Intelligent transaction method and system of digital bond, electronic equipment and storage medium

Abstract

The invention discloses an intelligent trading method and system of digital bonds, electronic equipment and a storage medium, the intelligent trading method of digital bonds introduces a dynamically changing bond default risk on the basis of a classic investment utility assessment model, thereby constructing an improved investment utility assessment model, and based on the model, the investment utility assessment model is established. The investor can accurately evaluate the default probability of each digital bond after acquiring the asset information and financing demand information of each bond issuer, so that the investment utility of each digital bond is accurately evaluated, the model can accurately simulate the avoidance behavior of the investor to the credit risk, and the investment efficiency of the digital bond is improved. When the default probability of the digital bond is increased, the digital bond can be actively avoided, the digital bond is closer to the actual investment behavior of an investor, the investment risk of the investor is greatly reduced, and especially when the digital bond is oriented to issuing subjects of small and medium-sized enterprises with large credit risks, large economic losses can be effectively avoided for the investor.

Description

Technical Field

[0001] The present invention relates to the field of digital financial technology, and in particular, to an intelligent trading method and system for digital bonds, an electronic device, and a computer-readable storage medium. Background Art

[0002] The traditional bond market lacks data sharing among various participants (such as issuers, investors, exchanges, and clearing houses). Trading information cannot be updated in real time, and the ability to monitor and respond to market dynamics is limited, making it difficult to quickly identify and respond to market abuse and fraud. Bond trading has low transparency and an imperfect credit management system, which can easily lead to a lack of trust between investors and issuers, and thus create fraud risks. The bond trading process is cumbersome and involves manual review by multiple parties, making it prone to operational errors, delays, fraud, and other risks. The application of blockchain technology and smart contract technology perfectly solves these problems. By introducing blockchain technology, the data transparency and security of the bond market can be improved, and the problem of information silos can be resolved. Smart contract technology can realize the automated issuance and trading process of bonds, reduce manual operations, and improve transaction efficiency. For example, patent CN112785422A discloses a corporate bond financing method based on blockchain.

[0003] In addition, investors currently generally use the mean-variance utility model when evaluating investment utility and choose bonds with the highest investment utility evaluation value for investment. However, the mean-variance utility model only considers the investor's static balance between risk and return, and does not consider the dynamically changing real-time credit risk of bonds, resulting in inaccurate investment utility evaluation results, which brings greater investment risks to investors, especially when targeting small and medium-sized enterprise issuers with higher credit risks, which may cause investors to suffer greater losses. Summary of the Invention

[0004] The present invention provides an intelligent trading method and system for digital bonds, an electronic device, and a computer-readable storage medium, which can greatly reduce investors' investment risks, especially when targeting small and medium-sized enterprise issuers with higher credit risks, and can effectively avoid causing large economic losses to investors.

[0005] According to one aspect of the present invention, a method for intelligent trading of digital bonds is provided, comprising the following:

[0006] Bond issuers and investors complete digital identity verification on the blockchain;

[0007] Determine the issuance price of digital bonds based on the financing demand information input by each bond issuer, and automatically issue digital bonds through smart contracts;

[0008] Obtain asset information and financing demand information of each bond issuer to assess the default probability of each bond issuer. After evaluating the investment utility of each digital bond based on the default probability, the digital bond with the highest investment utility is selected as the target digital bond for investors.

[0009] Investors and the bond issuers corresponding to the target digital bonds complete the transaction through smart contracts.

[0010] Furthermore, the default probability of the bond issuer is calculated based on the following formula:

[0011]

[0012] Among them, PD i (t) represents the default probability of bond issuer i at time t, A i (t) represents the current asset value of bond issuer i at time t, B i represents the debt maturity value of bond issuer i, μ represents the asset growth rate of the bond issuer, σ represents the asset volatility of the bond issuer, T represents the bond term, and Φ() represents the probability function.

[0013] Furthermore, the investment utility of each digital bond is evaluated based on the following formula:

[0014]

[0015] Among them, U i (t) represents the investment utility of the i-th digital bond at time t, E[R i (t)] represents the investor’s expected rate of return on the i-th digital bond, λ i represents the investor's risk aversion coefficient for the i-th digital bond, δ i It represents the default aversion coefficient of investors to the i-th digital bond.

[0016] Furthermore, the investor's expected rate of return for each digital bond is calculated based on the following formula:

[0017] E[R i (t)]=r i +CS i (t)

[0018] CS i (t) = γ0 + γ1 × PD i (t)+γ2×Liquidity i (t)+∈ i (t)

[0019] Among them, r i represents the discount rate of the i-th digital bond, estimated based on its yield curve, CSi (t) represents the credit spread of the i-th digital bond at time t, γ0, γ1, γ2 represent the regression coefficients, Liquidity i (t) represents the liquidity index of the i-th digital bond at time t, ∈ i (t) represents the disturbance term.

[0020] Furthermore, the yield curve is fitted using the least squares method based on real-time market data, and the fitting expression is:

[0021]

[0022] Among them, β0 represents the long-term interest rate component, β1 represents the short-term interest rate component, β2 represents the medium-term interest rate component, λ represents the curve shape parameter used to control the curvature of the yield curve, and r maxker (t i ) represents the yield to maturity observed in the market, r(t) represents the spot rate of return with a maturity of t, and t represents the maturity period.

[0023] Furthermore, it also includes the following:

[0024] Investors determine the market price of the next period based on the market price and supply and demand information of the digital bonds they hold in the current period.

[0025] Furthermore, the market price for the next period is calculated based on the following formula:

[0026] P t+1 =P t +α t (D t -S t )

[0027]

[0028] Among them, P t represents the market price in period t, P t+1 represents the market price in period t+1, α t Denotes the market price response sensitivity coefficient of period t, D t represents the number of buy orders in period t, S t Indicates the number of buy orders in period t, Volatility t Represents the market volatility of period t, Liquidity t It represents the liquidity index of period t, and η0, η1 and η2 are all constants.

[0029] In addition, the present invention also provides an intelligent trading system for digital bonds, including:

[0030] A digital identity verification module, used by bond issuers and investors to complete digital identity verification on the blockchain;

[0031] The digital bond issuance module is used to determine the issuance price of each digital bond based on the financing demand information input by each bond issuer and automatically issue digital bonds through smart contracts;

[0032] The investment utility evaluation module is used to obtain the asset information and financing demand information of each bond issuer to assess the default probability of each bond issuer. After evaluating the investment utility of each digital bond based on the default probability, the module selects the digital bond with the highest investment utility as the target digital bond for investors.

[0033] The digital bond trading module is used for investors to complete transactions with the bond issuers corresponding to the target digital bonds through smart contracts.

[0034] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the above method by calling the computer program stored in the memory.

[0035] In addition, the present invention also provides a computer-readable storage medium for storing a computer program for performing intelligent trading of digital bonds, wherein the computer program executes the steps of the above-described method when running on a computer.

[0036] The present invention has the following beneficial effects:

[0037] The intelligent digital bond trading method of the present invention introduces dynamically changing bond default risks based on the classic investment utility evaluation model, thereby constructing an improved investment utility evaluation model. Based on this model, investors can accurately assess the default probability of each digital bond after obtaining asset information and financing demand information of each bond issuer, and thus accurately assess the investment utility of each digital bond. This model can accurately simulate investors' avoidance of credit risk and actively avoid digital bonds when the default probability of the bond increases. This model is closer to investors' actual investment behavior and greatly reduces investors' investment risks. This is especially effective for small and medium-sized enterprise issuers with higher credit risks, effectively avoiding significant economic losses for investors.

[0038] In addition, the intelligent trading system for digital bonds of the present invention also has the above advantages.

[0039] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 This is a flow chart of the intelligent trading method for digital bonds according to a preferred embodiment of the present application;

[0042] Figure 2 This is another flowchart of the intelligent trading method for digital bonds according to the preferred embodiment of the present application;

[0043] Figure 3 This is another flowchart of the intelligent trading method for digital bonds according to the preferred embodiment of the present application;

[0044] Figure 4 This is a schematic diagram of the module structure of the intelligent trading system for digital bonds in another embodiment of the present application. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] Reference Figure 1 The preferred embodiment of the present application provides an intelligent trading method for digital bonds, including the following contents:

[0047] Step S1: The bond issuer and investor complete digital identity verification on the blockchain;

[0048] Step S2: Determine the issuance price of each digital bond based on the financing demand information input by each bond issuer, and automatically issue the digital bond through a smart contract;

[0049] Step S3: Obtain the asset information and financing demand information of each bond issuer to assess the default probability of each bond issuer. After evaluating the investment utility of each digital bond based on the default probability, the digital bond with the highest investment utility is selected as the target digital bond for the investor.

[0050] Step S4: The investor and the bond issuer corresponding to the target digital bond complete the transaction through a smart contract.

[0051] It can be understood that the intelligent trading method of digital bonds in this embodiment introduces dynamically changing bond default risks on the basis of the classic investment utility evaluation model, thereby constructing an improved investment utility evaluation model. Based on this model, investors can accurately evaluate the default probability of each digital bond after obtaining the asset information and financing demand information of each bond issuer, thereby accurately evaluating the investment utility of each digital bond. This model can accurately simulate investors' avoidance behavior of credit risk. When the probability of default of a digital bond increases, the investor will actively avoid the bond, which is closer to the investor's actual investment behavior and greatly reduces the investor's investment risk. In particular, when it comes to issuing small and medium-sized enterprises with higher credit risks, it can effectively avoid causing large economic losses to investors.

[0052] In step S1, the bond issuer must undergo digital identity verification on the blockchain, using asymmetric encryption technology to generate a unique digital identity for it and register it. This digital identity information is then stored on the blockchain, ensuring the security and legitimacy of digital identity authentication. Similarly, investors must register and complete identity verification on the blockchain platform. The system verifies their true identity using digital identity technology and generates a unique digital identifier for each investor. All registration information and credit records are stored on the blockchain, ensuring data transparency and security. Digital identity verification technology ensures the legitimacy and security of the identities of bond issuers and investors, effectively preventing fraud.

[0053] In addition, in step S2, the bond issuer (such as a company or government) enters its financing demand information into the blockchain platform. This financing demand information includes the financing amount, expected interest rate, and bond term. The agent installed in the blockchain platform will capture market data (including interest rate trends, investor demand, and economic indicators) in real time, analyze it, predict market acceptance and interest rate fluctuations, and determine the issue price of the digital bond based on historical issuance data, market interest rate curves, and liquidity of similar bonds. The agent is pre-trained, and the specific training process is prior art and will not be described here. Of course, in other embodiments of the present invention, the bond issuer can also set the issue price of the digital bond based on its own evaluation results. After determining the issue price, the bond issuer automatically generates specific issuance terms, such as the face rate, issuance amount, and bond term, through the system interface. These are automatically deployed to the blockchain via a smart contract to ensure the openness, transparency, and immutability of the terms. The bond issuer then issues a bond investment invitation to the market through the smart contract, including detailed terms such as the face rate, term, and issuance size, and displays them publicly and transparently through the smart contract, thereby achieving the automatic issuance of digital bonds.

[0054] In addition, in step S3, there are several bond issuers and investors on the blockchain platform. The intelligent body will select appropriate digital bonds on the blockchain platform and submit price-quantity subscription curves based on the investment needs of different investors, such as risk preferences and expected returns, based on a preset investment utility evaluation model. The cumulative subscription volume of each price level is recorded on the blockchain. In the prior art, investor behavior modeling generally uses the mean-variance utility model to evaluate investment utility, where the expression of the mean-variance utility model is: Among them, U represents the investment utility, μ represents the expected return, σ 2 represents the variance of returns, and λ0 represents the risk aversion coefficient. However, this model only considers the static balance between risk and return for investors, and does not consider the dynamically changing real-time credit risk of bonds, resulting in inaccurate investment utility evaluation results. In particular, when targeting small and medium-sized enterprise issuers with higher credit risks, it may cause greater losses to investors. Therefore, the present invention improves the investment utility evaluation model by introducing the probability of default into the investment utility evaluation model, which can more accurately evaluate the investment utility of each digital bond. The model can accurately simulate the investor's avoidance behavior towards credit risk. When the probability of default of a digital bond increases, the investor will actively avoid the bond. This is closer to the investor's actual investment behavior, greatly reducing the investor's investment risk, especially when targeting small and medium-sized enterprise issuers with higher credit risks, which can effectively avoid causing large economic losses to investors.

[0055] Specifically, the intelligent agent on the blockchain will first obtain the asset information and financing demand information of each bond issuer to assess the default probability of each bond issuer. The default probability of the bond issuer is calculated based on the following formula:

[0056]

[0057] Among them, PD i (t) represents the default probability of bond issuer i at time t, A i (t) represents the current asset value of bond issuer i at time t, B i represents the debt maturity value of bond issuer i, μ represents the asset growth rate of the bond issuer, σ represents the asset volatility of the bond issuer, T represents the bond term, and Φ() represents a probability function. For example, the cumulative distribution function of the standard normal distribution can be selected, and the standard normal variable can be mapped to the probability of falling below this value, reflecting the default probability corresponding to the asset value being lower than the default threshold in the structured model. It can be understood that the default probability model of the present invention dynamically analyzes the default probability based on multi-dimensional data indicators of the bond issuer's asset information and financing demand information, and can accurately assess the real-time default risk of digital bonds.

[0058] Then, the intelligent body will evaluate the investment utility of each digital bond based on the calculated default probability. The expression of the improved investment utility evaluation model is:

[0059]

[0060] Among them, U i (t) represents the investment utility of the i-th digital bond at time t, E[R i (t)] represents the investor’s expected rate of return on the i-th digital bond, λ i represents the investor's risk aversion coefficient for the i-th digital bond, δ i It represents the default aversion coefficient of investors to the i-th digital bond.

[0061] It can be understood that the present invention can accurately simulate investors' avoidance of credit risk by using the default probability of digital bonds as a negative utility item in investment utility evaluation, making the behavior of the intelligent agent closer to the investor's true investment psychology. When the default probability of a digital bond increases, the intelligent agent will actively avoid the bond, greatly reducing the investment risk of investors. Especially when it comes to issuing digital bonds to small and medium-sized enterprises with higher credit risks, it can effectively avoid causing large economic losses to investors.

[0062] In addition, the investor's expected rate of return for each digital bond is calculated based on the following formula:

[0063] E[R i (t)]=r i +CS i (t)

[0064] CS i (t) = γ0 + γ1 × PD i (t)+γ2×Liquidity i (t)+∈ i (t)

[0065] Among them, r i The discount rate of the i-th digital bond is estimated based on its yield curve. The specific estimation formula belongs to the existing technology and will not be repeated here. CS i (t) represents the credit spread of the i-th digital bond at time t, γ0, γ1, γ2 represent regression coefficients, which can be obtained by regression calculation based on historical data or through reinforcement learning, Liquidity i (t) represents the liquidity index of the i-th digital bond at time t (such as bid-ask spread, transaction frequency, etc.), ∈ i (t) represents the disturbance term, that is, the fitting error.

[0066] It is understandable that the expected rate of return is usually composed of a risk-free discount rate and a risky credit spread. Among them, the existing technology usually regards the credit spread of a bond as a static valuation parameter, which is generally estimated by a rating agency or manually preset. However, in real transactions, the default risk of a bond will change dynamically, and the liquidity risk will also change dynamically, causing the credit spread of the bond to also show dynamic changes. Therefore, the existing static credit spread strategy cannot accurately evaluate the expected rate of return. The present invention proposes a credit spread estimation model based on dynamic factor modeling, which dynamically evaluates the credit spread of each digital bond based on the default probability and liquidity index of the digital bond. The credit spread can be adjusted in time according to changes in the credit of the bond issuer, deterioration of the bond's liquidity, market sentiment shocks, etc. It can quickly perceive risks and adjust positions when emergencies occur in the market, so as to facilitate timely premium compensation for high-risk bonds, providing a more accurate and reliable basis for investment return evaluation, which can effectively help investors hedge risks.

[0067] In addition, in traditional bond valuation or interest rate modeling, existing technologies usually use the Nelson-Siegel model to fit the yield curve. The expression of this model is:

[0068]

[0069] Here, β0 represents the long-term interest rate component, β1 represents the short-term interest rate component, β2 represents the medium-term interest rate component, λ represents the curve shape parameter used to control the curvature of the yield curve, and t represents the maturity period. However, because the Nelson-Siegel model can only perform static interest rate analysis, it lacks a dynamic update mechanism for the individual characteristics, credit status, and transaction feedback of digital bonds.

[0070] In the present invention, the yield curve is obtained by dynamic sliding window fitting based on real-time market data, specifically using the least squares method, and the fitting expression is:

[0071]

[0072]

[0073] Among them, r maxker (t i ) represents the market-observed yield to maturity, and r(t) represents the spot rate of return with a maturity of t. Furthermore, λ can be fine-tuned based on the risk preferences and holding targets of different investors to accommodate different trading cycles and investment horizons.

[0074] It can be understood that the present invention uses real-time market data to fit the least squares method to obtain a dynamic and sensitive yield curve, which is conducive to the intelligent agent making rapid adjustments according to the real-time market interest rate and realizing the dynamic update of the discount rate. The dynamic prediction of the yield curve is also conducive to the intelligent agent being more forward-looking in the selection and maturity matching of different bonds, which is conducive to improving investors' returns.

[0075] In addition, in step S4, after the intelligent agent selects the target digital bond for the investor, the investor and the bond issuer confirm the transaction terms and execute digital signatures through the smart contract after reaching an agreement. The investor and the bond issuer verify the compliance of funds and bonds through zero-knowledge proof. Once the transaction is confirmed, the smart contract will automatically record all relevant transaction data, such as the investment amount, number of bonds and interest rate. After the transaction is confirmed, the smart contract will automatically handle the funds settlement to ensure that the funds and bonds are transferred safely and accurately in accordance with the predetermined terms.

[0076] Furthermore, during the bond holding period, the smart contract automatically calculates and transfers the interest payable to the investor's account on each payment date according to the bond terms. On the bond's maturity date, the smart contract also automatically calculates the principal repayment amount and automatically transfers the funds to the investor's account via the blockchain, completing the principal repayment. If the bond issuer fails to repay the principal or interest on time, the smart contract automatically records the default and uploads the details to the blockchain. This record directly affects the bond issuer's credit rating in the system, thereby updating its blacklist and whitelist status. Therefore, the blockchain not only records all bond-related transaction data, but also includes default records and interest payment information, allowing regulators to access and audit this data in real time for compliance checks. Furthermore, the blockchain also records investors' credit ratings and behavioral data. The smart agent dynamically assigns credit scores based on factors such as investors' trading history and repayment ability, adjusting their credit ratings in real time. Furthermore, the smart contract automatically calculates the applicable taxes for each bond transaction, generates tax reports, and automatically deducts the corresponding taxes from the transaction amount. This automated tax processing reduces manual calculation errors and delays, ensuring tax compliance and mitigating tax risks. Furthermore, in the secondary bond market, smart contracts automatically handle bond repurchase and resale operations. Furthermore, when investors need to exit their investments, smart contracts automatically calculate the redemption amount and securely and quickly transfer it to their accounts via the blockchain. This automated redemption process reduces transaction costs and complexity, while improving operational transparency and efficiency. Furthermore, when bonds mature or are repaid early, smart contracts automatically execute the clearing and settlement process, including fund transfers and bond cancellations. This automates the bond liquidation process, ensures rapid and accurate settlement, and significantly improves liquidation efficiency.

[0077] In addition, if Figure 2As shown, the intelligent trading method of digital bonds also includes the following contents:

[0078] Step S5: Investors determine the market price of the next period based on the market price and supply and demand information of the digital bonds they hold in the current period.

[0079] Specifically, investors can resell their digital bonds to the next-level trading counterparty. Before selling the digital bonds, investors need to first evaluate the current market price of the digital bonds, and then use the current market price as the selling price in the next trading cycle. The current market price is generally determined based on the basic bond pricing model, which is expressed as: Among them, P t represents the current market price of the bond, i.e., the market price in period t; C represents the interest payment per period, i.e., the coupon; F represents the face value of the bond; r represents the discount rate, which can be estimated based on the yield curve; and N represents the number of remaining periods. However, this bond pricing model fails to reflect the dynamic feedback process between investment behavior, information dissemination, and market reaction. Investors can only passively set the selling price, which makes it difficult to accurately reflect the true value of the digital bond and may cause losses to investors. Therefore, the present invention constructs a digital bond price update mechanism based on transaction behavior, specifically calculating the market price of the next period based on the following formula:

[0080] P t+1 =P t +α t (D t -S t )

[0081]

[0082] Among them, P t represents the market price in period t, P t+1 represents the market price in period t+1, α t Denotes the market price response sensitivity coefficient of period t, D t represents the number of buy orders in period t, S t Indicates the number of buy orders in period t, Volatility t Represents the market volatility of period t, Liquidity t represents the liquidity index of period t. η0, η1, and η2 are all constants. The market price response sensitivity coefficient depends on market volatility and liquidity tension. The smaller the market volatility and the better the liquidity, the more stable the digital bond is in the market and has a higher market value. The larger the market price response sensitivity coefficient, the higher the market price in the next period, which can more accurately reflect the true value of the digital bond.

[0083] It can be understood that the present invention constructs a digital bond price update mechanism based on trading behavior, which updates the market price of the next period through the trading situation of the bond in the current market, forming a feedback loop of micro behavior-macro market price. The price update is no longer a random disturbance or updated according to a preset path. It has a high degree of simulation and is conducive to capturing real market fluctuations such as price manipulation, herd effect, and spontaneous collapse.

[0084] In addition, if Figure 3 As shown, the intelligent trading method of digital bonds also includes the following contents:

[0085] Step S6: Detect whether there is any abnormal transaction of digital bonds, and issue an early warning if any abnormal transaction occurs.

[0086] Understandably, in traditional financial regulatory systems, abnormal transaction detection typically relies on manually set price thresholds or abnormal trading volume warning rules, which are often lagging and rigid. They are unable to provide dynamic warnings based on market microstructure and changes in investor behavior. In particular, it is difficult to identify abnormal fluctuations and deviations caused by information distortion or herd behavior, resulting in an inability to accurately detect abnormal transactions. In contrast, this invention proposes two evaluation indicators: price deviation and volatility. The calculation formula for the price deviation indicator is: Δ t Indicates price deviation, P t represents the actual market price, V t It represents the fundamental valuation calculated based on the discounted cash flow method. The price deviation can quantitatively represent the deviation between the bond price and its fundamental valuation. The calculation formula of the volatility indicator is: σ m (t) represents the short-term market price volatility, P i represents the price at each moment during the window period, Represents the average price within the window period, n represents the length of the window period, and the short-term market price volatility can characterize the intensity of the market price within a certain time window. Then, based on the weighted sum of the two indicators, a comprehensive evaluation index is obtained, which can be expressed as: R t =ω1×Δ t +ω2×σ m (t), where R t Represents a comprehensive evaluation index, ω1 and ω2 represent weight coefficients, if R t If the value is greater than the preset threshold, it is determined that there is abnormal trading behavior in the digital bond and an early warning reminder is issued.

[0087] It can be understood that the present invention comprehensively detects abnormal trading behaviors by evaluating whether prices deviate from reasonable valuations and whether prices experience abnormal fluctuations. It can quickly and accurately identify abnormal trading behaviors in extreme market scenarios (high deviation + high volatility), providing regulators with a quantitative, dynamic, and structured risk identification tool, filling the gap in existing technologies in "ex-ante abnormality identification."

[0088] In addition, if Figure 4 As shown, another embodiment of the present invention further provides an intelligent trading system for digital bonds, preferably using the intelligent trading method for digital bonds as described above, including:

[0089] A digital identity verification module, used by bond issuers and investors to complete digital identity verification on the blockchain;

[0090] The digital bond issuance module is used to determine the issuance price of each digital bond based on the financing demand information input by each bond issuer and automatically issue digital bonds through smart contracts;

[0091] The investment utility evaluation module is used to obtain the asset information and financing demand information of each bond issuer to assess the default probability of each bond issuer. After evaluating the investment utility of each digital bond based on the default probability, the module selects the digital bond with the highest investment utility as the target digital bond for investors.

[0092] The digital bond trading module is used for investors to complete transactions with the bond issuers corresponding to the target digital bonds through smart contracts.

[0093] It can be understood that the intelligent trading system of digital bonds in this embodiment introduces dynamically changing bond default risks on the basis of the classic investment utility evaluation model, thereby constructing an improved investment utility evaluation model. Based on this model, investors can accurately evaluate the default probability of each digital bond after obtaining the asset information and financing demand information of each bond issuer, thereby accurately evaluating the investment utility of each digital bond. This model can accurately simulate investors' avoidance behavior of credit risk. When the probability of default of a digital bond increases, the investor will actively avoid the bond. This is closer to the investor's actual investment behavior, greatly reducing the investor's investment risk, especially when it comes to issuing small and medium-sized enterprises with higher credit risks, which can effectively avoid causing large economic losses to investors.

[0094] In addition, the intelligent trading system for digital bonds also includes:

[0095] The price update module is used for investors to determine the market price of the next period based on the market price and supply and demand information of the digital bonds they hold in the current period.

[0096] In addition, the intelligent trading system for digital bonds also includes:

[0097] The anomaly detection module is used to detect whether there are abnormal transactions in digital bonds, and issue an early warning if abnormal transactions occur.

[0098] In addition, another embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the above method by calling the computer program stored in the memory.

[0099] In addition, another embodiment of the present invention further provides a computer-readable storage medium for storing a computer program for performing intelligent trading of digital bonds, wherein the computer program executes the steps of the above-described method when running on a computer.

[0100] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical medium with a pattern of holes, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash-erasable programmable read-only memory (FLASH-EPROM), any other memory chip or cartridge, or any other medium that can be read by a computer. Instructions can further be transmitted or received via a transmission medium. The term transmission medium may include any tangible or intangible medium that can be used to store, encode, or carry instructions for execution by a machine, and includes digital or analog communication signals or other intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires of a bus used to transmit a computer data signal.

[0101] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt 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.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] 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 an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] 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 the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0106] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

[0107] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An intelligent trading method for digital bonds, characterized in that: Includes the following: Bond issuers and investors complete digital identity verification on the blockchain; Determine the issuance price of digital bonds based on the financing demand information input by each bond issuer, and automatically issue digital bonds through smart contracts; Obtain asset information and financing demand information of each bond issuer to assess the default probability of each bond issuer. After evaluating the investment utility of each digital bond based on the default probability, the digital bond with the highest investment utility is selected as the target digital bond for investors. Investors and the bond issuers corresponding to the target digital bonds complete the transaction through smart contracts.

2. The intelligent trading method for digital bonds according to claim 1, characterized in that: The default probability of the bond issuer is calculated based on the following formula: Among them, PD i (t) represents the default probability of bond issuer i at time t, A i (t) represents the current asset value of bond issuer i at time t, B i represents the debt maturity value of bond issuer i, μ represents the asset growth rate of the bond issuer, σ represents the asset volatility of the bond issuer, T represents the bond term, and Φ() represents the probability function.

3. The intelligent trading method for digital bonds according to claim 2, characterized in that: The investment utility of each digital bond is evaluated based on the following formula: Among them, U i (t) represents the investment utility of the i-th digital bond at time t, E[R i (t)] represents the investor’s expected rate of return on the i-th digital bond, λ i represents the investor's risk aversion coefficient for the i-th digital bond, δ i It represents the default aversion coefficient of investors to the i-th digital bond.

4. The intelligent trading method for digital bonds according to claim 3, characterized in that: The expected rate of return for investors on each digital bond is calculated based on the following formula: E[R i (t)]=r i +CS i (t) CS i (t)=γ0+γ1×PD i (t)+γ2×Liquidity i (t)+∈ i (t) Among them, r i represents the discount rate of the i-th digital bond, estimated based on its yield curve, CS i (t) represents the credit spread of the i-th digital bond at time t, γ0, γ1, γ2 represent the regression coefficients, Liquidity i (t) represents the liquidity index of the i-th digital bond at time t, ∈ i (t) represents the disturbance term.

5. The intelligent trading method for digital bonds according to claim 4, characterized in that: in, The yield curve is fitted using the least squares method based on real-time market data. The fitting expression is: Among them, β0 represents the long-term interest rate component, β1 represents the short-term interest rate component, β2 represents the medium-term interest rate component, λ represents the curve shape parameter used to control the curvature of the yield curve, and r maxker (t i ) represents the yield to maturity observed in the market, r(t) represents the spot rate of return with a maturity of t, and t represents the maturity period.

6. The intelligent trading method for digital bonds according to claim 1, characterized in that: Also included: Investors determine the market price of the next period based on the market price and supply and demand information of the digital bonds they hold in the current period.

7. The intelligent trading method for digital bonds according to claim 6, characterized in that: The market price for the next period is calculated based on the following formula: P.S t+1＝ P.S t +α t (D t -S t ) Among them, P t represents the market price in period t, P t+1 represents the market price in period t+1, α t Denotes the market price response sensitivity coefficient of period t, D t represents the number of buy orders in period t, S t Indicates the number of buy orders in period t, Volatility t Represents the market volatility of period t, Liquidity t It represents the liquidity index of period t, and η0, η1 and η2 are all constants.

8. An intelligent trading system for digital bonds, characterized by: include: A digital identity verification module, used by bond issuers and investors to complete digital identity verification on the blockchain; The digital bond issuance module is used to determine the issuance price of each digital bond based on the financing demand information input by each bond issuer and automatically issue digital bonds through smart contracts; The investment utility evaluation module is used to obtain the asset information and financing demand information of each bond issuer to assess the default probability of each bond issuer. After evaluating the investment utility of each digital bond based on the default probability, the module selects the digital bond with the highest investment utility as the target digital bond for investors. The digital bond trading module is used for investors to complete transactions with the bond issuers corresponding to the target digital bonds through smart contracts.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the method according to any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium for storing a computer program for performing intelligent digital bond trading, characterized in that: When the computer program is run on a computer, the steps of the method according to any one of claims 1 to 7 are executed.

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