CN115689152A - Enterprise yield prediction method, enterprise yield prediction device, electronic equipment and medium - Google Patents
Enterprise yield prediction method, enterprise yield prediction device, electronic equipment and medium Download PDFInfo
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Abstract
The disclosure provides a method for predicting enterprise yield, which can be used in the financial field or other fields. The enterprise yield prediction method comprises the following steps: acquiring share fluctuation mean value and share fluctuation rate of an enterprise with yield to be predicted; determining an initial market share for the enterprise; calculating to obtain a market share prediction interval by utilizing a Monte Carlo method based on the share fluctuation mean, the share fluctuation rate and the initial market share; obtaining a prediction result of market share according to the prediction interval; acquiring industry development data and initial industry yield; calculating to obtain an industry yield prediction result based on the industry development data and the initial industry yield; and obtaining the forecast result of the enterprise yield based on the industry yield forecast result and the market share forecast result. The disclosure also provides an enterprise yield prediction apparatus, device, storage medium and program product.
Description
Technical Field
The present disclosure relates to the field of finance, and more particularly, to the field of financial wind control, and more particularly, to a method, apparatus, device, medium, and program product for predicting enterprise yield.
Background
Currently, climate risks are incorporated into a comprehensive risk management system, and a pressure test is an important tool for climate risk management and is a necessary means for promoting climate risk management, wherein a transformation risk system is a core system in the pressure test, and a large amount of data needs to be retrieved and stored, so that the accuracy of the pressure test is affected.
In the process of realizing the concept disclosed by the invention, the inventor finds that the current transformation risk system has low utilization rate of stored data, can only predict industry yield, cannot predict single enterprise yield, and causes waste of data resources and low accuracy of pressure test.
Disclosure of Invention
In view of the foregoing, the present disclosure provides a method, apparatus, device, medium, and program product for enterprise yield prediction.
According to a first aspect of the present disclosure, there is provided a method for forecasting enterprise yield, the method comprising: acquiring share fluctuation mean values and share fluctuation rates of enterprises with yields to be predicted; determining an initial market share for the business; calculating to obtain a prediction interval of the market share by utilizing a Monte Carlo method based on the share fluctuation mean value, the share fluctuation rate and the initial market share; obtaining a prediction result of market share according to the prediction interval; acquiring industry development data and initial industry yield; calculating to obtain an industry yield prediction result based on the industry development data and the initial industry yield; and obtaining the forecast result of the enterprise yield based on the industry yield forecast result and the market share forecast result.
According to an embodiment of the present disclosure, the step of calculating the prediction interval of the market share by using the monte carlo method includes: step length determination: determining a step S of the Monte Carlo simulation, wherein the step S corresponds to the period number of the market share; a random number generation step: generating a random number based on the share fluctuation ratio, wherein the random number obeys a normal distribution N (0, sigma), and the sigma is the share fluctuation ratio; calculating market share: calculating market share of the next period based on the market share of the previous period, the share fluctuation mean, and the random number, wherein the initial market share is taken as the market share of the 1 st period; and a market share calculation step of the S stage: and repeatedly executing the random number generation step and the market share calculation step until the market share of the S th stage is obtained, wherein the market share of the S th stage is used as an intermediate prediction result of the market share.
According to an embodiment of the present disclosure, the step of calculating the prediction interval of the market share by using the monte carlo method further includes: repeatedly executing the random number generation step, the market share calculation step and the market share calculation step of the S-th period M times to obtain M intermediate prediction results of the market shares, wherein M is more than or equal to 2; and taking the union of the M intermediate forecasted results as the forecast interval of market share.
According to the embodiment of the present disclosure, in the step of repeatedly executing the random number generation step and the market share calculation step until obtaining the market share of the S-th period, when the market share of the F-th period is equal to or less than 0, the market share of the S-th period is set to 0; and when the market share of the F-th stage is more than or equal to 1, the market share of the S-th stage is 1, wherein S is more than or equal to F and is more than or equal to 1.
According to an embodiment of the present disclosure, in the step of obtaining a predicted result of market share according to the prediction interval, the predicted result of market share includes a first predicted result, a second predicted result, and a third predicted result, wherein the market share in the first predicted result is greater than the market share in the second predicted result, and the market share in the second predicted result is greater than the market share in the third predicted result.
According to an embodiment of the present disclosure, the step of obtaining a prediction result of market share according to the prediction interval includes: taking the average value of the first K middle prediction results of the prediction interval to obtain a first prediction result of the market share; taking the average value of the middle K middle prediction results of the prediction interval to obtain a second prediction result of the market share; and taking the average value of the next K intermediate prediction results of the prediction interval to obtain a third prediction result of the market share, wherein K is more than or equal to 1/3M and more than or equal to 1.
According to an embodiment of the present disclosure, the step of obtaining the forecasted result of the enterprise yield based on the industry yield forecasted result and market share forecasted result comprises: multiplying the industry yield prediction result and the first prediction result of the market share to obtain a first prediction result of the enterprise yield; multiplying the industry yield prediction result and a second prediction result of the market share to obtain a second prediction result of the enterprise yield; and multiplying the industry yield prediction result and the third prediction result of the market share to obtain a third prediction result of the enterprise yield.
According to an embodiment of the present disclosure, the step of calculating an industry yield prediction result based on the industry development data and the initial industry yield includes: extracting an industry growth rate based on the industry development data; and calculating the industry yield of the S stage based on the initial industry yield and the industry growth rate to be used as an industry yield prediction result.
A second aspect of the present disclosure provides an enterprise yield prediction apparatus, including: the first acquisition module is used for acquiring share fluctuation mean values and share fluctuation rates of enterprises with yields to be predicted; a first determination module to determine an initial market share for the enterprise; the first calculation module is used for calculating a prediction interval of the market share by utilizing a Monte Carlo method based on the share fluctuation mean value, the share fluctuation rate and the initial market share; the second calculation module is used for obtaining a prediction result of the market share according to the prediction interval; the second acquisition module is used for acquiring industry development data and initial industry yield; the third calculation module is used for calculating to obtain an industry yield prediction result based on the industry development data and the initial industry yield; and the prediction module is used for obtaining the prediction result of the enterprise yield based on the industry yield prediction result and the market share prediction result.
According to an embodiment of the present disclosure, the first calculation module includes a first determination unit configured to perform the step size determination step: determining a step S of the Monte Carlo simulation, wherein the step S corresponds to the period number of the market share; a random number generation unit for performing the random number generation step of: generating a random number based on the share fluctuation ratio, wherein the random number obeys a normal distribution N (0, sigma), and the sigma is the share fluctuation ratio; a first calculation unit for the market share calculation step: calculating market share of the next period based on the market share of the previous period, the share fluctuation mean, and the random number, wherein the initial market share is taken as the market share of the 1 st period; and a second calculation unit for the market share calculation step of the S-th phase: and repeatedly executing the random number generation step and the market share calculation step until the market share of the S th stage is obtained, wherein the market share of the S th stage is used as an intermediate prediction result of the market share.
According to an embodiment of the present disclosure, the first calculating module further includes a repeating unit, configured to repeatedly execute the random number generating step, the market share calculating step, and the market share calculating step in the S-th period M times, so as to obtain M intermediate prediction results of market shares, where M is greater than or equal to 2; and a third calculation unit configured to use a union of the M intermediate prediction results as a prediction section of the market share.
According to an embodiment of the present disclosure, in the step of repeatedly executing the random number generation step and the market share calculation step until obtaining the market share in the S-th period, when the market share in the F-th period is equal to or less than 0, the market share in the S-th period is made 0; and when the market share of the F-th period is more than or equal to 1, the market share of the S-th period is 1, wherein S is more than or equal to F and more than or equal to 1.
According to an embodiment of the present disclosure, the forecasted results of the market share of the second computing module include a first forecasted result, a second forecasted result, and a third forecasted result, wherein the market share in the first forecasted result is greater than the market share in the second forecasted result, and the market share in the second forecasted result is greater than the market share in the third forecasted result.
According to an embodiment of the present disclosure, the second calculation module includes a fourth calculation unit, configured to take an average value of the first K intermediate prediction results of the prediction interval, to obtain a first prediction result of market share; the fifth calculation unit is used for taking the average value of the middle K middle prediction results in the prediction interval to obtain a second prediction result of the market share; and the sixth calculating unit is used for taking the average value of the last K middle prediction results of the prediction interval to obtain a third prediction result of the market share, wherein 1/3M is more than or equal to K and more than or equal to 1.
According to an embodiment of the present disclosure, the forecasting module includes a seventh calculating unit, configured to multiply the industry yield forecast result and the first forecast result of the market share to obtain a first forecast result of the enterprise yield; the eighth calculating unit is used for multiplying the industry yield prediction result and the second prediction result of the market share to obtain a second prediction result of the enterprise yield; and the ninth calculation unit is used for multiplying the industry yield prediction result and the third prediction result of the market share to obtain a third prediction result of the enterprise yield.
According to an embodiment of the present disclosure, the third computing module includes an extraction module for extracting an industry growth rate based on the industry development data; and the tenth calculating unit is used for calculating the industry yield of the S-th period based on the initial industry yield and the industry growth rate to be used as an industry yield prediction result.
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.
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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 method, apparatus, device, medium and program product for enterprise yield prediction according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a method of forecasting enterprise production in accordance with an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart for calculating a forecast interval for market share using a Monte Carlo method, according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a system architecture diagram of a method for enterprise yield prediction, in accordance with an embodiment of the present disclosure;
FIG. 5 is a block diagram schematically illustrating an apparatus for forecasting enterprise yield, in accordance with an embodiment of the present disclosure; and
FIG. 6 schematically illustrates a block diagram of an electronic device adapted to implement a prediction method for enterprise production in accordance with 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 to which this invention belongs, 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.
In those instances 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.). The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features.
The climate risk is already incorporated into a comprehensive risk management system, and a pressure test is an important tool for climate risk management and is a necessary means for promoting the climate risk management, wherein a transformation risk system is a core system in the pressure test, a large amount of data needs to be called and stored, but the research on the climate risk is just started, the analysis and utilization of the data are not sufficient, the used technical means are original, the extraction efficiency of precious data information is low, only current numerical values are independently utilized, only the industry yield can be predicted, the single enterprise yield cannot be predicted, the waste of data resources is caused, and the accuracy of the pressure test is low.
In view of the foregoing problems, embodiments of the present disclosure provide a method for predicting enterprise yield, the method including: acquiring share fluctuation mean value and share fluctuation rate of an enterprise with yield to be predicted; determining an initial market share for the enterprise; calculating to obtain a prediction interval of the market share by utilizing a Monte Carlo method based on the share fluctuation mean value, the share fluctuation rate and the initial market share; obtaining a prediction result of market share according to the prediction interval; acquiring industry development data and initial industry yield; calculating to obtain an industry yield prediction result based on the industry development data and the initial industry yield; and obtaining the forecast result of the enterprise yield based on the industry yield forecast result and the market share forecast result.
It should be noted that the method and apparatus for determining the enterprise yield according to the present disclosure may be used for predicting the enterprise yield in the financial field, and may also be used for predicting the enterprise yield in any field other than the financial field.
Fig. 1 schematically illustrates an application scenario diagram of a method, an apparatus, a device, a medium, and a program product for enterprise yield prediction according to an embodiment of the present disclosure.
As shown in fig. 1, the 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, among others.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the 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 background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the enterprise yield prediction method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the enterprise yield prediction device provided by the embodiment of the present disclosure may be generally disposed in the server 105. The enterprise yield prediction method provided by the embodiment of the present disclosure may also be performed by a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the enterprise yield prediction device provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and 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 enterprise yield prediction method of the disclosed embodiment will be described in detail with reference to fig. 2 to 4 based on the scenario described in fig. 1.
FIG. 2 schematically illustrates a flow chart of a method of forecasting enterprise yield in accordance with an embodiment of the present disclosure.
As shown in fig. 2, the enterprise yield prediction method of this embodiment includes operations S210 to S270.
In operation S210, a share fluctuation mean and a share fluctuation rate of the enterprise whose yield is to be predicted are obtained. The share fluctuation mean is used for describing the status of the company in the industry, if the mean is higher, the company is a monopoly of the industry, and if the mean is lower, the company is a common company in the industry. The share fluctuation rate data represents the industry characteristics, namely, the share low fluctuation rate represents the mature industry, and the share high fluctuation rate represents that the industry is the growth industry. The share fluctuation mean value mu and the share fluctuation rate sigma can be directly specified manually or calculated through historical data, and when a scheme of obtaining parameters from the historical data is adopted, the calculation process is as follows:
in the formula, p i The length of the time window selected for the historical data n is the historical data of the share of the enterprise, and may be selected according to a time situation, such as a month or a year, which is not limited in the embodiments of the present disclosure.
In operation S220, an initial market share of the business is determined, which may be extracted from the business data, for example.
In operation S230, a prediction interval of the market share is calculated using a monte carlo method based on the share fluctuation mean, the share fluctuation rate, and the initial market share.
Fig. 3 schematically illustrates a flow chart of calculating a prediction interval for market share using the monte carlo method according to an embodiment of the present disclosure.
As shown in fig. 3, the step of calculating the prediction interval of the market share by using the monte carlo method in this embodiment includes:
in operation S310, the step size determining step: determining a step size S of the Monte Carlo simulation, wherein the step size S corresponds to the number of periods for calculating the market share. The specific number of periods may be specified according to an actual situation, for example, 1000 periods or 10000 periods are selected, the larger the number of periods is, the more accurate the estimation result is, but the performance requirement on the computer is higher, and therefore, the actual required accuracy and the existing computing capability may be comprehensively considered for determination, which is not limited in the embodiment of the present disclosure.
In operation S320, a random number generation step: and generating a random number based on the share fluctuation ratio, wherein the random number obeys a normal distribution N (0, sigma), and the sigma is the share fluctuation ratio. N (0, sigma) represents that the generated random numbers obey a normal distribution with a mathematical expectation of 0 and a variance of sigma.
In operation S330, the market share calculating step: calculating the market share of the next period based on the market share of the previous period, the share fluctuation mean, and the random number, wherein the initial market share is the market share of the 1 st period. It should be noted that, each time the market share is calculated, the random number is regenerated to improve the accuracy of the monte carlo prediction.
Exemplary, the formula for calculating market share at stage 2 is:
P 1 ＝P 0 *exp(mu+ep) (2)
the market share for phase x is calculated as:
P x-1 ＝P x-2 *exp(mu+ep) (3)
in the formula, P 1 Is market share of stage 2, P 0 For market share at stage 1, mu is the mean of the share fluctuations, ep is a random number obeying a normal distribution N (0, sigma), and x is 2 ≦ S.
In operation S340, the market share calculating step of the S-th stage: and repeatedly executing the random number generation step and the market share calculation step until the market share of the S-th period is obtained, wherein the market share of the S-th period is used as an intermediate prediction result of the market share.
According to the embodiment of the present disclosure, in the step of repeatedly executing the random number generation step and the market share calculation step until obtaining the market share of the S-th period, when the market share of the F-th period is equal to or less than 0, the market share of the S-th period is made 0; and when the market share of the F-th stage is more than or equal to 1, the market share of the S-th stage is 1, wherein S is more than or equal to F and is more than or equal to 1. In the process of calculating the market share of the S-th stage, if the market share of a certain stage in the middle is less than or equal to 0, the enterprise is broken in production during simulation under the path, so that no market share exists, the market share of the S-th stage is directly judged to be 0, subsequent calculation is not performed, so that the calculation resources are saved, and the calculation speed is increased; similarly, if the market share in a certain period is greater than or equal to 1, it indicates that the enterprise forms monopoly during simulation in the path, so that the market share is directly judged to be 1, and subsequent calculation is not performed.
According to an embodiment of the present disclosure, the step of calculating the prediction interval of the market share by using the monte carlo method further includes: repeatedly executing the random number generation step, the market share calculation step and the market share calculation step of the S-th period M times to obtain M intermediate prediction results of the market shares, wherein M is more than or equal to 2; and intermediate forecasted results of the M market sharesAs a prediction interval for market share. The greater the specific numerical value of M, the more accurate the prediction result is, but the performance requirement on the computer is higher, so that the actually required precision and the existing computing capability may be comprehensively considered for determination, which is not limited in the embodiment of the present disclosure. Illustratively, M may be 10000, i.e. the above-mentioned random number generation step, the above-mentioned market share calculation step and the above-mentioned market share calculation step of the S-th stage are repeatedly executed 10000 times, so as to obtain 10000 market share prediction results P of the S-th stage s 。
Returning to fig. 2, in operation S240, a prediction result of the market share is obtained according to the prediction interval.
According to an embodiment of the present disclosure, in the step of obtaining a predicted result of market share according to the prediction interval, the predicted result of market share includes a first predicted result, a second predicted result, and a third predicted result, wherein the market share in the first predicted result is greater than the market share in the second predicted result, and the market share in the second predicted result is greater than the market share in the third predicted result. For example, in the enterprise market share forecasting scenario, the market share forecasting results include an optimistic forecasting result, a neutral forecasting result and a pessimistic forecasting result, that is, the first forecasting result corresponds to the optimistic forecasting result, the second forecasting result corresponds to the neutral forecasting result, and the third forecasting result corresponds to the pessimistic forecasting result.
According to an embodiment of the present disclosure, the step of obtaining a prediction result of market share according to the prediction interval includes: taking the average value of the first K middle prediction results of the prediction interval to obtain a first prediction result of market share; taking the average value of the middle K middle prediction results of the prediction interval to obtain a second prediction result of the market share; and taking the average value of the last K intermediate prediction results of the prediction interval to obtain a third prediction result of the market share, wherein K is more than or equal to 1/3M and more than or equal to 1. And the prediction interval containing the M intermediate prediction results is converted into a first prediction result, a second prediction result and a third prediction result, so that the subsequent computation amount of the system can be greatly reduced, and the computation speed is improved. The selection of the value K may be determined according to actual conditions, and if the value K is selected to be 50, when the first prediction result is obtained, the intermediate prediction results of the M market shares in the prediction interval need to be sorted from large to small, and an average value of the top 50 intermediate prediction results is taken as the first prediction result. Correspondingly, the second prediction result is the average of the middle 50 middle prediction results, and the third prediction result is the average of the latter 50 middle prediction results. In addition, when the prediction result is obtained, the median of 50 intermediate prediction results may also be taken, and the specific value taking method may be selected according to the actual situation, which is not limited in the embodiment of the present disclosure.
At operation S250, industry development data and initial industry production are obtained. The industry development data can be obtained from industry development data given by a regulatory agency, and can also be obtained from related industry data.
In operation S260, an industry yield prediction result is calculated based on the industry development data and the initial industry yield.
According to an embodiment of the present disclosure, the step of calculating an industry yield prediction result based on the industry development data and the initial industry yield includes: extracting an industry growth rate based on the industry development data; and calculating the industry yield of the S stage based on the initial industry yield and the industry growth rate to serve as an industry yield prediction result.
Illustratively, the industry development data obtained by the regulatory body is referred to as D { D } 1 ，D 2 ，…D S In which D is S Industry development data representing the S-th stage with an initial industry yield of N 0 . The specific calculation steps are as follows:
step 1: firstly, extracting an industry growth rate based on the industry development data, wherein the calculation formula of the industry growth rate in the 1 st stage is as follows:
ΔD 1 ＝D 1 /D 2 (4)
in the formula,. DELTA.D 1 Indicating stage 1 business increaseLength of growth, D 1 And D 2 The industry development data for stage 1 and stage 2 are shown separately.
Step 2: calculating according to the method, and calculating the industry growth rate of the No. 2 and No. 3 in turn, wherein the industry growth rate is respectively represented as: delta D 2 、ΔD 3 ...ΔD S 。
And step 3: based on the initial industry yield and the industry growth rate, a calculation formula for calculating the industry yield of the S-th stage is as follows:
N s ＝N 0 *ΔD 1 *ΔD 2 *ΔD 3 *...ΔD S (5)
in the formula, N s Indicating the industry production at S stage.
In operation S270, a forecast of the enterprise production is obtained based on the industry production forecast and the market share forecast.
According to an embodiment of the present disclosure, the step of obtaining the forecasted result of the enterprise yield based on the forecasted result of the industry yield and the forecasted result of the market share includes: multiplying the industry yield prediction result with the first prediction result of the market share to obtain a first prediction result of the enterprise yield; multiplying the industry yield prediction result and a second prediction result of the market share to obtain a second prediction result of the enterprise yield; and multiplying the industry yield forecast result and the third forecast result of the market share to obtain a third forecast result of the enterprise yield.
The enterprise yield prediction method provided by the embodiment of the disclosure predicts the yield of a single enterprise by using a Monte Carlo method based on the share fluctuation mean value, the share fluctuation rate and the initial market share of the enterprise, greatly improves the utilization rate of the transformation risk system on stored data, fully utilizes data resources, realizes the yield prediction of the single enterprise, reduces the waste of the data resources, and improves the accuracy of a pressure test, wherein the accuracy of predicting the yield-breaking resolution capability of the enterprise by using the pressure test is improved by more than 20%, preferably by 30%.
FIG. 4 schematically illustrates a system architecture diagram of a method for enterprise yield forecasting, in accordance with an embodiment of the present disclosure.
As shown in fig. 4, when a user predicts the yield of a single enterprise by using a transformation risk system, the user selects the enterprise to be predicted, determines the term S for calculating market share, i.e., the step length S of the monte carlo simulation, and the transformation risk system retrieves historical data and calculates the mean value mu and the share fluctuation rate sigma of share fluctuation, thereby triggering the monte carlo simulation.
The specific operating process of the Monte Carlo simulation is as follows:
s1, selecting a step size 1000 to be simulated and an initial known market share P 0 ；
S2, generating a random number ep obeying normal distribution N (0, sigma);
s3, generating the next predicted market share P 1 ；
S4, repeating S2 and S3 for 999 times to obtain the market share P of the 1000 th step 1000 As an intermediate predictor of market share;
s5, repeating the steps S2-S410000 times to obtain an intermediate prediction result of 10000 market shares;
s6, taking the union of the 10000 market share intermediate forecasts as the market share forecast interval, wherein the 10000 market share intermediate forecasts are sorted from large to small in the forecast interval.
After the market share prediction interval is obtained, the step of obtaining the market share prediction results under three scenes comprises the following steps:
s7, taking 50 highest intermediate prediction results in the prediction interval obtained by Monte Carlo simulation, and taking an average value to obtain an optimistic prediction result P p ；
S8, averaging 4975 th to 5025 th intermediate prediction results in the prediction interval obtained by Monte Carlo simulation to obtain a neutral prediction result P n ；
S9, taking 50 lowest intermediate prediction results in the prediction interval obtained by Monte Carlo simulation, and taking an average value to obtain a pessimistic prediction result P u 。
After the prediction results of market share under the three scenes are obtained, the industry development data given by the supervision institution are obtained, and the industry yield N of the S-th stage is obtained through calculation according to the formula (4) and the formula (5) s 。
Finally, the step of obtaining the forecasted outcome for the business's production based upon the industry production forecasted outcome and the forecasted outcome for market share includes:
s10, predicting the industry yield N s And an optimistic forecast of said market share p Multiplying to obtain an optimistic yield forecast for the enterprise;
s11, predicting the industry yield N s And a neutral forecast of said market share P n Multiplying to obtain the neutral yield forecast of the enterprise;
s12, predicting the industry yield N s And pessimistic forecasted results P for said market share u Multiplying to obtain the pessimistic yield forecast of the enterprise.
And finally, the transformation risk system sends the optimistic yield forecast, the neutral yield forecast and the pessimistic yield forecast of the enterprise to the user.
In the embodiment of the disclosure, at the ending stage of the current transformation risk system, namely, an enterprise yield prediction result part is deduced from an industry yield prediction result, a processing module is added, a Monte Carlo method with a limit is used for simulating the possibility of the share of the industry market occupied by the enterprise in the future by combining historical data, and optimistic estimation, neutral estimation and pessimistic estimation of the share occupied by the enterprise in the future are further provided, so that the original simple and direct processing procedure is replaced, the mean value and fluctuation rate data of the historical data of the share of the enterprise are fully extracted, the function of the climate risk transformation model system is further extended, the utilization rate of the transformation risk system on stored data is greatly improved, data resources are fully utilized, and the information processing capability of the system is improved. From a business perspective, the system gives the yield forecast from an enterprise level perspective and can bring the following benefits: 1. the loan credit granting object of the bank is a specific enterprise, and the accurate credit policy specification is facilitated aiming at the yield prediction of the enterprise. 2. The system can give out optimistic, neutral and pessimistic yield estimates of enterprises, and provides technical support for banks to pertinently give out various credit and pay schemes and face economic environment changes more steadily.
Based on the enterprise yield prediction method, the disclosure also provides an enterprise yield prediction device. The apparatus will be described in detail below with reference to fig. 5.
Fig. 5 schematically shows a block diagram of a prediction apparatus for enterprise yield according to an embodiment of the present disclosure.
As shown in fig. 5, the enterprise yield prediction apparatus 500 of this embodiment includes a first obtaining module 510, a first determining module 520, a first calculating module 530, a second calculating module 540, a second obtaining module 550, a third calculating module 560, and a predicting module 570.
The first obtaining module 510 is configured to obtain a share fluctuation mean and a share fluctuation rate of the enterprise whose yield is to be predicted. In an embodiment, the first obtaining module 510 may be configured to perform the operation S210 described above, which is not described herein again.
A first determination module 520 to determine an initial market share for the enterprise. In an embodiment, the first determining module 520 may be configured to perform the operation S220 described above, which is not described herein again.
A first calculating module 530, configured to calculate, by using a monte carlo method, a prediction interval of the market share based on the share fluctuation mean, the share fluctuation rate, and the initial market share. In an embodiment, the first calculating module 530 may be configured to perform the operation S230 described above, and is not described herein again.
And the second calculating module 540 is configured to obtain a prediction result of the market share according to the prediction interval. In an embodiment, the second calculating module 540 may be configured to perform the operation S240 described above, and is not described herein again.
A second obtaining module 550 for obtaining industry development data and initial industry production. In an embodiment, the second obtaining module 550 may be configured to perform the operation S250 described above, and is not described herein again.
And a third calculating module 560, configured to calculate an industry yield prediction result based on the industry development data and the initial industry yield. In an embodiment, the third calculating module 560 may be configured to perform the operation S260 described above, and is not described herein again.
And the forecasting module 570 is used for obtaining a forecasting result of the enterprise yield based on the industry yield forecasting result and the market share forecasting result. In an embodiment, the prediction module 570 may be configured to perform the operation S270 described above, which is not described herein again.
According to an embodiment of the present disclosure, the first calculation module includes a first determination unit configured to perform the step size determination step of: determining a step S of the Monte Carlo simulation, wherein the step S corresponds to the period number of the market share; a random number generation unit for performing the random number generation step: generating a random number based on the fraction fluctuation rate, wherein the random number obeys a normal distribution N (0, sigma), and sigma is the fraction fluctuation rate; a first calculation unit configured to perform a market share calculation step: calculating market shares for a next period based on the market shares for a previous period, the mean of fluctuation of shares, and the random number, wherein the initial market share is the market share for period 1; and a second calculation unit for the market share calculation step of the S-th phase: and repeatedly executing the random number generation step and the market share calculation step until the market share of the S-th period is obtained, wherein the market share of the S-th period is used as an intermediate prediction result of the market share.
According to an embodiment of the present disclosure, the first calculating module further includes a repeating unit, configured to repeatedly execute the random number generating step, the market share calculating step, and the market share calculating step of the S-th period M times to obtain intermediate prediction results of M market shares, where M is greater than or equal to 2; and a third calculation unit configured to use a union of the M intermediate prediction results as a prediction section of the market share.
According to an embodiment of the present disclosure, in the step of repeatedly executing the random number generation step and the market share calculation step until obtaining the market share in the S-th period, when the market share in the F-th period is equal to or less than 0, the market share in the S-th period is made 0; and when the market share of the F-th stage is more than or equal to 1, the market share of the S-th stage is 1, wherein S is more than or equal to F and is more than or equal to 1.
According to an embodiment of the present disclosure, the forecasted results of the market share of the second computing module include a first forecasted result, a second forecasted result, and a third forecasted result, wherein the market share in the first forecasted result is greater than the market share in the second forecasted result, and the market share in the second forecasted result is greater than the market share in the third forecasted result.
According to an embodiment of the present disclosure, the second calculation module includes a fourth calculation unit, configured to take an average value of the first K intermediate prediction results of the prediction interval, to obtain a first prediction result of market share; the fifth calculation unit is used for taking the average value of the middle K middle prediction results of the prediction interval to obtain a second prediction result of the market share; and the sixth calculating unit is used for taking the average value of the last K middle prediction results of the prediction interval to obtain a third prediction result of the market share, wherein 1/3M is more than or equal to K and more than or equal to 1.
According to an embodiment of the present disclosure, the forecasting module includes a seventh calculating unit, configured to multiply the industry yield forecasting result and the first forecasting result of the market share to obtain a first forecasting result of the enterprise yield; the eighth calculating unit is used for multiplying the industry yield prediction result and the second prediction result of the market share to obtain a second prediction result of the enterprise yield; and the ninth calculation unit is used for multiplying the industry yield prediction result and the third prediction result of the market share to obtain a third prediction result of the enterprise yield.
According to an embodiment of the present disclosure, the third computing module includes an extraction module for extracting an industry growth rate based on the industry development data; and the tenth calculating unit is used for calculating the industry yield in the S stage based on the initial industry yield and the industry growth rate to be used as an industry yield prediction result.
According to an embodiment of the present disclosure, any plurality of the first obtaining module 510, the first determining module 520, the first calculating module 530, the second calculating module 540, the second obtaining module 550, the third calculating module 560, and the predicting module 570 may be combined into one module to be implemented, 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 first obtaining module 510, the first determining module 520, the first calculating module 530, the second calculating module 540, the second obtaining module 550, the third calculating module 560 and the predicting module 570 may be at least partially implemented 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 by a suitable combination of any of them. Alternatively, at least one of the first obtaining module 510, the first determining module 520, the first calculating module 530, the second calculating module 540, the second obtaining module 550, the third calculating module 560 and the predicting module 570 may be at least partially implemented as a computer program module, which may perform a corresponding function when executed.
FIG. 6 schematically illustrates a block diagram of an electronic device adapted to implement a prediction method for enterprise yield in accordance with an embodiment of the present disclosure.
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. Processor 601 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the 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. A 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 a 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 a 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 an embodiment of the present disclosure, a computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.
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 perform 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 above described systems, devices, modules, units, etc. 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, and the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, downloaded and installed through the communication section 609, and/or installed from the 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 above described systems, devices, apparatuses, modules, units, etc. 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 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 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 which 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 in advantageous 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 (12)
1. A method for forecasting enterprise yield, the method comprising:
acquiring share fluctuation mean value and share fluctuation rate of an enterprise with yield to be predicted;
determining an initial market share for the enterprise;
calculating to obtain a prediction interval of the market share by utilizing a Monte Carlo method based on the share fluctuation mean value, the share fluctuation rate and the initial market share;
obtaining a prediction result of market share according to the prediction interval;
acquiring industry development data and initial industry yield;
calculating to obtain an industry yield prediction result based on the industry development data and the initial industry yield; and
and obtaining the forecast result of the enterprise yield based on the industry yield forecast result and the market share forecast result.
2. The method of claim 1, wherein the step of calculating the predicted market share interval using the monte carlo method comprises:
step length determination: determining a step S of the Monte Carlo simulation, wherein the step S corresponds to the period number of the market share;
a random number generation step: generating a random number based on the share fluctuation ratio, wherein the random number obeys a normal distribution N (0, sigma), and the sigma is the share fluctuation ratio;
and (3) calculating the market share: calculating market shares for a next period based on the market shares for a previous period, the mean of fluctuation of shares, and the random number, wherein the initial market share is the market share for period 1; and
and (5) calculating the market share in the S stage: and repeatedly executing the random number generation step and the market share calculation step until the market share of the S th stage is obtained, wherein the market share of the S th stage is used as an intermediate prediction result of the market share.
3. The method of claim 2, wherein the step of calculating the forecast interval for market share using a monte carlo method further comprises:
repeatedly executing the random number generation step, the market share calculation step and the market share calculation step of the S-th period M times to obtain M intermediate prediction results of the market shares, wherein M is more than or equal to 2; and
and taking the union of the M intermediate prediction results of the market shares as the prediction interval of the market shares.
4. The method according to claim 2, wherein in said step of repeatedly performing said random number generating step and said market share calculating step until obtaining the market share of the S-th period,
when the market share of the F-th period is less than or equal to 0, the market share of the S-th period is made to be 0; and
when the market share of the F-th stage is more than or equal to 1, the market share of the S-th stage is 1, wherein S is more than or equal to F and is more than or equal to 1.
5. The method as recited in claim 3, wherein the step of obtaining the forecasted market share according to the forecast interval comprises a first forecasted share, a second forecasted share, and a third forecasted share, wherein the market share in the first forecasted share is greater than the market share in the second forecasted share, and wherein the market share in the second forecasted share is greater than the market share in the third forecasted share.
6. The method of claim 5, wherein the step of deriving a prediction of market share based on the prediction interval comprises:
taking the average value of the first K middle prediction results of the prediction interval to obtain a first prediction result of the market share;
taking the average value of the middle K middle prediction results of the prediction interval to obtain a second prediction result of the market share; and
and taking the average value of the next K intermediate prediction results of the prediction interval to obtain a third prediction result of the market share, wherein K is more than or equal to 1/3M and more than or equal to 1.
7. The method of claim 6, wherein the step of deriving the forecasted business yield based on the industry yield forecasted and market share forecasted comprises:
multiplying the industry yield prediction result with the first prediction result of the market share to obtain a first prediction result of the enterprise yield;
multiplying the industry yield prediction result by a second prediction result of the market share to obtain a second prediction result of the enterprise yield; and
and multiplying the industry yield prediction result and the third prediction result of the market share to obtain a third prediction result of the enterprise yield.
8. The method of claim 2, wherein the step of calculating an industry production forecast based on the industry development data and an initial industry production comprises:
extracting an industry growth rate based on the industry development data; and
and calculating the industry yield of the S-th period based on the initial industry yield and the industry growth rate to be used as an industry yield prediction result.
9. An apparatus for forecasting enterprise yield, comprising:
the first acquisition module is used for acquiring share fluctuation mean values and share fluctuation rates of enterprises with yields to be predicted;
a first determination module to determine an initial market share for the enterprise;
the first calculation module is used for calculating a prediction interval of the market share by utilizing a Monte Carlo method based on the share fluctuation mean value, the share fluctuation rate and the initial market share;
the second calculation module is used for obtaining a prediction result of the market share according to the prediction interval;
the second acquisition module is used for acquiring industry development data and initial industry yield;
the third calculation module is used for calculating to obtain an industry yield prediction result based on the industry development data and the initial industry yield; and
and the forecasting module is used for obtaining the forecasting result of the enterprise yield based on the industry yield forecasting result and the forecasting result of the market share.
10. 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 of any of claims 1-8.
11. 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 8.
12. A computer program product comprising a computer program which, when executed by a processor, carries out the method according to any one of claims 1 to 8.
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