CN116663905A - Financial risk prediction method, apparatus, device, storage medium and program product - Google Patents

Financial risk prediction method, apparatus, device, storage medium and program product Download PDF

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CN116663905A
CN116663905A CN202310716101.8A CN202310716101A CN116663905A CN 116663905 A CN116663905 A CN 116663905A CN 202310716101 A CN202310716101 A CN 202310716101A CN 116663905 A CN116663905 A CN 116663905A
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周玮理
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China Construction Bank Corp
CCB Finetech Co Ltd
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CCB Finetech Co Ltd
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Abstract

The application discloses a financial risk prediction method, a financial risk prediction device, financial risk prediction equipment, a financial risk prediction storage medium and a financial risk prediction program product. The application relates to the technical field of financial data processing. The method comprises the following steps: acquiring index data of the setting industry in the last period; wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data; predicting the financial system risk of the current period based on the index data of the previous period and a target regression model; the target regression model characterizes the relationship between index data of the setting industry and financial system risks. According to the financial risk prediction method disclosed by the embodiment, the financial system risk is predicted based on the multiple index data of the setting industry, so that the accuracy of financial system risk prediction can be improved.

Description

Financial risk prediction method, apparatus, device, storage medium and program product
Technical Field
The embodiment of the application relates to the technical field of financial data processing, in particular to a financial risk prediction method, a financial risk prediction device, financial risk prediction equipment, a financial risk prediction storage medium and a financial risk prediction program product.
Background
Systematic risk is an important part of financial institution risk management. The prevention of systematic risks posed by the setting industry (e.g., the real estate industry) is an important job of the financial industry. Some important industries have great influence on employment, financial income, resident financial resources and financial stability, so that prediction setting industry has great significance on influence of financial system risks.
In the prior art, the system risk is calculated by predicting potential capital gaps of financial institutions when market crisis occurs, and the association relationship between financial system risks and set industries is considered, so that the accuracy is low.
Disclosure of Invention
The embodiment of the application provides a financial risk prediction method, a device, equipment, a storage medium and a program product, which are used for predicting financial systematic risk based on a plurality of index data of a setting industry and can improve the accuracy of financial systematic risk prediction.
In a first aspect, an embodiment of the present application provides a method for predicting financial risk, including:
acquiring index data of the setting industry in the last period; wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data;
predicting the financial system risk of the current period based on the index data of the previous period and a target regression model; the target regression model characterizes the relationship between index data of the setting industry and financial system risks.
In a second aspect, an embodiment of the present application further provides a financial risk prediction apparatus, including:
the index data acquisition module is used for acquiring index data of the last period of the setting industry; wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data;
the financial system risk prediction module is used for predicting the financial system risk of the current period based on the index data of the previous period and a target regression model; the target regression model characterizes the relationship between index data of the setting industry and financial system risks.
In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement a method for predicting financial risk according to the embodiment of the present application.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements a method for predicting financial risk as described in embodiments of the present application.
In a fifth aspect, embodiments of the present application also provide a computer program product comprising a computer program which, when executed by a processor, implements a method of predicting financial risk as described in embodiments of the present application.
The embodiment of the application discloses a financial risk prediction method, a device, equipment, a storage medium and a program product, which are used for acquiring index data of a setting industry in the last period; wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data; predicting the financial system risk of the current period based on the index data of the previous period and the target regression model; the target regression model characterizes the relationship between index data of the set industry and financial system risks. According to the financial risk prediction method disclosed by the embodiment, the financial system risk is predicted based on the multiple index data of the setting industry, so that the accuracy of financial system risk prediction can be improved.
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 as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a financial risk prediction method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a financial risk prediction apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.
Fig. 1 is a flowchart of a financial risk prediction method provided by an embodiment of the present application, where the method is applicable to predicting risk of a financial system, and the method may be performed by a financial risk prediction device, where the device may be implemented in software and/or hardware, and optionally, implemented by an electronic device, where the electronic device may be a mobile terminal, a PC side, a server, or the like. As shown in fig. 1, the method specifically includes the following steps:
s110, acquiring index data of the setting industry in the last period.
Wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data. The setting industry may be an industry that has a significant impact on employment, tax revenue, resident financial resources, financial stability, for example: real estate industry. The period may be one period of one year, half year, one quarter, or one month, and in this embodiment, one period of one quarter.
In this embodiment, the manner of acquiring the index data of the last period in the setting industry may be: and acquiring index data of a head enterprise of the setting industry in the last period. After obtaining the index data such as financial data, business data, violation data, credit data, tax data, etc., the index data may be normalized first to predict the risk of the financial system. Where a head enterprise may be understood as an enterprise that ranks in the head of market share in the setting industry. In this embodiment, the index data of the head enterprise in the last period of the setting industry is authorized by the head enterprise, and meets the relevant rules of national laws and regulations.
S120, predicting the financial system risk of the current period based on the index data of the previous period and the target regression model.
The target regression model characterizes the relationship between index data of the set industry and financial system risks. The financial system Risk may be represented by a Risk Value (Value at Risk, var) of the financial system. Specifically, the method for predicting the risk of the financial system in the current period based on the index data and the target regression model in the previous period may be: and inputting the index data of the previous period into a target regression model, and outputting the financial system risk of the current period.
In this embodiment, the target regression model is constructed based on a sample data set of a history period, where the sample data set includes history index data of a set industry, first risk information, and second risk information of a financial system.
Optionally, the determining manner of the target regression model is as follows: acquiring historical index data, first risk information and second risk information of a financial system of an industry set in a plurality of historical periods; a target regression model is determined based on the historical index data, the first risk information, and the second risk information.
Wherein the financial system includes at least one financial institution. Financial institutions can be understood as institutions engaged in financial business, and relate to industries such as banks, securities, insurance and the like, and the application scenario takes banks as examples, and index data of large banks are collected. The plurality of history periods may be a plurality of quarters in the past N years. The first risk information may be a Var value of a head enterprise of the setting industry, and the second risk information may be a Var value of a financial system.
Specifically, the manner of acquiring the historical index data of the industry set in the plurality of historical periods may be: acquiring initial index data of a set industry in a plurality of history periods; acquiring enterprises marked by risks in the setting industry; and screening the initial index data based on the enterprises identified by the risks to obtain historical index data.
Wherein, the initial index data may include financial data, business data, violation data, credit data, tax data, and the like. A risk-identified business may be understood as a business that has been risk alerted (ST). Specifically, taking a quarter as a period, extracting multi-dimensional initial index data such as financial data, business data, violation data, credit data and tax data of a head enterprise i in the setting industry in the T-th historical period, dividing the head enterprise into a risk-identified enterprise and a non-risk-identified enterprise, facilitating machine learning for modeling, and screening index data related to the risk-identified enterprise from the initial index data to serve as historical index data. Wherein t=1, 2,3,..n, represents the first quarter, the second quarter, the third quarter, and up to the nth quarter. In the embodiment, index data related to enterprises identified by risks are screened out, so that the accuracy of a target regression model constructed later can be improved, and the accuracy of predicting the financial risk of the system is improved.
Specifically, the process of acquiring the first risk information of the industry set in the plurality of history periods may be: for each history period, acquiring a plurality of first benefit information of the setting industry in the history period; ordering the plurality of first benefit information; and extracting first benefit information at a first setting dividing point in the sorted plurality of first benefit information, and determining the first benefit information as first risk information of the setting industry in a history period.
The first benefit information may be a daily rate of return of a head enterprise in the setting industry. In this embodiment, the manner of obtaining the plurality of first benefit information of the setting industry in the history period may be: acquiring a plurality of first market values of a setting industry in a history period; and determining a plurality of first benefit information corresponding to the plurality of first market values respectively.
Wherein the first market value may be characterized by a daily stock price of head enterprise i. Specifically, the daily stock prices of the head real estate enterprises in the last N years are collected, and then the daily gain rate is calculated according to the formula rt=100×ln (Pt/Pt-1), and the daily gain rate is taken as the first market value. Rt represents a daily gain rate, pt represents a daily stock price.
Wherein, the ranking of the plurality of first benefit information can be understood as: the plurality of first benefit information for each history period is ordered in order of from big to small or from small to big.
Wherein the first setting quantile may be set by the user to any value between 0-100%, for example to 90%, i.e. the first benefit information at 90% is extracted as the first risk information for the history period. Illustratively, taking the real estate industry as an example, the daily gain rates of real estate enterprises i in the T-th history period are ranked, and then the daily gain rate at the first set dividing point q1 is found as the first risk information, i.e. Var value, of the real estate enterprises i in the T-th history period.
Specifically, the manner of acquiring the second risk information of the financial system in the plurality of history periods may be: for each history period, acquiring a plurality of second benefit information of at least one financial institution in the history period respectively; ranking the plurality of second benefit information; extracting second benefit information at a second set dividing point from the sorted plurality of second benefit information; and fusing the second benefit information of at least one financial institution to obtain second risk information of the financial system in the history period.
Wherein the second revenue information may be a daily rate of return of the financial institution. In this embodiment, the manner of acquiring the plurality of second benefit information of the at least one financial institution in the history period may be: acquiring a plurality of second market values of at least one financial institution in a history period respectively; and determining a plurality of second benefit information corresponding to the second market values respectively.
Wherein the second market value may be characterized by a daily stock price of financial institution j. Specifically, the daily stock prices of the financial institutions in the last N years are collected, and then the daily gain rate is calculated according to the formula rt=100×ln (Pt/Pt-1), and the daily gain rate is taken as the second market value. Rt represents a daily gain rate, pt represents a daily stock price.
The manner of ordering the plurality of second benefit information may be: the plurality of second benefit information for each history period is ordered in order of from big to small or from small to big.
Wherein the second set quantile may be set by the user, as with the first set quantile, to any value between 0-100%, for example to 90%, i.e. the second benefit information at 90% is extracted as the second risk information for the history period. Illustratively, the daily rate of return of the Jierong institution j during the T-th history period is ranked, and then the daily rate of return at the second set split point q2 is found as the second risk information, i.e., var value, for the financial institution during the T-th history period.
The method for fusing the second benefit information of at least one financial institution to obtain the second risk information of the financial system in the history period may be: and carrying out weighted summation on the second benefit information of at least one financial institution to obtain second risk information of the financial system in the history period.
Specifically, the weight of each financial institution is determined according to the market share of the financial institution, and then weighted summation is performed on the basis of the second benefit information of at least one financial institution to obtain second risk information of the financial system in a history period.
Specifically, the manner of determining the target regression model based on the historical index data, the first risk information, and the second risk information may be: for every two adjacent history periods in the plurality of history periods, constructing a first regression model according to the history index data of the previous history period and the first risk information of the next history period; constructing a second regression model according to the first risk information of the later history period and the second risk information of the later history period; and determining a target regression model according to the first regression model and the second regression model.
Wherein the previous history period is denoted as T-1 and the next history period is denoted as T. In this embodiment, a first regression model is constructed based on the history index data of the T-1 th history period and the first risk information of the T-1 th history period. I.e. the first regression model characterizes the relationship between the history index data of the T-1 th history period and the first risk information of the T-th history period. And constructing a second regression model based on the first risk information of the T-th historical period and the second risk information of the T-th historical period, namely, the second regression model characterizes the relation between the first risk information of the T-th historical period and the second risk information of the T-th historical period. And finally, determining a target regression model according to the first regression model and the second regression model, namely determining the relation between the historical index data of the T-1 historical period and the second risk information of the T historical period, namely setting the relation between the index data of the industry and the risk of the financial system.
In this embodiment, the manner of constructing the first regression model according to the history index data of the previous history period and the first risk information of the subsequent history period may be: and taking the history index data of the previous history period as an independent variable, and taking the first risk information of the next history period as the dependent variable to construct a first regression model.
Specifically, the historical index data of the previous historical period is taken as an independent variable, the first risk information of the next historical period is taken as a dependent variable, and regression fitting is performed to obtain a first regression model.
In this embodiment, the manner of constructing the second regression model according to the first risk information of the subsequent history period and the second risk information of the subsequent history period may be: and taking the first risk information of the later history period as an independent variable and the second risk information of the later history period as an independent variable to construct a second regression model.
Specifically, the first risk information of the later history period is taken as an independent variable, the second risk information of the later history period is taken as an independent variable, and regression fitting is performed to obtain a second regression model.
According to the technical scheme, index data of the setting industry in the last period are obtained; wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data; predicting the financial system risk of the current period based on the index data of the previous period and the target regression model; the target regression model characterizes the relationship between index data of the set industry and financial system risks. According to the financial risk prediction method disclosed by the embodiment, the financial system risk is predicted based on the multiple index data of the setting industry, so that the accuracy of financial system risk prediction can be improved.
Fig. 2 is a schematic structural diagram of a financial risk prediction apparatus according to an embodiment of the present application, where, as shown in fig. 2, the apparatus includes:
an index data obtaining module 210, configured to obtain index data of a last period of a setting industry; wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data;
the financial system risk prediction module 220 is configured to predict a financial system risk of a current period based on the index data of a previous period and the target regression model; the target regression model characterizes the relationship between index data of the set industry and financial system risks.
Optionally, the method further comprises: the target regression model determining module is used for:
acquiring historical index data, first risk information and second risk information of a financial system of an industry set in a plurality of historical periods; wherein the financial system comprises at least one financial institution;
a target regression model is determined based on the historical index data, the first risk information, and the second risk information.
Optionally, the target regression model determination module is further configured to:
acquiring initial index data of a set industry in a plurality of history periods;
acquiring enterprises marked by risks in the setting industry;
and screening the initial index data based on the enterprises identified by the risks to obtain historical index data.
Optionally, the target regression model determination module is further configured to:
for each history period, acquiring a plurality of first benefit information of the setting industry in the history period;
ordering the plurality of first benefit information;
and extracting first benefit information at a first setting dividing point in the sorted plurality of first benefit information, and determining the first benefit information as first risk information of the setting industry in a history period.
Optionally, the target regression model determination module is further configured to:
acquiring a plurality of first market values of a setting industry in a history period;
and determining a plurality of first benefit information corresponding to the plurality of first market values respectively.
Optionally, the target regression model determination module is further configured to:
for each history period, acquiring a plurality of second benefit information of at least one financial institution in the history period respectively;
ranking the plurality of second benefit information;
extracting second benefit information at a second set dividing point from the sorted plurality of second benefit information;
and fusing the second benefit information of at least one financial institution to obtain second risk information of the financial system in the history period.
Optionally, the target regression model determination module is further configured to:
acquiring a plurality of second market values of at least one financial institution in a history period respectively;
and determining a plurality of second benefit information corresponding to the second market values respectively.
Optionally, the target regression model determination module is further configured to:
and carrying out weighted summation on the second benefit information of at least one financial institution to obtain second risk information of the financial system in the history period.
Optionally, the target regression model determination module is further configured to:
for every two adjacent history periods in the plurality of history periods, constructing a first regression model according to the history index data of the previous history period and the first risk information of the next history period;
constructing a second regression model according to the first risk information of the later history period and the second risk information of the later history period;
and determining a target regression model according to the first regression model and the second regression model.
Optionally, the target regression model determination module is further configured to:
and taking the history index data of the previous history period as an independent variable, and taking the first risk information of the next history period as the dependent variable to construct a first regression model.
Optionally, the target regression model determination module is further configured to:
and taking the first risk information of the later history period as an independent variable and the second risk information of the later history period as an independent variable to construct a second regression model.
The device can execute the method provided by all the embodiments of the application, and has the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in this embodiment can be found in the methods provided in all the foregoing embodiments of the application.
Fig. 3 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a financial risk prediction method.
In some embodiments, the method of predicting financial risk may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the financial risk prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the financial risk prediction method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
Embodiments of the present application also provide a computer program product comprising a computer program which, when executed by a processor, implements a method of predicting financial risk as provided by any of the embodiments of the present application.
Computer program product in the implementation, the computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.

Claims (15)

1. A method for predicting financial risk, comprising:
acquiring index data of the setting industry in the last period; wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data;
predicting the financial system risk of the current period based on the index data of the previous period and a target regression model; the target regression model characterizes the relationship between index data of the setting industry and financial system risks.
2. The method of claim 1, wherein the target regression model is determined by:
acquiring historical index data, first risk information and second risk information of a financial system of the set industry in a plurality of historical periods; wherein the financial system comprises at least one financial institution;
a target regression model is determined based on the historical index data, the first risk information, and the second risk information.
3. The method of claim 2, wherein obtaining historical index data for the set business over a plurality of historical cycles comprises:
acquiring initial index data of the setting industry in a plurality of history periods;
acquiring enterprises marked by risks in the setting industry;
and screening the initial index data based on the enterprises identified by the risks to obtain historical index data.
4. The method of claim 2, wherein obtaining first risk information for the setting industry over a plurality of history periods comprises:
for each history period, acquiring a plurality of first benefit information of the setting industry in the history period;
ranking the plurality of first benefit information;
and extracting first benefit information at a first setting dividing point in the sorted plurality of first benefit information, and determining the first benefit information as first risk information of the setting industry in the history period.
5. The method of claim 4, wherein obtaining a plurality of first revenue information for the setting industry over the history period comprises:
acquiring a plurality of first market values of the setting industry in the history period;
and determining a plurality of first benefit information corresponding to the plurality of first market values respectively.
6. The method of claim 2, wherein obtaining second risk information for the financial system over a plurality of history periods comprises:
for each history period, obtaining a plurality of second benefit information of the at least one financial institution in the history period respectively;
ranking the plurality of second benefit information;
extracting second benefit information at a second set dividing point from the sorted plurality of second benefit information;
and fusing the second benefit information of the at least one financial institution to obtain second risk information of the financial system in the history period.
7. The method of claim 6, wherein obtaining a plurality of second revenue information for the at least one financial institution over the history period, respectively, comprises:
acquiring a plurality of second market values of the at least one financial institution during the history period, respectively;
and determining a plurality of second benefit information corresponding to the second market values respectively.
8. The method of claim 6, wherein fusing the second revenue information for the at least one financial institution to obtain second risk information for the financial system during the history period, comprises:
and carrying out weighted summation on the second benefit information of the at least one financial institution to obtain second risk information of the financial system in the history period.
9. The method of claim 2, wherein determining a target regression model based on the historical indicator data, the first risk information, and the second risk information comprises:
for each two adjacent history periods in the plurality of history periods, constructing a first regression model according to the history index data of the previous history period and the first risk information of the next history period;
constructing a second regression model according to the first risk information of the later history period and the second risk information of the later history period;
and determining a target regression model according to the first regression model and the second regression model.
10. The method of claim 9, wherein constructing a first regression model from the historical index data of the previous historical period and the first risk information of the subsequent historical period comprises:
and constructing a first regression model by taking the history index data of the previous history period as an independent variable and the first risk information of the next history period as the dependent variable.
11. The method of claim 9, wherein constructing a second regression model from the first risk information for a subsequent history period and the second risk information for a subsequent history period comprises:
and constructing a second regression model by taking the first risk information of the later history period as an independent variable and the second risk information of the later history period as an independent variable.
12. A financial risk prediction apparatus, comprising:
the index data acquisition module is used for acquiring index data of the last period of the setting industry; wherein the index data includes at least one of: financial data, business data, violation data, credit data, and tax data;
the financial system risk prediction module is used for predicting the financial system risk of the current period based on the index data of the previous period and a target regression model; the target regression model characterizes the relationship between index data of the setting industry and financial system risks.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, wherein the processor implements the method of predicting financial risk according to any one of claims 1-11 when executing the computer program.
14. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of predicting financial risk as claimed in any one of claims 1 to 11.
15. A computer program product comprising a computer program which, when executed by a processor, implements the financial risk prediction method of any one of claims 1-11.
CN202310716101.8A 2023-06-15 2023-06-15 Financial risk prediction method, apparatus, device, storage medium and program product Pending CN116663905A (en)

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