CN109784652A - Fund style drift degree determines method, apparatus, equipment and storage medium - Google Patents
Fund style drift degree determines method, apparatus, equipment and storage medium Download PDFInfo
- Publication number
- CN109784652A CN109784652A CN201811540326.8A CN201811540326A CN109784652A CN 109784652 A CN109784652 A CN 109784652A CN 201811540326 A CN201811540326 A CN 201811540326A CN 109784652 A CN109784652 A CN 109784652A
- Authority
- CN
- China
- Prior art keywords
- style
- fund
- month
- time period
- preset time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012417 linear regression Methods 0.000 claims abstract description 49
- 230000001419 dependent effect Effects 0.000 claims description 10
- 230000009286 beneficial effect Effects 0.000 claims description 3
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 claims description 2
- 239000010931 gold Substances 0.000 claims description 2
- 229910052737 gold Inorganic materials 0.000 claims description 2
- 238000007405 data analysis Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000008859 change Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 239000004744 fabric Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000007115 recruitment Effects 0.000 description 1
Landscapes
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention belongs to big data analysis technical field, discloses a kind of fund style drift degree and determine method, apparatus, equipment and storage medium.The present invention traverses the preset time period according to month, multiple linear regression model is trained according to the daily earning rate of return on funds and each fund style index daily in the current month traversed, obtain each regression coefficient in the multiple linear regression model, the main style of the current month is determined further according to the regression coefficient, finally after being traversed to the preset time period, the style drift degree of the target fund is determined according to the main style in each month in the preset time period, it avoids directly determining risk income style as benchmark according to the style of FUNDING STATEMENT, it ensure that the accuracy of the main style in each month, and then it ensure that the accuracy of fund style drift degree.
Description
Technical field
The present invention relates to big data analysis technical fields more particularly to a kind of fund style drift degree to determine method, dress
It sets, equipment and storage medium.
Background technique
Public offering fund refers to the cards with publicity pattern to public investor fund raised and with security for investee
Certificate investment funds.Public offering fund is with the recruitment of mass media means, and promoter gathers public's fund and sets up investment funds, is demonstrate,proved
Certificate investment.These funds have information announcing, profit distribution, the industry standards such as run-limiting under the stringent supervision of law.
In capital investment management process, often there is so-called style drift phenomenon, i.e. capital investment style is received in risk
Changed in beneficial style, and investor would generally using the style of fund drift about degree as invest reference factor, therefore,
The reference factor is extremely important for investor, but due in the prior art determine style drift about degree when, usually with
The style of FUNDING STATEMENT is as benchmark, but since the fund is when being all displaced to another style the most of the time, it is led in fact
Style has occurred that variation, and lead to style drift degree determines inaccuracy.
Above content is only used to facilitate the understanding of the technical scheme, and is not represented and is recognized that above content is existing skill
Art.
Summary of the invention
The main purpose of the present invention is to provide a kind of fund style drift degree to determine method, apparatus, equipment and storage
Medium, it is intended to solve the determination for causing style to drift about degree in the prior art directly using the style of FUNDING STATEMENT as benchmark not
Accurate technical problem.
To achieve the above object, the present invention provides a kind of fund style drift degree and determines method, the fund style drift
Shifting degree determine method the following steps are included:
Target fund return on funds daily in each month within a preset period of time is obtained, and obtains the preset time
In section in each month different fund style indexes daily earning rate；
The preset time period is traversed according to month；
According to the daily earning rate of return on funds and each fund style index daily in the current month traversed to more
First linear regression model (LRM) is trained, and obtains each regression coefficient in the multiple linear regression model；
The main style of the current month is determined according to the regression coefficient；
After traversing to the preset time period, institute is determined according to the main style in each month in the preset time period
State the style drift degree of target fund.
Preferably, the day of daily return on funds and each fund style index in the current month that the basis traverses
Earning rate is trained multiple linear regression model, obtains each regression coefficient in the multiple linear regression model, comprising:
Using return on funds daily in the current month traversed as dependent variable, by each fund style index
Daily earning rate is trained the multiple linear regression model according to the dependent variable and independent variable as independent variable, obtains
Each regression coefficient in the multiple linear regression model.
Preferably, the fund style index belongs to nine style index；
The multiple linear regression model are as follows:
Wherein, YiFor i-th day in current month return on funds, XijIt is jth kind fund style index in current month
In daily earning rate on the i-thth, b0For constant, bjFor the corresponding regression coefficient of jth kind fund style index.
Preferably, the main style that the current month is determined according to the regression coefficient, comprising:
Each regression coefficient is compared, using the fund style of the corresponding fund style index of maximum regression coefficient as
The main style of the current month.
Preferably, described after being traversed to the preset time period, according to each month in the preset time period
Main style determines the style drift degree of the target fund, comprising:
After being traversed to the preset time period, the most main style of frequency of occurrence in the preset time period is made
For the benchmark style of the target fund；
According to the main style determination in each month in the benchmark style of the target fund and the preset time period
The style drift degree of target fund.
Preferably, in the benchmark style and the preset time period according to the target fund each month main wind
Lattice determine the style drift degree of the target fund, comprising:
Count the total quantity of the main style in each month in the preset time period；
Count the frequency of occurrence of other main styles in the preset time period in addition to the benchmark style；
Using the ratio between the frequency of occurrence and the total quantity as the style of target fund drift degree.
Preferably, described to obtain target fund return on funds daily in each month within a preset period of time, and obtain
In the preset time period in each month different fund style indexes daily earning rate, comprising:
Target fund day net value data daily in each month within a preset period of time are obtained, and obtain the preset time
In section in each month different fund style indexes day data；
According to the adjacent day net value data calculate the target fund within a preset period of time in each month it is daily
Return on funds, and according to different fund style indexes in each month in the adjacent day data calculating preset time period
Daily earning rate.
In addition, to achieve the above object, the present invention also proposes a kind of fund style drift degree determining device, described device
Include:
Earning rate obtains module, for obtaining target fund mutual fund earnings daily in each month within a preset period of time
Rate, and obtain the daily earning rate of different fund style indexes in each month in the preset time period；
Data traversal module, for being traversed to the preset time period according to month；
Model training module, for being referred to according to daily return on funds and each fund style in the current month traversed
Several daily earning rates are trained multiple linear regression model, obtain each recurrence system in the multiple linear regression model
Number；
Main style determining module, for determining the main style of the current month according to the regression coefficient；
Degree determining module is used for after traversing to the preset time period, according to each in the preset time period
The main style in month determines the style drift degree of the target fund.
In addition, to achieve the above object, the present invention also proposes that a kind of fund style drift degree determines equipment, the equipment
It include: the fund style drift journey that memory, processor and being stored in can be run on the memory and on the processor
It spends and determines program, it is true that the fund style drift degree determines that program is arranged for carrying out fund style drift degree as described above
The step of determining method.
In addition, to achieve the above object, the present invention also proposes a kind of storage medium, fund is stored on the storage medium
Style drift degree determines program, and the fund style drift degree determines to be realized as described above when program is executed by processor
The step of fund style drift degree determines method.
The present invention traverses the preset time period according to month, according to base daily in the current month traversed
The daily earning rate of golden earning rate and each fund style index is trained multiple linear regression model, obtains the multiple linear
Each regression coefficient in regression model determines the main style of the current month further according to the regression coefficient, finally to institute
After stating preset time period traversal, the wind of the target fund is determined according to the main style in each month in the preset time period
Lattice drift degree, avoids directly determining risk income style as benchmark according to the style of FUNDING STATEMENT, but by current
The mode that the earning rate in month is trained multiple linear regression model determines each recurrence in multiple linear regression model
Coefficient, and determine according to the regression coefficient the main style of the current month, it ensure that the accuracy of the main style in each month,
And then it ensure that the accuracy of fund style drift degree.
Detailed description of the invention
Fig. 1 is that the fund style drift degree for the hardware running environment that the embodiment of the present invention is related to determines the knot of equipment
Structure schematic diagram；
Fig. 2 is the flow diagram that fund style of the present invention drift degree determines method first embodiment；
Fig. 3 is the flow diagram that fund style of the present invention drift degree determines method second embodiment；
Fig. 4 is the structural block diagram of fund style of the present invention drift degree determining device first embodiment.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
Referring to Fig.1, Fig. 1 is that the fund style drift degree for the hardware running environment that the embodiment of the present invention is related to determines
Device structure schematic diagram.
As shown in Figure 1, fund style drift degree determines that equipment may include: processor 1001, such as central processing
Device (Central Processing Unit, CPU), communication bus 1002, user interface 1003, network interface 1004, memory
1005.Wherein, communication bus 1002 is for realizing the connection communication between these components.User interface 1003 may include display
Shield (Display), input unit such as keyboard (Keyboard), optional user interface 1003 can also include that the wired of standard connects
Mouth, wireless interface.Network interface 1004 optionally may include standard wireline interface and wireless interface (such as Wireless Fidelity
(WIreless-FIdelity, WI-FI) interface).Memory 1005 can be the random access memory (Random of high speed
Access Memory, RAM) memory, be also possible to stable nonvolatile memory (Non-Volatile Memory,
), such as magnetic disk storage NVM.Memory 1005 optionally can also be the storage device independently of aforementioned processor 1001.
Fund style drift degree is determined it will be understood by those skilled in the art that structure shown in Fig. 1 is not constituted
The restriction of equipment may include perhaps combining certain components or different component cloth than illustrating more or fewer components
It sets.
As shown in Figure 1, as may include operating system, network communication mould in a kind of memory 1005 of storage medium
Block, Subscriber Interface Module SIM and fund style drift degree determine program.
Fund style drift degree shown in Fig. 1 determines in equipment that network interface 1004 is mainly used for and other equipment
Carry out data communication；User interface 1003 is mainly used for carrying out data interaction with user；Fund style drift degree of the present invention is true
Processor 1001, memory 1005 in locking equipment can be set to be determined in equipment in fund style drift degree, the fund
Style drift degree determines that equipment calls the fund style stored in memory 1005 drift degree to determine by processor 1001
Program, and execute fund style drift degree provided in an embodiment of the present invention and determine method.
The embodiment of the invention provides a kind of fund style drift degree to determine method, is the present invention one referring to Fig. 2, Fig. 2
Kind fund style drift degree determines the flow diagram of method first embodiment.
In the present embodiment, fund style drift degree determine method the following steps are included:
S10: target fund return on funds daily in each month within a preset period of time is obtained, and is obtained described default
In period in each month different fund style indexes daily earning rate.
It should be noted that the executing subject of the method for the present embodiment is specially the server for being able to carry out data processing.
It should be understood that since style drift degree needs just decide within one period, it is described default
The time span of period can be configured as needed, such as: 5 can be set by the time span of the preset time period
Year, certainly, it may be alternatively provided as 1,2,3,4,6,7,8 year, may also be configured to 0.5 year, the present embodiment is without restriction to this.
It will be appreciated that in step S10, can be grabbed from certain financial web sites (such as: the website Wind) by web crawlers
Target fund return on funds daily in each month within a preset period of time is taken, and grabs each month in the preset time period
The daily earning rate of middle difference fund style index.
In the concrete realization, the fund that user needs to refer to style drift degree generally falls into public offering fund, therefore, described
Target fund belongs to public offering fund, and the public offering fund refers to publicity pattern to public investor fund raised and with card
Certificate is the security investment fund of investee.
Specifically, since public offering fund usually can just be held in the time removed except Sunday Saturday and legal festivals and holidays
Disk, therefore, daily return on funds belongs to the return on funds of opening quotation day in above-mentioned month each within a preset period of time, together
Sample, the daily earning rate of different fund style indexes belongs to the daily earning rate of opening quotation day in each month in above-mentioned preset time period.
Certainly, these financial web sites may also not show these earning rates sometimes, and usually can all show that each fund is daily
Day net value data and different fund style index day data, so, target fund can not be grabbed by web crawlers and existed
Return on funds daily in each month in preset time period, and grab in the preset time period different fund wind in each month
The daily earning rate of grid index can determine for guarantee fund style drift degree, in the present embodiment, can first obtain target fund and exist
Day net value data daily in each month in preset time period, and obtain in the preset time period different fund wind in each month
The day data of grid index；The target fund each month within a preset period of time is calculated further according to adjacent day net value data
In daily return on funds, and calculate in the preset time period different funds in each month according to the adjacent day data
The daily earning rate of style index.
Specifically, daily return on funds can be calculated by the following formula:
Yi=(Ji-Ji-1)/Ji-1
Wherein, YiFor return on funds on the i-thth, JiFor day net value data on the i-thth, Ji-1For JiPrevious day net value
Data.
Equally, the daily earning rate of different fund style indexes can be calculated by the following formula:
Xij=(Lij-Lij-1)/Lij-1
Wherein, XijDaily earning rate for jth kind fund style index on 1st, LijExist for jth kind fund style index
Day data on the i-thth, Lij-1For LijPrevious day data.
S20: the preset time period is traversed according to month.
It should be noted that since preset time period would generally include biggish data volume, and model training usually requires
Therefore certain data volume in the present embodiment, using month as processing unit, the preset time period is carried out according to month
Traversal.
S30: according to the daily earning rate of return on funds and each fund style index daily in the current month traversed
Multiple linear regression model is trained, each regression coefficient in the multiple linear regression model is obtained.
It will be appreciated that the current month is the month traversed when traversing to the preset time period,
Such as: the preset time period is in October, 2012~2018 year September, when being traversed to the preset time period, traversal
To in March, 2017, at this time in March, 2017 was the current month.
In the concrete realization, any one model is made of various variables, when analyzing these models, Ke Yixuan
The influence for studying some of variables to other variables is selected, then these variables selected are known as independent variable, and is affected
Amount be thus referred to as dependent variable, most for influence of the daily earning rate to return on funds convenient for determining which fund style index
Greatly, therefore, can be using return on funds daily in the current month traversed as dependent variable in the present embodiment, it will be described each
The daily earning rate of fund style index is as independent variable, according to the dependent variable and independent variable to the multiple linear regression model
It is trained, obtains each regression coefficient in the multiple linear regression model.
It should be noted that nine kinds of styles can be usually divided into for fund style, it specifically can be such as following table institute
Show:
| Deep bid value | Deep bid balance | Deep bid growth |
| Mid-game value | Mid-game balance | Mid-game growth |
| Shallow bid value | Shallow bid balance | Shallow bid growth |
Therefore, the fund style index belongs to nine style index.
To guarantee accuracy that main style determines, in the present embodiment, the multiple linear regression model are as follows:
Wherein, YiFor i-th day in current month return on funds, XijIt is jth kind fund style index in current month
In daily earning rate on the i-thth, b0For constant, bjFor the corresponding regression coefficient of jth kind fund style index.
It should be noted that for multiple linear regression model, it, can be in advance by b before being trained0And bjRespectively
It is set as initial value, such as: it may be configured as 1, due to excluding except Sunday Saturday and legal festivals and holidays in a month, it will usually
There are 20 days or so, therefore, in current month usually there will be 20 or so return on funds and daily earning rate, by these
When data successively substitute into the multiple linear regression model, b0And bjIt can constantly change, described in substituting into all data
After multiple linear regression model, model training completion is regarded as, at this point, can get each time in the multiple linear regression model
Return coefficient bj。
S40: the main style of the current month is determined according to the regression coefficient.
It will be appreciated that regression coefficient is bigger, illustrate the fund style of the corresponding fund style index of the regression coefficient with
The style of the target fund is closer, for the main style convenient for the determination current month, can compare each regression coefficient
Compared with using the fund style of the corresponding fund style index of maximum regression coefficient as the main style of the current month.
Assuming that b1Corresponding deep bid is worth style index, b2Corresponding deep bid balances style index, b3Corresponding deep bid growth style refers to
Number, b4Corresponding mid-game is worth style index, b5Corresponding mid-game balances style index, b6Corresponding mid-game growth style index, b7It is corresponding
Shallow bid is worth style index, b8Corresponding shallow bid balances style index, b9Corresponding shallow bid growth style index, if current month is more
B in first linear regression model (LRM)1~b4It is 0.1, b5It is 0.3, b6~b9It is 0.2, at this point, b5Mid-game can be balanced style by maximum
Main style as current month.
S50: after traversing to the preset time period, the main style according to each month in the preset time period is true
The style drift degree of the fixed target fund.
It should be noted that having the above is only for example, not constituting any restriction to technical solution of the present invention
During body is realized, those skilled in the art, which can according to need, to be configured, herein with no restrictions.
It will be appreciated that the style drift degree by fund can allow user more accurately to hold " equity share type base
The drift of the style of gold " and " inclined stock mixed type fund " and style change situation, and user can be allowed to float by the style of fund
Whether condition of shifting one's love understands market state in which, additionally it is possible to allow user to see fund manager clearly and have and select Shi Nengli, to be user
It is preferred that fund provides a selection dimension.
By foregoing description it is not difficult to find that the present embodiment traverses the preset time period according to month, according to time
The daily earning rate of daily return on funds and each fund style index is to multiple linear regression model in the current month gone through
It is trained, obtains each regression coefficient in the multiple linear regression model, work as further according to described in regression coefficient determination
The main style in preceding month, finally after being traversed to the preset time period, according to each month in the preset time period
Main style determines the style drift degree of the target fund, avoids directly being determined according to the style of FUNDING STATEMENT as benchmark
Risk income style, but it is more to determine in such a way that the earning rate of current month is trained multiple linear regression model
Each regression coefficient in first linear regression model (LRM), and determine according to the regression coefficient the main style of the current month, guarantee
The accuracy of the main style in each month, and then ensure that the accuracy of fund style drift degree.
With reference to Fig. 3, Fig. 3 is the process signal that a kind of fund style drift degree of the present invention determines method second embodiment
Figure.
Based on above-mentioned first embodiment, in the present embodiment, step S50 includes:
S51: after being traversed to the preset time period, by the most main wind of frequency of occurrence in the preset time period
Benchmark style of the lattice as the target fund；
It should be noted that if frequency of occurrence is most in the benchmark style claimed of target fund and the preset time period
When main style is inconsistent, it will be appreciated that have occurred that variation for the benchmark style of the target fund, therefore, described can will preset
Benchmark style of the most main style of frequency of occurrence as the target fund in period.
Assuming that the benchmark style that target fund is claimed is A style, but the main style of target fund is B wind in preset time period
The number of lattice is most, it can be understood as the benchmark style of the target fund has occurred that variation, therefore, can be by the B wind
Benchmark style of the lattice as the target fund.
S52: it is determined according to the main style in each month in the benchmark style and the preset time period of the target fund
The style drift degree of the target fund.
It drifts about degree for the style convenient for quickly determining the target fund, in the present embodiment, can first count described default
The total quantity of the main style in each month in period；Other in the preset time period in addition to the benchmark style are counted again
The frequency of occurrence of main style；Finally using the ratio between the frequency of occurrence and the total quantity as the wind of the target fund
Lattice drift degree.
Assuming that the total quantity of the main style in each month is 48 in preset time period, i.e., the length of the described preset time period is 4
Year, it is assumed that the benchmark style is B style, and it is 23 times that the main style in each month, which is the number of benchmark style, removes the base
The frequency of occurrence of other styles except quasi- style is 25 times, at this point, the mesh can be regard the ratio 0.52 between 25 and 48 as
Mark the style drift degree of fund.
In addition, the embodiment of the present invention also proposes a kind of storage medium, fund style drift is stored on the storage medium
Degree determines program, and the fund style drift degree, which determines, realizes fund wind as described above when program is executed by processor
The step of lattice drift degree determines method.
It is the structural block diagram of fund style of the present invention drift degree determining device first embodiment referring to Fig. 4, Fig. 4.
As shown in figure 4, the fund style drift degree determining device that the embodiment of the present invention proposes includes:
Earning rate obtains module 4001, and for obtaining target fund, fund daily in each month is received within a preset period of time
Beneficial rate, and obtain the daily earning rate of different fund style indexes in each month in the preset time period；
Data traversal module 4002, for being traversed to the preset time period according to month；
Model training module 4003, for according to return on funds and each fund wind daily in the current month traversed
The daily earning rate of grid index is trained multiple linear regression model, obtains each recurrence in the multiple linear regression model
Coefficient；
Main style determining module 4004, for determining the main style of the current month according to the regression coefficient；
Degree determining module 4005 is used for after traversing to the preset time period, according to the preset time period
The main style in interior each month determines the style drift degree of the target fund.
In the concrete realization, any one model is made of various variables, when analyzing these models, Ke Yixuan
The influence for studying some of variables to other variables is selected, then these variables selected are known as independent variable, and is affected
Amount be thus referred to as dependent variable, most for influence of the daily earning rate to return on funds convenient for determining which fund style index
Greatly, therefore, in the present embodiment, the model training module 4003, daily fund in the current month for being also used to traverse
Earning rate is as dependent variable, using the daily earning rate of each fund style index as independent variable, according to the dependent variable and certainly
Variable is trained the multiple linear regression model, obtains each regression coefficient in the multiple linear regression model.
For fund style, nine kinds of styles can be usually divided into, therefore, the fund style index belongs to nine
Style index；
To guarantee accuracy that main style determines, in the present embodiment, the multiple linear regression model are as follows:
Wherein, YiFor i-th day in current month return on funds, XijIt is jth kind fund style index in current month
In daily earning rate on the i-thth, b0For constant, bjFor the corresponding regression coefficient of jth kind fund style index.
It will be appreciated that regression coefficient is bigger, illustrate the fund style of the corresponding fund style index of the regression coefficient with
The style of the target fund is closer, for the main style convenient for the determination current month, in the present embodiment, and the main style
Determining module 4004 is also used to be compared each regression coefficient, by the corresponding fund style index of maximum regression coefficient
Main style of the fund style as the current month.
It can determine for guarantee fund style drift degree, in the present embodiment, the earning rate obtains module 4001, also uses
The daily day net value data in acquisition target fund within a preset period of time each month, and obtain each in the preset time period
The day data of different fund style indexes in month；The target fund is calculated default according to the adjacent day net value data
Return on funds daily in each month in period, and calculated in the preset time period respectively according to the adjacent day data
The daily earning rate of different fund style indexes in month.
It should be noted that being given above only a kind of concrete implementation mode, not to technical solution of the present invention
Constitute any restriction.
By foregoing description it is not difficult to find that the present embodiment traverses the preset time period according to month, according to time
The daily earning rate of daily return on funds and each fund style index is to multiple linear regression model in the current month gone through
It is trained, obtains each regression coefficient in the multiple linear regression model, work as further according to described in regression coefficient determination
The main style in preceding month, finally after being traversed to the preset time period, according to each month in the preset time period
Main style determines the style drift degree of the target fund, avoids directly being determined according to the style of FUNDING STATEMENT as benchmark
Risk income style, but it is more to determine in such a way that the earning rate of current month is trained multiple linear regression model
Each regression coefficient in first linear regression model (LRM), and determine according to the regression coefficient the main style of the current month, guarantee
The accuracy of the main style in each month, and then ensure that the accuracy of fund style drift degree.
It should be noted that workflow described above is only schematical, not to protection model of the invention
Enclose composition limit, in practical applications, those skilled in the art can select according to the actual needs part therein or
It all achieves the purpose of the solution of this embodiment, herein with no restrictions.
In addition, the not technical detail of detailed description in the present embodiment, reference can be made to provided by any embodiment of the invention
Fund style drift degree determines method, and details are not described herein again.
Based on the first embodiment of above-mentioned fund style drift degree determining device, fund style drift journey of the present invention is proposed
Spend determining device second embodiment.
In the present embodiment, the degree determining module 4005 is also used to after traversing to the preset time period,
Using the most main style of frequency of occurrence in the preset time period as the benchmark style of the target fund；According to the target
The main style in each month determines the style drift journey of the target fund in the benchmark style and the preset time period of fund
Degree.
It drifts about degree, in the present embodiment, the degree determining module for the style convenient for quickly determining the target fund
4005, it is also used to count the total quantity of the main style in each month in the preset time period；It counts and is removed in the preset time period
The frequency of occurrence of other main styles except the benchmark style；Ratio between the frequency of occurrence and the total quantity is made
For the style drift degree of the target fund.
It should be noted that workflow described above is only schematical, not to protection model of the invention
Enclose composition limit, in practical applications, those skilled in the art can select according to the actual needs part therein or
It all achieves the purpose of the solution of this embodiment, herein with no restrictions.
In addition, the not technical detail of detailed description in the present embodiment, reference can be made to provided by any embodiment of the invention
Fund style drift degree determines method, and details are not described herein again.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the system that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or system institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or system.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in one as described above
In storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that terminal device (it can be mobile phone,
Computer, server, air conditioner or network equipment etc.) execute method described in each embodiment of the present invention.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of fund style drift degree determines method, which is characterized in that the fund style drift degree determines method packet
Include following steps:
Target fund return on funds daily in each month within a preset period of time is obtained, and is obtained in the preset time period
The daily earning rate of different fund style indexes in each month；
The preset time period is traversed according to month；
According to the daily earning rate of return on funds and each fund style index daily in the current month traversed to polynary line
Property regression model is trained, and obtains each regression coefficient in the multiple linear regression model；
The main style of the current month is determined according to the regression coefficient；
After traversing to the preset time period, the mesh is determined according to the main style in each month in the preset time period
Mark the style drift degree of fund.
2. fund style drift degree as described in claim 1 determines method, which is characterized in that the basis traversed works as
The daily earning rate of daily return on funds and each fund style index is trained multiple linear regression model in preceding month,
Obtain each regression coefficient in the multiple linear regression model, comprising:
Using return on funds daily in the current month traversed as dependent variable, the day of each fund style index is received
Beneficial rate is trained the multiple linear regression model according to the dependent variable and independent variable, described in acquisition as independent variable
Each regression coefficient in multiple linear regression model.
3. fund style drift degree as claimed in claim 2 determines method, which is characterized in that the fund style index category
In nine style index；
The multiple linear regression model are as follows:
Wherein, YiFor i-th day in current month return on funds, XijIt is jth kind fund style index i-th in current month
The daily earning rate of day, b0For constant, bjFor the corresponding regression coefficient of jth kind fund style index.
4. fund style drift degree according to any one of claims 1 to 3 determines method, which is characterized in that described
The main style of the current month is determined according to the regression coefficient, comprising:
Each regression coefficient is compared, using the fund style of the corresponding fund style index of maximum regression coefficient as described in
The main style of current month.
5. fund style according to any one of claims 1 to 3 drift degree determines method, which is characterized in that it is described
After to preset time period traversal, the target fund is determined according to the main style in each month in the preset time period
Style drift about degree, comprising:
After being traversed to the preset time period, using the most main style of frequency of occurrence in the preset time period as institute
State the benchmark style of target fund；
The target is determined according to the main style in each month in the benchmark style and the preset time period of the target fund
The style drift degree of fund.
6. fund style drift degree as claimed in claim 5 determines method, which is characterized in that described according to the target base
The main style in each month determines the style drift degree of the target fund in the benchmark style and the preset time period of gold,
Include:
Count the total quantity of the main style in each month in the preset time period；
Count the frequency of occurrence of other main styles in the preset time period in addition to the benchmark style；
Using the ratio between the frequency of occurrence and the total quantity as the style of target fund drift degree.
7. fund style drift degree according to any one of claims 1 to 3 determines method, which is characterized in that described to obtain
Target fund return on funds daily in each month within a preset period of time is taken, and obtains each month in the preset time period
The daily earning rate of middle difference fund style index, comprising:
Target fund day net value data daily in each month within a preset period of time are obtained, and are obtained in the preset time period
The day data of different fund style indexes in each month；
Target fund fund daily in each month within a preset period of time is calculated according to the adjacent day net value data
Earning rate, and calculate in the preset time period according to the adjacent day data day of different fund style indexes in each month
Earning rate.
The degree determining device 8. a kind of fund style drifts about, which is characterized in that described device includes:
Earning rate obtains module, for obtaining target fund return on funds daily in each month within a preset period of time, and
Obtain the daily earning rate of different fund style indexes in each month in the preset time period；
Data traversal module, for being traversed to the preset time period according to month；
Model training module, for according to return on funds and each fund style index daily in the current month traversed
Daily earning rate is trained multiple linear regression model, obtains each regression coefficient in the multiple linear regression model；
Main style determining module, for determining the main style of the current month according to the regression coefficient；
Degree determining module is used for after traversing to the preset time period, according to each month in the preset time period
Main style determine the target fund style drift degree.
9. a kind of fund style drift degree determines equipment, which is characterized in that the equipment includes: memory, processor and deposits
The fund style drift degree that storing up can run on the memory and on the processor determines program, the fund style
Drift degree determines that program is arranged for carrying out the fund style drift degree determination side as described in any one of claims 1 to 7
The step of method.
10. a kind of storage medium, which is characterized in that it is stored with fund style drift degree on the storage medium and determines program,
The fund style drift degree, which determines, realizes fund as described in any one of claim 1 to 7 when program is executed by processor
The step of style drift degree determines method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811540326.8A CN109784652A (en) | 2018-12-14 | 2018-12-14 | Fund style drift degree determines method, apparatus, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811540326.8A CN109784652A (en) | 2018-12-14 | 2018-12-14 | Fund style drift degree determines method, apparatus, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109784652A true CN109784652A (en) | 2019-05-21 |
Family
ID=66497078
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811540326.8A Pending CN109784652A (en) | 2018-12-14 | 2018-12-14 | Fund style drift degree determines method, apparatus, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109784652A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114036208A (en) * | 2021-11-09 | 2022-02-11 | 建信金融科技有限责任公司 | Model training and sensitivity analysis method, device, equipment and medium |
-
2018
- 2018-12-14 CN CN201811540326.8A patent/CN109784652A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114036208A (en) * | 2021-11-09 | 2022-02-11 | 建信金融科技有限责任公司 | Model training and sensitivity analysis method, device, equipment and medium |
| CN114036208B (en) * | 2021-11-09 | 2024-07-30 | 建信金融科技有限责任公司 | Model training and sensitivity analysis method, device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108520460A (en) | Business datum calculates processing method, device, computer equipment and storage medium | |
| CN109583722B (en) | Channel data analysis system and method | |
| CN110246031A (en) | Appraisal procedure, system, equipment and the storage medium of business standing | |
| CN108090834A (en) | Fund evaluation method and device | |
| CN109360084A (en) | Appraisal procedure and device, storage medium, the computer equipment of reference default risk | |
| de Guevara et al. | Effects of the degree of financial constraint and excessive indebtedness on firms’ investment decisions | |
| CN112232944B (en) | Method and device for creating scoring card and electronic equipment | |
| US20140257938A1 (en) | System and method for analyzing hospitality industry data and providing analytical performance management tools | |
| CN109543952A (en) | Invest acquisition methods, device, computer equipment and the storage medium of target enterprise | |
| US9942416B2 (en) | Telecommunication price-based routing apparatus, system and method | |
| Bax | Do diverse and inclusive workplaces benefit investors? An Empirical Analysis on Europe and the United States | |
| Akdoğan et al. | Heterogeneous effect of exchange rates on firms’ exports: Role of labor intensity | |
| CN109784652A (en) | Fund style drift degree determines method, apparatus, equipment and storage medium | |
| US20030120580A1 (en) | Processing system for market efficiency value added | |
| US7974903B2 (en) | System and method for analyzing distributions for taxation analysis | |
| CN112598228B (en) | Enterprise competitiveness analysis method, device, equipment and storage medium | |
| CN117252677A (en) | Credit line determination method and device, electronic equipment and storage medium | |
| CN109034994A (en) | Credit rating method and device, computer readable storage medium, terminal | |
| CN107979478A (en) | A kind of definite method and device of customer experience index | |
| CN114091715A (en) | Method and system for predicting future refund of account receivable | |
| Liu et al. | Firm valuation over lifecycle: A perspective on growth option | |
| Anfuso et al. | A sound Basel III compliant framework for backtesting credit exposure models | |
| CN102096887A (en) | Step-by-step progressive screening method for tax payment assessment model | |
| Azevedo et al. | Uncertainty and competition in the adoption of complementary technologies | |
| CN116340306B (en) | Service logic-oriented data quality assessment method, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190521 |