CN114168814A

CN114168814A - Data processing method and related device

Info

Publication number: CN114168814A
Application number: CN202111502791.4A
Authority: CN
Inventors: 吴博; 朱富荣; 庄佳和; 何易超; 林宜领; 林妙真; 林凯; 陈文森; 石阳
Original assignee: China Construction Bank Corp
Current assignee: China Construction Bank Corp
Priority date: 2021-12-09
Filing date: 2021-12-09
Publication date: 2022-03-11

Abstract

The invention discloses a data processing method and a related device, wherein the method comprises the following steps: after receiving an evaluation request for a target company, acquiring a plurality of comparable companies corresponding to the target company; calculating the similarity between the target company and each comparable company through a similarity model based on the standardized Euclidean distance; and calling a relative evaluation model according to the similarity, and performing weighted calculation on the data of each comparable company to obtain the relative evaluation of the target company, wherein the higher the similarity is, the higher the weight of the comparable company is, and the data are financial characteristic data and other quantity characteristic data related to the evaluation of the company. The method can solve the problem that the prior art is poorer in accuracy than a company estimation method.

Description

Data processing method and related device

Technical Field

The present invention belongs to the field of data processing technologies, and in particular, to a data processing method, apparatus, device, storage medium, and computer program product.

Background

Currently, value evaluation for companies mainly includes asset basis evaluation, income evaluation, and comparable company evaluation. The comparative company estimation method has high performability and occupies an important position in the existing company value evaluation system. But the estimation accuracy of the existing comparable company estimation method is poor.

Disclosure of Invention

The embodiment of the invention provides a data processing method and a related device, which can solve the problem of poor accuracy of a comparable company evaluation method in the prior art.

In a first aspect, a data processing method is provided, including:

after receiving an evaluation request for a target company, acquiring a plurality of comparable companies corresponding to the target company;

calculating the similarity between the target company and each comparable company through a similarity model based on the standardized Euclidean distance;

and calling a relative evaluation model according to the similarity, and performing weighted calculation on the data of each comparable company to obtain the relative evaluation of the target company, wherein the higher the similarity is, the higher the weight of the comparable company is, and the data are financial characteristic data and other quantity characteristic data related to the evaluation of the company.

Optionally, the calculating, based on the normalized euclidean distance, a similarity between the target company and each comparable company through a similarity model includes:

standardizing first data of comparable companies and second data of a target company, wherein the data comprises the first data, and the first data corresponds to the second data;

taking the second data after the standardization processing and the first data of each comparable company after the standardization processing as the input of a distance arithmetic unit in the similarity model to operate the distance arithmetic unit to obtain the standardized Euclidean distance between the target company and each comparable company;

and acquiring the similarity corresponding to the standardized Euclidean distance through preset mapping of the distance and the similarity in the similarity model.

Optionally, the invoking a relative valuation model, performing a weighted calculation on data of each comparable company to obtain a relative valuation of the target company includes:

analyzing the data of each comparable company, and determining the relative valuation model adapted to the target company;

and calling an interface of the adaptive relative valuation model, and performing weighted calculation on the data of each comparable company according to the weight of each comparable company to obtain the relative valuation of the target company.

Optionally, the obtaining comparable companies corresponding to the target company includes:

acquiring the industry category of the target company;

and screening reference data matched with the industry categories from a preset database, wherein the companies corresponding to the reference data are comparable companies.

Optionally, the industry category comprises a primary industry category and a sub-category under the primary industry category; the step of screening out reference data matched with the industry categories from a preset database comprises the following steps:

acquiring first reference data matched with the sub-categories in the preset database;

when the number of companies corresponding to the first reference data is greater than or equal to a first preset threshold value, the companies corresponding to the first reference data are the comparable companies;

when the number of companies corresponding to the first reference data is smaller than a first preset threshold value, acquiring second reference data matched with the primary industry category in the preset database; the company corresponding to the second reference data is the comparable company.

Optionally, after obtaining the second reference data matched with the primary industry category in the preset database, the method further includes:

when the number of companies corresponding to the second reference data is larger than or equal to a second preset threshold value, the companies corresponding to the second reference data are the comparable companies;

when the number of companies corresponding to the second reference data is smaller than a second preset threshold value, acquiring third reference data matched with the comprehensive industry from a preset database; the third reference data matched company and the second reference data matched company are the comparable companies.

Optionally, after the obtaining of the comparable companies corresponding to the target company, the method further includes:

data cleansing is performed to screen comparable companies for data anomalies.

In a second aspect, a data processing apparatus is provided, including:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a plurality of comparable companies corresponding to a target company after receiving an evaluation request of the target company;

the similarity calculation module is used for calculating the similarity between the target company and each comparable company through a similarity model based on the standardized Euclidean distance;

and the weighting calculation module is used for calling a relative valuation model according to the similarity, carrying out weighting calculation on the data of each comparable company to obtain the relative valuation of the target company, wherein the higher the similarity is, the higher the weight of the comparable company is, and the data are financial characteristic data and other quantity characteristic data related to the valuation of the company.

In a third aspect, there is provided a data processing apparatus comprising a memory, a processor, and a computer program stored in the memory and running on the processor, the computer program performing the data processing method as in the first aspect.

In a fourth aspect, a computer storage medium is provided, which when executed by a processor implements the data processing method of the first aspect.

In a fifth aspect, a computer program product is provided, the computer program product comprising a computer program which, when executed by a processor, implements the data processing method of the first aspect.

Compared with the prior art, the data processing method and the related device provided by the embodiment of the application acquire a plurality of comparable companies corresponding to a target company after receiving an evaluation request for the target company; and calculating the relative estimation of the target company by the calculation of the comparable companies, calculating the similarity between the target company and each comparable company through a similarity model based on the standardized Euclidean distance, and realizing the weighted calculation of the relative estimation according to the similarity after the similarity calculation. According to the arrangement, the finally obtained relative estimation of the target company is weighted and calculated according to the similarity obtained by standardizing the Euclidean distance, so that the estimation accuracy can be greatly improved, and the problem that the comparative company estimation method in the prior art is poor in accuracy is solved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic flow chart diagram of an embodiment of a data processing method of the present invention.

Fig. 2 is a schematic detailed flow diagram of S110 in an embodiment of the data processing method of the present invention.

Fig. 3 is a schematic detailed flow chart of S130 in an embodiment of the data processing method of the present invention.

Fig. 4 is a schematic block diagram of a data processing apparatus according to an embodiment of the present invention.

FIG. 5 is a schematic block diagram of a data processing apparatus of an embodiment of the present invention.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The embodiments will be described in detail below with reference to the accompanying drawings.

In addition, it should be noted that, in the following embodiments of the present application, the acquisition, storage, use, processing, etc. of data all conform to relevant regulations of national laws and regulations.

Company valuation refers to evaluating the intrinsic value of a company looking at the company itself, and the actual company's assets and profitability determine the intrinsic value of the company. Accurate corporate valuations are more fundamental to pricing for various transactions. As described in the background, current corporate valuations primarily include asset basis valuation, revenue valuation, and comparable corporate valuation.

The asset basis valuation method is a method for determining the value of an evaluation object on the basis of reasonably evaluating all assets and liabilities of a company. The profit valuation method refers to a method of determining the value of an evaluation target by capitalizing or discounting the income expected by a company. Comparable company valuation refers to predicting the value of a target company with reference to the market values of comparable companies in the market, belonging to relative value valuation.

It should be noted that, although the comparable company valuation method is wider than the asset base valuation method and the income valuation method for the data acquisition channel, thereby improving the performability of the comparable company valuation method, the comparable company valuation method adopted in the prior art mainly uses the recent transaction price of the comparable company in the transaction market, and predicts the target company by a direct comparison method, and the method has lower accuracy than other methods.

In order to solve the above problem, an embodiment of the present application provides a data processing method, and referring to fig. 1, in an alternative embodiment, the method includes:

s110, after receiving an evaluation request for a target company, acquiring a plurality of comparable companies corresponding to the target company.

S120, calculating the similarity between the target company and each comparable company through a similarity model based on the standardized Euclidean distance;

s130, calling a relative evaluation model according to the similarity, and carrying out weighted calculation on the data of each comparable company to obtain the relative evaluation of the target company, wherein the higher the similarity is, the higher the weight of the comparable company is, and the data are financial characteristic data and other quantity characteristic data related to the evaluation of the company.

The method comprises the steps that after an evaluation request for a target company is received, a plurality of comparable companies corresponding to the target company are obtained; and calculating the relative estimation of the target company by the calculation of the comparable companies, calculating the similarity between the target company and each comparable company through a similarity model based on the standardized Euclidean distance, and realizing the weighted calculation of the relative estimation according to the similarity after the similarity calculation. According to the arrangement, the finally obtained relative estimation of the target company realizes weighted calculation according to the similarity obtained by standardizing the Euclidean distance, and the estimation accuracy can be greatly improved, so that the problem that the comparative company estimation method in the prior art is poor in accuracy is solved.

The above-described data processing method can be applied to an electronic device that performs company evaluation, for example, the electronic device may be a server or a computer device. For example, a user may select a display control corresponding to a target company through a touch panel of the server, and then initiate an evaluation request for the target company.

In S110, the target company refers to a target company to be subjected to value evaluation, and correspondingly, the comparable company is a company capable of participating in calculating and predicting the evaluation value of the target company and providing an important reference value for the evaluation value of the target company. In the application, the similarity between comparable companies and target companies and the similarity in aspects such as financial characteristics, core business, growth driving factors, risk factors and the like can be evaluated based on similarity calculation.

It should be noted that there may be multiple comparable companies for each target company, and the comparable companies for different target companies are not always identical. In an alternative example, the matching degree between the target company and all companies may be calculated, and comparable companies may be determined according to the matching degree between the target company and each company, where the matching degree may include similarity. The related companies of the target company may also be set as comparable companies, or comparable companies may also be determined depending on the industry to which the target company belongs.

Referring to fig. 2, taking the industry to which the target company belongs to determine comparable companies as an example, acquiring comparable companies corresponding to the target company in S110 may include:

s211, acquiring the industry category of the target company;

s212, screening reference data matched with the industry categories from a preset database, wherein the companies corresponding to the reference data are comparable companies.

The industry category may refer to an industry category or a market category in which a company is located, and the industry category may be divided according to international or national standards. The industry category may also be divided into sub-industry categories (also referred to hereafter as sub-categories or sub-industries), i.e., sub-divided industry categories under the same industry category. The company's industry can provide much information about the company including, for example, core drivers, risks, and opportunities.

The preset database may store reference data of all the selectable companies, and the reference data may include basic information, financial data, and the like of the selectable companies, wherein the basic data may include an industry category, a product category, a belonging area, a research and development situation, and the like. Financial data may include asset data, liability data, profit data, and the like.

For example, the process of obtaining comparable companies matched with the target company may be obtaining a national standard industry (i.e., an industry category of the target company) in which the target company is located, where the national standard industry corresponds to an industry code in a preset database, and then, the reference data including the industry code may be found from the preset database, and a company corresponding to the found reference data may be used as the screened comparable company. The industry code can be a combination of characters and/or numbers, for example, the industry code A is the industry X1, B is the industry X2, and C is the industry X3.

In another alternative example, the industry categories may include a first level industry category and sub-categories under the first level industry category on the basis of the above in order to select enough comparable company samples. In this case, the step S220 may include:

acquiring first reference data matched with the sub-categories in the preset database; and when the number of companies corresponding to the first reference data is greater than or equal to a first preset threshold value, the companies corresponding to the first reference data are the comparable companies. When the number of companies corresponding to the first reference data is smaller than a first preset threshold value, acquiring second reference data matched with the primary industry category in the preset database; the company corresponding to the second reference data is the comparable company.

In this example, the matching of the optional companies with the subcategories in the industry categories is preferentially specified, but since the application needs enough comparable company samples when subsequent similarity and relative evaluation calculation are carried out, a first preset threshold value can be set on the basis of the above, so as to confirm whether the quantity of the company samples matched based on the sub-industry screening is enough, and when the quantity of the company samples is larger than or equal to the first preset threshold value, the companies corresponding to the first reference data matched based on the subcategories are directly used as the comparable companies.

It will be appreciated that there is actually a smaller total number of businesses in some segments, in which case the number of businesses belonging to a segment is not sufficient to support accurate valuation of the target company if the target company exists for valuation in those segments. At the moment, the industry category can be expanded, namely comparable companies can be obtained according to the first-level industry category of the sub-industry, so that enough comparable company sample size can be selected, and the accuracy of the relative estimation calculation of the target company is guaranteed.

Because the number of companies in the first-level industry category may be too small, in such a case, the number of comparable companies corresponding to the target company in the category is also small, on the basis, after the second reference data matched with the first-level industry category in the preset database is obtained, whether the number of companies corresponding to the second reference data matched with the first-level industry category is greater than or equal to a second preset threshold value or not is judged.

When the number of companies corresponding to the second reference data is greater than or equal to a second preset threshold value, the companies corresponding to the second reference data are the comparable companies; when the number of companies corresponding to the second reference data is smaller than a second preset threshold value, acquiring third reference data matched with the comprehensive industry from a preset database; the third reference data matched company and the second reference data matched company are the comparable companies.

The number of the first preset threshold and the second preset threshold may be set consistently, for example, 10, or may be set differently. The comprehensive industry refers to the complex and various industries, and companies in the industries are in diversified operation. Companies within the integrated industry can represent a general situation throughout the industry, and can provide a complement in the number of comparable companies in the same industry category where they are not sufficient.

In the above example, the filtering setting from fine to coarse of the industry category helps to screen out the proper comparable companies of the target company, which is beneficial to making reference to the reasonable evaluation of the target company subsequently.

After obtaining comparable companies corresponding to the target company, the optional companies may be data washed to screen out comparable companies containing anomalous data. Outlier data may include missing or outlier data that may affect the accuracy of the model estimate.

For example, comparable companies in the reference data having an average profit that is less than M may be culled, e.g., M1; comparable companies with market profitability, N% before market net rate, e.g. 5% N, can be excluded. By screening out comparable companies for data anomalies, the accuracy of the model estimation process is prevented from being affected.

In S120, the similarity may be calculated using the normalized euclidean distance, and data comparison between each comparable company and the target company is required in calculating the similarity. In the present example, the first data of comparable companies and the second data of the target company are involved in the calculation of the euclidean distance, wherein the first data corresponds to the second data, and for example, the data types may be consistent, thereby facilitating the processing calculation of the similarity.

The first data of each comparable company used above can be obtained by screening at least one of the data related to each comparable company. In addition, a time dimension of the data may also be defined, such as data of the last five or ten years.

The data for each comparable company may be index data related to company valuations, which are quantifiable feature data that may include, for example, financial feature data and other quantitative feature data.

Other quantity characteristic data may include, for example, the amount of equipment, the amount of energy storage, and so forth. The financial characteristic data for each comparable company may include at least one of an enterprise asset, an owner equity, an equity rate, a total asset return rate, a net profit, a revenue growth rate for a business of venture, and a net asset return rate. Illustratively, the first data may be consistently set with reference to the financial characteristic data.

The standardized euclidean distance is an improvement of a common euclidean distance, and the standardized euclidean distance is based on the idea that the distribution of each dimension component of data is different, and the data needs to be standardized in advance, so that the components of each dimension are standardized to be equal in mean value and variance.

When the similarity between each comparable company and the target company is calculated, the first data of each comparable company for each dimension and the second data of the target company can be normalized to be equal in mean value and variance when being converted into vector representation.

The first data of each comparable company is actually a normalized euclidean distance calculation between two n-dimensional vectors a (x11, x12, …, x1n) (i.e., vectors into which the first data of any comparable company is converted) and b (x21, x22, …, x2n) (i.e., vectors into which the second data of the target company is converted) when being input to the distance operator of the similarity model together with the second data of the target company for calculation, wherein the distance operator can perform the calculation operation with reference to the following equation (1).

Wherein n is the data dimension, S is the standard deviation, and d is the normalized euclidean distance.

It can be understood that the normalized euclidean distance substantially represents the approximation degree between two vectors, and in the present application, the similarity degree between a comparable company and a target company can be represented, and the smaller the normalized euclidean distance of the data feature vectors corresponding to various data dimensions between the target company and the comparable company is, the higher the similarity degree is. According to the rule, preset mapping of the distance and the similarity can be set in the similarity model in advance, and the similarity between the target company and the comparable company can be found according to the preset mapping under the condition that the standardized Euclidean distance is obtained.

Through the introduction of the standardized Euclidean distance and the similarity model, the similarity between the target company and the comparable company can be obtained according to a data processing operation mode.

In S130, a weight may be assigned to the comparable company according to the calculated similarity and the similarity, and the higher the similarity is, the higher the weight of the comparable company is.

In an alternative example, the relative valuation model can be configured specifically for different characteristics of the target company. Referring to fig. 3, S130 may include:

s331, analyzing the data of each comparable company, and determining the relative estimation model adapted to the target company;

s332, calling an interface of the adaptive relative estimation model, and performing weighted calculation on data of each comparable company according to the weight of each comparable company to obtain the relative estimation of the target company.

The data of the target company is analyzed, and the analysis is mainly used for obtaining the enterprise development bias of the target company. While different development biases may correspond to different configurations of the relative estimation models.

For example, if the target company is analyzed to be a relatively mature company, a first relative valuation model can be used; if the target company is the development driving type department company, the second relative evaluation model can be used; if the target company is obtained by analysis and is a company with a high turnover rate, a third relative estimation model can be used.

After each relative estimation model is constructed, a corresponding interface can be set for each relative estimation model, and estimation operation can be realized by using the model by calling the interfaces when in use.

Specifically, when the relative estimation model is calculated, the data of each comparable company is referred to, the proportion weight of each comparable company is biased, the proportion weight is determined based on the similarity calculation between the comparable company and the target company, the calculation of the relative estimation is more biased through the weighting of different proportions, and the accuracy of the relative estimation can be improved.

The data processing method according to the embodiment of the present application is described in detail above with reference to fig. 1 to 3, and the data processing apparatus according to the embodiment of the present application is described in detail below with reference to fig. 4.

Referring to fig. 4, in an embodiment, a data processing apparatus may include:

an obtaining module 410, configured to obtain, after receiving an evaluation request for a target company, a plurality of comparable companies corresponding to the target company;

a similarity calculation module 420, configured to calculate, based on the normalized euclidean distance, a similarity between the target company and each comparable company through a similarity model;

and the weighting calculation module 430 is configured to invoke a relative valuation model according to the similarity, perform weighting calculation on data of each comparable company to obtain a relative valuation of the target company, where the higher the similarity is, the higher the weight of the comparable company is, and the data are financial feature data and other quantity feature data related to the valuation of the company.

In an alternative example, the similarity calculation module may include:

the processing unit is used for carrying out standardization processing on first data of comparable companies and second data of a target company, wherein the data comprises the first data, and the first data corresponds to the second data;

the operation unit is used for taking the second data after the standardization processing and the first data of each comparable company after the standardization processing as the input of a distance arithmetic unit in the similarity model so as to operate the distance arithmetic unit to obtain the standardized Euclidean distance between the target company and each comparable company;

and the obtaining unit is used for obtaining the similarity corresponding to the standardized Euclidean distance through the preset mapping of the distance and the similarity in the similarity model.

In another alternative example, the weight calculating module may include:

the determining unit is used for analyzing the data of each comparable company and determining the relative estimation model adapted to the target company;

and the calling unit is used for calling the adaptive interface of the relative evaluation model, and performing weighted calculation on the data of each comparable company according to the weight of each comparable company to obtain the relative evaluation of the target company.

In yet another alternative example, the obtaining module may include:

the acquisition unit is used for acquiring the industry category of the target company;

and the screening unit is used for screening out reference data matched with the industry categories from a preset database, wherein the companies corresponding to the reference data are comparable companies.

In yet another optional example, the industry category includes a level one industry category and a sub-category under the level one industry category; the screening unit may include:

the obtaining subunit is used for obtaining first reference data which are matched with the sub-categories and are in the preset database;

the setting subunit is configured to, when the number of companies corresponding to the first reference data is greater than or equal to a first preset threshold, determine that the company corresponding to the first reference data is the comparable company;

the obtaining subunit is further configured to obtain, when the number of companies corresponding to the first reference data is smaller than a first preset threshold, second reference data, which is matched with the first-level industry category, in the preset database; the company corresponding to the second reference data is the comparable company.

In yet another optional example, the setting subunit is further configured to, when the number of companies corresponding to the second reference data is greater than or equal to a second preset threshold, determine that the company corresponding to the second reference data is the comparable company;

the obtaining subunit is further configured to obtain third reference data matched with the comprehensive industry from a preset database when the number of companies corresponding to the second reference data is smaller than a second preset threshold; the third reference data matched company and the second reference data matched company are the comparable companies.

In yet another optional example, the apparatus further comprises:

and the screening module is used for cleaning the data to screen out comparable companies with data abnormality.

Fig. 5 shows a hardware structure diagram of a data processing device according to an embodiment of the present application. The data processing device may comprise, among other things, a processor 501 and a memory 502 in which computer program instructions are stored.

Specifically, the processor 501 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 502 may include mass storage for data or instructions. By way of example, and not limitation, memory 502 may include a Hard Disk Drive (HDD), a floppy Disk Drive, flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 502 may include removable or non-removable (or fixed) media, where appropriate. The memory 502 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 502 is non-volatile solid-state memory.

The memory 502 may include Read Only Memory (ROM), flash memory devices, Random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory 502 comprises one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods in accordance with the above-described aspects of the disclosure.

The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement any of the data processing methods in the above embodiments.

In one example, the data processing device may also include a communication interface 503 and a bus 510. As shown in fig. 5, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 to complete communication therebetween.

The communication interface 503 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application.

Bus 510 includes hardware, software, or both to couple the components of the data processing device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 510 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

The data processing device may be based on a data processing method, thereby implementing the data processing method and apparatus described in conjunction with fig. 1 to 4.

In addition, in combination with the data processing method in the foregoing embodiments, the embodiments of the present application may provide a computer storage medium to implement. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the data processing methods in the above embodiments.

In addition, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps and the corresponding contents of the foregoing method embodiments can be implemented.

In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

It should be understood that in the embodiment of the present application, "B corresponding to a" means that B is associated with a, from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may be determined from a and/or other information.

While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention, and these modifications or substitutions are intended to be included in the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A data processing method, comprising:

2. The method of claim 1, wherein calculating the similarity between the target company and each comparable company through a similarity model based on the normalized euclidean distance comprises:

3. The method of claim 2, wherein said invoking a relative valuation model to perform a weighted calculation of data for each comparable company to obtain a relative valuation for said target company comprises:

4. The method according to any one of claims 1 to 3, wherein the obtaining comparable companies corresponding to the target company comprises:

acquiring the industry category of the target company;

5. The method of claim 4, wherein the industry category comprises a level one industry category and a sub-category under the level one industry category; the step of screening out reference data matched with the industry categories from a preset database comprises the following steps:

6. The method of claim 5, wherein after obtaining the second reference data in the pre-defined database that matches the primary industry category, the method further comprises:

7. The method of claim 1, wherein after obtaining the comparable companies corresponding to the target company, the method further comprises:

data cleansing is performed to screen comparable companies for data anomalies.

8. A data processing apparatus, characterized in that the apparatus comprises:

9. A data processing apparatus, characterized in that the data processing apparatus comprises a memory, a processor and a computer program stored in the memory and running on the processor, the computer program performing the data processing method according to any one of claims 1 to 7.

10. A computer storage medium, wherein the computer storage medium, when executed by a processor, implements the data processing method of any one of claims 1 to 7.

11. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, carries out the steps of the data processing method according to any one of claims 1 to 7.