Performance attribution method and device for solid revenue asset combination, storage medium and computer program product

Abstract

The invention discloses a fixed revenue asset combination performance attribution method and device, a storage medium and a computer program product, and relates to the technical field of data analysis, and the method comprises the steps: collecting bottom data of a fixed revenue asset combination; inputting the underlying data into a pre-constructed attribution model based on profit and loss for hierarchical performance attribution disassembly to obtain a performance attribution result; the hierarchical performance attribution splitting comprises the steps of calculating a combined net profit, splitting the combined net profit, splitting a combined total income, splitting an income effect and a price effect; and selecting an attribution calculation mode in combination with a management mode of the fixed income asset combination, and aggregating the performance attribution results based on the attribution calculation mode to obtain multiple periods of attribution results. The accuracy and the flexibility of the performance attribution result are improved through hierarchical performance attribution disassembly, and accurate disassembly of the fixed revenue asset combination income is realized.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to fixed income portfolio performance attribution methods, devices, storage media, and computer program products. Background Technology

[0002] The Campisi attribution model, a widely used tool for fixed-income portfolio performance analysis, breaks down portfolio returns into key factors such as income and price effects through a top-down framework, providing a foundation for understanding the composition of returns. However, when applying the Campisi model, attribution results are calculated solely based on portfolio holdings and individual bond valuations, neglecting transaction costs from trading activity, portfolio changes, and daily profit and loss. This simplification leads to an unreasonable amplification of the residual terms in the attribution model, causing discrepancies between the attribution results and actual returns, and failing to accurately reflect the composition of portfolio returns. Furthermore, by ignoring the impact of changes in the risk-free yield curve when calculating bond price effects, the core components of the price effect cannot be accurately decomposed, affecting the reliability of the attribution results. These shortcomings make current mainstream solutions using the Campisi model unable to meet investors' demands for accuracy and flexibility in attribution results, and fail to truly reflect the sources of portfolio returns.

[0003] Therefore, how to accurately decompose the returns of a fixed-income portfolio and improve the accuracy and flexibility of performance attribution results has become a technical problem that this application urgently needs to solve.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a method, device, storage medium, and computer program product for attributing the performance of a fixed-income portfolio, aiming to solve the technical problem of how to accurately decompose the returns of a fixed-income portfolio and improve the accuracy and flexibility of the performance attribution results.

[0006] To achieve the above objectives, this application proposes a fixed-income portfolio performance attribution method, the method comprising: Collect underlying data for fixed-income asset portfolios; The underlying data is input into a pre-built profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results; the hierarchical performance attribution decomposition includes calculating portfolio net profit, splitting the portfolio net profit, splitting portfolio total revenue, splitting revenue effect and price effect; By selecting an attribution calculation method based on the management approach of the fixed-income asset portfolio, and aggregating the performance attribution results based on the attribution calculation method, multi-period attribution results are obtained.

[0007] In one embodiment, the underlying data includes portfolio data, bond data, and benchmark portfolio data. The step of inputting the underlying data into a pre-built profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results includes: The underlying data is input into a pre-built profit and loss-based attribution model, which extracts the interval profit and loss amount from the portfolio profit and loss statement of the portfolio data to obtain the portfolio net profit. The portfolio net profit is broken down into total portfolio revenue and cost of capital; The total income of the portfolio is decomposed into income effect and price effect according to the profit and loss-based attribution model. The income effect and the price effect are decomposed separately, and the risk-free asset effect is calculated. Based on the risk-free asset interest rate curve in the aforementioned bond data, the risk-free asset effect can be decomposed into the risk-free asset shift effect and the risk-free asset distortion effect. The performance attribution results are obtained based on the income effect, the price effect, the risk-free asset effect, the risk-free asset shift effect, and the risk-free asset distortion effect.

[0008] In one embodiment, the step of splitting the portfolio net profit into total portfolio revenue and cost of capital includes: The cost of capital is calculated based on the user-defined cost of capital rate and the average capital occupied by the investment portfolio. The total revenue of the portfolio is derived from the portfolio's net profit based on the cost of capital.

[0009] In one embodiment, the step of decomposing the total portfolio revenue into income effects and price effects according to the profit and loss-based attribution model includes: According to the profit and loss-based attribution model, the total portfolio revenue is decomposed into the revenue effect, the price effect, the trading capability effect, the profit and loss adjustment effect, and the stock selection effect; including, The coupon effect, convergence effect, tax exemption effect, and riding effect are calculated using the profit and loss-based attribution model, and the income effect is obtained based on the coupon effect, the convergence effect, the tax exemption effect, and the riding effect. The interest rate spread effect and convexity effect are calculated using the profit and loss-based attribution model, and the price effect is obtained based on the interest rate spread effect and the convexity effect.

[0010] In one embodiment, the steps of decomposing the income effect and the price effect respectively, and calculating the risk-free asset effect, include: The income effect is further broken down into the coupon effect, the convergence effect, the tax exemption effect, and the riding effect; The price effect is further decomposed into the interest rate spread effect and the convexity effect; The risk-free asset effect is calculated based on a pre-obtained risk-free asset interest rate curve.

[0011] In one embodiment, the step of decomposing the risk-free asset effect into a risk-free asset shift effect and a risk-free asset distortion effect by combining the risk-free asset interest rate curve in the bond data includes: Extract key maturity points from the risk-free asset interest rate curve in the bond data, and calculate the average interest rate based on the key maturity points; Calculate the risk-free asset shift effect based on the average interest rate mentioned above; Based on the risk-free asset effect and the risk-free asset translation effect, the risk-free asset distortion effect is obtained.

[0012] In one embodiment, the attribution calculation method includes a compound interest calculation model and a simple interest calculation model. The step of selecting the attribution calculation method based on the management method of the fixed-income asset portfolio, and aggregating the performance attribution results based on the attribution calculation method to obtain multi-period attribution results includes: If the fixed-income asset portfolio is managed using the net asset value method or the market value method, then the compound interest calculation mode should be selected. Based on the compound interest calculation model and the time-weighted rate of return theory, the performance attribution results are aggregated using a preset algorithm to obtain multi-period attribution results. If the fixed-income asset portfolio is managed using the cost method, then the simple interest calculation model should be selected; The performance attribution results are accumulated based on the simple interest calculation model to obtain multi-period attribution results.

[0013] In addition, to achieve the above objectives, this application also proposes a fixed-income portfolio performance attribution device, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the fixed-income portfolio performance attribution method as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the fixed income portfolio performance attribution steps as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the fixed income portfolio performance attribution steps as described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: The underlying data of the fixed-income asset portfolio is collected; the underlying data is input into a pre-constructed profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results; the hierarchical performance attribution decomposition includes calculating portfolio net profit, splitting portfolio net profit, splitting portfolio total revenue, and splitting revenue effect; the attribution calculation method is selected in combination with the management method of the fixed-income asset portfolio, and the performance attribution results are aggregated based on the attribution calculation method to obtain multi-period attribution results. First, by collecting underlying data from fixed-income asset portfolios, a comprehensive data foundation is provided for accurate attribution. Next, the underlying data is input into a profit-loss-based attribution model for hierarchical performance attribution decomposition. This decomposition calculates portfolio net profit, the portfolio's total revenue, and revenue efficiency, achieving a multi-dimensional and refined breakdown of returns. This addresses the issue of attribution items deviating from their true values ​​due to the failure to consider changes in the risk-free rate curve. Finally, by combining the fixed-income asset portfolio management methods with the selected attribution calculation method, performance attribution results are aggregated to obtain multi-period results. This allows for flexible adaptation to different portfolio management models, improving the accuracy and flexibility of performance attribution results and enabling effective and accurate decomposition of fixed-income asset portfolio returns. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the first embodiment of the fixed-income portfolio performance attribution method of this application; Figure 2 A flowchart illustrating the second embodiment of the fixed-income portfolio performance attribution method of this application; Figure 3 This is a schematic diagram of the underlying data structure proposed in this application; Figure 4 A diagram illustrating the hierarchical performance attribution breakdown provided in this application; Figure 5 This is a schematic diagram of the module structure of the fixed income portfolio performance attribution device in an embodiment of this application; Figure 6This is a schematic diagram of the equipment structure of the hardware operating environment involved in the fixed income asset portfolio performance attribution method in this application embodiment.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: collecting the underlying data of a fixed-income asset portfolio; inputting the underlying data into a pre-constructed profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results; the hierarchical performance attribution decomposition includes calculating portfolio net profit, splitting the portfolio net profit, splitting the portfolio total revenue, splitting the revenue effect and price effect; selecting an attribution calculation method based on the management method of the fixed-income asset portfolio, and aggregating the performance attribution results based on the attribution calculation method to obtain multi-period attribution results.

[0024] This application's embodiments take into account that: The Campisi attribution model, as a widely used tool for fixed-income portfolio performance analysis, decomposes portfolio returns into key factors such as income effects and price effects through a top-down framework, providing a foundation for understanding the composition of returns. However, when applying the Campisi model, attribution results are calculated only based on portfolio holdings and individual bond valuations, omitting transaction costs from trading activity, holding changes, and daily profit and loss. This simplification leads to an unreasonable amplification of the residual terms in the attribution model, causing discrepancies between the attribution results and actual returns, and failing to accurately reflect the composition of portfolio returns. Furthermore, because the impact of changes in the risk-free yield curve is ignored when calculating the bond price effect, the core components of the price effect cannot be accurately decomposed, affecting the reliability of the attribution results. These shortcomings make current mainstream solutions using the Campisi model unable to meet investors' needs for accuracy and flexibility in attribution results, and unable to truly reflect the sources of portfolio returns.

[0025] Therefore, this application provides a solution to collect underlying data of a fixed-income asset portfolio; input the underlying data into a pre-constructed profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results; the hierarchical performance attribution decomposition includes calculating portfolio net profit, splitting the portfolio net profit, splitting the portfolio total revenue, splitting the revenue effect and price effect; select an attribution calculation method based on the management method of the fixed-income asset portfolio, and aggregate the performance attribution results based on the attribution calculation method to obtain multi-period attribution results. First, by collecting underlying data from fixed-income asset portfolios, a comprehensive data foundation is provided for accurate attribution. Next, the underlying data is input into a profit-loss-based attribution model for hierarchical performance attribution decomposition. This decomposition calculates portfolio net profit, the portfolio's total revenue, and revenue efficiency, achieving a multi-dimensional and refined breakdown of returns. This addresses the issue of attribution items deviating from their true values ​​due to the failure to consider changes in the risk-free rate curve. Finally, by combining the fixed-income asset portfolio management methods with the selected attribution calculation method, performance attribution results are aggregated to obtain multi-period results. This allows for flexible adaptation to different portfolio management models, improving the accuracy and flexibility of performance attribution results and enabling effective and accurate decomposition of fixed-income asset portfolio returns.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or performance attribution system capable of performing the above functions. The following description uses a performance attribution system as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, the embodiments of this application provide a fixed-income portfolio performance attribution method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the fixed-income portfolio performance attribution method of this application.

[0028] In this embodiment, the fixed-income portfolio performance attribution method includes steps S10 to S30: Step S10: Collect the underlying data of the fixed-income asset portfolio; It should be noted that, in this embodiment of the application, the fixed-income asset portfolio refers to an investment portfolio composed of fixed-income financial assets such as bonds; the underlying data refers to the set of basic data supporting the performance attribution analysis of the fixed-income asset portfolio.

[0029] The purpose of collecting underlying data from fixed-income asset portfolios is to provide comprehensive and accurate data support for subsequent profit and loss-based performance attribution analysis, ensuring that the attribution results truly reflect the actual sources of portfolio returns. The system needs to acquire various types of data related to the fixed-income asset portfolio from multiple data sources, covering multiple dimensions such as trading, holdings, profit and loss, and valuation, to cover all the information required for attribution analysis.

[0030] In one possible implementation, the data sources for the underlying data include internal investment research systems, external bond valuation platforms, trading systems, accounting systems, etc.; in another possible implementation, the data collection frequency for the underlying data can be set to daily, weekly, or monthly according to user needs.

[0031] Step S20: Input the underlying data into a pre-constructed profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results; the hierarchical performance attribution decomposition includes calculating portfolio net profit, splitting the portfolio net profit, splitting portfolio total revenue, and splitting revenue effect and price effect; It should be noted that, in the embodiments of this application, the profit and loss-based attribution model refers to a model that combines the actual profit and loss of the fixed income asset portfolio to conduct performance attribution analysis; the hierarchical performance attribution decomposition refers to the process of gradually decomposing portfolio returns into multiple effects according to different levels.

[0032] Additionally, it should be noted that portfolio net profit refers to the net income of a fixed-income asset portfolio over a certain period; decomposing portfolio net profit refers to the process of breaking down portfolio net profit into the cost of capital and total portfolio income; decomposing total portfolio income refers to the process of breaking down total portfolio income into trading capacity, income effect, price effect, profit and loss adjustment, and security selection effect; decomposing income effect refers to the process of further decomposing income effect into coupon effect, convergence effect, tax exemption effect, and riding effect. Decomposing price effect refers to the process of breaking down price effect into interest rate spread effect and convexity effect, and calculating the risk-free asset effect.

[0033] The purpose of inputting underlying data into a pre-built profit and loss-based attribution model for hierarchical performance attribution decomposition is to achieve refined and multi-dimensional analysis of the sources of fixed-income asset portfolio returns, thereby improving the accuracy and interpretability of attribution results. The system utilizes a profit and loss-based attribution model to progressively decompose portfolio returns according to a hierarchical structure from the overall to the local level, with each layer of decomposition based on underlying data and specific algorithmic logic.

[0034] In one possible implementation, the number of levels in the hierarchical performance attribution breakdown can be adjusted according to user needs; in another possible implementation, the parameters in the attribution model (such as tax exemption rate, interest income tax rate, etc.) can be customized by the user.

[0035] Additionally, it should be noted that the purpose of calculating the portfolio net profit is to obtain the overall net income of the fixed-income asset portfolio during the analysis period, which serves as the basis for subsequent decomposition; the purpose of decomposing the portfolio net profit is to separate the cost of capital and the total income actually obtained by the portfolio, clarifying the core components of the portfolio income; the purpose of decomposing the portfolio total income is to decompose the total income into effects from different sources and identify the contribution of each effect to the portfolio income; the purpose of decomposing the income effect is to further refine the composition of the income component and accurately grasp the specific sources of the income effect.

[0036] Step S30: Select an attribution calculation method based on the management method of the fixed income asset portfolio, and aggregate the performance attribution results based on the attribution calculation method to obtain multi-period attribution results.

[0037] It should be noted that, in this application embodiment, the management method of the fixed income asset portfolio refers to the daily management mode of the portfolio, including net asset value method, market value method, cost method, etc.; the attribution calculation method refers to the calculation method of aggregating the performance attribution results of a single period to obtain the attribution results of multiple periods; the multi-period attribution results refer to the summary results of the performance attribution of the fixed income asset portfolio over multiple periods.

[0038] The purpose of selecting an attribution calculation method and aggregating performance attribution results based on the management approach of the fixed-income asset portfolio is to ensure that multi-period attribution results conform to the actual situation of portfolio management and improve the flexibility and applicability of the results. The system dynamically selects an appropriate calculation method based on the portfolio's management approach and aggregates the performance attribution results for each period.

[0039] In one possible implementation, a compound interest calculation model is selected for portfolios managed by net asset value or market value methods; a simple interest calculation model is selected for portfolios managed by cost methods; in another possible implementation, different time intervals (such as quarters or years) can be selected during aggregation to generate multi-period attribution results.

[0040] For example, in one specific implementation, if the fixed-income portfolio is managed using the net asset value method, the system selects the compound interest calculation mode. Based on the idea of ​​time-weighted return, it uses the Carino algorithm to aggregate the returns of each effect on each trading day within the interval to obtain multi-period attribution results. If the portfolio is managed using the cost method, the system selects the simple interest calculation mode, directly sums up the attribution profit and loss of each period within the multi-period interval to obtain the multi-period attribution profit and loss amount of each effect, and then divides each attribution amount by the portfolio interval profit and loss amount and multiplies it by the portfolio interval return to obtain multi-period attribution results.

[0041] This embodiment provides a fixed-income portfolio performance attribution method. By collecting underlying data of the fixed-income portfolio, it provides a comprehensive data foundation for accurate attribution. Furthermore, the underlying data is input into a profit-loss-based attribution model for hierarchical performance attribution decomposition. This hierarchical performance attribution decomposition calculates portfolio net profit, the split portfolio net profit, the split portfolio total revenue, and the split revenue effect, achieving a multi-dimensional and refined decomposition of returns. This addresses the issue of attribution items deviating from their true values ​​due to the failure to consider changes in the risk-free rate curve. Finally, by combining the attribution calculation method with the fixed-income portfolio management approach, the performance attribution results are aggregated to obtain multi-period results. This allows for flexible adaptation to different portfolio management models, improving the accuracy and flexibility of performance attribution results, and enabling effective and accurate decomposition of fixed-income portfolio returns.

[0042] Based on the first embodiment of this application, a second embodiment of this application is proposed. In the second embodiment of this application, content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter.

[0043] Based on this, please refer to Figure 2 , Figure 2 A schematic flowchart of the second embodiment provided in this application. Figure 2 As shown, the underlying data includes portfolio data, bond data, and benchmark portfolio data. Step S20, which inputs the underlying data into a pre-built profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain the performance attribution results, further includes steps S21 to S26: Step S21: Input the underlying data into a pre-built profit and loss-based attribution model, and extract the interval profit and loss amount from the portfolio profit and loss table of the investment data to obtain the portfolio net profit. It should be noted that, in this embodiment of the application, portfolio data refers to data such as transactions, holdings, and profits and losses related to the fixed-income asset portfolio; the portfolio profit and loss statement refers to a report that records the profit and loss of the portfolio within a certain period; and the interval profit and loss amount refers to the net profit and loss amount of the portfolio within a specific time period.

[0044] The purpose of this step is to obtain the overall net return of the portfolio, which serves as the core basis for subsequent stratified decomposition, ensuring that the attribution analysis starts from the actual profit and loss and improving the accuracy of the results. After the system inputs the underlying data into the model, the model automatically locates the portfolio profit and loss statement in the investment data, extracts the range profit and loss amount, and directly uses it as the portfolio net profit (because the profit and loss statement already reflects the net income after deducting costs).

[0045] Additionally, refer to Figure 3 , Figure 3 This is a schematic diagram of the underlying data structure proposed in this application. Figure 3As shown, the portfolio data includes portfolio transaction data, portfolio holding data, and portfolio profit and loss data; the bond data includes basic bond information data, bond valuation data, bond cash flow data, and bond type data; the daily market data for convertible bonds; the yield curve data; and the benchmark data includes the holding weight data of indices such as the ChinaBond Total Wealth (Total Return) Index.

[0046] Step S22: Decompose the combined net profit into total combined revenue and cost of capital; It should be noted that in this embodiment, total portfolio revenue refers to all revenue generated by the portfolio through operating activities within the specified period; cost of capital refers to the costs incurred by the portfolio due to the use of funds (such as interest expenses or opportunity costs). The purpose of this step is to separate the cost of capital from actual operating revenue, clarifying the core source of portfolio returns. The model calculates the cost of capital (based on the portfolio's average holding cost and the interest rate), and then subtracts the cost of capital from the portfolio's net profit to obtain the total portfolio revenue.

[0047] In one possible implementation, the cost of capital is calculated by multiplying the portfolio's average holding cost by the average funding rate; in another possible implementation, the funding rate may be either the market benchmark rate or the portfolio's internal cost of capital rate.

[0048] Step S23: Decompose the total portfolio revenue into revenue effect and price effect according to the profit and loss-based attribution model; It should be noted that, in the embodiments of this application, the income effect refers to the fixed income (such as coupon interest, tax-free income, etc.) obtained by the portfolio during the bond holding period; the price effect refers to the income obtained by the portfolio due to changes in bond prices (such as changes in the interest rate of risk-free assets, changes in interest rate spread, etc.).

[0049] The purpose of this step is to distinguish whether the returns come from "holding income" or "price volatility," providing direction for subsequent refined analysis. The model, based on Campisi attribution logic, decomposes total portfolio returns into two main categories: income effects and price effects.

[0050] In one possible implementation, the decomposition logic adopts a top-down hierarchical approach (first classifying into major categories and then into subcategories); in another possible implementation, the decomposition supports user-defined effect classification criteria.

[0051] Step S24: Decompose the income effect and the price effect respectively, and calculate the risk-free asset effect; It should be noted that, in the embodiments of this application, the risk-free asset effect refers to the portion of portfolio returns derived from changes in the risk-free asset interest rate.

[0052] The purpose of this step is to separate the returns from changes in the risk-free interest rate from the price effect, thereby improving attribution accuracy. The model analyzes the composition of the price effect, extracting and calculating the effect related to the risk-free asset interest rate as the risk-free asset effect (typically derived from the risk-free asset effect portion of the price effect). In one possible implementation, the extraction of the risk-free asset effect is based on calculations of changes in the risk-free asset spot rate curve; in another possible implementation, the risk-free asset type can be user-defined (e.g., selecting a risk-free asset or policy bank bonds).

[0053] Step S25: Combining the risk-free asset interest rate curve in the bond data, the risk-free asset effect is decomposed into the risk-free asset translation effect and the risk-free asset distortion effect. It should be noted that, in the embodiments of this application, the risk-free asset interest rate curve refers to the spot interest rate curve of risk-free assets (such as risk-free assets); the risk-free asset translation effect refers to the return brought about by the overall parallel shift of the risk-free interest rate curve; the risk-free asset distortion effect refers to the return brought about by the non-parallel change of the risk-free interest rate curve (such as changes in slope or curvature).

[0054] The purpose of this step is to refine the sources of the risk-free asset effect and accurately reflect the impact of the type of yield curve shift on returns. The model combines the risk-free asset yield curve from bond data, calculates the shift effect by adjusting duration, and then subtracts the shift effect from the risk-free asset effect to obtain the distortion effect.

[0055] In one possible implementation, the translation effect is calculated using the average of the interest rate spread at key maturity points; in another possible implementation, the distortion effect is calculated using principal component analysis.

[0056] Step S26: Obtain the performance attribution result based on the income effect, the price effect, the risk-free asset effect, the risk-free asset shift effect, and the risk-free asset distortion effect.

[0057] It should be noted that, in the embodiments of this application, the performance attribution results refer to the decomposition results of portfolio returns in each effect dimension (including return amount, rate of return, etc.).

[0058] The purpose of this step is to summarize all the decomposed effects and generate a complete performance attribution report to help users understand the sources of benefits. The model integrates data on income effects, price effects, risk-free asset effects, translation effects, distortion effects, etc., and outputs structured attribution results.

[0059] In one possible implementation, the performance attribution results support both revenue amount and rate of return display methods; in another possible implementation, the results can be exported as Excel or a visual chart format.

[0060] In this embodiment, the net profit of the portfolio is obtained by extracting the actual period profit and loss from the profit and loss statement of the fixed income portfolio, which solves the problem of residual amplification caused by the neglect of actual transaction costs and taxes in traditional models. The net profit is then broken down into total portfolio income and cost of funds to clarify the core return components. The total income is further broken down into income effect and price effect to distinguish the types of return sources. The risk-free asset effect is extracted from the price effect and refined into translation and distortion effects, which solves the defect of traditional price effect decomposition that does not consider the details of risk-free interest rate curve changes. Finally, all effects are integrated to obtain the performance attribution results, which realizes a refined and multi-dimensional accurate decomposition of the sources of portfolio returns, greatly improves the accuracy and interpretability of the attribution results, and provides a reliable single-period decomposition basis for subsequent multi-period attribution, meeting the user's needs for flexible and accurate performance attribution of fixed income portfolios.

[0061] In one feasible implementation, step S22 may include steps S221 to S222: Step S221: Calculate the cost of capital based on the user-defined cost of capital rate and the average capital occupation amount of the investment group; It should be noted that, in this embodiment of the application, the user-defined cost of funds rate refers to the interest rate set by the user according to their own investment strategy or internal management requirements to measure the cost of capital occupation; the average amount of capital occupied by the portfolio refers to the average amount of capital occupied by the fixed income asset portfolio over a certain period; and the cost of funds refers to the cost expenditure incurred by the portfolio due to the occupation of funds.

[0062] The purpose of this step is to accurately calculate the cost of capital for the portfolio, providing a basis for subsequently separating actual operating income from the portfolio's net profit and ensuring that attribution analysis can distinguish between the cost of capital and core returns. The system calculates the cost of capital through multiplication based on the user-inputted custom cost of capital rate and the pre-calculated average capital occupied by the portfolio. In one possible implementation, the user-defined cost of capital rate can be selected from the market benchmark interest rate, the internal funds transfer pricing rate, or a user-specified fixed interest rate; in another possible implementation, the average capital occupied by the portfolio can be calculated using the arithmetic mean of the initial and final holding costs, or the weighted average of daily holding costs.

[0063] Step S222: Based on the cost of capital, deduce the total revenue of the portfolio from the net profit of the portfolio.

[0064] It should be noted that, in this embodiment of the application, the portfolio net profit refers to the overall net income of the fixed income portfolio within a certain period (from the range of profit and loss in the portfolio income statement); the portfolio total income refers to all income obtained by the portfolio through operating activities during the period (before deducting the cost of capital).

[0065] The purpose of this step is to clarify the core operating income composition of the portfolio through reverse derivation, separating the cost of capital from actual returns, and laying the foundation for subsequent analysis of income and price effects. The system derives the total portfolio income through addition operations based on the portfolio's net profit and the calculated cost of capital.

[0066] In one possible implementation, the formula derived in reverse is: Total Portfolio Revenue = Net Profit + Cost of Capital; in another possible implementation, if there are non-recurring profit and loss adjustments, Total Portfolio Revenue = Net Profit + Cost of Capital + Non-recurring Profit and Loss Adjustments.

[0067] To better understand the hierarchical performance attribution decomposition using an attribution model proposed in the second embodiment of this application, the following will be combined with Figure 4 This application elaborates on the hierarchical performance attribution decomposition proposed in this paper, with reference to... Figure 4 , Figure 4 The hierarchical performance attribution decomposition diagram provided for this application is as follows: Figure 4 As shown: The first layer calculates the portfolio's net profit based on the assets and market value of the portfolio. The second layer calculates the cost of capital based on the average capital required by the portfolio, thus breaking down the portfolio's net profit in the first layer into the cost of capital and the total portfolio revenue. The third layer, using the Campisi model based on profit and loss, decomposes the total portfolio revenue into trading ability, revenue effect, price effect, profit and loss adjustment, and stock selection effect. The fourth layer further decomposes the revenue effect into coupon effect, convergence effect, tax exemption effect, and riding effect; the price effect can be further decomposed into risk-free asset effect, interest rate spread effect, and convexity effect. The fifth layer, considering the types of changes in the risk-free asset interest rate curve, decomposes the risk-free asset shift effect and risk-free asset distortion effect.

[0068] By analyzing the investment portfolio's revenue contribution in a detailed and multi-dimensional way, we can gain a more accurate understanding of the sources of revenue.

[0069] Based on the first and / or second embodiments of this application, a third embodiment of this application is proposed. In this third embodiment, content that is the same as or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0070] In this embodiment, step S23, which decomposes the total portfolio income into income effect and price effect according to the profit and loss-based attribution model, may include step S231: Step S231: According to the profit and loss-based attribution model, the total portfolio revenue is decomposed into the revenue effect, the price effect, the trading capability effect, the profit and loss adjustment effect, and the stock selection effect; It should be noted that, in the embodiments of this application, the trading ability effect refers to the revenue effect brought about by the trader's trading ability as measured by the deviation between the transaction price and the valuation price of the portfolio's bonds; the profit and loss adjustment effect refers to the effect of adjusting the portfolio's profit and loss; and the bond selection effect refers to the residual part that cannot be explained by other effects, reflecting the revenue effect brought about by the portfolio's ability to select bonds.

[0071] The purpose of this step is to decompose the total portfolio revenue into more granular effects, covering aspects not included in the traditional Campisi model such as trading capabilities and profit / loss adjustments, thereby reducing attribution residuals and improving the accuracy and interpretability of the results. The system uses a profit / loss-based attribution model to decompose the total portfolio revenue into the five effects mentioned above according to a pre-defined decomposition logic.

[0072] In one possible implementation, the decomposition order can prioritize the trading capacity effect and profit and loss adjustment effect, and then decompose the revenue effect and price effect; in another possible implementation, users can be allowed to customize whether certain effects are included.

[0073] In one specific embodiment, step S231 may further include steps S232 to S233: Step S232: Calculate the coupon effect, convergence effect, tax exemption effect, and riding effect using the profit and loss-based attribution model, and obtain the income effect based on the coupon effect, convergence effect, tax exemption effect, and riding effect. The system calculates each sub-effect separately using a profit-loss-based attribution model, and then sums them to obtain the income effect. Specifically, the riding effect is the portion of the income derived from the price effect solely due to the shortening of the remaining maturity of the bond and the decline of the yield along the yield curve. This is achieved by shifting the initial interest rate curve and repricing the bond to obtain the final net price, adding the coupon income during the period, and subtracting the income from the initial net price and initial yield. The calculation formula is as follows:

[0074] in: This indicates that the closing net price is obtained by repricing based on the initial spot interest rate curve. Indicates the initial net price; Indicates the coupon income during the period; This represents the yield to maturity at the beginning of the period; This indicates the annualized interval under the individual bond interest calculation rules; This indicates the initial full price.

[0075] Specifically, the formula for calculating the tax exemption effect is as follows:

[0076] in, This refers to the tax exemption effect, also known as tax shield revenue. Indicates the tax exemption rate; This refers to income tax on interest. This indicates the coupon income during the period.

[0077] Step S233: Calculate the interest rate spread effect and convexity effect using the profit and loss-based attribution model, and obtain the price effect based on the interest rate spread effect and the convexity effect.

[0078] It should be noted that, in the embodiments of this application, the interest rate spread effect refers to the effect of price changes caused by changes in the interest rate spread between bonds and risk-free assets; the convexity effect refers to the return effect caused by the second-order sensitivity of bond price changes to interest rate changes.

[0079] The purpose of this step is to refine the sources of the price effect and improve the accuracy of its decomposition. The system calculates the interest rate spread effect and convexity effect separately using a profit and loss-based attribution model, and then combines these with the risk-free asset effect obtained in the previous step to arrive at the price effect.

[0080] In one possible implementation, the interest rate spread effect can be calculated using the zero-fluctuation interest rate spread; in another possible implementation, the convexity effect can be considered by taking into account the impact of the square of the interest rate change on prices.

[0081] In this embodiment, the total portfolio income is decomposed into income effect, price effect, and trading capability effect, profit and loss adjustment effect, and bond selection effect, which are not included in the traditional Campisi model. This addresses the problem of amplified residuals caused by the traditional model ignoring transaction costs and taxes. Furthermore, the income effect is refined into coupon effect, convergence effect, tax exemption effect, and riding effect. The introduction of the tax exemption effect improves the accuracy of income source explanation. The price effect is decomposed into interest rate spread effect and convexity effect, addressing the deficiency of traditional price effect calculation in not considering interest rate spread and convexity details. This achieves a refined and multi-dimensional accurate decomposition of the income sources of fixed-income asset portfolios, significantly reducing attribution residuals and improving the accuracy and interpretability of performance attribution results, thus meeting users' needs for a deeper understanding of income sources.

[0082] Based on the above embodiments of this application, a fourth embodiment of this application is proposed. In this fourth embodiment, content that is the same as or similar to that in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0083] In this embodiment, step S24, which involves decomposing the income effect and the price effect and calculating the risk-free asset effect, may include steps S241 to S243: Step S241: The income effect is decomposed into the coupon effect, the convergence effect, the tax exemption effect, and the riding effect; It should be noted that, in the embodiments of this application, the income effect refers to the sum of the effects related to fixed income obtained by the fixed income portfolio during the bond holding period; the coupon effect refers to the effect brought about by the interest income obtained during the bond holding period; the convergence effect refers to the income effect generated during the process of bond price convergence to face value; the tax exemption effect refers to the tax shield income effect obtained by holding tax-exempt bonds; and the riding effect refers to the income effect brought about by the shortening of the remaining maturity of the bond and the decline of the yield along the yield curve.

[0084] The purpose of this step is to accurately identify the specific sources of portfolio income by decomposing the income effect into multiple sub-effects, thus addressing the issue of insufficient attribution precision in the traditional Campisi model due to its omission of sub-items such as tax exemption effects. The system uses a profit-loss-based attribution model to decompose the income effect into the four sub-effects mentioned above according to pre-defined logic. The calculation of each sub-effect incorporates the actual profit and loss information and cash flow data of the bonds, ensuring the accuracy of the decomposition results.

[0085] Step S242: The price effect is decomposed back into the interest rate spread effect and the convexity effect.

[0086] The risk-free asset effect refers to the effect of changes in bond prices caused by changes in the risk-free interest rate curve.

[0087] The purpose of this step is to decompose the price effect into the interest rate spread effect and the convexity effect, and then combine these two effects to calculate the risk-free asset effect. This achieves a high-precision decomposition of the price effect and solves the attribution bias problem caused by traditional models neglecting the details of changes in the risk-free interest rate curve. The system uses a profit-loss-based attribution model to first decompose the price effect into the interest rate spread effect and the convexity effect. Then, referring to the calculation logic of these two effects, it calculates the risk-free asset effect through methods such as discounted cash flow of bonds or modified duration.

[0088] In one feasible implementation, the calculation approach for the interest rate differential effect is as follows: The effective duration is replaced by the spread duration, and the zero-volatility spread is replaced by the T-spread, in order to more accurately calculate the portion of the bond price change caused solely by changes in the spread between the individual bond and the risk-free asset. The calculation formula is as follows:

[0089]

[0090]

[0091] in, This indicates the interest rate differential effect; This indicates the initial spread duration, representing the volatility in bond prices caused by a 10bp change in the spread between the bond and the risk-free asset. Indicates the beginning interest rate spread and the ending interest rate spread; This represents 10bp; This represents the full price of a bond discounted on the risk-free spot rate curve when the interest rate spread changes upward by 10 basis points. This represents the full price of a bond discounted on the risk-free spot rate curve when the interest rate spread changes downward by 10 basis points. This indicates the interest rate spread.

[0092] Step S243: Calculate the risk-free asset effect based on the pre-obtained risk-free asset interest rate curve.

[0093] In one feasible implementation, the calculation approach for the risk-free asset effect is as follows: To accurately extract bond price changes caused by shifts in the risk-free yield curve, we discount the bond cash flows onto the risk-free spot yield curve to derive the initial and final risk-free yields, rather than simply using the initial and final risk-free rates for the same duration. The specific calculation formula is as follows:

[0094]

[0095] = *

[0096] in, This indicates the distortion effect of risk-free assets; This indicates the risk-free asset transfer effect; Indicates modified duration; This represents the yield to maturity of risk-free assets at the end of the period; This represents the yield to maturity of the risk-free asset at the beginning of the period; It represents the average interest rate spread between key maturity points on the risk-free spot rate curve at the end of the period and the beginning of the period; This represents the return on risk-free assets.

[0097] In this embodiment, by refining the income effect into coupon effect, convergence effect, tax exemption effect (not included in the traditional Campisi model), and riding effect, and decomposing the price effect into interest rate spread effect and convexity effect, and combining them to calculate the risk-free asset effect, the problem of the traditional Campisi model's coarse decomposition of income effect and failure to consider interest rate spread and convexity details in price effect is solved. This achieves a multi-dimensional and refined decomposition of income sources, reduces attribution residuals, and significantly improves the interpretability and accuracy of performance attribution results.

[0098] Based on the above embodiments of this application, a fifth embodiment of this application is proposed. In this fifth embodiment, content that is the same as or similar to that in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0099] In this embodiment, step S25, which decomposes the risk-free asset effect into the risk-free asset shift effect and the risk-free asset distortion effect based on the risk-free asset interest rate curve in the bond data, may include steps S251 to S253: Step S251: Extract key maturity points on the risk-free asset interest rate curve from the bond data, and calculate the average interest rate based on the key maturity points; It should be noted that, in this embodiment of the application, bond data refers to all data related to bonds, including basic bond information, valuation data, cash flow data, etc.; the risk-free asset yield curve refers to the curve formed by the current yield of risk-free assets. Key maturity points refer to representative maturity points on the risk-free yield curve. Average interest rates refer to the average interest rate values ​​at these key maturity points.

[0100] The purpose of this step is to extract key maturity points and calculate the average interest rate, providing foundational data for subsequent calculations of the risk-free asset shift effect and ensuring that the calculation of the shift effect reflects the overall trend of the yield curve. The system filters key maturity points from the bond data to identify the key maturity points of the risk-free asset yield curve, and then averages the interest rates at these points. The selected key maturity points must cover the major market trading periods to ensure the representativeness of the results.

[0101] In one possible implementation, the key time point can be selected from five commonly used time points: 1 year, 3 years, 5 years, 7 years, and 10 years. In another possible implementation, the key time point can be customized by the user according to actual needs.

[0102] Step S252: Calculate the risk-free asset shifting effect based on the average interest rate; It should be noted that, in the embodiments of this application, the average interest rate refers to the average interest rate at key maturity points of the risk-free asset interest rate curve; the risk-free asset shift effect refers to the return effect generated by the overall parallel shift of the risk-free asset interest rate curve.

[0103] The purpose of this step is to quantify the impact of the overall shift in the risk-free yield curve on portfolio returns, and it is an important component of deconstructing the risk-free asset effect. The system calculates the risk-free asset shift effect based on changes in the average interest rate (the difference between the average interest rate at the end of the period and the beginning of the period), combined with a modified duration. The calculation logic references the risk-free asset shift effect formula in the documentation to ensure the accuracy of the results.

[0104] Step S253: Based on the risk-free asset effect and the risk-free asset translation effect, the risk-free asset distortion effect is obtained.

[0105] It should be noted that, in the embodiments of this application, the risk-free asset effect refers to the effect of bond price changes caused by changes in the risk-free asset interest rate curve; the risk-free asset translation effect refers to the effect produced by the overall parallel shift of the risk-free asset interest rate curve; and the risk-free asset distortion effect refers to the effect produced by the non-overall parallel shift of the risk-free asset interest rate curve (such as distortion, steepening, or flattening).

[0106] The purpose of this step is to isolate the portion of the risk-free asset effect caused by non-shifting changes in the interest rate curve, further refine the decomposition of the risk-free asset effect, and improve attribution accuracy. The system obtains the risk-free asset distortion effect by subtracting the risk-free asset shifting effect from the risk-free asset effect.

[0107] In this embodiment, the average interest rate is calculated by extracting key maturity points of the risk-free asset interest rate curve, thereby obtaining the risk-free asset shift effect. The distortion effect is then obtained by subtracting the shift effect from the risk-free asset effect. This solves the problem that the traditional method only uses the difference between the return of the risk-free asset at the end of the period and the beginning of the period to calculate interest rate fluctuations, which causes the risk-free asset effect to deviate from the true value. It realizes a refined decomposition of the changes (shift + distortion) of the risk-free asset interest rate curve, accurately separates the impact of the overall shift of the interest rate curve and non-shift changes on returns, and improves the attribution accuracy of the risk-free asset effect in the price effect.

[0108] Based on the above embodiments of this application, a sixth embodiment of this application is proposed. In the sixth embodiment of this application, content that is the same as or similar to the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0109] In this embodiment, step S30, which selects the attribution calculation method based on the management method of the fixed-income asset portfolio and aggregates the performance attribution results based on the attribution calculation method to obtain multi-period attribution results, may include steps S31 to S34: Step S31: If the management method of the fixed-income asset portfolio is net asset value method or market value method, then select the compound interest calculation mode; It should be noted that, in this embodiment of the application, a fixed-income asset portfolio refers to an investment portfolio composed of fixed-income assets (such as bonds, money market instruments, etc.); management method refers to the method of pricing and calculating returns for portfolio assets; net asset value method refers to a management method that prices portfolio assets according to their market net value and reflects returns through changes in net asset value; market value method refers to a management method that prices portfolio assets according to their current market value; and compound interest calculation model refers to a multi-period return calculation method that includes the returns of each period into the principal and generates returns in the next period.

[0110] The purpose of this step is to select an appropriate multi-period attribution calculation model based on the management method of the fixed-income asset portfolio, ensuring that the multi-period attribution results conform to the actual logic of portfolio management—under both net asset value (NAV) and market value (V / V) methods, portfolio returns are rolled over into the principal, thus requiring a compound interest calculation model. The system identifies the portfolio's management method and automatically selects the compound interest calculation model when it detects either the NAV or V / V method.

[0111] In one possible implementation, the system can automatically identify the management method by reading the basic configuration information of the combination; in another possible implementation, the user can manually specify the management method of the combination, and the system selects the calculation mode based on the user input.

[0112] Step S32: Based on the compound interest calculation model and the time-weighted rate of return theory, the performance attribution results are aggregated using a preset algorithm to obtain multi-period attribution results; It should be noted that, in this embodiment of the application, the compound interest calculation mode refers to the calculation method in which the income of each period is included in the principal; the time-weighted rate of return theory refers to the theory of dividing the investment period into several independent segments and compounding the rate of return of each segment to calculate the total return; the preset algorithm refers to the algorithm pre-set by the system for aggregating the performance attribution results of multiple periods; the performance attribution result refers to the income amount or rate of return of each effect (such as income effect, price effect, etc.) in a single period; the multi-period attribution result refers to the cumulative income or rate of return of each effect in multiple periods.

[0113] The purpose of this step is to aggregate single-period attribution results using a preset algorithm based on the compound interest model and time-weighted rate of return theory, thereby obtaining accurate multi-period attribution results and solving the problem of rolling calculation of multi-period returns. The system uses a preset algorithm to aggregate single-period attribution results, and the preset algorithm must conform to the logic of time-weighted rate of return.

[0114] In one possible implementation, the preset algorithm is the Carino algorithm; in another possible implementation, the preset algorithm is a compound rate of return algorithm based on geometric mean.

[0115] Step S33: If the management method of the fixed income asset portfolio is the cost method, then select the simple interest calculation mode; It should be noted that, in the embodiments of this application, a fixed-income asset portfolio refers to an investment portfolio composed of fixed-income assets; management method refers to the method of pricing and calculating income of the portfolio assets; cost method refers to a management method in which the portfolio assets are priced according to their initial investment cost and the income only reflects the actual interest or dividends received; simple interest calculation mode refers to a multi-period income calculation method in which income is calculated only based on the initial principal and the income is not included in the principal.

[0116] The purpose of this step is to select the appropriate multi-period attribution calculation mode based on the portfolio's management method—under the cost method, portfolio income is not rolled over into the principal, thus requiring a simple interest calculation mode. The system identifies the portfolio's management method, and when the cost method is detected, it automatically selects the simple interest calculation mode. In one possible implementation, the system automatically identifies the cost method by reading the portfolio's accounting policy; in another possible implementation, the user can manually select the cost method management method, and the system will switch the calculation mode accordingly.

[0117] Step S34: Based on the simple interest calculation model, the performance attribution results are accumulated to obtain multi-period attribution results.

[0118] It should be noted that, in the embodiments of this application, the simple interest calculation mode refers to the method of calculating the income based solely on the initial principal; the cumulative method refers to the operation of directly adding the income amounts of each effect in multiple periods; the performance attribution result refers to the income amount of each effect in a single period; and the multi-period attribution result refers to the cumulative income amount of each effect in multiple periods.

[0119] The purpose of this step is to accumulate the single-period attribution results based on the simple interest model to obtain multi-period attribution results that conform to the cost approach management logic. The system directly sums up the benefit amounts of each effect in each period across multiple periods to obtain the cumulative benefit amount for multiple periods.

[0120] In one possible implementation, the summation operation is to directly add up the gains and losses of each effect in each period; in another possible implementation, the summation operation is to multiply the rate of return of each effect in each period by the initial principal and then add them together.

[0121] In this embodiment, a multi-period attribution calculation mode is selected by dynamically adapting the management method of the fixed-income asset portfolio. For portfolios managed by net asset value method or market value method, a compound interest calculation mode is adopted (the Carino algorithm is used to aggregate the results of a single period based on the time-weighted return theory), and a simple interest calculation mode is adopted for portfolios managed by cost method (the attribution profit and loss of a single period is directly accumulated). This solves the problem that the traditional method does not distinguish between management methods, which leads to the deviation of the multi-period attribution results from the true value. It meets the user's need for flexibility in the calculation of the portfolio's historical returns and improves the accuracy and adaptability of multi-period performance attribution.

[0122] This application also provides a fixed-income portfolio performance attribution device, please refer to... Figure 5 The fixed-income portfolio performance attribution device includes: The underlying data module 10 is used to collect the underlying data of the fixed income asset portfolio; The hierarchical decomposition module 20 is used to input the underlying data into a pre-built profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results; the hierarchical performance attribution decomposition includes calculating portfolio net profit, decomposing the portfolio net profit, decomposing the portfolio total revenue, and decomposing the revenue effect and price effect; The aggregation module 30 is used to select an attribution calculation method based on the management method of the fixed income asset portfolio, and aggregate the performance attribution results based on the attribution calculation method to obtain multi-period attribution results.

[0123] The fixed-income portfolio performance attribution device provided in this application, employing the fixed-income portfolio performance attribution method in the above embodiments, can solve the technical problems of fixed-income portfolio performance attribution. Compared with the prior art, the beneficial effects of the fixed-income portfolio performance attribution device provided in this application are the same as those of the fixed-income portfolio performance attribution method provided in the above embodiments, and other technical features in the fixed-income portfolio performance attribution device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0124] This application provides a fixed-income portfolio performance attribution device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the fixed-income portfolio performance attribution method in Embodiment 1 above.

[0125] The following is for reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing a fixed-income portfolio performance attribution device according to embodiments of this application. The fixed-income portfolio performance attribution device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The fixed-income portfolio performance attribution device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0126] like Figure 6As shown, the fixed-income portfolio performance attribution device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the fixed-income portfolio performance attribution device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the fixed-income portfolio performance attribution device to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows fixed-income portfolio performance attribution devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0127] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0128] The fixed-income portfolio performance attribution device provided in this application, employing the fixed-income portfolio performance attribution method described in the above embodiments, can solve the technical problems of fixed-income portfolio performance attribution. Compared with the prior art, the beneficial effects of the fixed-income portfolio performance attribution device provided in this application are the same as those of the fixed-income portfolio performance attribution method provided in the above embodiments, and other technical features in this fixed-income portfolio performance attribution device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0129] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0131] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the fixed-income portfolio performance attribution method in the above embodiments.

[0132] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0133] The aforementioned computer-readable storage medium may be included in the fixed income portfolio performance attribution device; or it may exist independently and not be incorporated into the fixed income portfolio performance attribution device.

[0134] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the fixed-income portfolio performance attribution device, the fixed-income portfolio performance attribution device performs the following: collects underlying data of the fixed-income portfolio; inputs the underlying data into a pre-constructed profit-loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results; the hierarchical performance attribution decomposition includes calculating portfolio net profit, splitting the portfolio net profit, splitting the portfolio total revenue, splitting the revenue effect and price effect; selects an attribution calculation method based on the management method of the fixed-income portfolio, and aggregates the performance attribution results based on the attribution calculation method to obtain multi-period attribution results.

[0135] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0137] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0138] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described fixed-income portfolio performance attribution method, thereby solving the technical problem of fixed-income portfolio performance attribution. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the fixed-income portfolio performance attribution method provided in the above embodiments, and will not be repeated here.

[0139] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the fixed income portfolio performance attribution method described above.

[0140] The computer program product provided in this application can solve the technical problem of fixed-income portfolio performance attribution. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the fixed-income portfolio performance attribution method provided in the above embodiments, and will not be repeated here.

[0141] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A fixed-income portfolio performance attribution method, characterized in that, The fixed-income asset portfolio performance attribution method comprises: Collecting underlying data of the fixed-income asset portfolio; Inputting the underlying data into a pre-constructed profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results; the hierarchical performance attribution decomposition comprises calculating portfolio net profit, splitting the portfolio net profit, splitting portfolio total income, splitting income effect and price effect; Selecting an attribution calculation method in combination with the management mode of the fixed-income asset portfolio, and aggregating the performance attribution results based on the attribution calculation method to obtain multi-period attribution results.

2. The method of claim 1, wherein, The underlying data comprises investment group data, bond data and benchmark portfolio data, and the step of inputting the underlying data into a pre-constructed profit and loss-based attribution model for hierarchical performance attribution decomposition to obtain performance attribution results comprises: Inputting the underlying data into a pre-constructed profit and loss-based attribution model, and extracting interval profit and loss amounts from a portfolio profit and loss table of the investment group data by the profit and loss-based attribution model to obtain portfolio net profit; Splitting the portfolio net profit into portfolio total income and fund cost; Decomposing the portfolio total income into income effect and price effect according to the profit and loss-based attribution model; Respectively decomposing the income effect and the price effect, and calculating risk-free asset effect; Decomposing the risk-free asset effect into risk-free asset translation effect and risk-free asset distortion effect in combination with the risk-free asset interest rate curve in the bond data; Obtaining performance attribution results based on the income effect, the price effect, the risk-free asset effect, the risk-free asset translation effect and the risk-free asset distortion effect.

3. The securitized portfolio performance attribution method of claim 2, wherein, The step of splitting the portfolio net profit into portfolio total income and fund cost comprises: Calculating fund cost according to the fund cost rate and the average fund occupancy amount of the investment group defined by the user; Reverse deriving the portfolio total income from the portfolio net profit based on the fund cost.

4. The securitized portfolio performance attribution method of claim 2, wherein, The step of decomposing the portfolio total income into income effect and price effect according to the profit and loss-based attribution model comprises: Decomposing the portfolio total income into the income effect, the price effect, transaction ability effect, profit and loss adjustment effect and bond selection effect according to the profit and loss-based attribution model; including, Calculating coupon interest effect, convergence effect, tax exemption effect and riding effect by the profit and loss-based attribution model, and obtaining the income effect according to the coupon interest effect, the convergence effect, the tax exemption effect and the riding effect; Calculating interest rate spread effect and convexity effect based on the interest rate spread effect and the convexity effect to obtain the price effect.

5. The securitized portfolio performance attribution method of claim 3, wherein, The step of respectively decomposing the income effect and the price effect, and calculating risk-free asset effect comprises: Re-decomposing the income effect into the coupon interest effect, the convergence effect, the tax exemption effect and the riding effect; Re-decomposing the price effect into the interest rate spread effect and the convexity effect; Calculating risk-free asset effect according to a pre-obtained risk-free asset interest rate curve.

6. The securitized portfolio performance attribution method of claim 2, wherein, The step of disassembling the risk-free asset effect in the bond data into a risk-free asset translation effect and a risk-free asset distortion effect comprises: extracting key term points on a risk-free asset interest rate curve in the bond data, calculating an interest rate average value based on the key term points; calculating a risk-free asset translation effect according to the interest rate average value; obtaining the risk-free asset distortion effect based on the risk-free asset effect and the risk-free asset translation effect.

7. The portfolio performance attribution method of claim 1, wherein, The attribution calculation mode comprises a compound interest calculation mode and a simple interest calculation mode, the step of selecting an attribution calculation mode based on the management mode of the fixed income asset portfolio, aggregating the performance attribution results based on the attribution calculation mode, and obtaining multi-period attribution results comprises: if the management mode of the fixed income asset portfolio is a net value method or a market value method, the compound interest calculation mode is selected; based on the compound interest calculation mode and the theory of time-weighted yield, the performance attribution results are aggregated using a preset algorithm to obtain multi-period attribution results; if the management mode of the fixed income asset portfolio is a cost method, the simple interest calculation mode is selected; the performance attribution results are accumulated based on the simple interest calculation mode to obtain multi-period attribution results.

8. A fixed income portfolio performance attribution apparatus, comprising: The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the fixed income asset portfolio performance attribution method according to any one of claims 1 to 7.

9. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the fixed income asset portfolio performance attribution method according to any one of claims 1 to 7.

10. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by a processor to implement the steps of the fixed income asset portfolio performance attribution method according to any one of claims 1 to 7.

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