expression comprising parameters based on public factors, wherein the public factors comprise a real estate starting area, a domestic total power generation amount, a domestic coal yield and an international crude oil yield, and the initial standard value parameter expression is as follows:

Lamda＝A*T+B*REAL+C*COAL+D*OIL+E*EL

in the formula, lamda is an initial standard value parameter, T is a time factor of historical estimation data, REAL is the REAL estate construction area, EL is the domestic total power generation amount, COAL is the domestic COAL yield, OIL is the international crude OIL yield, and A, B, C, D and E are parameters.

5. The method of claim 2, wherein the risk types include low risk, medium risk, and high risk.

6. The method of claim 1, wherein the step of obtaining an estimate of the data to be estimated comprises:

extracting default probability of the data to be evaluated from the data to be evaluated;

calculating to obtain the actual default probability of the data to be evaluated based on the default probability;

calculating to obtain the neutral default probability of the data to be evaluated by utilizing the actual default probability and the standard value parameter; and

and calculating to obtain the estimated value of the data to be estimated based on the neutral default probability of the data to be estimated.

7. A data processing device applied to a bond valuation system comprises:

the acquisition module is used for extracting an estimation parameter from the data to be estimated, wherein the estimation parameter comprises a time factor of the data to be estimated;

the first calculation module is used for determining a standard value parameter expression by using a loss function;

the second calculation module is used for substituting the evaluation parameter into the standard value parameter expression to calculate and obtain a standard value parameter of the data to be evaluated, wherein the standard value parameter is a positive value and is right deviation distribution; and

and the evaluation module is used for inputting the standard value parameters into a bond evaluation system and calculating the evaluation of the data to be evaluated.

8. An electronic device, comprising:

one or more processors;

a storage device to store one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method recited in any of claims 1-6.

9. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.