CN107818416A - The method and apparatus for generating Corporate Finance index forecast model - Google Patents
The method and apparatus for generating Corporate Finance index forecast model Download PDFInfo
- Publication number
- CN107818416A CN107818416A CN201711058600.3A CN201711058600A CN107818416A CN 107818416 A CN107818416 A CN 107818416A CN 201711058600 A CN201711058600 A CN 201711058600A CN 107818416 A CN107818416 A CN 107818416A
- Authority
- CN
- China
- Prior art keywords
- data
- facility personnel
- financial
- real data
- financial product
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/12—Accounting
- G06Q40/125—Finance or payroll
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Marketing (AREA)
- Finance (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Technology Law (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of method and apparatus for generating Corporate Finance index forecast model.This method includes：For predetermined amount of time, the historical forecast data that multiple facility personnels generate for the Corporate Finance index corresponding with one or more financial products is obtained, and obtains the real data for Corporate Finance index, multiple facility personnels are associated with multiple mechanisms；Historical forecast data and real data are screened；Based on the historical forecast data and real data screened, multiple facility personnels and multiple mechanisms are scored；Appraisal result based on multiple mechanisms and multiple facility personnels, corresponding weight when determining the prediction result for each financial product generation that multiple facility personnels are directed in one or more financial products as combination forecasting, to predict Corporate Finance index.The dynamic that the prediction result that the present invention is issued by comprehensive different institutions personnel is combined forecast model weight adjusts, more accurately to predict Corporate Finance index.
Description
Technical field
The invention belongs to field of computer data processing, more particularly to a kind of side for generating Corporate Finance index forecast model
Method and device.
Background technology
In financial industry, the facility personnel of mechanism (financial institution of stock trader, bank etc etc.) is (for example, analysis
Teacher etc.) all the time by basic side information, investigation and macro-indicators go to analyze and predicts in secondary market listing public affairs on the spot
The earning performance and financial index of department.For financial product, such as stock, Earning per share (EPS), net profit, operating income etc.
The prediction of financial index, the change of the relative assessment level of listed company may be influenced whether, so as to influence share price level.These
Prediction index constitutes the basis of stock basic side investment, is the stock investor on secondary market and investment managers' emphasis pass
The target of note.
Traditional financial index Forecasting Methodology is based on the subjects such as company's main business, income from sales, cash flow and establishes prediction
Model, every subject of financial statement is predicted one by one, finally predicts financial index.On this basis, the consistent prediction of finance utilizes
The prediction result of analyst, the prediction result progress such as simple arithmetic mean of the facility personnel of such as analyst etc is come in advance
Survey financial index.
Typically, the analysts of mechanism can irregularly update the prediction of the financial index of the financial product of its covering.
For example, for a listed company, tens to one or two hundred analyst is might have in a fiscal year to its following finance
The prediction adjustment of index.In reality, because the difference of Forecasting Methodology, the difference of predicted time and inconsistent using data,
Prediction between different analysts often has no small difference.
With the arrival in big data epoch, sharing and collecting for various data is gradually replaced by machine, the calculating of data by
Machine substitutes.Analysts judge rapidly to update the prediction result of respective financial index based on each for market.Finance
The metadata provider of industry embodies multiple mechanisms and analyst's finance using simple average method in a period of time to integrate at present
The consistent prediction result of index.The deficiency of this method is：The ageing for final prediction knot of information can not be captured
Influenceed caused by fruit accuracy, for the delay of response of accident, because financial index prediction is generally sent out with time and market
Changing, but the property of simple average method determines, analyst's prediction of the past period is made no exception, once it is so public
There is major event in department, and company's basic side situation changes, and the time, remote grinding report and predict just can not clearly reflect this
Class changes, and the prediction nearer from real time shaft is then more accurate；Analyst/mechanism be have ignored for different financial products
The difference of professional predictive ability, from the point of view of historical data, the prediction of authoritative analysis teacher is often more accurate；And fail more
Reflect prediction tendency of the analyst as a colony exactly.
Therefore, a kind of prediction result that can preferably integrate different institutions personnel issue is needed badly more accurately to predict public affairs
The method for taking charge of financial index.
The content of the invention
The present invention generates comprehensive difference by being scored mechanism and facility personnel in view of the above-mentioned problems, proposing one kind
The method and apparatus of the Corporate Finance index forecast model of the prediction result of facility personnel's issue.
The first aspect of the present invention proposes a kind of method for generating Corporate Finance index forecast model, including：For pre-
Fix time section, obtain multiple facility personnels and being gone through for what the Corporate Finance index corresponding with one or more financial products generated
History prediction data, and obtain the actual number for the Corporate Finance index corresponding with one or more of financial products
According to, wherein, the multiple facility personnel is associated with the multiple mechanism；To the historical forecast data and the real data
Screened, the historical forecast data and the real data screened with determination do not include exceptional value；For described one
Each financial product in individual or multiple financial products, based on the historical forecast data and the real data screened,
The multiple facility personnel is scored；It is pre- based on the history screened for one or more of financial products
Data and the real data are surveyed, the multiple mechanism is scored；And based on the multiple mechanism and the multiple machine
The appraisal result of structure personnel, for each financial product in one or more of financial products, determine the multiple mechanism
Personnel for each financial product generation prediction result as combination forecasting when corresponding weight, and then predict and
The corresponding Corporate Finance index of each financial product.
The second aspect of the present invention proposes a kind of tangible computer-readable recording medium, and the medium includes instruction, should
Instruction causes computing device to be at least used for when executed：For predetermined amount of time, obtain multiple facility personnels and be directed to and one
Or the historical forecast data of the corresponding Corporate Finance index generation of multiple financial products, and obtain be directed to it is one or
The real data of the corresponding Corporate Finance index of multiple financial products, wherein, the multiple facility personnel and the multiple machine
Structure is associated；The historical forecast data and the real data are screened, to determine the historical forecast screened
Data and the real data do not include exceptional value；For each financial product in one or more of financial products, base
In the historical forecast data and the real data screened, the multiple facility personnel is scored；For described
One or more financial products, based on the historical forecast data and the real data screened, to the multiple mechanism
Scored；And the appraisal result based on the multiple mechanism and the multiple facility personnel, for one or more of
Each financial product in financial product, determine prediction result of the multiple facility personnel for each financial product generation
Corresponding weight during as combination forecasting, and then predict the Corporate Finance index corresponding with each financial product.
The third aspect of the present invention proposes a kind of device for generating Corporate Finance index forecast model, including：Data obtain
Unit is taken, it is configured as being directed to predetermined amount of time, obtains multiple facility personnels for relative with one or more financial products
The historical forecast data for the Corporate Finance index generation answered, and obtain for corresponding with one or more of financial products
Corporate Finance index real data, wherein, the multiple facility personnel is associated with multiple mechanisms；Data screening unit,
It is configured as screening the historical forecast data and the real data, to determine the historical forecast screened
Data and the real data；Facility personnel's scoring unit, it is configured as being directed in one or more of financial products
Each financial product, based on the historical forecast data and the real data screened, the multiple facility personnel is entered
Row scoring；Mechanism scoring unit, it is configured as being directed to one or more of financial products, based on the history screened
Prediction data and the real data, score the multiple mechanism；Model prediction unit, it is configured as based on described
Multiple mechanisms and the appraisal result of the multiple facility personnel, produced for each finance in one or more of financial products
Product, determine that institute is right when the multiple facility personnel is directed to the prediction result of each financial product generation as combination forecasting
The weight answered, and then predict the Corporate Finance index corresponding with each financial product.
The above method, computer-readable recording medium and device can include one or more of following aspect：
In an aspect, scoring is carried out to the multiple facility personnel to further comprise：Gone through according to being screened
Prediction error between history prediction data and the real data, is ranked up to the multiple facility personnel, and based on sequence
As a result the multiple facility personnel is scored.
In an aspect, scoring is carried out to the multiple facility personnel to further comprise：Based on facility personnel's
Open ranking, scores the multiple facility personnel.
In an aspect, scoring is carried out to the multiple mechanism to further comprise：For one or more of finance
Each financial product in product, missed according to the prediction between the historical forecast data and the real data screened
Difference, the multiple mechanism is ranked up, and each financial product is directed to obtain the multiple mechanism based on ranking results
Score；And the score of each financial product is directed to based on the multiple mechanism, the multiple mechanism is scored.
In an aspect, determine that the weight is based further on the multiple facility personnel for one or more of
Each financial product in financial product generates time during prediction result.
In an aspect, updated historical forecast data and real data are obtained in a manner of time window rolls；
Updated historical forecast data and real data are screened, with determine the updated historical forecast data screened and
Real data does not include exceptional value；For each financial product in one or more of financial products, based on what is screened
Updated historical forecast data and real data, scores the multiple facility personnel；For one or more of
Financial product, based on the updated historical forecast data and real data screened, the multiple mechanism is scored；With
And the appraisal result based on the multiple mechanism and the multiple facility personnel, in one or more of financial products
Each financial product, the multiple facility personnel is updated for the prediction result of each financial product generation as combined prediction
Corresponding weight during model, and then predict the Corporate Finance index corresponding with each financial product.
In an aspect, the instruction causes the computing device further when executed：According to being screened
Prediction error between historical forecast data and the real data, is ranked up to the multiple facility personnel, and based on row
Sequence result scores the multiple facility personnel.
In an aspect, the instruction causes the computing device further when executed：Based on facility personnel
Open ranking, the multiple facility personnel is scored.
In an aspect, the instruction causes the computing device further when executed：For one or more
Each financial product in individual financial product, according to pre- between the historical forecast data and the real data screened
Error is surveyed, the multiple mechanism is ranked up, and is produced based on ranking results to obtain the multiple mechanism for each finance
The score of product；And the score of each financial product is directed to based on the multiple mechanism, the multiple mechanism is scored.
In an aspect, the instruction causes the computing device further when executed：Based on the multiple mechanism
Time when personnel generate prediction result for each financial product in one or more of financial products is come described in determining
Weight.
In an aspect, the instruction causes the computing device when executed：Obtained in a manner of time window rolls
Learn from else's experience the historical forecast data and real data of renewal；Updated historical forecast data and real data are screened, with
The updated historical forecast data and real data that determination is screened do not include exceptional value；For one or more of finance
Each financial product in product, based on the updated historical forecast data and real data screened, to the multiple machine
Structure personnel are scored；For one or more of financial products, based on the updated historical forecast data screened and
Real data, the multiple mechanism is scored；And the scoring based on the multiple mechanism and the multiple facility personnel
As a result, for each financial product in one or more of financial products, it is every for this to update the multiple facility personnel
Corresponding weight when the prediction result of individual financial product generation is as combination forecasting, and then predict and produced with each finance
Corporate Finance index corresponding to condition.
In an aspect, facility personnel's scoring unit is configured to：For one or more of gold
Melt each financial product in product, according to the prediction error between the historical forecast data and the real data, to institute
State multiple facility personnels to be ranked up, and the multiple facility personnel is scored based on ranking results.
In an aspect, facility personnel's scoring unit is configured to：Based on the public affairs on facility personnel
Begun to rehearse name, and the multiple facility personnel is scored.
In an aspect, the mechanism scoring unit is configured to：For one or more of finance productions
Each financial product in product, according to the prediction error between the historical forecast data and the real data screened,
The multiple mechanism is ranked up, and obtaining for each financial product is directed to obtain the multiple mechanism based on ranking results
Point；And the score of each financial product is directed to based on the multiple mechanism, the multiple mechanism is scored.
In an aspect, the model prediction unit is configured to：It is directed to based on the multiple facility personnel
Time during each financial product generation prediction result in one or more of financial products determines the weight.
In an aspect, the data capture unit obtains updated historical forecast in a manner of time window rolls
Data and real data；The data screening unit is configured as sieving updated historical forecast data and real data
Choosing, to determine the updated historical forecast data and real data screened；Facility personnel's scoring unit is configured as
For each financial product in one or more of financial products, based on the updated historical forecast data screened and
Real data, the multiple facility personnel is scored；The mechanism scoring unit is configured as one or more
Individual financial product, based on the updated historical forecast data and real data screened, the multiple mechanism is scored；
The model prediction unit is configured as the appraisal result based on the multiple mechanism and the multiple facility personnel, for described
Each financial product in one or more financial products, the multiple facility personnel is updated for each financial product generation
Prediction result as combination forecasting when corresponding weight, and then predict and produce the corresponding public affairs of the condition with each finance
Take charge of financial index.
The present invention is by the historical forecast degree of accuracy of the multiple mechanisms of synthesis and multiple facility personnels to financial product to mechanism
Scored with facility personnel, and integrate time point when multiple facility personnels generate the prediction result to financial product, most
The Corporate Finance index forecast model of weighting is generated using the prediction result of multiple facility personnels issue, so that during to future eventually
The Corporate Finance index of section is predicted.Due to considering the predictive ability difference of facility personnel, facility personnel issues prediction and tied
Fruit ageing simultaneously combines facility personnel and is weighted as the prediction result of a colony, can more accurately, synthetically
Embody the prediction to Corporate Finance index of all facility personnels.In addition, all processes can all be completed by computer disposal, it is not required to
Want manual intervention, time efficiency is higher.
Brief description of the drawings
Refer to the attached drawing shows and illustrates embodiment.These accompanying drawings be used for illustrate general principle, so as to illustrate only for
Understand the necessary aspect of general principle.These accompanying drawings are not in proportion.In the accompanying drawings, identical reference represents similar
Feature.
Fig. 1 is the flow chart of the generation Corporate Finance index forecast model according to the embodiment of the present invention；
Fig. 2 is the structure chart of the device of the generation Corporate Finance index forecast model according to the embodiment of the present invention.
Embodiment
In the specific descriptions of following preferred embodiment, by with reference to the appended accompanying drawing for forming a present invention part.Institute
Attached accompanying drawing, which has been illustrated by way of example, can realize specific embodiment.The embodiment of example is not intended as
Limit is according to all embodiments of the invention.It is appreciated that without departing from the scope of the present invention, it can utilize other
Embodiment, structural or logicality modification can also be carried out.Therefore, following specific descriptions and nonrestrictive, and this
The scope of invention is defined by the claims appended hereto.
It may be not discussed in detail for technology, method and apparatus known to person of ordinary skill in the relevant, but suitable
In the case of, the technology, method and apparatus should be considered as part for specification.For between each unit in accompanying drawing
Line, it is only for be easy to illustrate, it represents that the unit at least line both ends is in communication with each other, it is not intended that limitation does not connect
It can not be communicated between the unit of line.
Inventor has found that traditional financial index Forecasting Methodology is based on company's main business, income from sales, showed by studying
The subjects such as gold stream establish forecast model, and on this basis, for example simple arithmetic of prediction result progress of facility personnel's issue is put down
Predict financial index.But traditional Forecasting Methodology have ignored facility personnel and be predicted for the specialty of different financial products
The difference of ability, while also fail to reflect prediction tendency of the facility personnel as a colony exactly.In addition, traditional prediction
Method also have ignored the ageing of the prediction result that facility personnel is issued, and for financial field, information instant ten thousand sometimes
Become, time prediction remote can not clearly reflect such change.
Some terms used in the application are illustrated first.Financial index refers to that financial shape is summarized and evaluated in enterprise
The relative indicatrix of condition and management performance, China《General rules governing enterprise financial affairs》In be that three kinds of financial index are as defined in enterprise：Payment of debts energy
Power index, including asset-liability ratio, liquidity ratio, current rate；Operation ability index, including accounts receivable turnover, stock
Turnover rate；Profitability Index, including capital profit margin, profit ratio of sales (operating income profit-tax rate), cost profit
Rate etc..
Conceived based on foregoing invention, the present invention is proposed based on the facility personnel generation associated with mechanism on company
The historical forecast data and real data of financial index scores mechanism and facility personnel, then according to appraisal result come
The Corporate Finance index forecast model of the prediction result of the comprehensive each facility personnel's generation of generation.
Fig. 1 is the flow chart of the method for the generation Corporate Finance index forecast model according to the embodiment of the present invention.Such as flow
Described in figure, this method comprises the following steps：
Step S101：For predetermined amount of time, multiple facility personnels are obtained for relative with one or more financial products
The historical forecast data for the Corporate Finance index generation answered, and obtain for corresponding with one or more financial products
The real data of Corporate Finance index, wherein, the plurality of facility personnel is associated with the plurality of mechanism.
In this step, for predetermined amount of time (for example, in the past several years), from specific storage device or server or
Otherwise wait and obtain multiple facility personnels (for example, analyst etc.) for one or more financial products (for example, stock
Deng) the historical forecast number of corresponding Corporate Finance index (for example, Earning per share (EPS), net profit, operating income etc.) generation
According to, and the real data for the Corporate Finance index corresponding with one or more financial products is obtained, wherein, this is more
Individual facility personnel is associated with the plurality of mechanism (mechanism of stock trader, bank etc etc.).Because facility personnel may be from one
Individual mechanism leaves office and/or registration is to another mechanism, on some historical time point, member of the facility personnel as institutional affiliation
Issue is predicted for the financial index of one or more financial products.For mechanism, within a period, each mechanism can
With including several facility personnels, and the quantity of facility personnel and/or composition may change within another period.It is right
For facility personnel, within a period, it can be under the jurisdiction of a mechanism, financial index of the issue for some financial products
Prediction, and within another period, it is under the jurisdiction of another mechanism, issue refers to for the finance of some identical or different financial products
Mark prediction.In other words, for the financial forecast each time of financial product, can all there are corresponding facility personnel and corresponding machine
Structure.
Step S102：Historical forecast data and real data are screened, to determine screened historical forecast data
Do not include exceptional value with real data.
In this step, historical forecast data and real data are screened, to determine screened historical forecast number
Do not include exceptional value according to real data, that is, reject exceptional value that may be present in historical forecast data and real data.In number
During according to statistics, probably due to human error or other reasons cause individual data to deviate expected or a large amount of statistics values
As a result situation.Such as the error of decimal point, cause prediction data to produce the difference of the order of magnitude.If counted individually in one group of data
Remaining observation of its affiliated data group is deviated considerably from according to value, then referred to as abnormal data, outlier.That is, exceptional value
Refer in one group of predicted value and value that the deviation of mean predicted value is larger.For example, the expectation of one group of predicted value and variance are respectively μ
And σ, the σ of μ+2 can be will be greater than or the value less than μ -2 σ regards as exceptional value.The purpose for rejecting exceptional value that may be present is
Exceptional value is avoided to produce larger deviation effects to overall prediction.
Step S103：It is pre- based on the history screened for each financial product in one or more financial products
Data and real data are surveyed, the plurality of facility personnel is scored.
In this step, based on historical forecast data of the plurality of facility personnel for the generation of each financial product and institute
The real data of acquisition, the plurality of facility personnel is scored.Due to and not all facility personnel can be directed to each finance
Product is predicted, and only considers the facility personnel being predicted for the financial product, can be according to historical forecast data and reality
Border data, the facility personnel is scored, if there is no historical forecast data (for example, it may be possible to which the facility personnel is newly to enter
Capable people, or the financial product are products of new issue, etc.), it is particular value (for example, 0) by facility personnel scoring.
In step s 103, alternatively, in one embodiment, the further bag that scores is carried out to the plurality of facility personnel
Include：According to the prediction error between the historical forecast data and real data screened, the plurality of facility personnel is ranked up,
And the plurality of facility personnel is scored based on ranking results.Sorted according to prediction error, i.e., according to the standard of historical forecast
Exactness can determine the difference of the predictive ability of different facility personnels, and more generally, the ranking in sequence is higher, generally represent
Higher predictive ability, and the ranking in sorting is lower, generally represents relatively low predictive ability.Therefore, based on ranking results pair
Facility personnel scored can be considered as to a certain extent the difference of the predictive ability based on different facility personnels come pair
They are scored.
In step s 103, alternatively, in one embodiment, the further bag that scores is carried out to the plurality of facility personnel
Include：Based on the open ranking on facility personnel, the plurality of facility personnel is scored.In the affiliated industry field of mechanism,
Some influential open rankings are generally had, part body personnel are carried out with ranking, this open ranking is to a certain degree
On also embody facility personnel predictive ability height.
Step S104：For one or more financial products, based on the historical forecast data and real data screened,
The plurality of mechanism is scored.
In this step, because facility personnel is associated with mechanism, therefore scoring is carried out to mechanism to be based on to based on machine
Historical forecast data and acquired real data of the structure personnel for financial product generation, score mechanism.Due to
All facility personnels associated by mechanism can be predicted for several financial products, can be produced according to several finance
The historical forecast data and real data of the Corporate Finance index of condition association, score the mechanism.
In step S104, alternatively, in one embodiment, scoring is carried out to the plurality of mechanism and further comprised：
For each financial product in one or more financial products, according to the historical forecast data and real data screened it
Between prediction error, the plurality of mechanism is ranked up, and the plurality of mechanism is obtained for each finance based on ranking results
The score of product；And the score of each financial product is directed to based on the plurality of mechanism, the plurality of mechanism is scored.It is first
First, the plurality of mechanism is obtained for each financial product for each financial product in one or more financial products
Score be similar to as above for facility personnel score it is described, sorted according to error is predicted, i.e., it is pre- according to history
The degree of accuracy of survey can determine the difference of the predictive ability of the facility personnel of different institutions, and more generally, mechanism is in the ranking
Ranking is higher, generally represents higher predictive ability, and the ranking in sorting is lower, generally represents relatively low predictive ability.From
And the score that the plurality of mechanism is directed to each financial product is obtained based on ranking results.Secondly, it is directed to based on the plurality of mechanism
The score of each financial product, scores the plurality of mechanism.Herein, due to consideration that facility personnel is associated with mechanism,
Mechanism is combined to the predictive ability of each financial product to score mechanism, to embody mechanism to multiple financial products
Macro-forecast ability.
Step S105：Appraisal result based on the plurality of mechanism and the plurality of facility personnel, for one or more gold
Melt each financial product in product, determine prediction result conduct of the plurality of facility personnel for each financial product generation
Corresponding weight during combination forecasting, and then predict the Corporate Finance index corresponding with each financial product.
In this step, for each financial product, the facility personnel to financial product issue prediction result is combined
And its predictive ability of associated mechanism determines respective weights of the prediction result in combination forecasting, so as to generate
Corporate Finance index forecast model, and the Corporate Finance index corresponding with the financial product is predicted using the model.
In step S105, alternatively, in one embodiment, determine that the weight is based further on the plurality of mechanism
Personnel are directed to time during each financial product generation prediction result in one or more financial products.Herein, it is contemplated that
Ageing possessed by time during facility personnel's issue prediction result, more generally, generally, the time for issuing prediction result gets over
Early (that is, more remote apart from current time), its referring to property is lower, and the time for issuing prediction result is more late (that is, apart from current time
It is nearer), its referring to property is higher.
Optionally, in addition, in one embodiment, in step S101, warp is obtained in a manner of time window rolls
The historical forecast data and real data of renewal, and based on updated historical forecast data and real data, repeat above-mentioned step
Rapid S102, S103, S104 and S105 are to update the prediction result of facility personnel corresponding weight in combination forecasting, i.e., more
New company's financial index forecast model.In other words, should as historical forecast data and real data are with the dynamic change of time
Method can dynamically update Corporate Finance index forecast model.
The present invention has carried out dynamic evaluation using the financial forecast data of history to mechanism, the predictive ability of facility personnel,
Adjustment is weighted to time factor, following crucial financial index of company can be more accurately predicted, carried for market investment
More preferable guidance is supplied.
It is below analyst using mechanism as stock trader, facility personnel, financial product to preferably express the design of the present invention
To be illustrated exemplified by stock to the above method, but this is only illustrated, and is not to be limited.
In step S101, from the multi-party financial database being stored on particular memory device or server, during for one section
Between (for example, in past 3 years), obtain the historical forecast data that all analysts of financial index corresponding to each stock provide
(such as the history financial index prediction data in history wealth year) and real data (such as the actual financial index number in history wealth year
According to).For example, the financial index includes earnings per share, operating income, net profit etc..
In step S102, the historical forecast data and real data of all financial index are screened, with rejecting abnormalities
Value, so as to avoid exceptional value from producing larger deviation effects to overall prediction.For example, expectation and the variance of one group of predicted value
Respectively μ and σ, can will be greater than the σ of μ+2 or the value less than μ -2 σ regards as exceptional value.
After step S102 screening, in step S103, for each stock, finance of the analyst for stock are taken
The historical forecast value of index is P={ p1,...,pn, actual value is R={ r1,...,rn, based on historical forecast value P and reality
Value R scores analyst.
In step S103, alternatively, analyst is carried out based on the prediction error between historical forecast value P and actual value R
Sequence, and analyst is scored based on ranking results.In one example, prediction error rate E={ e are calculated1,...,en,
Wherein ei=| pi-ri|/|ri|, the average forecasting error for calculating analyst for the historical forecast of single stock losesK is lost for the average forecasting error of the historical forecast of all analysts of the single stock
It is ranked up from small to large, and analyst is scored based on ranking results.In this example, such as analyst scores and tied
Fruit is F1=(Rank-α)log(1+log(Nanalyst)), wherein NanalystIt is the analysis that historical forecast is have ever made to the single stock
Teacher's sum, Rank is ranking of the analyst in ranking results, and α is constant, for example 0.5.In this example, ranking results are higher
Analyst there is lower average forecasting error to lose, to a certain extent it is considered that having higher predictive ability, because
This appraisal result is higher.
In step S103, it is alternatively possible to further be scored using the open ranking on analyst analyst.
The example continued the above, it is changed into F using the open ranking on analyst, such as analyst's appraisal result1=β * (Rank-α)
log(1+log(Nanalyst)), wherein β is the regulation coefficient of the open ranking on analyst, for example known on analyst
Open ranking have new wealth ranking, crystal ball prize list, east wealth analyst's index etc..For not appearing in open row
Analyst in name, is not adjusted to appraisal result.Adjustment reflection is to analyst's predictive ability height in industry
Evaluation, generally authoritative analyst, its predictive ability is higher, and referring to property of prediction result is stronger.
It is similar with to analyst score in step S104, for one or more stocks, based on stock trader (i.e. by with
The analyst that stock trader is associated makes) historical forecast value P={ p1,...,pmAnd actual value R={ r1,...,rmTo stock trader
Scored.
In step S104, alternatively, for each stock, based on the prediction error between historical forecast value P and actual value R
Mechanism is ranked up, and the score of each stock of historical forecast is have ever made for it to obtaining stock trader based on ranking results
score.In one example, prediction error rate E={ e are calculated1,...,em, wherein ei=| pi-ri|/|ri|, calculate mechanism pair
In the average forecasting error loss of the historical forecast of single stockFor the single stock
The average forecasting error loss k of the historical forecast of all mechanisms is ranked up from small to large, and based on ranking results come to mechanism
Scored.In this example, score1=(Rank are scored at for stock 1, such as mechanism-θ)log(1+log(Ndealer)),
Wherein NdealerIt is stock trader's sum that historical forecast is have ever made to the single stock, Rank is ranking of the stock trader in ranking results,
θ is constant, for example 0.5.For each stock trader, if its score to N number of stock be respectively score1, score2 ...,
ScoreN, then the score based on the stock trader to each stock, scores the stock trader.In this example, such as stock trader scores
As a result it is the arithmetic average F of above-mentioned score2=(score1+score2+...+scoreN)/N.
In step S105, for the financial forecast achievement data of new wealth year single stock, generated and predicted according to analyst
As a result time, for example, based within past 180 days analyst issue the date of prediction result to the number of days T of current date, base
In the appraisal result of the issue analyst obtained as previously described be F1, the appraisal result for issuing stock trader is F2, determine distributional analysis teacher
For stock generation prediction result as combination forecasting when corresponding weight w.It is pre- for not have ever made history
The stock trader and analyst of survey, can be F by the appraisal result of issue analyst1Appraisal result with issue stock trader is F2It is set to special
Definite value, for example 0.In one example, calculateWherein, C1And C2For constant, such as C1It is just
Value.In this example, the weight shared by the more early prediction result of issuing time will be smaller, the more late prediction result of issuing time
Shared weight will be bigger, therefore, embody issue analyst prediction result it is ageing.After weight w is obtained, for
All prediction results of single stock can be weighted according to weight, be referred to a pair Corporate Finance corresponding with the single stock
Mark is predicted.By being scored analyst and stock trader and weighted model, whole financial forecast method utilizes the wealth of history
Business prediction data has carried out dynamic evaluation to the research strength of stock trader, analyst, and adjustment is weighted to time factor, can be more
Add and predict following crucial financial index of company exactly, more preferable guidance is provided for market investment.
It should be appreciated that the specific formula and calculating process in above-mentioned example are used only for explaining the design of the present invention, it is right
For those skilled in the art, the specific formula can be modified with calculating process or otherwise be realized similar
Process.
Based on the above method, the present invention proposes a kind of device for generating Corporate Finance index forecast model.Fig. 2 is foundation
The Organization Chart of the device 200 of the generation Corporate Finance index forecast model of the embodiment of the present invention.
Device 200 includes：Data capture unit 201；Data screening unit 202；Facility personnel's scoring unit 203；Mechanism
Score unit 204；And model prediction unit 205.
Data capture unit 201 is configured as performing the function such as Fig. 1 described by step S101.
Data screening unit 202 is configured as performing the function such as Fig. 1 described by step S102.
Facility personnel's scoring unit 203 is configured as performing the function such as Fig. 1 described by step S103.
Mechanism scoring unit 204 is configured as performing functions of the Fig. 1 described by step S104.
Model prediction unit 205 is configured as performing the function such as Fig. 1 described by step S105.
Optionally, in addition, in one embodiment, data capture unit 201 obtains in a manner of time window rolls
Updated historical forecast data and real data, data screening unit 202, facility personnel's scoring unit 203, mechanism judge paper
Member 204 and model prediction unit 205 are configured as performing as above based on updated historical forecast data and real data
Described function, to update the prediction result of facility personnel corresponding weight in combination forecasting, Ji Geng new companies wealth
Business index forecast model.
The flow of data processing method in Fig. 1 also represents machine readable instructions, and the machine readable instructions are included by handling
The program that device performs.The program can be by hypostazation in the software for being stored in tangible computer computer-readable recording medium, the tangible calculating
Machine computer-readable recording medium such as CD-ROM, floppy disk, hard disk, digital versatile disc (DVD), the memory of Blu-ray Disc or other forms.Replace
Generation, some steps or all steps in the exemplary method in Fig. 1 can utilize application specific integrated circuit (ASIC), FPGA
Any combination of device (PLD), field programmable logic device (EPLD), discrete logic, hardware, firmware etc. is implemented.In addition,
Although the flow chart shown in Fig. 1 describes the data processing method, the step in the processing method can be modified, deleted
Or merge.
As described above, realizing Fig. 1 instantiation procedure using coded command (such as computer-readable instruction), the programming refers to
Order is stored on tangible computer computer-readable recording medium, such as hard disk, flash memory, read-only storage (ROM), CD (CD), digital universal light
Disk (DVD), Cache, random access storage device (RAM) and/or any other storage medium, believe on the storage medium
Breath can store random time (for example, for a long time, for good and all, of short duration situation is interim to buffer, and/or the caching of information).Such as
As used herein, the term tangible computer computer-readable recording medium is expressly defined to include any type of computer-readable storage
Signal.Additionally or alternatively, Fig. 1 instantiation procedure, the coding are realized using coded command (such as computer-readable instruction)
Instruction is stored in non-transitory computer-readable medium, such as hard disk, flash memory, read-only storage, CD, digital versatile disc, height
Fast buffer, random access storage device and/or any other storage medium, random time can be stored in the storage-medium information
(for example, for a long time, for good and all, of short duration situation, interim buffering, and/or the caching of information).
The present invention does not have using traditionally predicting public affairs to the prediction result of facility personnel progress such as simple arithmetic mean
Financial index is taken charge of, but the prediction knot of comprehensive different institutions personnel issue is generated by being scored mechanism and facility personnel
The Corporate Finance index forecast model of fruit is to predict Corporate Finance index so that more accurately reflects facility personnel as a group
The prediction tendency of body, and better profit from predictive ability difference of the facility personnel/mechanism for different financial products.In addition,
The present invention can capture the ageing for influence caused by final prediction result accuracy of information.Moreover, in generation model
The cycle of operation in, all processes can be completed by computer disposal, it is no longer necessary to manual intervention, greatly save cost, possess
The characteristics of intellectuality, high efficiency, time efficiency is higher.
Therefore, although describing the present invention with reference to specific example, wherein these specific examples are merely intended to be to show
Example property, rather than limit the invention, but it will be apparent to those skilled in the art that do not taking off
On the basis of spirit and scope from the present invention, the disclosed embodiments can be changed, increased or deleted.
Claims (18)
- A kind of 1. method for generating Corporate Finance index forecast model, it is characterised in that including：For predetermined amount of time, obtain multiple facility personnels and refer to for the Corporate Finance corresponding with one or more financial products The historical forecast data of generation is marked, and obtains and is directed to the Corporate Finance index corresponding with one or more of financial products Real data, wherein, the multiple facility personnel is associated with the multiple mechanism；The historical forecast data and the real data are screened, with determine the historical forecast data screened and The real data does not include exceptional value；For each financial product in one or more of financial products, based on the historical forecast data screened and The real data, the multiple facility personnel is scored；It is right based on the historical forecast data and the real data screened for one or more of financial products The multiple mechanism is scored；AndAppraisal result based on the multiple mechanism and the multiple facility personnel, in one or more of financial products Each financial product, determine that prediction result of the multiple facility personnel for each financial product generation is pre- as combination Weight corresponding during model is surveyed, and then predicts the Corporate Finance index corresponding with each financial product.
- 2. the method as described in claim 1, it is characterised in that scoring is carried out to the multiple facility personnel and further comprised： According to the prediction error between the historical forecast data and the real data screened, the multiple facility personnel is entered Row sequence, and the multiple facility personnel is scored based on ranking results.
- 3. method as claimed in claim 2, it is characterised in that scoring is carried out to the multiple facility personnel and further comprised： Based on the open ranking on facility personnel, the multiple facility personnel is scored.
- 4. the method as described in claim 1, it is characterised in that scoring is carried out to the multiple mechanism and further comprised：For each financial product in one or more of financial products, according to the historical forecast data screened and Prediction error between the real data, is ranked up to the multiple mechanism, and described more to obtain based on ranking results Individual mechanism is directed to the score of each financial product；AndThe score of each financial product is directed to based on the multiple mechanism, the multiple mechanism is scored.
- 5. the method as described in claim 1, it is characterised in that determine that the weight is based further on the multiple facility personnel Time during prediction result is generated for each financial product in one or more of financial products.
- 6. the method as described in claim 1, it is characterised in that also include：Updated historical forecast data and real data are obtained in a manner of time window rolls；Updated historical forecast data and real data are screened, to determine the updated historical forecast number screened Do not include exceptional value according to real data；For each financial product in one or more of financial products, based on the updated historical forecast number screened According to and real data, the multiple facility personnel is scored；It is right based on the updated historical forecast data and real data screened for one or more of financial products The multiple mechanism is scored；AndAppraisal result based on the multiple mechanism and the multiple facility personnel, in one or more of financial products Each financial product, it is pre- as combination to update prediction result of the multiple facility personnel for each financial product generation Weight corresponding during model is surveyed, and then predicts the Corporate Finance index corresponding with each financial product.
- 7. a kind of tangible computer-readable recording medium, the medium includes instruction, and the instruction causes calculating to set when executed It is used for less to the utmost：For predetermined amount of time, obtain multiple facility personnels and refer to for the Corporate Finance corresponding with one or more financial products The historical forecast data of generation is marked, and obtains and is directed to the Corporate Finance index corresponding with one or more of financial products Real data, wherein, the multiple facility personnel is associated with the multiple mechanism；The historical forecast data and the real data are screened, with determine the historical forecast data screened and The real data does not include exceptional value；For each financial product in one or more of financial products, based on the historical forecast data screened and The real data, the multiple facility personnel is scored；It is right based on the historical forecast data and the real data screened for one or more of financial products The multiple mechanism is scored；AndAppraisal result based on the multiple mechanism and the multiple facility personnel, in one or more of financial products Each financial product, determine that prediction result of the multiple facility personnel for each financial product generation is pre- as combination Weight corresponding during model is surveyed, and then predicts the Corporate Finance index corresponding with each financial product.
- 8. computer-readable recording medium as claimed in claim 7, it is characterised in that the instruction causes described when executed Computing device is further：According to the prediction error between the historical forecast data and the real data screened, to institute State multiple facility personnels to be ranked up, and the multiple facility personnel is scored based on ranking results.
- 9. computer-readable recording medium as claimed in claim 8, it is characterised in that the instruction causes described when executed Computing device is further：Based on the open ranking on facility personnel, the multiple facility personnel is scored.
- 10. computer-readable recording medium as claimed in claim 7, it is characterised in that the instruction causes institute when executed It is further to state computing device：For each financial product in one or more of financial products, according to the historical forecast data screened and Prediction error between the real data, is ranked up to the multiple mechanism, and described more to obtain based on ranking results Individual mechanism is directed to the score of each financial product；AndThe score of each financial product is directed to based on the multiple mechanism, the multiple mechanism is scored.
- 11. computer-readable recording medium as claimed in claim 7, it is characterised in that the instruction causes institute when executed It is further to state computing device：The each finance production being directed to based on the multiple facility personnel in one or more of financial products Time when product generate prediction result determines the weight.
- 12. computer-readable recording medium as claimed in claim 7, it is characterised in that the instruction causes institute when executed State computing device：Updated historical forecast data and real data are obtained in a manner of time window rolls；Updated historical forecast data and real data are screened, to determine the updated historical forecast number screened Do not include exceptional value according to real data；For each financial product in one or more of financial products, based on the updated historical forecast number screened According to and real data, the multiple facility personnel is scored；It is right based on the updated historical forecast data and real data screened for one or more of financial products The multiple mechanism is scored；AndAppraisal result based on the multiple mechanism and the multiple facility personnel, in one or more of financial products Each financial product, it is pre- as combination to update prediction result of the multiple facility personnel for each financial product generation Weight corresponding during model is surveyed, and then predicts the Corporate Finance index corresponding with each financial product.
- A kind of 13. device for generating Corporate Finance index forecast model, it is characterised in that including：Data capture unit, it is configured as being directed to predetermined amount of time, obtains multiple facility personnels and is directed to and one or more gold Melt the historical forecast data of the corresponding Corporate Finance index generation of product, and obtain and be directed to and one or more of finance The real data of the corresponding Corporate Finance index of product, wherein, the multiple facility personnel is associated with multiple mechanisms；Data screening unit, it is configured as screening the historical forecast data and the real data, to determine The historical forecast data and the real data of screening；Facility personnel's scoring unit, it is configured as each financial product being directed in one or more of financial products, base In the historical forecast data and the real data screened, the multiple facility personnel is scored；Mechanism scoring unit, it is configured as being directed to one or more of financial products, pre- based on the history screened Data and the real data are surveyed, the multiple mechanism is scored；Model prediction unit, it is configured as the appraisal result based on the multiple mechanism and the multiple facility personnel, for Each financial product in one or more of financial products, determine that the multiple facility personnel is directed to each financial product Corresponding weight when the prediction result of generation is as combination forecasting, and then predict corresponding with each financial product Corporate Finance index.
- 14. device as claimed in claim 13, it is characterised in that facility personnel's scoring unit is configured to： For each financial product in one or more of financial products, according to the historical forecast data and the real data Between prediction error, the multiple facility personnel is ranked up, and the multiple facility personnel is entered based on ranking results Row scoring.
- 15. device as claimed in claim 14, it is characterised in that facility personnel's scoring unit is configured to： Based on the open ranking on facility personnel, the multiple facility personnel is scored.
- 16. device as claimed in claim 13, it is characterised in that the mechanism scoring unit is configured to：For each financial product in one or more of financial products, according to the historical forecast data screened and Prediction error between the real data, is ranked up to the multiple mechanism, and described more to obtain based on ranking results Individual mechanism is directed to the score of each financial product；AndThe score of each financial product is directed to based on the multiple mechanism, the multiple mechanism is scored.
- 17. device as claimed in claim 13, it is characterised in that the model prediction unit is configured to：It is based on The multiple facility personnel be directed to one or more of financial products in each financial product generation prediction result when when Between determine the weight.
- 18. device as claimed in claim 13, it is characterised in thatThe data capture unit obtains updated historical forecast data and real data in a manner of time window rolls；The data screening unit is configured as screening updated historical forecast data and real data, to determine The updated historical forecast data and real data of screening；Facility personnel's scoring unit is configured as each financial product being directed in one or more of financial products, base In the updated historical forecast data and real data screened, the multiple facility personnel is scored；Mechanism scoring unit is configured as being directed to one or more of financial products, updated is gone through based on what is screened History prediction data and real data, the multiple mechanism is scored；The model prediction unit is configured as the appraisal result based on the multiple mechanism and the multiple facility personnel, for Each financial product in one or more of financial products, update the multiple facility personnel and be directed to each financial product Corresponding weight when the prediction result of generation is as combination forecasting, and then predict corresponding with each financial production condition Corporate Finance index.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711058600.3A CN107818416A (en) | 2017-11-01 | 2017-11-01 | The method and apparatus for generating Corporate Finance index forecast model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711058600.3A CN107818416A (en) | 2017-11-01 | 2017-11-01 | The method and apparatus for generating Corporate Finance index forecast model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107818416A true CN107818416A (en) | 2018-03-20 |
Family
ID=61604567
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711058600.3A Pending CN107818416A (en) | 2017-11-01 | 2017-11-01 | The method and apparatus for generating Corporate Finance index forecast model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107818416A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109472498A (en) * | 2018-11-15 | 2019-03-15 | 陶明 | A kind of tracking evaluation and transaction system of open quantitatively concentration security analysis report |
| CN110210959A (en) * | 2019-06-10 | 2019-09-06 | 广发证券股份有限公司 | Analysis method, device and the storage medium of financial data |
| CN112258322A (en) * | 2020-10-22 | 2021-01-22 | 上海携宁计算机科技股份有限公司 | Information prediction method, information prediction device, electronic equipment and storage medium |
| CN112907267A (en) * | 2019-12-03 | 2021-06-04 | 顺丰科技有限公司 | Method and device for predicting cargo quantity, computer equipment and storage medium |
| CN113240213A (en) * | 2021-07-09 | 2021-08-10 | 平安科技（深圳）有限公司 | Method, device and equipment for selecting people based on neural network and tree model |
| CN114692941A (en) * | 2021-12-30 | 2022-07-01 | 江南大学 | Multi-attention-based company financial prediction method |
-
2017
- 2017-11-01 CN CN201711058600.3A patent/CN107818416A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109472498A (en) * | 2018-11-15 | 2019-03-15 | 陶明 | A kind of tracking evaluation and transaction system of open quantitatively concentration security analysis report |
| CN110210959A (en) * | 2019-06-10 | 2019-09-06 | 广发证券股份有限公司 | Analysis method, device and the storage medium of financial data |
| CN112907267A (en) * | 2019-12-03 | 2021-06-04 | 顺丰科技有限公司 | Method and device for predicting cargo quantity, computer equipment and storage medium |
| CN112258322A (en) * | 2020-10-22 | 2021-01-22 | 上海携宁计算机科技股份有限公司 | Information prediction method, information prediction device, electronic equipment and storage medium |
| CN112258322B (en) * | 2020-10-22 | 2021-10-22 | 上海携宁计算机科技股份有限公司 | Information prediction method, information prediction device, electronic equipment and storage medium |
| CN113240213A (en) * | 2021-07-09 | 2021-08-10 | 平安科技（深圳）有限公司 | Method, device and equipment for selecting people based on neural network and tree model |
| CN114692941A (en) * | 2021-12-30 | 2022-07-01 | 江南大学 | Multi-attention-based company financial prediction method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107818416A (en) | The method and apparatus for generating Corporate Finance index forecast model | |
| Carpenter et al. | The disparate labor market impacts of monetary policy | |
| US20140164290A1 (en) | Database for risk data processing | |
| Arráiz et al. | Psychometrics as a tool to improve screening and access to credit | |
| Kariyawasam | Analysing the impact of financial ratios on a company’s financial performance | |
| Bleyen et al. | Measurement, incorporation and use of performance information in the budget: A methodological survey approach to map performance budgeting practices in local government | |
| Pekkaya et al. | DETERMINING THE PRIORITIES OF CRITERIA IN ASSESSING THE BANKRUPTCY RISK OF THE BANKS VIA AHP2 | |
| Gemmell et al. | Estimating quality-adjusted productivity in tertiary education: Methods and evidence for New Zealand | |
| Soekarno et al. | Performance assessment framework for small and medium enterprise | |
| Park | Municipal fragmentation and local financial condition | |
| O'Mahony | Measuring performance and accountability in higher education: A review article on productivity in higher education | |
| Zuccollo et al. | Private returns to tertiary education-how does New Zealand compare to the OECD? | |
| Mainali | Government expenditure and economic growth in Nepal | |
| Vargha et al. | What Do Competitiveness Rankings Show Us?—Substantiation and Informativeness of the World Economic Forum’s Competitiveness Ranking | |
| Faccini et al. | The role of news about TFP in US recessions and booms | |
| Gupta et al. | The Gamification of Indian Higher Education: Trends, Pitfalls and Ideas for Future | |
| Cassim | The value of financial ratio analysis in predicting the failure of JSE listed companies | |
| Vetakova et al. | Economic growth forecasting apparatus in regions with different reproduction structure | |
| Fund | Aid transparency assessment 2010 | |
| De Bosscher et al. | Comparing apples with oranges in international elite sport studies: is it possible? | |
| Saleheen | Taxation and good governance: The case of value-added tax in Bangladesh | |
| Krstić | Improving Macroeconomic Competitiveness | |
| İkiz | The Nexus for Education on Economic Growth: The Case of Turkey | |
| Ray et al. | A COMPARATIVE STUDY ON THE FINANCIAL PERFORMANCE BETWEEN PUBLIC SECTOR AND PRIVATE SECTOR BANKS IN INDIA WITH SPECIAL REFERENCE TO SBI AND HDFC BANK | |
| Toonen | Dissecting Anomaly Returns Among High and Low ESG Stocks |
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 |
Application publication date: 20180320 |
|
| RJ01 | Rejection of invention patent application after publication |