WO2021103571A1 - Procédé et appareil permettant de générer des informations de suggestion d'investissement d'actifs et support de stockage lisible - Google Patents

Procédé et appareil permettant de générer des informations de suggestion d'investissement d'actifs et support de stockage lisible Download PDF

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WO2021103571A1
WO2021103571A1 PCT/CN2020/101644 CN2020101644W WO2021103571A1 WO 2021103571 A1 WO2021103571 A1 WO 2021103571A1 CN 2020101644 W CN2020101644 W CN 2020101644W WO 2021103571 A1 WO2021103571 A1 WO 2021103571A1
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year
asset
sequence
assets
investment
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PCT/CN2020/101644
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Chinese (zh)
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林晓明
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华泰证券股份有限公司
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    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • 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
    • G06Q40/06—Asset management; Financial planning or analysis

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  • This application relates to the field of computer technology, and in particular to a method, device and readable storage medium for generating asset investment advice information.
  • the main purpose of this application is to provide a method, device and readable storage medium for generating asset investment advice information, aiming to solve the problem of poor reliability of capital investment methods in the prior art.
  • this application provides a method for generating asset investment advice information.
  • the method for generating asset investment advice information includes the following steps:
  • the step of determining the predicted fluctuation range corresponding to each type of the asset according to each of the year-on-year sequence data includes:
  • a fitting prediction of the same-year sequence data is performed on each type of asset to determine the predicted increase or decrease corresponding to the various types of assets.
  • the step of combining the filter sequences corresponding to the various types of assets to obtain the combined sequence of each of the preset observation periods includes:
  • the step of inputting each of the year-on-year sequence data and the combined sequence into a linear regression model to obtain linear regression coefficients includes:
  • Each of the year-on-year sequence data is used as a dependent variable, and the combined sequence is used as an independent variable to input into a linear regression model to obtain a linear regression coefficient.
  • the step of determining the investment weights corresponding to the various types of assets according to each of the predicted fluctuations includes:
  • the fitting prediction of the same-year sequence data is performed on each type of asset, so as to determine the predicted fluctuation rate corresponding to each type of asset
  • the steps include:
  • the predicted value and the fitted value corresponding to each type of asset are differentiated to obtain the predicted increase or decrease corresponding to each type of asset.
  • the step of determining the filter sequence corresponding to each type of asset in each preset observation period according to the year-on-year sequence data includes:
  • the present application also provides a device for generating asset investment advice information.
  • the device for generating asset investment advice information includes: a memory, a processor, and a memory, a processor, and a memory that is stored on the memory and can run on the processor.
  • a program for generating asset investment recommendation information which implements the steps of the method for generating asset investment recommendation information as described above when the program for generating asset investment recommendation information is executed by the processor.
  • the present application also provides a readable storage medium, the readable storage medium stores an asset investment advice information generation program, and the asset investment advice information generation program is executed by a processor to achieve the above The steps of the method for generating asset investment advice information described above.
  • the method, device and readable storage medium for generating asset investment recommendation information proposed by the embodiments of the application obtains various types of assets and the corresponding year-on-year sequence data of various types of assets, and determines the corresponding assets of each type according to the same-year sequence data. Forecast the rise and fall, and then determine the investment weight of various assets based on each predicted rise and fall. Because the device can determine the asset type classification or the investment weight of various assets after the investment risk classification through the year-on-year sequence data of various assets, the device can generate investment advice information for various assets according to the investment weight reasonably, and then provide users with reliable Investment advice for wealth management products with stable income.
  • FIG. 1 is a schematic diagram of the hardware structure of the device for generating asset investment recommendation information involved in the scheme of the embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a first embodiment of a method for generating investment advice information for assets in an application
  • FIG. 3 is a schematic flowchart of a second embodiment of a method for generating investment advice information of an application asset
  • FIG. 4 is a schematic flowchart of a third embodiment of a method for generating investment advice information for an application for assets
  • Fig. 5 is a schematic flowchart of a fourth embodiment of a method for generating asset investment advice information for the application.
  • the main solution of the embodiment of this application is to obtain various types of assets to determine the year-on-year sequence data corresponding to the various types of assets.
  • the classification methods of the various types of assets include asset type classification and investment risk classification.
  • the year-on-year sequence data determines the predicted rise and fall of each type of asset; the investment weight of each category of the asset is determined according to each of the predicted rises and falls.
  • the device can determine the asset type classification or the investment weight of various assets after the investment risk classification through the year-on-year sequence data of various assets, the device can generate investment advice information for various assets according to the investment weight reasonably, and then provide users with reliable Investment advice for wealth management products with stable income.
  • Fig. 1 is a schematic diagram of the hardware structure of the device for generating asset investment recommendation information involved in the solution of the embodiment of the present application.
  • the device for generating asset investment recommendation information may include: a processor 1001, such as a CPU, a communication bus 1002, and a memory 1003.
  • the communication bus 1002 is used to implement connection and communication between these components.
  • the memory 1003 may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as a magnetic disk memory.
  • the memory 1003 may also be a storage device independent of the aforementioned processor 1001.
  • the structure of the device for generating asset investment advice information shown in FIG. 1 does not constitute a limitation on the device for generating asset investment advice information, and may include more or less components than shown in the figure, or a combination Certain components, or different component arrangements.
  • the memory 1003 which is a computer storage medium, may include an operating system and a program for generating asset investment advice information.
  • the processor 1001 may be used to call the asset investment recommendation information generation program stored in the memory 1003, and perform the following operations:
  • the investment weights of the various types of assets are determined.
  • the processor 1001 may call a processing program for generating asset investment recommendation information stored in the memory 1003, and also perform the following operations:
  • the processor 1001 may call a processing program for generating asset investment recommendation information stored in the memory 1003, and also perform the following operations:
  • the extrapolated combined sequence and the linear regression coefficient perform a fitting prediction of the year-on-year sequence data for each type of asset to determine the predicted increase or decrease corresponding to the various types of assets;
  • the processor 1001 may call a processing program for generating asset investment recommendation information stored in the memory 1003, and also perform the following operations:
  • the processor 1001 may call a processing program for generating asset investment recommendation information stored in the memory 1003, and also perform the following operations:
  • Each of the year-on-year sequence data is used as a dependent variable, and the combined sequence is used as an independent variable to input into a linear regression model to obtain a linear regression coefficient.
  • the processor 1001 may call a processing program for generating asset investment recommendation information stored in the memory 1003, and also perform the following operations:
  • the processor 1001 may call a processing program for generating asset investment recommendation information stored in the memory 1003, and also perform the following operations:
  • the predicted value and the fitted value corresponding to each type of asset are differentiated to obtain the predicted increase or decrease corresponding to each type of asset.
  • the processor 1001 may call a processing program for generating asset investment recommendation information stored in the memory 1003, and also perform the following operations:
  • the first embodiment of the present application provides a method for generating asset investment advice information, the method includes:
  • Step S100 acquiring various types of assets to determine the year-on-year sequence data corresponding to the various types of assets
  • the execution subject is the device for generating asset investment recommendation information.
  • the device below refers to the device for generating asset investment recommendation information.
  • the device can be regarded as a server, and the device can be loaded into the client in the form of an APP, so that the client communicates with the device based on the APP.
  • the assets can be funds, bonds, stocks, commodities, and so on.
  • the user can open the APP loaded on the client.
  • the APP is the investment program of the wealth management product; the user can select the wealth management product that needs to be invested based on the APP. For example, if the user wants to invest in A stocks and B bonds, the user can select the interface based on the assets of the APP Select A stocks and B bonds on the app, and the APP sends the A stocks and B bonds selected by the user to the device, so that the device can obtain various assets based on the A stocks and B bonds.
  • the wealth management product needs to be classified into assets to obtain various assets.
  • the method of asset classification can be classified according to the type of asset. For example, wealth management products can be classified into stocks, bonds, commodities, etc.; the method of asset classification can also be classified according to the level of investment risk, for example, the risk of investment can be higher. Stocks and commodities are regarded as one type of assets, and bonds with lower investment risks are regarded as another type of assets. It is understandable that the classification methods include asset type classification and investment risk classification.
  • the device After acquiring various types of assets, the device needs to determine the corresponding year-on-year sequence data for each type of asset.
  • the data included in the year-on-year series data are all monthly year-on-year data.
  • Each month’s year-on-year data is calculated in a fixed 12-month cycle. For example, if the asset is a stock, the ratio of the closing price of the stock at the end of September 2019 to the closing price of the stock at the end of September 2018 or the logarithm of the ratio is taken as 2019 The year-on-year serial data in September of 2009, and September 2019 is the time of observation.
  • the year-on-year serial data Calculated according to the following formula:
  • the period of monthly year-on-year data is 12 months.
  • Step S200 Determine the predicted increase or decrease corresponding to each type of asset according to each of the year-on-year sequence data
  • step S200 After determining each year-on-year sequence data, the device obtains the predicted rise and fall of each type of asset based on each year-on-year sequence data. Specifically, referring to FIG. 3, that is, step S200 includes:
  • Step S210 Determine the filter sequence corresponding to each type of asset in each preset observation period according to the year-on-year sequence data;
  • the device After determining the year-on-year sequence data of various types of assets, the device then determines the filter sequence corresponding to each type of asset in each preset observation period according to the year-on-year sequence data.
  • the asset data sequence in a continuous period of time has a time dimension.
  • the stock price data is a numerical sequence in continuous time, so the resulting year-on-year sequence data of assets can be regarded as a time sequence.
  • the time series can be analogous to the time-domain signal generated by the movement of an economic and financial asset, and the time-domain signal has the expression of Fourier series. Therefore, Fourier transform can be performed on the year-on-year sequence data to obtain the corresponding frequency. Data in the frequency domain is analyzed and processed in the frequency domain.
  • the business cycle is the preset observation cycle.
  • the year-on-year data of assets show regular changes in the preset observation cycle, and the preset observation cycle is 42 Months, 100 months and 200 months, the preset observation period is set in the device.
  • each preset observation period corresponds to a target frequency signal.
  • These target frequency signals are stable and continuous, while other unstable or unsustainable frequency signals can be regarded as noise. Therefore, the device performs frequency domain filtering on the year-on-year sequence data of each type of asset to retain the target frequency signal and reduce noise interference.
  • the year-on-year sequence data of the reserved target frequency signal is the frequency domain data.
  • a set of filter coefficients gauss win is determined according to each preset observation period period, and according to each set of filter coefficients gauss win and frequency domain data wave fft Get a set of intermediate sequences
  • nfft is the zero-padded length of the year-on-year data
  • the gauss index is a series of numbers from 1 to nfft
  • center frequency represents the center frequency, that is, the frequency corresponding to the period factor to be extracted
  • gauss alpha is a parameter that affects the Gaussian filter bandwidth.
  • the formula (5) is obtained through the transformation of the above formula.
  • the transformation process is: perform inverse Fourier transform on each set of intermediate sequences to obtain a set of second intermediate sequences; intercept data from the second intermediate sequences according to the preset sequence length LEN Click to get the filtering sequence of each type of asset under each preset observation period Among them, the preset sequence length LEN is equal to the sum of the corresponding sequence length L1 and the extrapolated length L2.
  • Real(Z) is the real part of Z.
  • Step S220 Combine the filter sequences corresponding to the various types of assets to obtain the combined sequence of each of the preset observation periods, and determine the output length of the combined sequence to perform a preset length of the output length Modify to get the extrapolated combined sequence;
  • the merging sequence of the preset observation period can be synthesized by referring to the following process:
  • Step A Obtain a first filter matrix according to the filter sequence of the various types of assets
  • various types of assets can be stocks, bonds, and commodities
  • the filter sequences of stocks, bonds, and commodities are Taking each filter sequence as a vector, the three vectors are combined to form a filter matrix M1, and M1 is the first filter matrix.
  • Step B Hilbert transform is performed on the first filter matrix to obtain a corresponding second filter matrix
  • Step C Perform iterative calculation of merging weights according to the second filter matrix.
  • the combined weight vector with the preset observation period is a vector of all 1s with length N, where N is the number of vectors in matrix M2, that is, the number of asset categories, and the value is 3 when the asset only includes stocks, bonds, and commodities.
  • weight m mean(weight) (7)
  • M*N represents the multiplication of the matrix M and N
  • (M)′ is the conjugate transpose of M
  • diag(W) contains W on the main diagonal Diagonal matrix
  • conj(M) is the complex conjugate of M
  • M.*N represents the dot product of matrix M and N
  • mean(M) is the mean value of each column of M.
  • the filter sequences of various assets can be regarded as time-domain signals, they are affected by the cycle of the entire economic and financial system and are similar to the principles of signal propagation. Most of them are interfered by strong noise, and the signal-to-noise ratio is usually not high.
  • the iterative calculation method of merging weights given in the step reduces the phase difference estimation error and obtains the optimal weights of the merging sequence of various assets, thereby effectively improving the signal-to-noise ratio and stability of the merging sequence of the filter sequence of various assets .
  • Step D Obtain the combined sequence according to the second filter matrix and the combined weight vector.
  • the synthesized sequence X period whose preset observation period takes the value of period is a vector, namely
  • abs(W) is the complex magnitude of each element of W (each element of W is a complex number), and sum(W) is the sum of the elements of W.
  • the classification methods of various assets in the above examples are classified according to asset types; when the classification of assets is classified as investment risk, each asset that needs to be classified as one type of investment risk needs to be filtered first.
  • the merger of various assets is performed again in the merger sequence of various assets to obtain the final merger sequence under the preset observation period.
  • the stocks and commodities are divided into one category, and the other is bonds.
  • the preset observation period is 42 months.
  • the filters and commodities are determined
  • the 42-month first merging sequence is combined again with the first merging sequence and the bond's 42-month filtering sequence to obtain the 42-month merging sequence of the two types of assets.
  • the device After determining the combined sequence corresponding to each preset observation period, the device needs to perform Gaussian filtering extrapolation on the combined sequence to obtain an extrapolated combined sequence.
  • the combined sequence is obtained, the output length of the combined sequence is determined, and the output length is modified by a preset length, thereby obtaining the extrapolated combined sequence.
  • the output length of a 42-month merged sequence is 120, and the extrapolated filter one period will get an extrapolated merged sequence with an output length of 121.
  • the extrapolated filter one period is the preset length, and the preset length can be based on actual needs.
  • the setting is not limited to 1.
  • Step S230 input each of the year-on-year sequence data and the combined sequence into a linear regression model to obtain a linear regression coefficient
  • the linear regression model is stored in the device, and the device can use the synchronized sequence data corresponding to various assets as the dependent variable, and the combined sequence as the independent variable, which is input into the linear regression model to obtain the linear regression coefficient.
  • the linear regression model has a corresponding formula, that is, the linear regression formula, and the linear regression formula is: Among them, X 42 , X 100 , and X 200 are the merged sequences corresponding to 42 months, 100 months, and 200 months without predicting the preset observation period, b 1 is the intercept term, and b 2 , b 3 , and b 4 are linear Regression coefficients, It is the year-on-year serial data.
  • the linear regression model uses the least squares estimation algorithm to obtain the estimated value of the linear regression coefficient according to the linear regression formula.
  • the classification of various assets can be classified according to asset category or investment risk. If the asset classification method is asset category classification, the above formula is used to obtain the linear regression coefficient; if the asset classification method is investment risk classification , You need to differentiate the year-on-year sequence data of various assets to obtain the differential data, and then use the differential data as the dependent variable and the combined sequence as the independent variable to substitute the linear regression coefficient to obtain the linear regression coefficient.
  • the difference data is obtained by the difference of the year-on-year serial data, as follows:
  • Step S240 according to the extrapolated and merged sequence and the linear regression coefficient, perform a fitting prediction of the year-on-year sequence data for each type of asset to determine the predicted increase or decrease corresponding to each type of the asset;
  • the device can perform a fitting prediction of the same-year sequence data for each type of asset according to the extrapolated combined sequence and the linear regression coefficients to obtain the predicted value.
  • the fitted value at the current time is So as to get the predicted rise and fall
  • step S300 the investment weights of the various types of assets are determined according to each of the predicted fluctuations.
  • the target weights of various assets can be determined according to the predicted rise and fall.
  • the predicted fluctuation can be positive or negative. When the predicted fluctuation is greater than 0, it indicates that the asset corresponding to the predicted fluctuation will have greater returns in the next period, and when the predicted fluctuation is less than 0 , It indicates that the assets corresponding to the predicted rise and fall will have a lower return in the next period.
  • the device can determine the investment weight of various assets according to the magnitude of the predicted rise and fall of various assets. The greater the predicted rise and fall, the greater the investment weight.
  • the device can generate investment advice information for various assets. For example, if various types of assets are stocks, commodities, and bonds, and the corresponding weights of stocks, commodities, and bonds are 0.2, 0.5, and 0.3, it is recommended to divide the capital into 20%, 50%, and 30%. Invest funds in stocks, invest 50% of funds in commodities, and invest 30% of funds in bonds". In addition, there are multiple assets in a certain type of asset. For example, if Class A assets include stocks and bonds, the investment weight of stocks and bonds is half of the investment weight of Class A assets.
  • the device can also modify the investment recommendation information according to the desired wealth management product set by the user as a reference. For example, if the desired wealth management product set by the user is a medium-low risk and stable product, while the bond risk is low and stable, it is recommended to divide the funds into 20%, 50%, and 30%, and invest 20% of the funds in stocks, 50% of funds will be invested in commodities and 30% of funds will be invested in bonds", amended to "it is recommended to divide funds into 15%, 45%, and 40%, 15% of funds will be invested in stocks, 45% of funds will be invested in commodities, and Invest 40% of funds in bonds”.
  • the desired wealth management product set by the user is a medium-low risk and stable product, while the bond risk is low and stable, it is recommended to divide the funds into 20%, 50%, and 30%, and invest 20% of the funds in stocks, 50% of funds will be invested in commodities and 30% of funds will be invested in bonds", amended to "it is recommended to divide funds into 15%, 45%, and 40%, 15% of funds will be invested in stocks, 45% of funds will be invested
  • the investment suggestion information is fed back to the user's corresponding client, so that various financial products can be invested based on the investment suggestion information.
  • the device obtains various types of assets and year-on-year sequence data corresponding to each type of asset, and determines the predicted fluctuations corresponding to each type of asset based on the year-on-year sequence data, and then determines the predicted fluctuations according to the respective predicted fluctuations.
  • Investment weight of various assets Since the device can determine the asset type classification or the investment weight of various assets after the investment risk classification through the year-on-year sequence data of various assets, the device can generate investment advice information of various assets according to the investment weight reasonably, and then provide users with reliable Investment advice for wealth management products with stable income.
  • each year-on-year sequence data can be expressed as:
  • R K+1 is the normalization coefficient, which can prevent the weight range from becoming unstable due to continuous accumulation. It ensures that the square sum of the weight coefficient of each year-on-year serial data is equal to the number of year-on-year serial data, namely
  • the weight coefficient of each year-on-year sequence data is converged through a preset number of iterations, that is, the number of iterations for the weight coefficient convergence is determined in advance through experiments and stored as The preset number of times, when the SUMPLE algorithm is used to synthesize each year-on-year sequence data, when the number of iterations reaches the preset number of times, the iteration is stopped, and the synthesized combined sequence is output.
  • the system-level financial data movement law represented by the merged sequence is more stable and reliable, and more predictable.
  • the SUMPLE algorithm is suitable for the synthesis of low signal-to-noise ratio data
  • the optimal weight of each year-on-year sequence data can be calculated, so that the synthesized combined sequence signal noise Than higher.
  • n cor is the duration corresponding to the overall data
  • the relevant time interval n cor is the preset duration, and the preset duration can be set according to the actual situation For example, the preset duration can be set to 4 months (or 120 days).
  • the number of iterations is limited by the total duration of the sampled data. Therefore, the number of iterations may be less due to insufficient data length, which affects the convergence of the weight coefficients.
  • the number of iterations of the iterative method is not limited by the length of the same-year sequence data. Therefore, in this embodiment, preferably, when the SUMPLE algorithm is used to synthesize each year-on-year sequence data, the overall iterative method is selected for synthesis, which can ensure that the weight coefficients of each year-on-year sequence data converge.
  • Figure 4 is a second embodiment of the method for generating asset investment advice information of the application. Based on the first embodiment, acquiring various assets in step S100 to determine the year-on-year sequence data corresponding to each category of assets includes :
  • Step S110 Obtain the observation time, the preset lag period, and the preset year-on-year sequence length;
  • the original asset price lags its year-on-year serial data, and there is a lag period.
  • the original asset price is about 5 months slower than the year-on-year serial data, that is, the change in the cycle phase will be reflected in the asset return after 5 months. Rate.
  • the corresponding asset return rate will rise at the predicted time t+5, and vice versa if It is believed that the return on assets corresponding to period t+5 will decrease.
  • a preset lag period For example, set the preset lag period to 5 months.
  • Step S120 Obtain an end time according to the observation time and the preset hysteresis period, and obtain a start time according to the end time and the preset year-on-year sequence length;
  • Step S130 Acquire year-on-year sequence data of various types of the assets from the start time to the end time.
  • the required year-on-year sequence data is [t-(120+5)month, t-5month], that is, the date starts from 2000
  • the monthly sequence of stock prices starting on January 31, 2009 and ending on December 31, 2009, is used to predict the stock price trend on May 31, 2010.
  • the year-on-year sequence data of multiple types of assets are obtained according to the lag period of the original asset price relative to the same-year sequence data, which reduces the error of predicting the trend of each type of asset.
  • FIG. 5 is a third embodiment of the method for generating asset investment advice information of this application. Based on any one of the first to third embodiments, the filtering corresponding to each type of asset is performed in step S220. The sequence is combined to obtain the combined sequence of each of the preset observation periods, including:
  • Step S221 Determine investment risk parameters corresponding to various types of assets, and classify the various types of assets according to the investment risk parameters to obtain various sets, and the investment risk parameters corresponding to the various types of assets in the set belong to the same value.
  • Step S222 Combine the filter sequences corresponding to the various types of assets in the collection to obtain an intermediate combined sequence of each of the collections in each of the preset observation periods;
  • Step S223 Combine each of the intermediate combined sequences to obtain a combined sequence corresponding to each of the preset observation periods.
  • each asset that needs to be classified as investment risk is first merged with a filter sequence, and the merge sequence of various assets is merged again to obtain The final combined sequence under the preset observation period.
  • the device first determines the filter sequence corresponding to each type of asset, and the classification method of each type of asset is asset type classification.
  • the device determines the investment risk parameter corresponding to each type of asset.
  • the investment risk parameter represents the investment risk of the asset. The higher the investment risk parameter, the greater the investment risk of the asset.
  • the device can classify various types of assets according to the investment risk parameters of each type of asset, and obtain each set.
  • the investment risk parameters of various assets in the set belong to the same numerical range. For example, there are two sets.
  • the various assets in one set are stocks and commodities.
  • the value range of investment risk parameters corresponding to stocks and commodities corresponds to high-risk investments; the assets in the other set are bonds, and bonds correspond to investments.
  • the numerical range of risk parameters corresponds to low-risk investments.
  • the preset observation period is 42 months.
  • the method of generating asset investment advice information can be divided into eight steps, specifically:
  • Step 1 Determine the year-on-year sequence data of various types of assets.
  • the classification methods of various types of assets include asset type classification and investment risk classification. Therefore, step 1 can be divided into two situations, one of which is derived from asset type classification
  • the year-on-year sequence data of various types of assets is defined as step 1.1; the other is the year-on-year sequence data of various assets obtained by the investment risk classification method, which is defined as step 1.2;
  • Step 2 Determine the filter sequence corresponding to the year-on-year sequence data in each preset observation period
  • Step 3 Combine different filter sequences corresponding to the same preset observation period to obtain a combined sequence.
  • merging methods There are two merging methods used, preferably the sum algorithm is used to merge the filter sequences, and there are three merging methods, which are defined as steps 3.1.
  • Steps 3.2 and 3.3 where step 3.1 is the combination of filtering sequences of various assets classified by asset type, step 3.2 is the combination of filtering sequences of various assets classified by investment risk, and step 3.3 is It is to merge the filter sequences of different types of assets in the same category of assets with investment risk classification, and then merge the preliminary merged sequences corresponding to each type of asset;
  • Step 4 Extrapolate the combined sequence, that is, perform Gaussian filter extrapolation on the combined sequence to obtain an extrapolated combined sequence;
  • Step 5 Input the year-on-year sequence data and the combined sequence into the linear regression model to obtain the linear regression coefficients.
  • Step 5 includes two different steps, namely Step 5.1 and Step 5.2.
  • Step 5.1 uses the synchronized sequence data corresponding to the asset as the dependent variable.
  • the combined sequence is used as the independent variable to perform linear regression with intercept term to obtain the linear regression coefficient
  • step 5.2 is to use the difference of the synchronized sequence data corresponding to the asset as the dependent variable and the combined sequence as the independent variable to perform linear regression with intercept term , Get the linear regression coefficient;
  • Step 6 Extrapolate the combined sequence as the independent variable and linear regression coefficient as the coefficient, perform fitting prediction on the asset synchronization sequence data to obtain the predicted value, and obtain the current time, that is, the fitted value of the current period, through the predicted value and fitting Make the difference between the value and adjust the lag period to get the predicted rise and fall;
  • Step 7 Sort all kinds of assets according to the magnitude of the predicted rise and fall, so as to determine the investment weights corresponding to all kinds of assets;
  • Step 8 Generate asset investment advice information based on the investment weight of various assets.
  • step 1 includes two processing situations
  • step 3 includes three processing situations
  • step 5 includes two processing situations
  • the different processing situations of these three steps are sorted and combined, and the investment advice information is the most accurate after actual measurement.
  • strategy one is: Step 1.1+Step 2+Step 3.1+Step 4+Step 5.1+Step 6+Step 7+Step 8;
  • the second strategy is: Step 1.2+Step 2+Step 3.3+Step 4+Step 5.1+Step 6+Step 7+Step 8.
  • the present application also provides a device for generating asset investment advice information.
  • the device for generating asset investment advice information includes: a memory, a processor, and asset investment advice information stored on the memory and running on the processor
  • the generating program of the asset investment recommendation information is executed by the processor to implement the steps of the method for generating asset investment recommendation information as described in the above embodiment.
  • the present application also provides a readable storage medium on which a program for generating asset investment advice information is stored.
  • a program for generating asset investment advice information is stored on a readable storage medium on which a program for generating asset investment advice information is stored.
  • the program for generating asset investment advice information is executed by a processor, the asset as described in the above embodiment is implemented. Steps of the method of generating investment advice information.

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Abstract

Procédé et appareil permettant de générer des informations de suggestion d'investissement d'actifs et support de stockage lisible. Le procédé de génération d'informations de suggestion d'investissement d'actifs comprend les étapes suivantes consistant : à acquérir divers actifs de façon à déterminer des données séquentielles d'une année sur l'autre correspondant aux divers actifs, les moyens de classification pour les divers actifs comprenant une classification de type d'actif et une classification de risque d'investissement (S100) ; à déterminer, en fonction des diverses données séquentielles d'une année sur l'autre, une amplitude de fluctuation prédite correspondant à chaque actif (S200) ; et à déterminer, en fonction des différentes amplitudes de fluctuation prédites, les pondérations d'investissement des divers actifs (S300).
PCT/CN2020/101644 2019-11-25 2020-07-13 Procédé et appareil permettant de générer des informations de suggestion d'investissement d'actifs et support de stockage lisible WO2021103571A1 (fr)

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CN110458352A (zh) * 2019-08-06 2019-11-15 华泰证券股份有限公司 预测资产价格走势的方法、服务器及计算机可读存储介质
CN111126666A (zh) * 2019-11-25 2020-05-08 华泰证券股份有限公司 资产投资建议信息的生成方法、装置和可读存储介质

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CN109978214A (zh) * 2017-12-28 2019-07-05 广州潽蓝信息科技有限公司 一种筛选目标金融产品的方法和装置
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