Quantitative product risk early warning method and model

Abstract

The invention relates to a risk early warning method for quantified products, which is characterized by comprising the following steps: selecting K quantified products of n industries to obtain a quantified product matrix P; setting a deviation time proportion and a deviation threshold value of the ith industry in the n industries; determining time windows based on multiple dimensions, and obtaining the number N of the time windows; calculating indexes of the quantized product on the N time windows; obtaining index values of a plurality of dimensions on the time window to obtain an index matrix, and performing operation correction on the index matrix; and screening the deviated products, and generating early warning information of the quantified products according to the deviation time proportion. According to the method, risk early warning of each index of the quantitative product is realized, the quantitative product with abnormal performance in a specific time window can be identified in time, fine management is realized, and a basis is provided for making related decisions.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a risk early warning method and model for quantitative products. Background Art

[0002] In the current financial market, investment in quantitative products is an important investment activity, and the establishment of risk management and early warning mechanisms is crucial for quantitative product holders and managers. However, traditional quantitative product risk-return early warning methods have certain limitations, which are mainly reflected in the following aspects:

[0003] Existing early warning methods are often based on a single risk-return indicator for comparison, such as focusing only on the product's rate of return or volatility, while ignoring the comprehensive analysis of multiple indicators, which limits the comprehensiveness and effectiveness of the early warning system;

[0004] In addition, traditional early warning methods often lack real-time performance and are unable to respond to market changes in a timely manner, resulting in a lag in early warning information. Finally, traditional risk control early warning methods have poor adaptability and are unable to adjust relevant judgment thresholds in real time according to market changes, thereby improving effectiveness. Summary of the invention

[0005] In order to address the shortcomings of the prior art, the present invention provides a risk-return deviation early warning method for quantitative products. Through quantitative calculation of product risks and returns, combined with adjustable preset thresholds, a reliable basis is provided for relevant management decisions of quantitative products, thereby improving the real-time and adaptability of risk management.

[0006] A first aspect of the present invention provides a risk warning method for a quantitative product, characterized by comprising:

[0007] S1. Select K quantitative products from n industries and obtain the quantitative product matrix P;

[0008] S2. Set the deviation time proportion and deviation threshold of the i-th industry among n industries;

[0009] S3, determining the time window based on multiple dimensions to obtain the number of time windows N;

[0010] S4. Calculate the index of the quantitative product in N time windows;

[0011] S5, obtaining the indicator values ​​of multiple dimensions in the time window to obtain an indicator matrix and performing operations to correct the indicator matrix;

[0012] S6. Screen the deviated products and generate early warning information for quantitative products based on the deviation time ratio.

[0013] Exemplarily, the S3 determines the time window based on multiple dimensions to obtain the number of time windows N, including:

[0014] S301 selects a suitable time unit as a window for measuring the indicator;

[0015] S302 determines the time span range, traverses the endpoints of each time unit, and determines whether to include it in the window quantity N according to a preset rule.

[0016] Exemplarily, the step S4 calculates the index of the quantitative product in N time windows, including:

[0017] S401 Calculate the row matrix p in the quantization product matrix P i,j ={p i,1 ,

[0018] p i,2 …p i,j …p i,k}, where 1≤i≤n;

[0019] S402 calculates the index value of the quantitative product in each time window N, that is, the sequence of a certain index of the j-th product in the i-th dimension in the N time windows, q i,j = {q i,j,1 ,q i,j,2 , ...q i,j,N};

[0020] S403 obtains a K*N indicator matrix A of the same dimension i and the same type for the K products in the row matrix, expressed as

[0021] , where q i,j,t Represents the indicator value of the j-th product in the i-th dimension in the t-th time window, 1≤i≤n, 1≤j≤K, 1≤t≤N.

[0022] Exemplarily, the step S5 of modifying the indicator matrix operation includes:

[0023] S501 obtains the tth column of matrix A to obtain the index value of a single window, which is represented by a column matrix B.

[0024] , where q i,j,t Indicates the tth index value of the jth product in the ith dimension, 1≤i≤n, 1≤j≤K, 1≤t≤N;

[0025] S502 uses a normal distribution quantile algorithm to obtain two quantiles of the column matrix B, which are represented as z1′ and z2′, where z1′≤z2′;

[0026] S503 performs a de-extreme transformation on each element of the column matrix B to obtain

[0027]

[0028] S504 performs a de-extreme transformation on each element of the matrix A to obtain a transformed matrix

[0029] S505 calculates the mean index of all products in the i-th dimension in a single time window t to obtain a row matrix Q, in, is the average value of the elements in the t-th column of the matrix, 1≤t≤N.

[0030] Exemplarily, the S6 screens the deviated products and generates warning information of the quantitative products according to the deviation time ratio, including:

[0031] S601 obtains any row matrix from the quantization product matrix P, PP = {p i,1 , p i,2 …p i,j …p i,k}, where each element is the j-th product of dimension i, where 1≤i≤n;

[0032] S602 From the transformation, it can be seen that the index of the quantitative product after extreme value transformation in time window t is q i,j,t ',Compare Is it greater than the preset deviation threshold X? i , if so, then p i,j If it is a deviation product in time window t, then it is not a deviation product in time window t.

[0033] S603 summarizes the number of the determined deviated products in the entire time window as count, and if count / total number of time windows N is greater than the deviation time ratio, generates corresponding warning information.

[0034] Exemplarily, the method further includes: repeating steps S4-S6 to obtain a predetermined quantitative product sequence of all set dimensions, and obtaining a warning quantitative product for each dimension and each indicator parameter.

[0035] The present invention also provides a risk warning model for a quantitative product, which is characterized by comprising:

[0036] The product selection module is used to select K quantitative products from n industries and obtain the quantitative product matrix P;

[0037] The deviation parameter setting module is used to set the deviation time ratio and deviation threshold of the i-th industry among n industries;

[0038] A time window determination module is used to determine the time window based on multiple dimensions to obtain the number of time windows N;

[0039] An index calculation module, used to calculate the index of the quantitative product in N time windows;

[0040] A correction module, used for obtaining the index values ​​of multiple dimensions in a time window to obtain an index matrix and performing correction operations on the index matrix;

[0041] The screening module is used to screen out deviated products and generate early warning information for quantitative products based on the deviation time ratio.

[0042] The present invention also provides a computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

[0043] The present invention also provides an electronic device, characterized in that it includes a processor and a memory;

[0044] The memory is used to store computer programs;

[0045] The processor is configured to execute the method according to any one of claims 1 to 6 by calling a stored computer program.

[0046] The present invention also relates to a computer program product, comprising a computer program and / or instructions, characterized in that the computer program and / or instructions implement the steps of the above method when executed by a processor.

[0047] The beneficial effects of the present invention are as follows: (1) by setting the deviation threshold and the deviation time ratio, the present application can timely identify quantitative products that behave abnormally within a specific time window, and provide risk warnings for decision-making; (2) by segmenting and calculating indicators for quantitative products in each dimension, refined management of the risk-return of quantitative products is achieved, which helps to control risks more accurately; (3) automated time window division and indicator calculation reduce manual operations and improve the efficiency of risk management; (4) it can be flexibly adjusted according to different business scenarios and product characteristics, and has strong adaptability and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention is a flow chart of a risk warning method for a quantitative product.

[0049] Figure 2 This is a schematic diagram of the structure of a risk warning model for a quantitative product of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0052] This application provides a risk warning method for quantitative products, such as Figure 1 As shown, including:

[0053] S1. Select K quantitative products from n industries and obtain the quantitative product matrix P;

[0054] The specific scenario in the application can be the following settings. The preset dimension is industry, and n=4, namely {agriculture, forestry, animal husbandry and fishery, steel, electronics, medicine and biology}. In actual applications, the industry can use existing general industries or customize industries according to specific business needs.

[0055] For the above four industries, K=3 products are selected in each industry dimension, and the industry product matrix table is as follows:

[0056] Table 1 Industry product matrix

[0057] industry Product Matrix Product matrix for short Agriculture, Forestry, Animal Husbandry and Fishery <![CDATA[P 1,1 ,P 1,2 ,P 1,3 ]]> A, B, C Steel <![CDATA[P 2,1 ,P 2,2 ,P 2,3 ]]> D, E, F electronic <![CDATA[P 3,1 ,P 3,2 ,P 3,3 ]]> G, H, I medicine <![CDATA[P 4,1 ,P 4,2 ,P 4,3 ]]> J.K.L.

[0058] S2. Set the deviation time proportion and deviation threshold of the i-th industry among n industries;

[0059] Specifically, in the industry dimension of this hypothesis, it is assumed that the Sharpe ratio is used as the risk measurement indicator, and the deviation threshold X i =0.05, deviation time ratio Y i = 50%. In real business operations, multiple indicators are usually selected for comprehensive consideration. Here, for the sake of simplicity, only one measurement indicator is selected.

[0060] S3, determining the time window based on multiple dimensions to obtain the number of time windows N;

[0061] Specifically, assuming that the measurement time span of the risk-return indicator value is [D1, D2] = [20230101, 20230303], the frequency window is weekly, 7 days, D1 = 20230101 is Sunday, and D2 = 20230303 is Friday, then according to the set rules:

[0062] a. The frequency window where time point D2 is located is not counted;

[0063] b. When the time span between the location of time point D1 and the cutoff point of the frequency weekend is not 2 / 3 or more, the window is not included.

[0064] c. According to the above two steps, the number of time windows N=8 is determined, which is:

[0065] [2023 / 1 / 2, 2023 / 1 / 8], [2023 / 1 / 9, 2023 / 1 / 15], [2023 / 1 / 16, 2023 / 1 / 22],

[0066] [2023 / 1 / 23, 2023 / 1 / 29], [2023 / 1 / 30, 2023 / 2 / 5], [2023 / 2 / 6, 2023 / 2 / 12], [2023 / 2 / 13, 2023 / 2 / 19], [2023 / 2 / 20, 2023 / 2 / 26].

[0067] S4. Calculate the index of the quantitative product in N time windows;

[0068] Calculate the risk-return index: For the three products A, B, and C in the agriculture, forestry, animal husbandry, and fishery industries, calculate the Sharpe ratios in the eight time windows in the above steps, and obtain a 3*8 product-Sharp ratio matrix in the agriculture, forestry, animal husbandry, and fishery industry dimension:

[0069]

[0070] Assume the values ​​are as follows:

[0071] Table 2 Sharpe ratio values ​​of different industries in the divided intervals

[0072]

[0073] S5, obtaining the indicator values ​​of multiple dimensions in the time window to obtain an indicator matrix and performing operations to correct the indicator matrix;

[0074] Specifically, the index corrections are:

[0075] a. For each column of the above matrix table, obtain two values ​​at the quantiles 2.5% and 97.5% according to the normal distribution, the values ​​are z1′ and z2′ respectively, where z1′≤z2′

[0076] b. De-extreme transformation of each element in matrix B:

[0077]

[0078] Perform a similar transformation on the matrix table, assuming that the transformed matrix remains unchanged (it will change in actual situations), and A′=A:

[0079] Table 3 Sharpe ratio values ​​after extreme value removal

[0080]

[0081] Calculate the mean of the indicators in each time window t:

[0082] Table 4 Calculation of the average Sharpe ratio for each time window

[0083]

[0084] S6. Screen the deviated products and generate early warning information for quantitative products based on the deviation time ratio.

[0085] If |product transformed index value - corresponding index mean value| > preset deviation threshold X i =0.05, it is the deviation product in this time window;

[0086] After the indicator values ​​in the matrix table are subtracted from the corresponding indicator means, the matrix table is obtained:

[0087] Table 5 Differences between the Sharpe ratio matrix and the mean matrix

[0088]

[0089] The screening results are:

[0090] Table 6 Deviation of each product in the time window

[0091]

[0092] Calculate the deviation ratio:

[0093] Based on the above steps, the quantitative products are summarized over the entire time window to meet the set threshold amplitude, and the number of time windows t is calculated, which is counted;

[0094] If count / total number of time windows N> deviation time ratio Y i=50%, thus obtaining the deviation time proportion Y in the entire time window at a specific frequency on the dimension i i The product is the product that should generate warning information on dimension i.

[0095] The following table is obtained:

[0096] Table 7 The deviation time ratio of each product in the time window

[0097]

[0098] Therefore, the early warning benchmark product based on the Sharpe ratio of the agriculture, forestry, animal husbandry and fishery industry dimension is A. Therefore, the preliminary guidance basis for business operations is: in the agriculture, forestry, animal husbandry and fishery industry dimension, when measured by the Sharpe ratio, products with similar or similar composition to product A all have similar characteristics of product A under this dimension indicator.

[0099] By repeating the above steps, we can obtain the early warning indicator benchmark products under all industry dimensions.

[0100] The present invention also provides a risk warning model for quantitative products, such as Figure 2 As shown, including:

[0101] The product selection module is used to select K quantitative products from n industries and obtain the quantitative product matrix P;

[0102] The deviation parameter setting module is used to set the deviation time ratio and deviation threshold of the i-th industry among n industries;

[0103] A time window determination module is used to determine the time window based on multiple dimensions to obtain the number of time windows N;

[0104] An index calculation module, used to calculate the index of the quantitative product in N time windows;

[0105] A correction module, used for obtaining the index values ​​of multiple dimensions in a time window to obtain an index matrix and performing correction operations on the index matrix;

[0106] The screening module is used to screen out deviated products and generate early warning information for quantitative products based on the deviation time ratio.

[0107] The above model can execute the above processing method and achieve the corresponding technical effects.

[0108] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps in the method in the above embodiment, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps in the method in the above embodiment are implemented.

[0109] An embodiment of the present invention also provides an electronic device for executing the above method. As an implementation device of the method, the electronic device has at least a processor and a memory, and in particular, the memory stores data and related computer programs required for executing the method, and the processor calls the data and programs in the memory to execute all steps of the implementation method and obtains the corresponding technical effect.

[0110] Preferably, the electronic device may include a bus architecture, the bus may include any number of interconnected buses and bridges, and the bus will include various circuits linked together by one or more processors and memories. The bus may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are all well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the receiver and transmitter. The receiver and transmitter may be the same component, i.e., a transceiver, providing a unit for communicating with various other systems over a transmission medium. The processor is responsible for managing the bus and general processing, while the memory may be used to store data used by the processor when performing operations.

[0111] In addition, the electronic device may further include components such as a communication module, an input unit, an audio processor, a display, and a power supply. The processor (or controller, operation control) used may include a microprocessor or other processor device and / or a logic device, which receives input and controls the operation of various components of the electronic device; the memory may be one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices, which may store the above-mentioned related data information, and may also store a program for executing related information, and the processor may execute the program stored in the memory to realize information storage or processing, etc.; the input unit is used to provide input to the processor, for example, it may be a key or a touch input device; the power supply is used to provide power to the electronic device; the display is used to display display objects such as images and text, for example, it may be an LCD display. The communication module is a transmitter / receiver that sends and receives signals via an antenna. The communication module (transmitter / receiver) is coupled to the processor to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal. Based on different communication technologies, multiple communication modules may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless local area network module, etc. The communication module (transmitter / receiver) is also coupled to a speaker and a microphone via an audio processor to provide an audio output via the speaker and receive an audio input from the microphone, thereby realizing a common telecommunication function. The audio processor may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor is also coupled to a central processor, so that recording can be performed on the machine through the microphone, and the sound stored on the machine can be played through the speaker.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0114] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0116] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A risk warning method for a quantitative product, characterized in that: include: S1. Select K quantitative products from n industries and obtain the quantitative product matrix P; S2. Set the deviation time proportion and deviation threshold of the i-th industry among n industries; S3, determining the time window based on multiple dimensions to obtain the number of time windows N; S4. Calculate the index of the quantitative product in N time windows; S5, obtaining the indicator values ​​of multiple dimensions in the time window to obtain an indicator matrix and performing operations to correct the indicator matrix; S6. Screen the deviated products and generate early warning information for quantitative products based on the deviation time ratio.

2. The method according to claim 1, characterized in that The S3 determines the time window based on multiple dimensions to obtain the number of time windows N, including: S301 selects a suitable time unit as a window for measuring the indicator; S302 determines the time span range, traverses the endpoints of each time unit, and determines whether to include it in the window quantity N according to a preset rule.

3. The method according to claim 1, characterized in that The S4 calculates the index of the quantitative product in N time windows, including: S401 Calculate the row matrix p in the quantization product matrix P i,j ={p i,1 , p i,2 …p i,j …p i,k }, where 1≤i≤n; S402 calculates the index value of the quantitative product in each time window N, that is, the sequence of a certain index of the jth product of the i-th dimension in the N time windows, q i,j = {q i,j,1 ,q i,j,2 , ...q i,j,N }; S403 For the row matrix p i,j The K products in get the K*N indicator matrix A of the same dimension i and the same type, expressed as Among them, q i,j,t Represents the indicator value of the j-th product in the i-th dimension in the t-th time window, 1≤i≤n, 1≤j≤K, 1≤t≤N.

4. The method according to claim 3, characterized in that The S5 operates and modifies the indicator matrix, including: S501 obtains the tth column of matrix A to obtain the index value of a single window, which is represented by a column matrix B. Among them, q i,j,t Indicates the tth index value of the jth product in the ith dimension, 1≤i≤n, 1≤j≤K, 1≤t≤N; S502 uses a normal distribution quantile algorithm to obtain two quantiles of the column matrix B, which are represented as z1′ and z2′, where z1′≤z2′; S503 performs a de-extreme transformation on each element of the column matrix B to obtain S504 performs a de-extreme transformation on each element of the matrix A to obtain a transformed matrix S505 calculates the mean index of all products in the i-th dimension in a single time window t to obtain a row matrix Q, in, is the average value of the elements in the tth column of matrix A′, 1≤t≤N.

5. The method according to claim 4, characterized in that: The S6 screens the deviated products and generates early warning information of the quantitative products according to the deviation time ratio, including: S601 obtains any row matrix from the quantization product matrix P, PP = {p i,1 , p i,2 …p i,j …p i,k }, where each element p i,j is the j-th product of dimension i, where 1≤i≤n; S602 From the transformed A', it can be seen that the quantized product p i,j The index after extreme value transformation in time window t is q i,j,t ',Compare Is it greater than the preset deviation threshold X? i , if so, then p i,j If it is a deviation product in time window t, then it is not a deviation product in time window t. S603 summarizes the p in the entire time window based on the determined deviation product i,j The quantity is counted. If count / total number of time windows N is greater than the deviation time ratio, a corresponding warning message is generated.

6. The method according to claim 1, characterized in that The method further includes: repeating steps S4-S6 to obtain a predetermined quantitative product sequence of all set dimensions, and obtaining a warning quantitative product for each dimension and each indicator parameter.

7. A risk warning model for quantitative products, characterized in that: include: The product selection module is used to select K quantitative products from n industries and obtain the quantitative product matrix P; The deviation parameter setting module is used to set the deviation time ratio and deviation threshold of the i-th industry among n industries; A time window determination module is used to determine the time window based on multiple dimensions to obtain the number of time windows N; An index calculation module, used to calculate the index of the quantitative product in N time windows; A correction module, used for obtaining the index values ​​of multiple dimensions in a time window to obtain an index matrix and performing correction operations on the index matrix; The screening module is used to screen out deviated products and generate early warning information for quantitative products based on the deviation time ratio.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the method according to any one of claims 1 to 6 by calling a stored computer program.

10. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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