CN112270431A

CN112270431A - Method and device for configuring large-class assets, computer equipment and storage medium

Info

Publication number: CN112270431A
Application number: CN202010962493.2A
Authority: CN
Inventors: 杨朝军; 丁专鑫; 杨晃
Original assignee: Shanghai Zhonglu Zhaoye Financial Consulting Co ltd
Current assignee: Shanghai Zhonglu Zhaoye Financial Consulting Co ltd
Priority date: 2020-09-14
Filing date: 2020-09-14
Publication date: 2021-01-26

Abstract

The application specifically discloses a method, a device, a terminal and a storage medium for configuring large assets, wherein the method comprises the following steps: collecting historical data of profitability of various assets, risk preference and expert opinions of investors; determining an expected profit and a maximum risk threshold for the investor; determining the configured target asset type according to the historical data of the profitability of various assets, and acquiring the future profitability and risk prediction of various assets by adopting a BL model; quantifying the expert opinions according to a preset scoring system; correcting future earnings and risk predictions of various assets according to the quantified expert opinions; and determining a large-class asset allocation scheme by combining the corrected future earning rate of various assets, risk prediction, expected earnings of investors, a maximum risk threshold value and an investment period.

Description

Method and device for configuring large-class assets, computer equipment and storage medium

Technical Field

The present application relates to the field of asset management technologies, and in particular, to a method and an apparatus for configuring a large class of assets, a computer device, and a storage medium.

Background

At present, the financial institution manages huge funds, and the supervision department requires that independent accounting should be performed between financial products, and risk isolation is needed. On the other hand, the investors' need for long-term stable investment increases; therefore, it is necessary to allocate the investment funds for collective operations to various large financial assets, and ensure stable investment yield and expected income through risk dispersion. The large-class asset allocation means that according to the risk preference and the investment period of an investor, the optimal income after risk adjustment is achieved by dynamically selecting the optimal proportion of large-class assets such as bonds, stocks, commodities, currencies, overseas and the like. Most of the current asset allocation schemes are limited to allocation schemes of equity assets, namely stocks and stock funds, and do not consider allocation schemes of large assets including but not limited to stocks, bonds, currencies, commodities and the like, or only adopt fixed ratio schemes of the large assets. The key problem is how to dynamically predict the expected income and risk of the assets according to the dynamic changes of the income rate and risk of the assets of the large class and dynamically adjust the configuration scheme of the assets of the large class based on the prediction.

Disclosure of Invention

The application provides a method, a device, a terminal and a storage medium for configuring large assets so as to obtain a better large asset configuration scheme.

In a first aspect, the present application provides a method for configuring a large class of assets, the method comprising:

collecting historical data of profitability of various assets, risk preference and expert opinions of investors;

determining an expected profit and a maximum risk threshold for the investor;

determining the configured target asset type according to the historical data of the profitability of various assets, and acquiring the future profitability and risk prediction of various assets by adopting a BL (basic block hierarchy) model;

quantifying the expert opinions according to a preset scoring system;

correcting future earnings and risk predictions of various assets according to the quantified expert opinions;

determining a large-class asset allocation scheme by combining the corrected future earning rate and risk prediction of various assets, the expected earning of investors, the maximum risk threshold value and the investment period;

and determining an optimal large-class asset allocation scheme by adopting a BL model according to future earnings, risk prediction, investor risk preference and investment duration of various assets.

In the method for configuring a large class of assets of the present application, the method further includes:

and storing and outputting the configuration schemes of the large-class assets.

In the method for configuring assets of the present application, the quantifying of the expert opinions includes: strong reduction, flattening, increase and strong increase.

In the method for configuring the large-class assets, 1-5 points in the scoring rule respectively correspond to strong persistence, strong persistence and strong persistence in sequence.

In a second aspect, the present application further provides a generic asset configuration device, including:

the data acquisition unit is used for collecting various asset profitability historical data, risk preference of investors and expert opinions;

a risk determination unit for determining an expected profit for the investor and a maximum risk threshold;

the model prediction unit is used for determining the configured target asset type according to the historical data of the profitability of various assets and establishing a large asset prediction model capable of describing the profitability and the risk dynamic change of the large asset so as to obtain the future profit and risk prediction of various assets;

a quantization unit for quantizing the expert opinions;

and the correcting unit is used for correcting the future earning rate and the risk prediction of various assets according to the quantified expert opinions.

And the asset configuration unit determines a large class of asset configuration schemes by adopting a BL model according to the future earning rate, risk prediction, expected earnings of investors, a maximum risk threshold value, an investment period and quantified expert opinions of various assets.

In a third aspect, the present application also provides a terminal comprising a processor and a memory for storing computer instructions, the processor implementing the steps of the method for configuring a broad category of assets as described above when executing the computer program.

In a fourth aspect, the present application also provides a storable medium storing a computer program which, when executed by a processor, implements a method for configuring a broad class of assets as described above.

The application discloses a method, a device, equipment and a storage medium for configuring large assets, which are characterized in that the method comprises the steps of collecting historical data of the rate of return of various assets, risk preference of investors and expert opinions; determining an expected profit and a maximum risk threshold for the investor; determining the configured target asset types according to the historical data of the profitability of various assets, and acquiring the future profitability and risk prediction of various assets by adopting a BL model; quantifying the expert opinions according to a preset scoring system; correcting future earning rate and risk prediction of various assets according to quantified expert opinions; and determining a large-class asset allocation scheme by combining the corrected future earning rate of various assets, risk prediction, expected income of investors, a maximum risk threshold value and an investment period. According to the method and the device, the expected income and the risk of the assets are dynamically predicted according to the dynamic changes of the income rate and the risk of the assets, and the assets are dynamically adjusted based on the prediction. The method and the system also combine the expert opinions to correct the future earnings and the risk prediction of various assets, and obtain a better scheme for configuring the large assets.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, 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 application, 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 schematic flow chart diagram of a broad asset configuration method provided by an embodiment of the present application;

fig. 2 is a schematic block diagram of a generic asset configuration device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it should be understood that the described embodiments are some, but not all embodiments of the present application. All other embodiments that can be derived by a person skilled in the art from the embodiments given herein without making any inventive effort fall within the scope of protection of the present application.

The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

The embodiment of the application provides a method and a device for configuring large-class assets, computer equipment and a storage medium. The method for configuring the large-class assets can be applied to a terminal or a server to calculate an optimized large-class asset configuration scheme.

Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.

Referring to fig. 1, fig. 1 is a schematic flow chart of a method for configuring a large class of assets according to an embodiment of the present application. The configuration method includes steps S101 to S106.

S101, collecting historical data of profitability of various assets, risk preference and expert opinions of investors.

And acquiring subjective opinions of experts on various assets in the period t +1, market audience data of various assets in the period t and before and risk preferences of investors. The investor's preferences can be obtained through a questionnaire.

And S102, determining expected income and a maximum risk threshold of the investor.

It should be noted that, since the investor usually has no concept on the maximum sustainable standard deviation, the maximum sustainable standard deviation of the investor can be obtained through analysis by designing a set of questionnaire forms starting from the indexes familiar to the investor, such as expected income, maximum sustainable withdrawal, and the like. That is, the investor's bearable maximum withdrawal and revenue targets for various assets are obtained through questionnaires. And classifying the investor into one of a plurality of risk preference types according to the expected income target and the sustainable maximum withdrawal information, namely judging which of the plurality of risk preference types the current investor belongs 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 tolerant investor	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.

S103, determining the configured target asset type according to the historical data of the profitability of various assets, and obtaining the future profitability and the risk prediction of various assets by adopting a BL model.

The BL model is the Black-Litterman asset allocation model (Black and Litterman 1992). The model takes the asset profitability deduced by market equilibrium hypothesis as a starting point, and finally determines the profitability of investment products and the optimal investment portfolio configuration by combining the active judgment of investors on the profitability of different investment products. The core of the method is Bayesian shrinkage of the yield. The Black-Litterman model differs most from the "mean-variance" model in the prediction of profitability. In the aspect of rate of return prediction, the most essential core is that the prior rate of return and innovation are used to obtain the posterior rate of return in a Bayes framework, and the posterior rate of return is a Bayes shrinkage (Bayes shrinkage) on the rate of return. Bayesian contraction takes expected profitability obtained by a certain method as prior (prior), obtains sample expected profitability by using the latest T-period profitability data as new innovation, and finally calculates posterior expected profitability (posterior) by combining the former two. The method "shrinks" innovation-based predictions from a priori predictions in an optimal proportion that minimizes the error in the a posteriori expected yield.

And S104, quantifying the expert opinions according to a preset grading system.

Specifically, the opinions of the experts are scored according to a preset scoring system, thereby performing the following operations. For example, a large group of assets includes stocks, bonds, currency, and gold, and the experts score the stocks, bonds, currency, and gold.

And S105, correcting future earnings and risk predictions of various assets according to the quantified expert opinions.

Respectively subtracting the median of the total score in the scoring rule from the score of each asset, multiplying the obtained numerical value by the expected standard deviation of the corresponding asset, and obtaining the expected adjustment amplitude e of the corresponding profitability of each asset_A。

Adjusting the expected adjustment range e of the corresponding profitability of various assets_AThe standardization makes the expected income adjustment amplitude zero after weighted average of the current market value of various assets, and the income adjustment amplitude obtained after the standardization is the final adjustment amplitude alpha_A. Thereby obtaining the corrected future earnings and risk predictions of various assets.

In some embodiments, the expert opinions include: strong reduction, flattening, increase and strong increase. In the preset scoring rule, 1-5 points correspond to strong persistence, increasing persistence and strong persistence respectively. For example, in the expert opinion, if the opinion of a stock is withholding, the score of the stock asset is 2 points; if the opinion of gold is strongly increasing, the gold asset has a score of 5.

Defining the total score in the scoring rule to be 5 scores, and then the median is 3; and (3) defining the expert configuration opinions of various assets as 5 grades, wherein 1 to 5 grades respectively correspond to expected income minus two times of standard deviation, minus one time of standard deviation, unchanged, increased by one time of standard deviation and increased by two times of standard deviation on a specific processing mode.

e_A＝((o_s-3)σ_s(o_b-3)σ_b(o_m-3)σ_m(o_g-3)σ_g)^T。

e＝w_s(o_s-3)σ_s+w_b(o_b-3)σ_b+w_m(o_m-3)σ_m+w_g(o_g-3)σ_g；

α_A＝((o_s-3)σ_s-e(o_b-3)σ_b-e(o_m-3)σ_m-e(o_g-3)σ_g-e)^T；

Wherein o is_s，o_b，o_m，o_gIs the expert's score for stocks, bonds, currency and gold; sigma_s，σ_b，σ_m，σ_gInitial expected standard deviations for stocks, bonds, currencies, prime t periods, respectively; w is a_s，w_b，w_m，w_gIs the weight of stock, bond, currency and various kinds of asset market values in prime t period in the sum of the four kinds of asset market values; t is matrix transposition.

Adjusting the final amplitude alpha of each type of assets_AInitial expected profitability r of various assets_t+1Adding to obtain the final expected profitability r of each type of assets after the adjustment of the expert opinions_A,t+1Wherein r is_A,t+1＝r_t+1+α_A。

And S106, determining a large-class asset allocation scheme by combining the corrected future earning rate, risk prediction, expected earning of investors, a maximum risk threshold and an investment period of various assets.

According to the method and the system, the expected income and the risk of the assets are dynamically predicted according to the dynamic changes of the income rate and the risk of the assets, and the assets are dynamically adjusted based on the prediction. The method and the system also combine the expert opinions to correct the future earnings and the risk prediction of various assets, and obtain a better scheme for configuring the large-class assets.

In an optional embodiment, the method for configuring the large-class asset further includes step S107 of storing and outputting the large-class asset configuration scheme.

Referring to fig. 2, fig. 2 is a schematic block diagram of a generic asset allocation apparatus according to an embodiment of the present application, which is configured to execute the foregoing generic asset allocation method.

As shown in fig. 2, the asset allocation apparatus 200 of the broad category includes: a data acquisition unit 201, a risk determination unit 202, a model prediction unit 203, and an asset configuration unit 204.

The data acquisition unit 201 is used for collecting various asset profitability historical data and investment preference data of investors.

A risk determination unit 202 for determining an expected benefit of the investor and a maximum risk threshold.

And the model prediction unit 203 is used for determining the configured target asset category according to the historical data of the profitability of various assets, and acquiring the future profitability and the risk prediction of various assets by adopting a BL model.

And the expert opinion quantizing unit 204 is used for quantizing the expert opinions according to a preset grading system.

And the correcting unit 205 is used for correcting future profitability and risk prediction of various assets according to the quantified expert opinions.

And the asset allocation unit 206 is configured to determine a large class of asset allocation schemes according to the corrected future earnings, risk prediction, expected earnings of investors, maximum risk threshold and investment period of various assets.

In an optional embodiment, the generic asset allocation device 200 of the present application further includes a storage output unit 207 for storing and outputting the generic asset allocation plan.

It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus and the units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

According to an embodiment of the present application, there is provided a terminal, including a processor and a memory for storing computer instructions, where the processor, when executing the computer program, implements the steps in the method for configuring a large class of assets as described in any one of the above embodiments.

According to an embodiment of the present application, there is provided a computer-readable storage medium storing a computer program, which when executed by a processor implements a method for configuring a large class of assets 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 above functions may be distributed as needed and performed by different functional units and modules, that is, the internal structure of the device may be divided into different functional units or modules to perform all or part of the functions described above. 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 for parts that are not described or recited in detail in a certain embodiment, reference may be made to the descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the elements and method steps of the various embodiments 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 the actual implementation is performed, for example, multiple 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.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered by 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

1. A method for configuring assets of a broad category, comprising:

determining an expected profit and a maximum risk threshold for the investor;

determining the configured target asset type according to the historical data of the profitability of various assets, and acquiring the future profitability and risk prediction of various assets by adopting a BL model;

quantifying the expert opinions according to a preset scoring system;

and determining a large-class asset allocation scheme by combining the corrected future earning rate of various assets, risk prediction, expected earnings of investors, a maximum risk threshold value and an investment period.

2. The method of claim 1, further comprising:

and storing and outputting the configuration schemes of the large-class assets.

3. The method of claim 1, wherein the expert opinion quantification comprises: strong reduction, flattening, increase and strong increase.

4. The method according to claim 3, wherein the scoring rules are such that 1-5 points correspond to strong decreasing, leveling, increasing and strong increasing respectively.

5. A generic asset allocation device, comprising:

a quantization unit for quantizing the expert opinions;

And the asset allocation unit determines a large class of asset allocation schemes by adopting a BL model according to the future earning rate, the risk prediction, the expected earnings of investors, the maximum risk threshold, the investment period and the quantified expert opinions of various assets.

6. The generic asset configuration device of claim 5, further comprising:

and the storage output unit is used for storing and outputting the configuration scheme of the large-class assets.

7. A terminal comprising a processor and a memory for storing computer instructions, the processor implementing the steps of the generic asset configuration method as claimed in any of claims 1-4 when executing the computer program.

8. A storable medium storing a computer program which, when executed by a processor, implements a method of configuring a generic asset as claimed in any one of claims 1-4.