CN112016765A - Asset allocation method and device based on quantitative investment risk preference and terminal - Google Patents
Asset allocation method and device based on quantitative investment risk preference and terminal Download PDFInfo
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Abstract
The invention discloses an asset allocation method based on quantitative investment risk preference, which comprises the following steps: acquiring an expected income target and bearable maximum withdrawal information of the investor for various assets, and dividing the investor into various risk preference types according to the expected income target and the bearable maximum withdrawal information; acquiring historical data of various assets and calculating according to the historical data to obtain a covariance matrix; enumerating all possible asset allocation combinations of various assets, and calculating the maximum withdrawal and the minimum profit for each asset allocation combination according to corresponding historical data; and matching the expected income target and the maximum admissible withdrawal information with the maximum withdrawal and the minimum income of each asset configuration combination to obtain a plurality of reference combinations which are respectively matched with the plurality of risk preference types to the highest degree. The method quantifies the risk preference of the investor based on the corresponding benchmark combination, and then performs asset allocation according to the quantified indexes.
Description
Technical Field
The invention relates to the field of asset configuration, in particular to an asset configuration method, an asset configuration device, a terminal and a computer-readable storage medium based on quantified investment risk preference.
Background
Asset Allocation (Asset Allocation) refers to the Allocation of investment funds between different Asset classes, typically between low-risk, low-yield securities and high-risk, high-yield securities, according to investment requirements. The 'method for managing appropriateness of investors in securities futures' issued by the testimony shall be implemented from 7 months and 1 days in 2017, and the method requires that an operating organization establishes a system for classifying investors according to multi-dimensional indexes, and unifies the classification standards and management requirements of the investors. With the accumulation of social wealth, a scientific investor risk preference classification system is necessary.
The risk preferences of an investor include the risk tolerance attitude and ability of the investor, which refers to the degree to which the investor can tolerate risk in pursuing profits. Risks have many metrics, and the most widely used is the definition method given by modern financial theory: the parameter reflecting the risk preference of investors in the traditional optimization framework is lambda (namely, the maximum standard deviation), but the theoretical level only assumes the characteristics of the parameter, and the value of lambda is difficult to accurately obtain in practical operation. Therefore, how to determine the risk preference of the investor and convert the risk preference of the investor into the corresponding asset allocation proportion so as to meet the requirements of different customers is a technical problem in the current market.
Disclosure of Invention
The invention provides an asset allocation method based on quantitative investment risk preference, aiming at solving the problem of how to quantify the risk preference of an investor, and reasonably allocating assets according to quantified indexes to meet the investment requirements of customers.
According to a first aspect of the embodiments of the present application, there is provided an asset allocation method based on quantified investment risk preferences, comprising the following steps:
acquiring an expected income target and bearable maximum withdrawal information of an investor for various assets, and dividing the investor into various risk preference types according to the expected income target and the bearable maximum withdrawal information;
acquiring historical data of various assets and calculating to obtain a covariance matrix according to the historical data; wherein the assets comprise stocks, bonds, currencies and gold;
enumerating all possible asset allocation combinations of the various assets, and calculating the maximum withdrawal and the minimum profit for each asset allocation combination according to the corresponding historical data;
and matching the expected income target and the maximum admissible withdrawal information with the maximum withdrawal and the minimum income of each asset configuration combination to obtain a plurality of benchmark combinations which are respectively matched with the plurality of risk preference types to the highest degree.
In the asset allocation method based on the quantified investment risk preference, the invention also comprises the following steps:
and calculating the maximum standard deviation which can be borne by investors of each risk preference type according to the covariance matrix and various benchmark combinations.
In the asset allocation method based on the quantified investment risk preference, the invention also comprises the following steps:
updating historical data of various assets and calculating to obtain a new covariance matrix according to the updated historical data;
and calculating to obtain a new maximum standard deviation which can be borne by investors of each risk preference type according to the new covariance matrix and the multiple benchmark combinations.
In the asset allocation method based on the quantitative investment risk preference, the risk preference types are divided into five types from low to high, and the types of the benchmark combinations are five types corresponding to the risk preference types.
The invention provides an asset allocation device based on quantified investment risk preference, which comprises:
the investment information acquisition module is used for acquiring the expected income targets and the bearable maximum withdrawal information of investors for various assets, and dividing the investors into various risk preference types according to the expected income targets and the bearable maximum withdrawal information;
the historical data acquisition module is used for acquiring historical data of various assets and calculating to obtain a covariance matrix according to the historical data; wherein the assets comprise stocks, bonds, currencies and gold;
the asset configuration combination generating module is used for enumerating all possible asset configuration combinations of the various assets and calculating the maximum withdrawal and the minimum profit for each asset configuration combination according to the corresponding historical data;
and the asset allocation combination matching module is used for matching the expected income target and the bearable maximum withdrawal information with the maximum withdrawal and the minimum income of each asset allocation combination to obtain a plurality of reference combinations which are respectively matched with the plurality of risk preference types to the highest degree.
In the asset allocation device based on the quantified investment risk preference, the invention further comprises:
and the maximum standard deviation calculation module is used for calculating the maximum standard deviation which can be born by investors of each risk preference type according to the covariance matrix and the multiple benchmark combinations.
In the asset allocation device based on the quantified investment risk preference, the invention further comprises:
the historical data updating module is used for updating the historical data of various assets and calculating to obtain a new covariance matrix according to the updated historical data;
and the maximum standard deviation updating module is used for calculating and obtaining a new maximum standard deviation which can be born by investors of each risk preference type according to the new covariance matrix and the multiple benchmark combinations.
A third aspect of the present invention provides a terminal comprising a processor and a memory for storing computer instructions, the processor implementing the steps of the asset allocation method based on quantified investment risk preferences according to any of the above embodiments when executing the computer program.
A fourth aspect of the present invention provides a storable medium storing a computer program which, when executed by a processor, implements the asset allocation method based on quantified investment risk preferences as described in any one of the above embodiments.
According to the invention, an investor is divided into a plurality of risk preference types according to expected income targets and bearable maximum withdrawal information, all possible asset allocation combinations are enumerated at the same time, and a plurality of reference combinations with the highest matching degree corresponding to each risk preference type are found out according to the matching degree, so that the risk preference of the investor is quantized on the basis of the corresponding reference combinations, and then asset allocation is carried out according to quantized indexes. In other embodiments, the maximum standard deviation corresponding to the investor risk preference may also be derived from the normalized benchmark portfolio.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for asset allocation based on quantified investment risk preferences provided by the present invention;
FIG. 2 is a flow diagram of another embodiment of the asset configuration method based on quantified investment risk preferences shown in FIG. 1;
FIG. 3 is a block diagram of an asset configuration facility based on quantified investment risk preferences provided by the present invention;
fig. 4 is a block diagram of another embodiment of the asset configuration device based on quantified investment risk preferences shown in fig. 3.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In an embodiment of the present invention, an asset allocation method based on quantified investment risk preferences is provided, which is used for quantifying the investment risk preferences of investors, and further obtaining more accurate parameters (such as maximum tolerable standard deviation) reflecting the investment risk preferences in a traditional optimization framework, so as to facilitate risk control of investors.
As shown in fig. 1, the asset allocation method based on quantified investment risk preferences includes the following steps S101-S104.
In step S101, expected income targets and sustainable maximum withdrawal information of investors for various assets are obtained, and the investors are classified into a plurality of risk preference types according to the expected income targets and sustainable maximum withdrawal information.
It should be noted that, since the investor usually has no concept on the maximum sustainable standard deviation, the investor can obtain the maximum sustainable standard deviation by analyzing the form of a set of questionnaires starting from the indexes familiar to the investor, such as expected income, sustainable maximum withdrawal, etc. That is, the investor's bearable maximum withdrawal and revenue targets for various types of assets are obtained through questionnaires. And classifying the investors into one of a plurality of risk preference types according to the expected income target and the maximum admissible withdrawal information, namely judging which of the plurality of risk preference types the current investors belong to. Specifically, the risk preference types are divided into five categories from low to high. For example, low risk tolerant investors, lower risk tolerant investors, medium risk tolerant investors, higher risk tolerant investors, high risk tolerant investors can be categorized, with the maximum allowable withdrawal and revenue targets for each type of investor being as follows:
| expected gain rate of return | Can bear maximum withdrawal | |
| Low risk tolerant investor | 8％-10％ | 5％ |
| Lower risk |
10％-15％ | 10％ |
| Investor with risk tolerance | 15％-20％ | 15％ |
| Investor with higher risk of bearing capacity | 15％-20％ | 20％ |
| High risk tolerant investor | 15％-20％ | 25％ |
It is understood that the above classification is only an illustrative example, and the empirical operation can be flexibly set according to the specific asset type, investment proportion and other factors, and the present invention is not limited thereto.
In step S102, historical data of various assets are obtained, and a covariance matrix is obtained through calculation according to the historical data; wherein the assets include stocks, bonds, currency and gold.
Specifically, historical data of various assets in the period t and before are obtained, and then the expected profitability and the covariance matrix of the future period t + j are predicted through an existing asset configuration model (such as a mean variance model). The maximum standard deviation which can be borne by the five types of investors is convenient to calculate subsequently.
In step S103, enumerating all possible asset allocation combinations of various assets, and calculating the maximum withdrawal and the minimum profit for each asset allocation combination according to the corresponding historical data.
Specifically, a large class of assets is divided into stocks, bonds, currency and gold, and all possible asset configuration combinations are enumerated programmatically (also known as "grids") with maximum withdrawal and minimum profit calculated for each combination, respectively.
In step S104, the expected revenue targets and the maximum admissible pullback information are matched with the maximum pullback and the minimum revenue of each asset allocation combination, and a plurality of reference combinations respectively matched with a plurality of risk preference types to the highest degree are obtained.
In this step, specifically, five combinations which are most matched with five investors are found out from all possible asset allocation combinations, and the historical data of the five asset allocation combinations is used as the quantitative basis of the five investors, so that the risk preference of the five investors is quantified.
As shown in the table, the five types of investor benchmark configuration schemes obtained according to the grid method can better meet the risk preference requirements of investors in different levels. Meanwhile, the classic economic and financial concept is well reflected in the table above: high revenues are expected to be willing to bear high risk.
In the specific operation of the invention, investors are divided into five categories according to the expected income target and the bearable maximum withdrawal of the investors, and the five categories correspond to different risk preference levels; and then obtaining the most consistent five asset configuration combinations through matching according to the types of the risk preferences of different levels, thereby completing the quantification of the asset configuration.
Further, in an embodiment, as shown in fig. 2, a step S105 is further included.
In step S105, the maximum standard deviation that can be borne by each investor of each risk preference type is calculated according to the covariance matrix and various benchmark combinations.
Further, steps S106-S107 are included.
In step S106, updating historical data of various assets and calculating to obtain a new covariance matrix according to the updated historical data;
in step S107, a new maximum standard deviation that can be borne by each investor of each risk preference type is calculated from the new covariance matrix and various benchmark combinations.
Specifically, when the market environment changes, the steps of the technique can generate dynamic results with the maximum standard deviation which can be borne by five types of investors by combining with the rolling historical data so as to fit the change of the market.
In summary, it can be understood that, in the present invention, the investor is divided into multiple risk preference types according to the expected profit target and the bearable maximum withdrawal information, and enumerates all possible asset configuration combinations at the same time, and finds out multiple reference combinations with the highest matching degree corresponding to each risk preference type according to the matching degree, so as to quantify the risk preference of the investor based on the corresponding reference combinations, and further perform asset configuration according to the quantified indexes. In a preferred embodiment, a maximum standard deviation corresponding to the investor risk preference can also be derived from the normalized benchmark portfolio.
The second aspect of the present invention is to provide an asset allocation device 100 based on quantified investment risk preferences, which is used to quantify the risk preferences of investors, and provide asset allocation combinations that best meet the types of risk preferences, so as to complete the quantification of the risk preferences. It should be noted that the implementation steps and the implementation principle of the present apparatus are consistent with the asset allocation method based on the quantified investment risk preference, and therefore, the detailed description thereof is omitted below.
As shown in fig. 3, the asset allocation apparatus 100 based on quantified investment risk preferences includes:
the investment information acquisition module 10 is used for acquiring the expected income targets and the bearable maximum withdrawal information of the investors for various assets, and dividing the investors into various risk preference types according to the expected income targets and the bearable maximum withdrawal information;
the historical data acquisition module 20 is used for acquiring historical data of various assets and calculating a covariance matrix according to the historical data; wherein the assets comprise stocks, bonds, currencies and gold;
an asset allocation combination generating module 30, configured to enumerate all possible asset allocation combinations of the various assets, and calculate the maximum withdrawal and the minimum profit for each asset allocation combination according to the corresponding historical data;
and the asset allocation combination matching module 40 is used for matching the expected income target and the sustainable maximum withdrawal information with the maximum withdrawal and the minimum income of each asset allocation combination to obtain a plurality of reference combinations which are respectively matched with the plurality of risk preference types to the highest degree.
Further, in an embodiment, as shown in fig. 4, the method further includes:
and the maximum standard deviation calculation module 50 is used for calculating the maximum standard deviation which can be borne by investors of each risk preference type according to the covariance matrix and various benchmark combinations.
Still further, the method further comprises:
a historical data updating module 60, configured to update historical data of various assets and calculate a new covariance matrix according to the updated historical data;
and a maximum standard deviation updating module 70, configured to calculate, according to the new covariance matrix and the multiple benchmark combinations, a new maximum standard deviation that can be borne by each investor of each risk preference type.
A third aspect of the present invention provides a terminal comprising a processor and a memory for storing computer instructions, the processor implementing the steps of the asset allocation method based on quantified investment risk preferences according to any of the above embodiments when executing the computer program.
A fourth aspect of the present invention provides a storable medium storing a computer program which, when executed by a processor, implements the asset allocation method based on quantified investment risk preferences as described in any one of the above embodiments.
It should be noted that, for convenience and simplicity of description, it is clearly understood by those skilled in the art that the foregoing functional units and modules are merely illustrated in terms of division, and in practical applications, the foregoing functional allocation may be performed by different functional units and modules as needed, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. An asset allocation method based on quantified investment risk preferences, characterized by comprising the steps of:
acquiring an expected income target and bearable maximum withdrawal information of an investor for various assets, and dividing the investor into various risk preference types according to the expected income target and the bearable maximum withdrawal information;
acquiring historical data of various assets and calculating to obtain a covariance matrix according to the historical data; wherein the assets comprise stocks, bonds, currencies and gold;
enumerating all possible asset allocation combinations of the various assets, and calculating the maximum withdrawal and the minimum profit for each asset allocation combination according to the corresponding historical data;
and matching the expected income target and the maximum admissible withdrawal information with the maximum withdrawal and the minimum income of each asset configuration combination to obtain a plurality of benchmark combinations which are respectively matched with the plurality of risk preference types to the highest degree.
2. The asset allocation method based on quantified investment risk preferences according to claim 1, further comprising the steps of:
and calculating the maximum standard deviation which can be borne by investors of each risk preference type according to the covariance matrix and various benchmark combinations.
3. The asset allocation method based on quantified investment risk preferences according to claim 2, further comprising the steps of:
updating historical data of various assets and calculating to obtain a new covariance matrix according to the updated historical data;
and calculating to obtain a new maximum standard deviation which can be borne by investors of each risk preference type according to the new covariance matrix and the multiple benchmark combinations.
4. The asset allocation method based on quantified investment risk preferences according to claim 1, wherein the risk preference types are divided into five categories from low to high, and the categories of the benchmark groups are five categories corresponding to the risk preference types.
5. An asset allocation device based on quantified investment risk preferences, comprising:
the investment information acquisition module is used for acquiring the expected income targets and the bearable maximum withdrawal information of investors for various assets, and dividing the investors into various risk preference types according to the expected income targets and the bearable maximum withdrawal information;
the historical data acquisition module is used for acquiring historical data of various assets and calculating to obtain a covariance matrix according to the historical data; wherein the assets comprise stocks, bonds, currencies and gold;
the asset configuration combination generating module is used for enumerating all possible asset configuration combinations of the various assets and calculating the maximum withdrawal and the minimum profit for each asset configuration combination according to the corresponding historical data;
and the asset allocation combination matching module is used for matching the expected income target and the bearable maximum withdrawal information with the maximum withdrawal and the minimum income of each asset allocation combination to obtain a plurality of reference combinations which are respectively matched with the plurality of risk preference types to the highest degree.
6. The asset allocation device according to claim 5, further comprising:
and the maximum standard deviation calculation module is used for calculating the maximum standard deviation which can be born by investors of each risk preference type according to the covariance matrix and the multiple benchmark combinations.
7. The asset allocation device according to claim 6, further comprising:
the historical data updating module is used for updating the historical data of various assets and calculating to obtain a new covariance matrix according to the updated historical data;
and the maximum standard deviation updating module is used for calculating and obtaining a new maximum standard deviation which can be born by investors of each risk preference type according to the new covariance matrix and the multiple benchmark combinations.
8. A terminal comprising a processor and a memory for storing computer instructions, the processor, when executing the computer program, implementing the steps of the asset configuration method based on quantified investment risk preferences according to any of claims 1-4.
9. A storable medium storing a computer program, characterized in that the program, when being executed by a processor, is adapted to carry out a method of asset allocation based on quantified investment risk preferences according to any of the claims 1-4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112767132A (en) * | 2021-01-26 | 2021-05-07 | 北京国腾联信科技有限公司 | Data processing method and system |
| CN113256086A (en) * | 2021-05-11 | 2021-08-13 | 北京同邦卓益科技有限公司 | Method, device and storage medium for determining asset configuration combination |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112767132A (en) * | 2021-01-26 | 2021-05-07 | 北京国腾联信科技有限公司 | Data processing method and system |
| CN112767132B (en) * | 2021-01-26 | 2024-02-02 | 北京国腾联信科技有限公司 | Data processing method and system |
| CN113256086A (en) * | 2021-05-11 | 2021-08-13 | 北京同邦卓益科技有限公司 | Method, device and storage medium for determining asset configuration combination |
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