A stock sector bull fund performance indicator ranking method and system

Abstract

This invention discloses a method and system for ranking the performance indicators of stock-oriented top-performing funds, comprising: obtaining configuration parameters for the evaluation calculation task, wherein the configuration parameters are defined in the form of seven-tuples; generating a sample pool of eligible participating funds based on the classification, operation mode, and eligibility conditions of the participating funds; extracting historical trading data during the observation period for each participating fund in the sample pool, performing basic indicator pre-calculation operations, and generating and storing a basic indicator dataset for the participating funds; independently calculating multiple performance evaluation indicators for each participating fund during the observation period using a parallel computing method based on the basic indicator dataset; calculating a comprehensive score by weighting and summing the standard scores of multiple performance evaluation indicators of all participating funds according to the weight configuration in the scoring model, and sorting and outputting the funds in the sample pool according to the comprehensive score from largest to smallest.

Description

Technical Field

[0001] This invention relates to the field of financial data processing technology, and in particular to a method and system for ranking the performance indicators of stock-oriented top-performing funds. Background Technology

[0002] Fund industry rankings help investors quickly filter out historically high-performing funds from a vast pool of options, improving decision-making efficiency. A consistently high ranking usually reflects strong stock selection, market timing, and risk control capabilities from fund managers and their research teams. Meanwhile, ranking changes can reflect shifts in market style, competitive distribution channels, and the success or failure of company strategies.

[0003] The ranking of my country's fund industry is an important indicator reflecting the competitive landscape of the industry. Its current situation is characterized by concentration among the top players and intensified competition. It has important reference value for investors' decision-making, but there are also problems such as data transparency, evaluation standards, and short-term behavior.

[0004] When faced with massive amounts of data such as net asset value, size, and government bond yield of thousands of funds over a long period (5 or 7 years), using traditional serial calculations or real-time join queries can lead to problems such as database I / O bottlenecks, memory overflows, or extremely long calculation times (high system response latency).

[0005] How to effectively process the aforementioned financial data and obtain a fair and effective ranking is a technical problem that needs to be solved. Summary of the Invention

[0006] In view of the problems existing in the prior art, the purpose of this invention is to provide a method for ranking the performance indicators of stock-oriented golden bull funds. This method can accurately and quickly obtain relevant rankings by effectively processing financial data. Another purpose of this invention is to provide a stock-oriented golden bull fund performance indicator ranking system that implements the above method.

[0007] To achieve the above objectives, the present invention provides a method for ranking the performance indicators of stock-oriented top-performing funds, characterized in that the method includes the following steps: Obtain the configuration parameters for the evaluation calculation task. The configuration parameters are defined in the form of a seven-tuple, including the task name, the category of the participating fund, the operation mode of the participating fund, the deadline for observation, the number of years of the observation period, the eligibility conditions for participation, and the scoring model. Based on the classification of participating funds, their operation methods, and eligibility criteria, the publicly disclosed information data of public funds in the Golden Bull Award selection database are queried and screened to generate a sample pool of eligible participating funds. For each participating fund in the sample pool, historical transaction data during the observation period is extracted, basic indicator pre-calculation is performed, and a basic indicator dataset of the participating fund is generated and stored. The dataset includes at least the rolling fund return, rolling benchmark return, rolling risk-free return, and the weighted comparable net asset value of the participating fund for each trading day during the observation period. Based on the basic indicator dataset of the participating funds, a parallel computing approach is used to independently calculate multiple performance evaluation indicators for each participating fund during the observation period. The performance evaluation indicators include at least risk-adjusted return indicators and absolute return ranking stability indicators. Based on the weighting configuration in the scoring model, the standard scores of multiple performance evaluation indicators of all participating funds are weighted and summed to calculate the comprehensive score. The funds in the sample pool are then sorted and output according to the comprehensive score from largest to smallest.

[0008] Furthermore, based on the classification of participating funds, their operating methods, and eligibility criteria, the publicly disclosed information data of public funds in the Golden Bull Award selection database is queried and screened to generate a sample pool of eligible participating funds, specifically including: The initial sample set that matches the classification and operating method is selected from the database; Funds whose initial sample operation period was shorter than the specified number of months were excluded; Calculate the average net asset value of each fund in the initial sample set during the observation period and sort them in descending order. Then, sum up the average net asset value after sorting and select the funds whose cumulative total size accounts for the top 90% of the total size of similar products. We obtain data on fund managers' tenure, remove funds managed by current fund managers whose tenure during the probationary period is less than half of the probationary period, and generate the final sample pool of participating funds.

[0009] Furthermore, each data point in the basic indicator dataset for the participating funds consists of a six-tuple, formally represented as:

[0010] in, Indicates the fund code for the evaluation. This indicates one trading day during the observation period. This represents the rolling 1-year fund return. This represents the rolling 1-year benchmark yield. This represents the rolling one-year risk-free rate of return. This indicates the size weight of the participating fund on that trading day.

[0011] Furthermore, performance evaluation indicators also include risk-adjusted relative return indicators, relative return ranking stability indicators, and investor satisfaction evaluation indicators.

[0012] Furthermore, the risk-adjusted return metric and the risk-adjusted relative return metric are calculated using the pseudo-Sotinau ratio and the pseudo-Sotinau information ratio, respectively; When calculating the pseudo-Sotinho ratio, the rolling 1-year risk-free rate data series of a specific participating fund is read from the basic indicator dataset of the participating funds. The weighted median and weighted lower standard deviation of the risk-free rate data series are calculated using the size weight of each trading day as the weighting weight, and the ratio of the two is output as the pseudo-Sotinho ratio.

[0013] Furthermore, when calculating the stability index of absolute return ranking, a dynamic rolling window mechanism is used for calculation. The specific steps include: Obtain the percentile ranking of the participating fund's rolling 1-year fund return on each trading day during the observation period among similar funds, and perform an inverse normal transformation to obtain the first eigenvalue sequence; Calculate the average value and maximum drawdown of the first eigenvalue sequence for each participating fund throughout the entire observation period; Using the set trading day time span as the rolling window size, window data is extracted starting from the first trading day of the observation period. The average value and maximum drawdown of the first feature value of each participating fund within each window are calculated. The first feature value of the first trading day after the window is used as the predicted value to construct a training set. Multiple linear regression is performed to obtain the local adjustment coefficient corresponding to the window. The regression calculation is repeated by sliding the window forward with a step size of one trading day to obtain multiple local adjustment coefficients. The average of all local adjustment coefficients is then calculated as the empirical adjustment coefficient for similar participating funds. The stability index of the fund's absolute return ranking is obtained by adding the average value to the product of the empirical adjustment factor and the maximum drawdown.

[0014] Furthermore, the specific steps for calculating the investor satisfaction evaluation index include: The calculation period is based on each six-month period within the observation period, and publicly disclosed data of the fund is extracted. Within each semi-annual cycle, the simple return rate of investors, the weighted average net asset value return rate, and the investor profit and loss ratio of the participating funds are calculated separately. For each semi-annual cycle, the three sub-indicators are converted into standard scores relative to similar participating funds. Using the average net asset value of each semi-annual period as the weight, the weighted average score of the three sub-indicators was calculated over the entire observation period. The weighted average score of the three sub-indicators is used to calculate the arithmetic mean, which yields the investor satisfaction evaluation index for the participating fund.

[0015] Furthermore, when calculating the simple return rate, weighted average net asset value return rate, and investor profit / loss ratio for the second half of each year, the corresponding basic data for the second half of the year is obtained by calling the fund's annual report data and semi-annual report data for that year and using numerical deduction to calculate and use the data.

[0016] Furthermore, when extracting and processing data for funds with multiple share classes, data including the net asset value per unit is extracted from the fund's main share data for calculation, while data other than the net asset value per unit is extracted from the consolidated data disclosed in the fund's periodic reports for calculation.

[0017] A performance index ranking system for equity-oriented top-performing funds, the system being used to implement the aforementioned performance index ranking method for equity-oriented top-performing funds, the system comprising: The task definition module is used to receive and parse the evaluation calculation task configuration parameters defined in the form of a seven-tuple, and extract the task name, the category of the participating fund, the operation mode, the observation deadline, the number of years of the observation period, the eligibility conditions for participation, and the scoring model. The sample screening module is used to query and screen the publicly disclosed information data of public funds in the basic database of the Golden Bull Award selection based on the category of the participating funds, the operation mode of the participating funds, and the eligibility conditions for participation, and generate a sample pool of participating funds that meet the conditions. The basic data pre-calculation module is used to extract historical transaction data during the observation period for each participating fund in the sample pool of participating funds, perform basic indicator pre-calculation operations, generate and store the basic indicator dataset of the participating funds. The dataset includes at least the rolling fund return, rolling benchmark return, rolling risk-free return and the converted comparable net asset value size weight of the participating fund for each trading day during the observation period. The multi-threaded indicator calculation module is used to call the basic indicator dataset of the participating funds and start multiple calculation threads or processes to execute the independent calculation of multiple performance evaluation indicators in parallel. The performance evaluation indicators include at least risk-adjusted return indicators and absolute return ranking stability indicators. The comprehensive scoring and ranking module is used to calculate the comprehensive score by weighting and summing the standard scores of multiple performance evaluation indicators of all participating funds according to the weight configuration in the scoring model, and then sorting and outputting the participating funds in descending order of comprehensive score.

[0018] This invention provides a method for ranking the performance indicators of top-performing equity funds, which has the following advantages: 1. System architecture and computing performance optimization: By using the "pre-calculation" mechanism of basic indicators (pre-calculating and storing frequently accessed data such as rolling return rate, risk-free return rate, and scale weight into the dataset), redundant data I / O and repetitive calculations are significantly reduced when calculating complex indicators in the future.

[0019] 2. Decoupled design of multi-threaded / parallel computing: The calculation process of each evaluation indicator (risk-adjusted return, ranking stability, etc.) is designed to be independent of each other, relying on pre-computation datasets to support highly parallel computing, thereby improving the processing efficiency of large-scale fund data.

[0020] 3. Dynamic sliding window and algorithm optimization: When calculating the Ranking Stability Index (DAR), a dynamic sliding window with a step size of one trading day is used for multiple linear regression to obtain a more robust empirical adjustment coefficient, which has a significant technical effect on adaptive fitting of data processing. Attached Figure Description

[0021] Figure 1 This is a flowchart showing the overall ranking of the performance indicators of top-performing equity funds. Figure 2 This is a flowchart illustrating the calculation process for the performance indicators and overall score of equity-oriented Golden Bull Funds. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0024] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] The following combination Figures 1-2Specific embodiments of the present invention will be described in detail below. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the present invention.

[0026] This invention discloses a method for ranking the performance indicators of stock-oriented top-performing funds, the method comprising the following steps: Obtain the configuration parameters for the evaluation calculation task. The configuration parameters are defined in the form of a seven-tuple, including the task name, the category of the participating fund, the operation mode of the participating fund, the deadline for observation, the number of years of the observation period, the eligibility conditions for participation, and the scoring model. Based on the classification of participating funds, their operation methods, and eligibility criteria, the publicly disclosed information data of public funds in the Golden Bull Award selection database are queried and screened to generate a sample pool of eligible participating funds. For each participating fund in the sample pool, historical transaction data during the observation period is extracted, basic indicator pre-calculation is performed, and a basic indicator dataset of the participating fund is generated and stored. The dataset includes at least the rolling fund return, rolling benchmark return, rolling risk-free return, and the weighted comparable net asset value of the participating fund for each trading day during the observation period. Based on the basic indicator dataset of the participating funds, a parallel computing approach is used to independently calculate multiple performance evaluation indicators for each participating fund during the observation period. The performance evaluation indicators include at least risk-adjusted return indicators and absolute return ranking stability indicators. Based on the weighting configuration in the scoring model, the standard scores of multiple performance evaluation indicators of all participating funds are weighted and summed to calculate the comprehensive score. The funds in the sample pool are then sorted and output according to the comprehensive score from largest to smallest.

[0027] 1.1 Evaluation and Calculation Tasks This invention uses the Golden Bull Award selection in the fund industry as a background and example to illustrate the detailed technical solution of this invention. The calculation task for the Golden Bull Fund Product Award selection is defined by the following seven-tuple: ProductAwardTask = (TaskName, FundType, RunType, EvalEndDate, EvalYears, Qualifications, ScoreModel). Wherein: TaskName: Product Award Selection Calculation Task Name FundType: The third-level classification to which the participating fund belongs (or second-level if none exists). RunType: The operating mode of the participating fund (open-ended / closed-ended) EvalEndDate: Deadline for evaluation (e.g., the deadline for the Golden Bull Award evaluation is 2024-12-31). EvalYears: The number of years for the observation period (five or seven years). Qualifications: Eligibility criteria for participation, expressed as a Boolean expression. ScoreModel: Scoring Model Each evaluation calculation task can be described as follows: Based on the sample pool of participating funds defined by the task (determined by FundType, RunType, and Qualifications), calculate the relevant evaluation indicators for each participating fund during the corresponding observation period (determined by EvalYears and EvalEndDate), further calculate the comprehensive score of each participating fund during the observation period according to the ScoreModel, and sort all participating funds in descending order of comprehensive score, which is the quantitative evaluation output of the task.

[0028] Based on the established award categories and the Golden Bull Award fund classification system, there are a total of 10 calculation tasks for the selection of Golden Bull equity fund products, as shown in Table 1 below: Table 1. Calculation Task for Equity-Oriented Golden Bull Fund Selection

[0029] For each calculation task in the table above, the subsequent calculation steps and processes need to be executed independently to obtain the quantitative evaluation ranking results for each task.

[0030] 1.2 Evaluation Index System The quantitative evaluation indicators for equity-oriented Golden Bull Funds are shown in Table 2 below.

[0031] Table 2 Quantitative Evaluation Index System for Equity-Oriented Golden Bull Funds

[0032] When ranking similar funds, the most crucial calculation task is to calculate the five evaluation indicators mentioned above for each fund that meets the eligibility criteria (hereinafter referred to as the participating fund) during the corresponding observation period, and then to sum them up according to their respective weights to obtain the comprehensive score of each participating fund and rank them accordingly.

[0033] 1.2.1 Indicators for Individual Funds remember Let F be the set of all such trading days for fund F during the observation period, arranged in chronological order: If a fund is purchased on the trading day preceding each specified date and held for h trading days before redemption, the redemption date must still be within the observation period. This is especially true when h is d, w, m, or y. These represent a set consisting of a trading day, a trading week, a trading month, and a trading year, respectively.

[0034] Performance: Absolute return and excess return (1) Sample of Fund F's return: for any given time period ,investor The return on fund F purchased on the previous trading day and held for h trading days is denoted as . A set arranged in chronological order is abbreviated as . ,Right now ; (2) Sample of Fund F's Risk-Free Return: For any given time period ,investor The annualized return of fund F purchased on the previous trading day and held for h trading days minus The yield of a 10-year Treasury bond with a maturity of h on the previous trading day is denoted as A set arranged in chronological order is abbreviated as . ,Right now The yields of government bonds of various maturities are all annualized, so the fund yields are also required to be annualized, and the geometric method is used for annualization. (3) Sample of excess returns of Fund F: for any given time period ,investor The return of buying fund F on the previous trading day and holding it for h trading days minus the investor's... The return on the benchmark fund F purchased on the previous trading day and held for h trading days is denoted as . A set arranged in chronological order is abbreviated as . ,Right now .

[0035] 1.2.2 Risk-Adjusted Performance Indicators (1) The total size and trading day weighting of Fund F during the observation period: On any trading day during the observation period , record fund On the trading day The net asset value disclosed at the end of the most recent quarter, after being converted using the conversion factor, yields the comparable net asset value (hereinafter referred to as "size"), which is denoted as follows: , ,time The number of trading days between the most recent disclosed trading volume and the last disclosed trading volume were respectively , If time It is precisely because of this large-scale announcement that... = , = like definition:

[0036] This can be referred to as the total size of Fund F during the observation period. (Note:) ; For the return vector and The common weighted weights, where For set The number of elements.

[0037] (2) The pseudo-sorcinol ratio and pseudo-sorcinol information ratio are respectively: ;

[0038] (M) represents the median. express The median of this dataset; This represents the lower standard deviation of the sample, where the "sample mean" used in calculating the standard deviation is the median. express The lower standard deviation of the samples in this dataset.

[0039] 1.2.3 Ranking Adjusted by Fund Performance Drawdown Calculate the fund using the following steps. In the fund category The ranking was adjusted based on the performance pullback.

[0040] (1) First calculate the fund Set of returns and excess return set ,in ,and Similarly, this is the set of all such trading days in the fund market during the observation period, arranged in chronological order: If a fund is purchased on the trading day preceding each of the specified dates and redeemed on trading day h, the redemption date must still be within the observation period. (2) For any ,calculate In the set quantiles on and In the set quantiles on ; (3) Record Let be the inverse function of the standard normal distribution function, calculate and ; (4) Calculate the samples separately. and samples average , and maximum drawdown , ; (5) Calculate the samples separately. and samples average , For maximum drawdown , empirical adjustment coefficient , : ; It is a fund In the fund category The ranking of the maximum drawdown adjustment of absolute returns and the ranking of the maximum drawdown adjustment of relative returns.

[0041] 1.2.4 Investor satisfaction evaluation indicators The assessment of investor satisfaction is primarily based on a comprehensive consideration of three indicators: simple rate of return, weighted average net asset value return, and investor profit / loss ratio. The specific calculation methods for each indicator are as follows: (a) For the participating fund product F in the (semi-annual) reporting period, calculate the following three indicators respectively. 1. Simple rate of return for investors

[0042] in, Indicates the reporting period, for Net asset value of Fund F at the end of the reporting period. The net asset value of fund product F at the beginning of the reporting period is u. for Total subscription amount of Fund F during the reporting period for Net subscription and redemption amount of Fund F during the reporting period This refers to the dividends distributed by Fund Product F during the reporting period.

[0043] 2. Weighted average net asset value return rate: disclosed in the fund's semi-annual report, denoted as... . 3. Investor Profit / Loss Ratio: Given a target rate of return (Set to 0 for this instance), the investor's profit and loss calculation method (Omega ratio) is as follows: Where vector , It represents the number of trading days for Fund F during the reporting period. This indicates the size weight of fund F on trading day t (i.e., the size of comparable assets on that day). Let be the rate of return of fund F on trading day t.

[0044] (II) Calculation of Standard Scores for the Three Indicators in the (Semi-annual) Reporting Period: For all funds in the category to which Fund F belongs and which disclosed reports in the reporting period of u, the above three indicators are standardized and standard scores are obtained. The standard scores for Fund F in these three indicators are as follows: , and .

[0045] (III) Summarize the scores of the participating fund F on the three indicators during the evaluation period: (Record) This represents the average net asset value of Fund F over the three quarters of the reporting period (six months). , Given the total number of reporting periods for Fund F during the observation period, the three indicators for Fund F during the observation period are as follows: (1) Simple rate of return for investors: ;

[0046] (2) Weighted average net profit margin:

[0047] (3) Investor profit / loss ratio:

[0048] (iv) The investor satisfaction score for Fund F during the evaluation period was:

[0049] 1.2.5 Calculation Method for Score Summary of Multiple Scoring Indicators under the Same Category Individual awards for fund products and fund company awards often have multiple scoring indicators. This award scheme uses a weighted summation method to aggregate these indicators into a single indicator, where the weights are predetermined. Because individual awards and company awards are selected within a single category, and categories are comparable, but the dimensions of each scoring indicator may not be identical, standardization is necessary before weighted summation. The specific method is as follows: (1) Set up an individual award or company award in the category of Selected from the middle, and have Each scoring indicator is set in category Members , The scores on these indicators are respectively , .

[0050] (2) For all members in the class, the first... A vector composed of indicators After standardization, it is denoted as . express The standard score.

[0051] (3) Record The weighted summation weight of each scoring indicator is: ;

[0052] (4) Then the members of the class Overall score for:

[0053] Then according to The size of the award determines which members are eligible to be nominated.

[0054] 1.2.6 Calculation Scope of Indicators In fund evaluation, to ensure consistent calculation methods, funds with multiple share classes will be handled according to the following principles: 1) Except for the unit net value, all other indicators (such as fund size, weighted average net value return rate, etc.) use the consolidated data disclosed in the fund's periodic reports.

[0055] 2) The net asset value per unit uses data from the fund's main share.

[0056] The above principles are mainly based on the following considerations: 1) Consolidated data reflects the overall operation of the fund and is more representative and comparable.

[0057] 2) The information required to weight or combine the net asset value per unit of different shares is incomplete, and there are technical difficulties; the difference in net asset value per unit of different shares of the same fund is small, and the net asset value of the main share is representative, so the net asset value per unit of the main share is used for calculation.

[0058] 1.3 Overall Calculation Process The overall calculation process for the comprehensive ranking of equity-oriented Golden Bull Fund performance indicators is as follows: Figure 1 As shown.

[0059] The overall data processing and calculation process consists of five steps: The first step is to prepare the basic data. Based on publicly disclosed information from public funds, a basic database for the Golden Bull Awards selection will be constructed. This database will store and record the basic data related to the entire Golden Bull Awards selection calculation process. In addition, based on the Golden Bull Awards fund classification system, all funds in the market will be classified, tagged with category labels, and any historical changes in fund classifications will be recorded.

[0060] The second step is the selection of participating funds. Based on the eligibility criteria for the Golden Bull Fund award in the equity sector, funds meeting the eligibility requirements are selected from the Golden Bull Award database to form the participating fund sample pool. All subsequent calculations are performed within this pool.

[0061] The third step is to pre-calculate the basic indicators of the participating funds. To facilitate the verification of the calculation process, improve the overall calculation performance, and avoid repeatedly calculating some basic indicators (such as "the return rate of buying this fund on the previous trading day and holding it for one year") when calculating various evaluation indicators later, these indicators can be pre-calculated and saved to the Golden Bull Award selection database.

[0062] The fourth step is to calculate the performance evaluation indicators for participating funds. For each participating fund, calculate its various scoring indicators during the evaluation period (e.g., the evaluation period for the 22nd Golden Bull Award is from January 1, 2020 to December 31, 2024) as shown in Table 2.

[0063] The fifth step is to calculate and rank the comprehensive scores of the participating funds. For each participating fund, the relevant scoring indicators are weighted and summed according to the scoring model to obtain a comprehensive score. Then, the funds are ranked from highest to lowest based on their comprehensive scores.

[0064] The data processing flow, indicator calculation methods, or related algorithms involved in each of the above steps are described below.

[0065] I. Basic Data Preparation Based on publicly disclosed information from public funds, a foundational database for the Golden Bull Awards selection was constructed (technology selection: MySQL relational database). This database stores and records historical basic data related to the entire Golden Bull Awards selection calculation process, mainly including: basic information and changes of fund products, fund manager appointment and change information, daily net asset value data of funds, fund dividend or split data, 10-year Treasury bond yield data, daily benchmark index data, quarterly reports of funds (net asset value, asset allocation, etc.), and semi-annual or annual reports of funds (including ending net asset value, beginning net asset value, fund subscription proceeds, fund redemption proceeds, income distribution to holders, weighted average net asset value return rate, and profit for the period).

[0066] In practice, the basic database for the Golden Bull Award selection can be built and maintained based on the Hengsheng Juyuan database, Wind database, or similar databases in the industry through ETL, data fusion, cross-validation, and incremental updates.

[0067] Based on the fund contracts' provisions regarding the fund's asset investment scope and proportions, and the equity and bond composition ratio of the performance benchmark, all funds in the market are classified (including liquidated funds). The classification information for each fund (including historical changes) is recorded in the Golden Bull Awards' basic database. If a fund's classification has changed historically due to changes in its investment scope or performance benchmark, the database must record the classification information before and after the change (including the fund's category label, effective date, and expiration date at each stage before and after the change).

[0068] II. Selection of Participating Funds Based on the Golden Bull Award selection database, funds that meet the eligibility criteria for Golden Bull Funds in the equity sector are selected to form a sample pool of participating funds (each subsequent calculation step is carried out in this sample pool of funds).

[0069] Funds participating in the selection for the Golden Bull Equity Fund Award must meet the following eligibility criteria: (1) Operation time requirement: For funds participating in the five-year (or seven-year) product award, the operation time from the product's establishment to the end of the evaluation period shall not be less than 60 months (or 84 months).

[0070] (2) Asset size requirements: For each type of fund, the average net asset value during the observation period is ranked from high to low. The average net asset value after ranking is added up in sequence, and the products with the cumulative total size accounting for the top 90% of the total size of similar products are selected.

[0071] (3) Fund manager tenure requirement: The current fund manager should have managed the product for no less than half of the probationary period.

[0072] The following is the process for selecting candidates for funding.

[0073]

[0074] Taking the "Open-ended Hybrid - Active Allocation - 5Y" task of the "Five-Year Open-ended Hybrid Fund Golden Bull Award" in Table 1 as an example, the input parameters when using the above screening algorithm are: FundType = "Active Allocation", RunType = "Open-ended", EvalEndDate = "2024-12-31", EvalYears = 5. After executing this screening algorithm, the resulting fund sample set is the sample pool of participating funds for the "Open-ended Hybrid - Active Allocation - 5Y" evaluation task. Subsequent processes will only need to perform indicator calculations and scoring rankings for each participating fund in this sample pool.

[0075] III. Preliminary Calculation of Basic Indicators for Participating Funds For each participating fund, its relevant basic indicators for each trading day during the evaluation period are calculated and saved to the Golden Bull Award evaluation database. This avoids redundant calculations during subsequent indicator calculations, effectively improving overall computational performance. These basic indicators for each participating fund on each trading day collectively form a six-tuple, forming the overall basic indicator dataset for the participating funds.

[0076] Where: F represents the participating fund (usually identified by the fund code). This indicates one trading day during the observation period. This represents the return on an investor who bought F on the previous trading day of t and held it for one year (referred to as the "rolling 1-year fund return"). This represents the return on an investor's purchase of the benchmark F on the previous trading day of t and holding it for one year (referred to as the "rolling 1-year benchmark return"). This represents the risk-free rate of return for an investor who buys a 10-year Treasury bond on the previous trading day of t and holds it for one year (referred to as the "rolling 1-year risk-free rate"). This indicates the size weight (comparable net asset value after conversion) of the participating fund F on that trading day.

[0077] IV. Calculation of Performance Evaluation Indicators for Participating Funds Based on the basic indicator dataset D of the participating funds obtained in the previous steps, the specific algorithms and calculation processes of each performance evaluation indicator in "Table 2 Quantitative Evaluation Indicator System for Stock Direction Golden Bull Funds" are described below. The figure below shows the overall calculation process from the calculation of each evaluation indicator to the final comprehensive score (since the indicators are independent of each other, they can be calculated in parallel to speed up the overall performance).

[0078] 4.1 Calculation of Risk-Adjusted Return Indicator The risk-adjusted return of the participating funds during the evaluation period is measured using the "prototype Sortino ratio," calculated as follows:

[0079] in, This represents the dataset of the risk-free rate of return for the participating fund F, where each data point represents the risk-free rate of return of the participating fund F in ascending order of trading days during the observation period; This represents the weighted median of the ultra-risk-free rate dataset for the participating fund F; This represents the weighted lower standard deviation of the ultra-risk-free rate dataset for the participating fund F.

[0080] The calculation process for the risk-adjusted return indicator of participating fund F during the evaluation period is as follows.

[0081]

[0082] Similarly, the above process is repeated for each participating fund to obtain its pro forma Sortino ratio during the observation period, which is the risk-adjusted return indicator value of that fund during the observation period. Since the calculation of the pro forma Sortino ratio of each participating fund is independent of each other, parallel computing methods can be used to speed up the overall performance (for example, by using multi-threading or multi-processing to execute the above process in parallel).

[0083] 4.2 Calculation of Risk-Adjusted Relative Return Indicator The risk-adjusted relative return of the participating funds during the evaluation period is measured using the "prototype Sortino information ratio," calculated as follows:

[0084] in, This represents the excess return data of the participating fund F relative to its performance benchmark, where each data point represents the excess return of the participating fund F in ascending order of trading days during the observation period; This represents the weighted median of the excess return dataset for the participating fund F; This represents the weighted lower standard deviation of the excess return dataset of the participating fund F.

[0085] The calculation process for the risk-adjusted relative return of participating fund F during the evaluation period is as follows.

[0086]

[0087] Similarly, the above process is repeated for each participating fund to obtain its proposed Sortino Information Ratio (PIR) during the observation period, which is the risk-adjusted relative return indicator for that fund during the observation period. Since the PIRs of each participating fund are independent of each other, parallel computing can be used to speed up the overall performance (e.g., by using multi-threading or multi-processing to execute the above process in parallel).

[0088] 4.3 Calculation of Stability Indicators for Absolute Return Ranking The Ranking Stability of Absolute Returns (DAR) is a comprehensive measure of the stability of a participating fund's absolute return performance among similar funds during the evaluation period.

[0089] The specific algorithm flow is given below:

[0090]

[0091]

[0092] A few additional notes regarding the above algorithm: 1. The DAR indicator reflects the stability of a participating fund's ranking relative to similar funds during the evaluation period, based on a certain basic indicator (such as "rolling 1-year fund return" or "rolling 1-year benchmark return").

[0093] 2. In order to calculate the empirical adjustment coefficient coeff, in practice, data from several months after the observation period are usually used as "target data" for regression fitting.

[0094] 3. To make the final empirical adjustment coefficient coeff more robust, multiple linear regressions based on the window rolling method are needed to obtain the empirical adjustment coefficient w_coeff corresponding to different windows. Finally, the average of all windows is used as the empirical adjustment coefficient for this type of fund.

[0095] 4.4 Calculation of Relative Return Ranking Stability Indicators The calculation process for this indicator is basically the same as the "Absolute Return Ranking Stability Indicator Algorithm Process" mentioned above. The only difference is that the basic indicator "Rolling 1 Fund Return" in the algorithm is replaced with "Rolling 1-Year Excess Return".

[0096] 4.5 Calculation of Investor Satisfaction Index This indicator comprehensively measures participating funds from the perspective of investors' experience and satisfaction with investing in the fund. When calculating this indicator, each half-year period within the evaluation period is used as a calculation cycle (for example, an award with a five-year evaluation period includes 10 half-years). Based on the publicly disclosed semi-annual or annual reports of the funds, three basic indicators related to investor satisfaction can be calculated for each participating fund in each half-year cycle: simple investor return rate, weighted average net asset value return rate, investor profit / loss ratio, and average net asset value size in each half-year cycle.

[0097] Based on these three fundamental indicators, the standard score of each participating fund relative to similar funds in each semi-annual period can be calculated. For each fundamental indicator, based on its standard score and the fund's net asset value in each semi-annual period, the weighted average score of that fundamental indicator for each participating fund over the entire evaluation period can be further calculated. The final investor satisfaction score of the participating fund is the average of the scores of the three fundamental indicators.

[0098] The calculation method for the investor satisfaction index has been formalized according to section 1.2.6. The specific algorithm and calculation process are given below:

[0099]

[0100] It should be noted that since fund information disclosure does not include the semi-annual report for the second half of the year, the relevant basic data required for calculating the above-mentioned sub-indicators (simple return rate of investors, weighted average net asset value return rate, and investor profit / loss ratio) for the second half of each year need to be indirectly calculated based on the fund's annual report and semi-annual report for that year through deduction.

[0101] V. Calculation and Ranking of the Overall Score of Participating Funds After the evaluation indicators for all participating funds were calculated, each indicator was converted into a standard score. Then, the scores were weighted and summed according to the weights of each indicator in "Table 2. Quantitative Evaluation Indicator System for Equity-Oriented Golden Bull Funds" to obtain the comprehensive score for each participating fund. Overall Score = Standard Score of Risk-Adjusted Return Indicator +Risk-adjusted relative return indicator standard score +Stability index standard score for absolute return ranking +Stability index standard score of relative return ranking +Investor satisfaction evaluation index standard score .

[0102] The final ranking of participating funds in this category is obtained by sorting them from highest to lowest based on their overall scores.

[0103] VI. Simultaneously, this invention also provides a ranking system for stock-oriented top-performing funds based on performance indicators, comprising: The task definition module is used to receive and parse the evaluation calculation task configuration parameters defined in the form of a seven-tuple, and extract the task name, the category of the participating fund, the operation mode, the observation deadline, the number of years of the observation period, the eligibility conditions for participation, and the scoring model. The sample screening module is used to query and screen the publicly disclosed information data of public funds in the basic database of the Golden Bull Award selection based on the category of the participating funds, the operation mode of the participating funds, and the eligibility conditions for participation, and generate a sample pool of participating funds that meet the conditions. The basic data pre-calculation module is used to extract historical transaction data during the observation period for each participating fund in the sample pool of participating funds, perform basic indicator pre-calculation operations, generate and store the basic indicator dataset of the participating funds. The dataset includes at least the rolling fund return, rolling benchmark return, rolling risk-free return and the converted comparable net asset value size weight of the participating fund for each trading day during the observation period. The multi-threaded indicator calculation module is used to call the basic indicator dataset of the participating funds and start multiple calculation threads or processes to execute the independent calculation of multiple performance evaluation indicators in parallel. The performance evaluation indicators include at least risk-adjusted return indicators and absolute return ranking stability indicators. The comprehensive scoring and ranking module is used to calculate the comprehensive score by weighting and summing the standard scores of multiple performance evaluation indicators of all participating funds according to the weight configuration in the scoring model, and then sorting and outputting the participating funds in descending order of comprehensive score.

[0104] Any process or method described in the flowcharts of this invention or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, which can be implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device. The computer-readable medium can be any medium containing a program for storage, communication, propagation, or transmission for use by the execution system, apparatus, or device, including read-only memory, magnetic disks, or optical disks.

[0105] In the description of this specification, references to terms such as "embodiment," "example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, those skilled in the art can combine or combine the different embodiments or examples described in this specification and the features therein without causing contradiction.

[0106] While embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and alterations to the above embodiments within the scope of the present invention.

Claims

1. A method for ranking the performance indicators of stock-oriented top-performing funds, characterized in that, The method includes the following steps: Obtain the configuration parameters for the evaluation calculation task. The configuration parameters are defined in the form of a seven-tuple, including the task name, the category of the participating fund, the operation mode of the participating fund, the deadline for observation, the number of years of the observation period, the eligibility conditions for participation, and the scoring model. Based on the classification of participating funds, their operation methods, and eligibility criteria, the publicly disclosed information data of public funds in the Golden Bull Award selection database are queried and screened to generate a sample pool of eligible participating funds. For each participating fund in the sample pool, historical transaction data during the observation period is extracted, basic indicator pre-calculation is performed, and a basic indicator dataset of the participating fund is generated and stored. The dataset includes at least the rolling fund return, rolling benchmark return, rolling risk-free return, and the weighted comparable net asset value of the participating fund for each trading day during the observation period. Based on the basic indicator dataset of the participating funds, a parallel computing approach is used to independently calculate multiple performance evaluation indicators for each participating fund during the observation period. The performance evaluation indicators include at least risk-adjusted return indicators and absolute return ranking stability indicators. Based on the weighting configuration in the scoring model, the standard scores of multiple performance evaluation indicators of all participating funds are weighted and summed to calculate the comprehensive score. The funds in the sample pool are then sorted and output according to the comprehensive score from largest to smallest.

2. The method according to claim 1, characterized in that, Based on the classification, operation mode, and eligibility criteria of the participating funds, the publicly disclosed information data of public funds in the Golden Bull Award selection database are queried and screened to generate a sample pool of eligible participating funds, specifically including: The initial sample set that matches the classification and operating method is selected from the database; Funds whose initial sample operation period was shorter than the specified number of months were excluded; Calculate the average net asset value of each fund in the initial sample set during the observation period and sort them in descending order. Then, sum up the average net asset value after sorting and select the funds whose cumulative total size accounts for the top 90% of the total size of similar products. We obtain data on fund managers' tenure, remove funds managed by current fund managers whose tenure during the probationary period is less than half of the probationary period, and generate the final sample pool of participating funds.

3. The method according to claim 1, characterized in that, Each data point in the basic indicator dataset for the participating funds consists of a six-tuple, formally represented as: in, Indicates the fund code for the evaluation. This indicates one trading day during the observation period. This represents the rolling 1-year fund return. This represents the rolling 1-year benchmark yield. This represents the rolling one-year risk-free rate of return. This indicates the size weight of the participating fund on that trading day.

4. The method according to claim 1, characterized in that, Performance evaluation indicators also include risk-adjusted relative return indicators, relative return ranking stability indicators, and investor satisfaction evaluation indicators.

5. The method according to claim 4, characterized in that, Risk-adjusted return and risk-adjusted relative return metrics are calculated using the pseudo-Sotinau ratio and pseudo-Sotinau information ratio, respectively. When calculating the pseudo-Sotinho ratio, the rolling 1-year risk-free rate data series of a specific participating fund is read from the basic indicator dataset of the participating funds. The weighted median and weighted lower standard deviation of the risk-free rate data series are calculated using the size weight of each trading day as the weighting weight, and the ratio of the two is output as the pseudo-Sotinho ratio.

6. The method according to claim 4, characterized in that, When calculating the stability index of absolute return ranking, a dynamic rolling window mechanism is used. The specific steps include: Obtain the percentile ranking of the participating fund's rolling 1-year fund return on each trading day during the observation period among similar funds, and perform an inverse normal transformation to obtain the first eigenvalue sequence; Calculate the average value and maximum drawdown of the first eigenvalue sequence for each participating fund throughout the entire observation period; Using the set trading day time span as the rolling window size, window data is extracted starting from the first trading day of the observation period. The average value and maximum drawdown of the first feature value of each participating fund within each window are calculated. The first feature value of the first trading day after the window is used as the predicted value to construct a training set. Multiple linear regression is performed to obtain the local adjustment coefficient corresponding to the window. The regression calculation is repeated by sliding the window forward with a step size of one trading day to obtain multiple local adjustment coefficients. The average of all local adjustment coefficients is then calculated as the empirical adjustment coefficient for similar participating funds. The stability index of the fund's absolute return ranking is obtained by adding the average value to the product of the empirical adjustment factor and the maximum drawdown.

7. The method according to claim 4, characterized in that, The specific steps for calculating investor satisfaction rating indicators include: The calculation period is based on each six-month period within the observation period, and publicly disclosed data of the fund is extracted. Within each semi-annual cycle, the simple return rate of investors, the weighted average net asset value return rate, and the investor profit and loss ratio of the participating funds are calculated separately. For each semi-annual cycle, the three sub-indicators are converted into standard scores relative to similar participating funds. Using the average net asset value of each semi-annual period as the weight, the weighted average score of the three sub-indicators was calculated over the entire observation period. The weighted average score of the three sub-indicators is used to calculate the arithmetic mean, which yields the investor satisfaction evaluation index for the participating fund.

8. The method according to claim 7, characterized in that, When calculating the simple return rate, weighted average net asset value return rate, and investor profit / loss ratio for the second half of each year, the basic data for the second half of the year is obtained by calling the fund's annual report data and semi-annual report data for that year and using numerical deduction.

9. The method according to claim 1, characterized in that, When extracting and processing data for funds with multiple share classes, data including the net asset value per unit is extracted from the fund's main share data for calculation, while data other than the net asset value per unit is extracted from the consolidated data disclosed in the fund's periodic reports for calculation.

10. A performance ranking system for stock-oriented top-performing funds, characterized in that, The system is used to implement the stock-oriented golden bull fund performance indicator ranking method according to any one of claims 1-9, the system comprising: The task definition module is used to receive and parse the evaluation calculation task configuration parameters defined in the form of a seven-tuple, and extract the task name, the category of the participating fund, the operation mode, the observation deadline, the number of years of the observation period, the eligibility conditions for participation, and the scoring model. The sample screening module is used to query and screen the publicly disclosed information data of public funds in the basic database of the Golden Bull Award selection based on the category of the participating funds, the operation mode of the participating funds, and the eligibility conditions for participation, and generate a sample pool of participating funds that meet the conditions. The basic data pre-calculation module is used to extract historical transaction data during the observation period for each participating fund in the sample pool of participating funds, perform basic indicator pre-calculation operations, generate and store the basic indicator dataset of the participating funds. The dataset includes at least the rolling fund return, rolling benchmark return, rolling risk-free return and the converted comparable net asset value size weight of the participating fund for each trading day during the observation period. The multi-threaded indicator calculation module is used to call the basic indicator dataset of the participating funds and start multiple calculation threads or processes to execute the independent calculation of multiple performance evaluation indicators in parallel. The performance evaluation indicators include at least risk-adjusted return indicators and absolute return ranking stability indicators. The comprehensive scoring and ranking module is used to calculate the comprehensive score by weighting and summing the standard scores of multiple performance evaluation indicators of all participating funds according to the weight configuration in the scoring model, and then sorting and outputting the participating funds in descending order of comprehensive score.

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