CN117745443A - Asset expected benefit estimation method, device, electronic equipment and storage medium - Google Patents
Asset expected benefit estimation method, device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a method, a device, electronic equipment and a storage medium for estimating expected benefits of assets, which are used for responding to asset type selection operation to determine and acquire historical benefit data of an asset combination; based on the risk budget model, aiming at minimizing tracking errors of the risk contribution ratio and the expected risk ratio of the asset combination, determining the asset combination weight of the asset combination; simulating the expected yield of the asset combination in the target time period by combining the historical yield data and the asset combination weight to obtain the comprehensive yield of the asset combination in the specific time period; and repeatedly executing the profitability simulation process until the comprehensive profitability of the preset number is simulated, analyzing the comprehensive profitability of the preset number, and determining the profitability analysis data of the asset combination. Thus, the estimation of the asset combination weight of the asset combination is realized by combining the historical benefit data of the asset combination, and the design planning efficiency of the asset investment scheme and the accuracy of the benefit estimation are improved.
Description
Technical Field
The present disclosure relates to the field of revenue estimation technologies, and in particular, to a method and apparatus for estimating expected revenue of an asset, an electronic device, and a storage medium.
Background
When domestic banks make asset planning for clients, related staff for asset configuration usually make asset design planning according to industry experience; however, the associated staff of asset configuration can only design investment weights to plan for each asset class based on industry experience; obviously, the investment weights configured for each asset class according to experience are not any basis, and because the capability of the staff related to asset configuration is good and bad, for the staff with certain experience priority, a great amount of time is required to carry out the design planning of the asset investment scheme, and feedback cannot be carried out to clients in time, so that the planning efficiency of the asset investment scheme is lower; on the other hand, since the empirically configured investment weights do not have any basis, the profit situation cannot be accurately estimated.
Disclosure of Invention
In view of the foregoing, an object of the present application is to provide a method, an apparatus, an electronic device and a storage medium for estimating expected benefits of an asset, so as to solve the problem that the prior art cannot accurately estimate the benefits.
The embodiment of the application provides an estimation method of expected benefits of an asset, which comprises the following steps:
Responsive to an asset type selection operation, determining an asset portfolio of a plurality of asset categories having investment intent for a user and obtaining historical revenue data for the asset portfolio;
determining, based on a risk budget model, portfolio weights assigned to the portfolios at investment time using historical revenue data for the portfolios, targeting a minimum tracking error of a risk contribution ratio and a desired risk ratio for the portfolios;
combining the historical revenue data and the portfolio weight, and obtaining the comprehensive revenue rate generated by the portfolio in a specific time period by simulating the expected revenue rate of the portfolio in a target time period;
and repeatedly executing the profitability simulation process until a preset number of comprehensive profitability is simulated, and determining the profitability analysis data of the portfolio according to the portfolio weight by analyzing the preset number of comprehensive profitability.
In one possible implementation, the determining, based on the risk budget model, the portfolio weight allocated for the portfolio at the time of investment, using historical revenue data for the portfolio, targeting that a tracking error of a risk contribution ratio of the portfolio to a desired risk ratio is minimized, includes:
Acquiring a plurality of groups of candidate combination weights by calling a python layer;
determining, using historical revenue data for the portfolio, revenue covariances generated by the portfolio on different trade days;
determining, for each set of candidate portfolio weights, a standard deviation of risk resulting from investing in the portfolio in accordance with the candidate portfolio weight in combination with the profit covariance and the candidate portfolio weight; wherein the risk standard deviation is used for representing the profit dispersion degree of the asset combination;
determining a portfolio asset risk of investing in the portfolio in accordance with the candidate portfolio weight based on the risk standard deviation, the portfolio weight, and the profit covariance;
weighting the portfolio risk by using the portfolio weight, and determining a risk contribution duty ratio of the portfolio by determining a weighted asset risk of investing in the portfolio according to the candidate portfolio weight;
determining a risk contribution ratio tracking error resulting from investing in the portfolio according to the candidate portfolio weight based on the risk contribution ratio and the expected risk ratio of the portfolio;
and determining the candidate portfolio weight with the minimum risk ratio tracking error as the portfolio weight of the portfolio.
In one possible implementation, the combining the historical revenue data and the portfolio weight to obtain the integrated revenue rate generated by the portfolio over a specific time period by simulating the expected revenue rate of the portfolio over the target time period includes:
simulating expected revenue data acquired by each asset class in the asset portfolio within a target time period by combining the historical revenue data of the asset class;
determining an expected rate of return generated by the asset class during the target time period based on the expected rate of return data;
and weighting the expected yield rate generated by each asset class in the target time period according to the asset combination weight, and simulating to obtain the comprehensive yield rate generated by the asset combination in the specific time period.
In one possible implementation, the obtaining historical revenue data for the portfolio includes:
for each asset class in the asset portfolio, acquiring each performance benchmark index under the asset class;
weighting each performance benchmark index under the asset class according to the index weight of each performance benchmark index to obtain historical revenue data of the asset class;
Historical revenue data for the portfolio is determined based on the historical revenue data for each asset class.
In one possible embodiment, the determining the revenue analysis data for investing in the portfolio according to the portfolio weight by analyzing the preset number of integrated profitability includes:
determining a combined benefit distribution and an average benefit rate of investing in the portfolio according to the portfolio weight by analyzing the preset number of integrated benefit rates;
based on the combined revenue distribution, a revenue confidence interval for the portfolio is determined.
In one possible embodiment, the estimation method further includes:
and feeding back the income analysis data generated by the asset combination to a Java layer so as to display the income analysis data in a graphical user interface of the terminal equipment through the Java layer.
In one possible implementation, the desired risk ratio is configured by:
the desired risk duty cycle is configured by the duty cycle configuration operation in response to the duty cycle configuration operation.
The embodiment of the application also provides an estimation device of expected benefits of the asset, which comprises:
An asset class selection module for determining an asset portfolio made up of a plurality of asset classes having investment intents for a user in response to an asset type selection operation, and obtaining historical revenue data for the asset portfolio;
a weight estimation module for determining, based on a risk budget model, a portfolio weight allocated to the portfolio at the time of investment, using historical revenue data of the portfolio, targeting that a tracking error of a risk contribution ratio and an expected risk ratio of the portfolio is minimum;
the profit simulation module is used for combining the historical profit data and the asset combination weight, and obtaining the comprehensive profit rate of the asset combination in a specific time period by simulating the expected profit rate of the asset combination in the target time period;
and the profit determination module is used for repeatedly executing the profit rate simulation process until a preset number of comprehensive profit rates are simulated, and determining the profit analysis data of the asset combination according to the asset combination weight by analyzing the preset number of comprehensive profit rates.
In one possible implementation, the weight estimation module is configured to determine, using historical revenue data of the portfolio based on a risk budget model, a portfolio weight allocated for the portfolio at the time of investment, with a goal of minimizing tracking error of a risk contribution ratio and a desired risk ratio of the portfolio, the weight estimation module being configured to:
Acquiring a plurality of groups of candidate combination weights by calling a python layer;
determining, using historical revenue data for the portfolio, revenue covariances generated by the portfolio on different trade days;
determining, for each set of candidate portfolio weights, a standard deviation of risk resulting from investing in the portfolio in accordance with the candidate portfolio weight in combination with the profit covariance and the candidate portfolio weight; wherein the risk standard deviation is used for representing the profit dispersion degree of the asset combination;
determining a portfolio asset risk of investing in the portfolio in accordance with the candidate portfolio weight based on the risk standard deviation, the portfolio weight, and the profit covariance;
weighting the portfolio risk by using the portfolio weight, and determining a risk contribution duty ratio of the portfolio by determining a weighted asset risk of investing in the portfolio according to the candidate portfolio weight;
determining a risk contribution ratio tracking error resulting from investing in the portfolio according to the candidate portfolio weight based on the risk contribution ratio and the expected risk ratio of the portfolio;
and determining the candidate portfolio weight with the minimum risk ratio tracking error as the portfolio weight of the portfolio.
In one possible implementation, the profit modeling module, when configured to combine the historical profit data and the portfolio weights to obtain the integrated profit sharing rate generated by the portfolio over a particular time period by modeling the expected profit sharing rate of the portfolio over a target time period, is configured to:
simulating expected revenue data acquired by each asset class in the asset portfolio within a target time period by combining the historical revenue data of the asset class;
determining an expected rate of return generated by the asset class during the target time period based on the expected rate of return data;
and weighting the expected yield rate generated by each asset class in the target time period according to the asset combination weight, and simulating to obtain the comprehensive yield rate generated by the asset combination in the specific time period.
In one possible implementation, the asset class selection module, when configured to obtain historical revenue data for the portfolio, is configured to:
for each asset class in the asset portfolio, acquiring each performance benchmark index under the asset class;
Weighting each performance benchmark index under the asset class according to the index weight of each performance benchmark index to obtain historical revenue data of the asset class;
historical revenue data for the portfolio is determined based on the historical revenue data for each asset class.
In one possible implementation, the profit determination module, when configured to determine, by analyzing the preset number of integrated profit margins, profit analysis data for investing in the portfolio according to the portfolio weight, is configured to:
determining a combined benefit distribution and an average benefit rate of investing in the portfolio according to the portfolio weight by analyzing the preset number of integrated benefit rates;
based on the combined revenue distribution, a revenue confidence interval for the portfolio is determined.
In one possible implementation, the estimation device further includes a display module for:
and feeding back the income analysis data generated by the asset combination to a Java layer so as to display the income analysis data in a graphical user interface of the terminal equipment through the Java layer.
In one possible implementation, the estimation device further comprises a duty cycle configuration module for configuring the desired risk duty cycle by:
The desired risk duty cycle is configured by the duty cycle configuration operation in response to the duty cycle configuration operation.
The embodiment of the application also provides electronic equipment, which comprises: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the method of estimating expected benefits of an asset as described above.
Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method of estimating expected benefits of an asset as described above.
The method, the device, the electronic equipment and the storage medium for estimating expected benefits of assets, provided by the embodiment of the application, respond to asset type selection operation, determine an asset combination consisting of a plurality of asset categories with investment intention for a user, and acquire historical benefit data of the asset combination; determining, based on the risk budget model, portfolio weights assigned to the portfolios at investment using historical revenue data for the portfolios, targeting a minimum tracking error of the portfolio's risk contribution duty to the desired risk duty; combining the historical profit data and the asset combination weight, and obtaining the comprehensive profit rate of the asset combination in a specific time period by simulating the expected profit rate of the asset combination in the target time period; and repeatedly executing the profitability simulation process until a preset number of comprehensive profitability is simulated, and determining the profitability analysis data of the investment portfolio according to the portfolio weight by analyzing the preset number of comprehensive profitability. In this way, the historical profit data of the asset combination can be combined to realize the estimation of the asset combination weight of the asset combination so as to reasonably plan asset investment schemes of a plurality of asset categories and realize the estimation of the profit situation, and further, the design planning efficiency of the asset investment schemes and the accuracy of the profit estimation can be improved; in addition, in order to facilitate the user to check the profit effect generated by the asset combination, after the python layer finishes the calculation of the profit rate, the display of the investment effect is realized through the Java layer, so that the user can know the investment effect in time.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for estimating expected benefits of an asset provided by embodiments of the present application;
FIG. 2 is a schematic diagram of a yield estimation process according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of an apparatus for estimating expected benefits of an asset according to an embodiment of the present application;
FIG. 4 is a second schematic diagram of an apparatus for estimating expected benefits of an asset according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments of the present application, every other embodiment that a person skilled in the art would obtain without making any inventive effort is within the scope of protection of the present application.
According to research, when domestic banks make asset planning for clients, related staff for asset configuration usually make asset design planning according to industry experience; however, the associated staff of asset configuration can only design investment weights to plan for each asset class based on industry experience; obviously, the investment weights configured for each asset class according to experience are not any basis, and because the capability of the staff related to asset configuration is good and bad, for the staff with certain experience priority, a great amount of time is required to carry out the design planning of the asset investment scheme, and feedback cannot be carried out to clients in time, so that the planning efficiency of the asset investment scheme is lower; on the other hand, since the empirically configured investment weights do not have any basis, the profit situation cannot be accurately estimated.
Based on this, the embodiment of the application provides a method for estimating expected benefits of assets, which can combine historical benefit data of an asset combination to estimate the weight of the asset combination, so as to reasonably plan asset investment schemes of a plurality of asset categories and estimate the benefits, and further, can improve the design planning efficiency of the asset investment schemes and the accuracy of the benefits estimation.
Referring to fig. 1, fig. 1 is a flowchart of a method for estimating expected benefits of an asset according to an embodiment of the present application. As shown in fig. 1, a method for estimating expected benefits of an asset provided by an embodiment of the present application includes:
s101, responding to an asset type selection operation, determining an asset combination formed by a plurality of asset categories with investment intention for a user, and acquiring historical income data of the asset combination.
S102, determining the asset combination weight allocated to the asset combination in investment by using the historical benefit data of the asset combination and aiming at the minimum tracking error of the risk contribution duty ratio and the expected risk duty ratio of the asset combination based on the risk budget model.
S103, combining the historical income data and the asset combination weight, and obtaining the comprehensive income ratio of the asset combination in a specific time period by simulating the expected income ratio of the asset combination in the target time period.
S104, repeatedly executing the profitability simulation process until a preset number of comprehensive profitability is simulated, and determining the revenue analysis data of the asset combination according to the asset combination weight by analyzing the preset number of comprehensive profitability.
In the method for estimating expected benefits of assets, an asset combination formed by a plurality of asset categories with investment intention for a user can be determined in response to asset category selection operation applied by the user, and historical benefit data of the asset combination is acquired; based on the risk budget model, aiming at minimizing tracking errors of the risk contribution proportion and the expected risk proportion of the asset combination, determining the asset combination weight allocated to the asset combination in investment, thereby realizing the configuration of the asset combination weight according to the asset risk contribution proportion; and the historical revenue data of a plurality of asset classes are combined to realize the estimation of the combined revenue rate of the asset classes of the asset combination, so that the design planning efficiency of the asset investment scheme and the accuracy of the revenue estimation can be improved.
Here, the asset classes may divide the existing performance benchmark indexes into a plurality of asset classes in a custom manner through the Java layer according to definition standards given by each financial institution; the asset classes may include classes such as cash, fixed, equity, and alternative, on which the staff may further anchor the performance benchmark indices involved under each asset class.
In one embodiment, asset classes are configured by the steps of;
s1, dividing the plurality of asset classes by analyzing definition standards given by a financial institution.
In this step, the staff can input definition standards given by financial institutions through the Java layer, and the Java layer classifies the existing performance benchmark indexes into a plurality of asset classes by parsing the definition standards given by the respective financial institutions.
Here, the staff may also input the divided asset classes through the Java layer, thereby completing the definition of the asset classes autonomously.
S2, for each asset class, responding to an index configuration operation, and configuring at least one performance benchmark index related to the asset class through the index configuration operation.
In the step, a worker can determine the asset class to which each performance reference index belongs according to the data such as the historical trend of each performance reference index, and the configuration of at least one performance reference index related under each asset class is completed through the index configuration operation; specifically, for each of the divided asset classes, responsive to an index configuration operation applied by a user, at least one performance benchmark index related to the asset class is selected through the index configuration operation to complete the configuration of the at least one performance benchmark index related to the asset class.
Illustratively, when the asset class is a cash class, the performance benchmark index to which the cash class pertains may include a demand deposit interest rate, a monetary fund index, and the like; when the asset class is a fixed receipts class, the performance benchmark indices referred to by the fixed receipts class may include the regular deposit interest rate and the medium debt composite index, etc.
Here, when the user has an investment will, a plurality of performance reference indexes desired to be invested may be selected from the plurality of asset classes configured in accordance with the actual investment situation, and thus, by specifying the asset class to which each performance reference index belongs, a plurality of asset classes to which the user has an investment will may be specified, and the portfolio may be obtained by combining.
In step S101, the user may call the Java layer, select an asset class having an investment intention by applying an asset class selection operation, so as to determine an asset combination by combining the selected asset classes; specifically, in response to an asset class selection operation applied by a user, determining a plurality of asset classes having investment intents selected by the user through the asset class selection operation, and combining the plurality of asset classes to obtain an asset combination.
And, in order to estimate the combined yield obtained by the portfolio in the subsequent process, the historical yield data of the portfolio is obtained while the portfolio is determined.
Here, the portfolio is composed of a plurality of asset classes, and it can be seen that the historical revenue data of the portfolio refers to the historical revenue data of each asset class included in the portfolio; the asset class is actually a "class" and does not have historical revenue data, at which point the historical revenue data for each asset class may be determined with reference to the performance benchmark index included under that asset class.
In some cases the user may define the asset class with the intent of the investment by selecting a performance benchmark index, in which case the historical revenue data for the asset class may be determined by weighting each performance benchmark index as it relates to a plurality of performance benchmark indexes.
In one embodiment, the obtaining historical revenue data for the plurality of asset classes includes:
s1011, aiming at each asset class in the asset combination, acquiring each performance benchmark index under the asset class.
In this step, for each asset class for which the user has an investment intention, the performance benchmark index for which the user has an investment intention under that asset class is acquired one by one.
And S1012, weighting each performance benchmark index under the asset class according to the index weight of each performance benchmark index to obtain the historical profit data of the asset class.
In the step, for each performance benchmark index, when a user configures an asset class to which the performance benchmark index belongs, a corresponding index weight is configured for the performance benchmark index under each asset class; if the user has an investment intention for an asset class, the historical revenue data for the asset class may be determined by weighting each performance benchmark index by means of the index weight of each performance benchmark index under the asset class; specifically, the historical revenue data for the asset class is determined by weighting each performance benchmark index according to an index weight pre-configured for each performance benchmark index.
In the above embodiment, the description is made taking the asset class as an example, and two performance reference indexes, namely, the demand deposit interest rate and the monetary fund index, are related under the cash class, and at this time, an index weight of 0.5 may be assigned to the demand deposit interest rate, and an index weight of 0.5 may be assigned to the monetary fund index.
S1013, determining the historical revenue data of the asset combination based on the historical revenue data of each asset class.
In the process of investing in a portfolio, the investment weight (i.e., the investment proportion) occupied by each asset class in the portfolio affects the profitability, so in order to pursue a greater profitability, the class investment weight allocated to each asset class at the time of investment needs to be reasonably planned, so as to define the portfolio weight of the portfolio as a whole; that is, the portfolio weight is composed of category investment weights assigned to each asset category.
In the process of portfolio, the investment weight (i.e., the investment ratio) occupied by each asset class affects the profitability, so in order to pursue a larger profitability, the investment weight occupied by each asset class when invested needs to be reasonably planned, so as to define the portfolio weights of the asset classes.
In step S102, considering the size of the risk duty tracking error generated by the portfolio, the return of investment and the risk situation can be reflected, so that the design planning of the portfolio weight of the portfolio can be reasonably realized by means of the risk duty tracking error; specifically, a risk budget model is adopted, tracking errors of the risk contribution ratio and the expected risk ratio of the portfolio are minimized, and the portfolio weight allocated to the portfolio during investment is determined.
In one embodiment, step S102 includes:
s1021, a plurality of groups of candidate combination weights are obtained by calling the python layer.
In the step, a plurality of sets of preset candidate combination weights are acquired by calling a python layer, and in the subsequent process, the asset combination weight suitable for the asset combination can be selected from the acquired plurality of sets of candidate combination weights by calculating the summer ratio generated by the asset combination under each set of candidate combination weights.
S1022, determining the profit covariance Cov (r) generated by the asset combination on different transaction days by utilizing the historical profit data of the asset combination i ,r j )。
In this step, by analyzing the historical revenue data generated by the portfolio during the historical investment process, a determination is made of the revenue covariance Cov (r) generated by the portfolio during the different trade days of the historical investment process i ,r j )。
In the investment process, the asset combination weight of the asset combination can influence the risk brought by the investment; thus, in order to reasonably plan out portfolio weights, the portfolio weights applicable to the portfolio can also be selected from the multiple sets of candidate portfolio weights by means of the standard deviation of risk generated by the portfolio under the candidate portfolio weights.
S1023, determining the risk standard deviation generated by investing the asset combination according to the candidate combination weight by combining the gain covariance and the candidate combination weight aiming at each group of candidate combination weight.
In the step, for each set of obtained and preset candidate combination weights, the candidate combination weights are combined with the profit covariance generated by the asset combination on different transaction days, and the risk standard deviation generated by the asset combination when the asset combination is invested according to the candidate combination weights is determined.
Wherein the risk standard deviation is used for representing the profit dispersion degree of the asset combination;
here, the risk standard deviation generated by the portfolio is calculated by the following formula:
wherein sigma is the standard deviation of risk,w i for portfolio weights, cov (r i ,r j ) The generated income covariance r for different trade days i The rate of return on day i for multiple asset classes, r j The profitability for the j-th day of the plurality of asset classes.
The portfolio asset risk refers to uncertainty of asset value generated by the portfolio, and can be used for representing investment risk generated by the portfolio, and the investment risk of each asset class in the portfolio also affects the investment weight occupied by the asset class, and further affects the overall portfolio weight of the portfolio; thus, in calculating the portfolio weights for a portfolio, the portfolio weights applicable to the portfolio can be selected from the multiple sets of portfolio weights by virtue of the portfolio risk created by the portfolio.
And S1024, determining the combined asset risk of investing the asset combination according to the candidate combination weight based on the risk standard deviation, the asset combination weight and the profit covariance.
In this step, the portfolio risk of investing in the portfolio according to the candidate portfolio weight is determined using the standard deviation of risk generated by the portfolio, the candidate portfolio weight, and the covariance of revenue generated by the portfolio on different trade days.
Calculating a portfolio asset risk created by a portfolio by the following formula:
wherein the MRC is a combined asset risk generated by a plurality of asset classes.
S1025, weighting the combined asset risk by using the asset combination weight, and determining the risk contribution duty ratio of the asset combination by determining the weighted asset risk of the asset combination according to the candidate combination weight.
In this step, the weighted asset risk for the portfolio is calculated by the following formula:
TRC＝w i ·MRC；
wherein the TRC is a weighted asset risk for the portfolio.
And S1026, determining a risk ratio tracking error generated by investing the asset combination according to the candidate combination weight based on the risk contribution ratio and the expected risk ratio of the asset combination.
In this step, a risk ratio tracking error generated by investing in the portfolio with the candidate portfolio weight is determined based on the risk contribution ratio and the desired risk ratio of the portfolio, so as to select an optimal portfolio weight suitable for the portfolio from the plurality of candidate portfolio weights according to the risk ratio tracking error.
The risk duty tracking error generated by the portfolio is calculated by the following formula:
wherein MSE is a risk duty tracking error generated by asset combination, R T Is the desired risk duty cycle.
S1027, determining the candidate portfolio weight with the minimum risk duty ratio tracking error as the portfolio weight of the portfolio.
Here, the expected profitability of the portfolio of multiple asset classes over the target time period may be simulated with the aid of the historical revenue data for the multiple asset classes to determine a composite profitability of the portfolio of asset classes over the specific time period based on the simulated expected profitability.
Here, the expected rate of return generated by the portfolio over the target time period may be simulated with the aid of the historical rate of return data for the portfolio to determine a composite rate of return that the portfolio can generate over a particular time period based on the simulated expected rate of return.
In step S103, the expected rate of return generated by the portfolio over the target time period is determined by simulating the investment of the portfolio in terms of the portfolio weights in combination with the historical rate of return of the portfolio and the portfolio weights, and the integrated rate of return generated by the portfolio over the specific time period is determined.
In one embodiment, step S103 includes:
s1031, combining the historical revenue data of each asset class in the asset combination, and simulating expected revenue data acquired by the asset class in the target time period.
In this step, the historical revenue data for each asset class in the portfolio is combined, and the expected revenue data for that asset class taken over a target period of time (e.g., a trade day) is randomly simulated; further, expected revenue data expected to be achieved by the portfolio over a target time period (e.g., a year trading day) may be simulated by combining expected revenue data achieved by each asset class over the target time period (e.g., a trade day).
In order to ensure the credibility of the expected revenue data obtained by simulation, the expected revenue data of each asset class obtained by simulation is required to be ensured to be consistent with the deviation of the historical revenue data, so that the simulated expected revenue data is required to be limited by the average value and the standard value of the historical revenue data in the process of obtaining the expected revenue data by simulation; that is, the mean and standard value of expected revenue data obtained by the simulation are identical to the mean and standard value of historical revenue data.
Here, considering that the profitability can intuitively show the profitability of the portfolio, when estimating the overall revenue of the portfolio, the overall profitability of each asset class generated during a specific time period can be further determined by means of the expected revenue data generated during the target time period by the asset class.
S1032, determining an expected rate of return generated by the asset class within the target time period based on the expected rate of return data.
In this step, an expected rate of return that can be generated in the target time period by investing in the asset class is calculated based on the simulated expected rate of return generated in the target time period by the asset class.
Specifically, the expected rate of return generated by each asset class over the target time period is determined by the following formula:
R i ＝∏(1+r i )-1；
wherein R is i The expected rate of return, r, generated for each asset class over the target period of time i Expected revenue data expected to be achieved within the target time period for each asset class.
S1033, weighting expected profitability generated by each asset class in the target time period according to the asset combination weight, and simulating to obtain the comprehensive profitability generated by the asset combination in the specific time period.
In this step, the overall rate of return generated by the portfolio over a particular time period is determined by the following equation:
R＝∑(R i ×w i )；
wherein R is the comprehensive yield rate generated by the asset combination in a specific time period.
Here, considering that the data obtained by performing the simulation only once cannot describe the profit distribution of the portfolio, in order to improve the accuracy of the simulation result, the simulated profit rate is more similar to the actual produced profit rate, and multiple rounds of simulation are required to obtain the accurate profit data distribution of the portfolio.
In step S104, the simulation process of the expected profitability and the integrated profitability is repeatedly performed until the simulation obtains a preset number of integrated profitability, and the revenue analysis data generated by investing the portfolio according to the portfolio weight is determined by analyzing the preset number of integrated profitability.
Wherein the comprehensive yield of the preset quantity obtained by simulation accords with normal distribution; the revenue analysis data includes a revenue distribution, a revenue confidence interval, and an average revenue rate.
In one embodiment, said determining the revenue analysis data for investing in said portfolio in accordance with said portfolio weight by analyzing said preset number of integrated profitability comprises:
Determining a combined benefit distribution and an average benefit rate of investing in the portfolio according to the portfolio weight by analyzing the preset number of integrated benefit rates; and determining that the portfolio is in a revenue confidence interval based on the combined revenue distribution.
In the step, considering that the comprehensive profit margin of the preset quantity accords with normal distribution, in order to facilitate analysis of the profit situation of the asset combination, utilizing the comprehensive profit margin of the preset quantity to construct a combined profit margin for investment on the asset combination according to the weight investment of the asset combination, and determining the average profit margin of the comprehensive profit margin of the preset quantity; further, by analyzing the combined revenue distribution, a revenue confidence interval for the portfolio is determined.
Here, in order to facilitate the user to view the profit effect generated by the portfolio, after the python layer completes the calculation of the profit rate, the display of the investment effect is realized through the Java layer, so that the user can know the investment effect in time.
In one embodiment, the estimation method further comprises: and feeding back the income analysis data generated by the asset combination to a Java layer so as to display the income analysis data in a graphical user interface of the terminal equipment through the Java layer.
In the step, after the python layer simulates and calculates the combined yield rate generated by combining a plurality of asset types, the yield analysis data generated by the asset combination is fed back to the Java layer, so that the yield analysis data is displayed in a graphical user interface of the terminal equipment through the Java layer, and a user can timely check the investment effect expected to be obtained by the asset combination.
Here, the asset risk contribution ratio of each asset class is different for different asset classes due to the different investment risks present, and thus the asset risk contribution ratio of each asset class may be defined in combination with its own properties.
In one embodiment, the desired risk ratio is configured by:
the desired risk duty cycle is configured by the duty cycle configuration operation in response to the duty cycle configuration operation.
Referring to fig. 2, fig. 2 is a schematic diagram of a yield estimation process according to an embodiment of the present application. As shown in fig. 2, in step 201, the staff may manually divide the asset classes to obtain the asset classes such as cash class, fixed class, equity class, and alternative class;
step 202, the user can select a plurality of asset classes with investment intents from the divided asset classes;
Step 203, configuring asset risk weights for each asset class;
step 204, determining the asset combination weight allocated to the asset combination at the time of investment based on the risk budget model by calling the python layer;
step 205, determining revenue analysis data generated by investing in portfolios in accordance with portfolio weights by modeling the estimated portfolio yield generated by the portfolio over a particular time period.
In the method for estimating expected benefits of assets, provided by the embodiment of the application, in response to an asset type selection operation, an asset combination formed by a plurality of asset categories with investment intentions for a user is determined, and historical benefit data of the asset combination is obtained; determining, based on the risk budget model, portfolio weights assigned to the portfolios at investment using historical revenue data for the portfolios, targeting a minimum tracking error of the portfolio's risk contribution duty to the desired risk duty; combining the historical profit data and the asset combination weight, and obtaining the comprehensive profit rate of the asset combination in a specific time period by simulating the expected profit rate of the asset combination in the target time period; and repeatedly executing the profitability simulation process until a preset number of comprehensive profitability is simulated, and determining the profitability analysis data of the investment portfolio according to the portfolio weight by analyzing the preset number of comprehensive profitability. In this way, the historical profit data of the asset combination can be combined to realize the estimation of the asset combination weight of the asset combination so as to reasonably plan asset investment schemes of a plurality of asset categories and realize the estimation of the profit situation, and further, the design planning efficiency of the asset investment schemes and the accuracy of the profit estimation can be improved; in addition, in order to facilitate the user to check the profit effect generated by the asset combination, after the python layer finishes the calculation of the profit rate, the display of the investment effect is realized through the Java layer, so that the user can know the investment effect in time.
Referring to fig. 3 and 4, fig. 3 is a schematic structural diagram of a device for estimating expected benefits of an asset according to an embodiment of the present application, and fig. 4 is a schematic structural diagram of a second device for estimating expected benefits of an asset according to an embodiment of the present application. As shown in fig. 3, the estimation apparatus 300 includes:
an asset class selection module 310 for determining an asset portfolio of a plurality of asset classes having an investment intention for a user in response to an asset type selection operation and obtaining historical revenue data for the asset portfolio;
a weight estimation module 320 configured to determine, based on the risk budget model, a portfolio weight allocated to the portfolio at the time of investment, using historical revenue data of the portfolio, with a goal of minimizing a tracking error of a risk contribution ratio and a desired risk ratio of the portfolio;
a profit modeling module 330, configured to combine the historical profit data and the portfolio weights to obtain a comprehensive profit margin generated by the portfolio in a specific time period by modeling an expected profit margin of the portfolio in the target time period;
and a profit determination module 340, configured to repeatedly perform a profit margin simulation process until a preset number of comprehensive profit margins are simulated, and determine, by analyzing the preset number of comprehensive profit margins, profit analysis data for investing in the portfolio according to the portfolio weight.
Further, the weight estimation module 320 is configured to determine, based on the risk budget model, a portfolio weight allocated to the portfolio at the time of investment, using historical revenue data of the portfolio, with a goal of minimizing a tracking error of a risk contribution ratio of the portfolio to a desired risk ratio, the weight estimation module 320 is configured to:
acquiring a plurality of groups of candidate combination weights by calling a python layer;
determining, using historical revenue data for the portfolio, revenue covariances generated by the portfolio on different trade days;
determining, for each set of candidate portfolio weights, a standard deviation of risk resulting from investing in the portfolio in accordance with the candidate portfolio weight in combination with the profit covariance and the candidate portfolio weight; wherein the risk standard deviation is used for representing the profit dispersion degree of the asset combination;
determining a portfolio asset risk of investing in the portfolio in accordance with the candidate portfolio weight based on the risk standard deviation, the portfolio weight, and the profit covariance;
weighting the portfolio risk by using the portfolio weight, and determining a risk contribution duty ratio of the portfolio by determining a weighted asset risk of investing in the portfolio according to the candidate portfolio weight;
Determining a risk contribution ratio tracking error resulting from investing in the portfolio according to the candidate portfolio weight based on the risk contribution ratio and the expected risk ratio of the portfolio;
and determining the candidate portfolio weight with the minimum risk ratio tracking error as the portfolio weight of the portfolio.
Further, when the profit modeling module 330 is configured to combine the historical profit data and the portfolio weight to obtain the comprehensive profit sharing rate generated by the portfolio in a specific time period by modeling the expected profit sharing rate of the portfolio in the target time period, the profit modeling module 330 is configured to:
simulating expected revenue data acquired by each asset class in the asset portfolio within a target time period by combining the historical revenue data of the asset class;
determining an expected rate of return generated by the asset class during the target time period based on the expected rate of return data;
and weighting the expected yield rate generated by each asset class in the target time period according to the asset combination weight, and simulating to obtain the comprehensive yield rate generated by the asset combination in the specific time period.
Further, the asset class selection module 310 is configured to, when configured to obtain historical revenue data for the portfolio, the asset class selection module 310:
for each asset class in the asset portfolio, acquiring each performance benchmark index under the asset class;
weighting each performance benchmark index under the asset class according to the index weight of each performance benchmark index to obtain historical revenue data of the asset class;
historical revenue data for the portfolio is determined based on the historical revenue data for each asset class.
Further, the profit determining module 340, when determining the profit analysis data for investing in the portfolio according to the portfolio weight by analyzing the preset number of integrated profit margins, the profit determining module 340 is configured to:
determining a combined benefit distribution and an average benefit rate of investing in the portfolio according to the portfolio weight by analyzing the preset number of integrated benefit rates;
based on the combined revenue distribution, a revenue confidence interval for the portfolio is determined.
Further, as shown in fig. 4, the estimation device 300 further includes a display module 350, where the display module 350 is configured to:
And feeding back the income analysis data generated by the asset combination to a Java layer so as to display the income analysis data in a graphical user interface of the terminal equipment through the Java layer.
Further, as shown in fig. 4, the estimation device 300 further includes a duty configuration module 360, where the duty configuration module 360 is configured to configure the desired risk duty ratio by:
the desired risk duty cycle is configured by the duty cycle configuration operation in response to the duty cycle configuration operation.
The device for estimating expected benefits of assets, provided by the embodiment of the application, responds to an asset type selection operation, determines an asset combination composed of a plurality of asset categories with investment intentions for a user, and acquires historical benefit data of the asset combination; determining, based on the risk budget model, portfolio weights assigned to the portfolios at investment using historical revenue data for the portfolios, targeting a minimum tracking error of the portfolio's risk contribution duty to the desired risk duty; combining the historical profit data and the asset combination weight, and obtaining the comprehensive profit rate of the asset combination in a specific time period by simulating the expected profit rate of the asset combination in the target time period; and repeatedly executing the profitability simulation process until a preset number of comprehensive profitability is simulated, and determining the profitability analysis data of the investment portfolio according to the portfolio weight by analyzing the preset number of comprehensive profitability. In this way, the historical profit data of the asset combination can be combined to realize the estimation of the asset combination weight of the asset combination so as to reasonably plan asset investment schemes of a plurality of asset categories and realize the estimation of the profit situation, and further, the design planning efficiency of the asset investment schemes and the accuracy of the profit estimation can be improved; in addition, in order to facilitate the user to check the profit effect generated by the asset combination, after the python layer finishes the calculation of the profit rate, the display of the investment effect is realized through the Java layer, so that the user can know the investment effect in time.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 5, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.
The memory 520 stores machine-readable instructions executable by the processor 510, and when the electronic device 500 is running, the processor 510 communicates with the memory 520 through the bus 530, and when the machine-readable instructions are executed by the processor 510, the steps of the method for estimating expected benefits of assets in the method embodiment shown in fig. 1 can be executed, and detailed implementation is referred to in the method embodiment and will not be repeated herein.
The embodiment of the present application further provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, and the computer program may execute the steps of the method for estimating expected benefits of assets in the method embodiment shown in fig. 1 when the computer program is executed by a processor, and the specific implementation manner may refer to the method embodiment and will not be described herein.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present application, and are not intended to limit the scope of the present application, but the present application is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, the present application is not limited thereto. Any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the technical scope of the disclosure of the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of estimating expected revenue for an asset, the method comprising:
responsive to an asset type selection operation, determining an asset portfolio of a plurality of asset categories having investment intent for a user and obtaining historical revenue data for the asset portfolio;
determining, based on a risk budget model, portfolio weights assigned to the portfolios at investment time using historical revenue data for the portfolios, targeting a minimum tracking error of a risk contribution ratio and a desired risk ratio for the portfolios;
combining the historical revenue data and the portfolio weight, and obtaining the comprehensive revenue rate generated by the portfolio in a specific time period by simulating the expected revenue rate of the portfolio in a target time period;
and repeatedly executing the profitability simulation process until a preset number of comprehensive profitability is simulated, and determining the profitability analysis data of the portfolio according to the portfolio weight by analyzing the preset number of comprehensive profitability.
2. The method of estimating according to claim 1, wherein the determining, based on the risk budget model, the portfolio weight allocated for the portfolio at the time of investment using the historical revenue data of the portfolio, targeting a minimum tracking error of a risk contribution ratio and a desired risk ratio of the portfolio, includes:
Acquiring a plurality of groups of candidate combination weights by calling a python layer;
determining, using historical revenue data for the portfolio, revenue covariances generated by the portfolio on different trade days;
determining, for each set of candidate portfolio weights, a standard deviation of risk resulting from investing in the portfolio in accordance with the candidate portfolio weight in combination with the profit covariance and the candidate portfolio weight; wherein the risk standard deviation is used for representing the profit dispersion degree of the asset combination;
determining a portfolio asset risk of investing in the portfolio in accordance with the candidate portfolio weight based on the risk standard deviation, the portfolio weight, and the profit covariance;
weighting the portfolio risk by using the portfolio weight, and determining a risk contribution duty ratio of the portfolio by determining a weighted asset risk of investing in the portfolio according to the candidate portfolio weight;
determining a risk contribution ratio tracking error resulting from investing in the portfolio according to the candidate portfolio weight based on the risk contribution ratio and the expected risk ratio of the portfolio;
and determining the candidate portfolio weight with the minimum risk ratio tracking error as the portfolio weight of the portfolio.
3. The method of estimating according to claim 1, wherein said combining the historical revenue data and the portfolio weights to obtain the integrated revenue rate generated by the portfolio over a specific time period by modeling an expected revenue rate of the portfolio over a target time period comprises:
simulating expected revenue data acquired by each asset class in the asset portfolio within a target time period by combining the historical revenue data of the asset class;
determining an expected rate of return generated by the asset class during the target time period based on the expected rate of return data;
and weighting the expected yield rate generated by each asset class in the target time period according to the asset combination weight, and simulating to obtain the comprehensive yield rate generated by the asset combination in the specific time period.
4. The method of estimating according to claim 1, wherein said obtaining historical revenue data for the portfolio comprises:
for each asset class in the asset portfolio, acquiring each performance benchmark index under the asset class;
weighting each performance benchmark index under the asset class according to the index weight of each performance benchmark index to obtain historical revenue data of the asset class;
Historical revenue data for the portfolio is determined based on the historical revenue data for each asset class.
5. The estimation method of claim 1, wherein the determining the profit analysis data for investing in the portfolio according to the portfolio weight by analyzing the preset number of integrated profitability includes:
determining a combined benefit distribution and an average benefit rate of investing in the portfolio according to the portfolio weight by analyzing the preset number of integrated benefit rates;
based on the combined revenue distribution, a revenue confidence interval for the portfolio is determined.
6. The estimation method according to claim 1 or 5, characterized in that the estimation method further comprises:
and feeding back the income analysis data generated by the asset combination to a Java layer so as to display the income analysis data in a graphical user interface of the terminal equipment through the Java layer.
7. The estimation method according to claim 1, characterized in that the desired risk ratio is configured by:
the desired risk duty cycle is configured by the duty cycle configuration operation in response to the duty cycle configuration operation.
8. An apparatus for estimating expected revenue for an asset, the apparatus comprising:
an asset class selection module for determining an asset portfolio made up of a plurality of asset classes having investment intents for a user in response to an asset type selection operation, and obtaining historical revenue data for the asset portfolio;
a weight estimation module for determining, based on a risk budget model, a portfolio weight allocated to the portfolio at the time of investment, using historical revenue data of the portfolio, targeting that a tracking error of a risk contribution ratio and an expected risk ratio of the portfolio is minimum;
the profit simulation module is used for combining the historical profit data and the asset combination weight, and obtaining the comprehensive profit rate of the asset combination in a specific time period by simulating the expected profit rate of the asset combination in the target time period;
and the profit determination module is used for repeatedly executing the profit rate simulation process until a preset number of comprehensive profit rates are simulated, and determining the profit analysis data of the asset combination according to the asset combination weight by analyzing the preset number of comprehensive profit rates.
9. An electronic device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory in communication via said bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of the method of estimating expected benefit of an asset according to any of claims 1 to 7.
10. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, performs the steps of the method for estimating expected benefits of an asset according to any of claims 1 to 7.
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