Intelligent fund product evaluation method based on big data

Abstract

The invention relates to the technical field of financial data processing, and discloses a fund product intelligent evaluation method based on big data, comprising the following steps: step 1, acquiring recruitment information of a fund product currently sold; step 2, acquiring basic information of all investment users; step 3, obtaining all dynamic policies issued in the investment market; step 4, analyzing and generating a corresponding investment cycle for the investment users, then analyzing the average total amount of assets and the average income duration of different investment users, and determining the economic level and investment preference of the users; 5, the matching index of each fund product is analyzed, a fund product list conforming to the economic level and the investment preference of the investment user is screened out, and the user investment intention positioning is more accurate through multi-dimensional analysis; and 6, extracting all dynamic policies issued in the investment cycle, analyzing and generating a trend data set, assisting an investment user to select a fund product with more stable income, and enabling the user experience to be good and the long-term investment willingness to be high.

Description

Technical Field

[0001] This invention relates to the field of financial data processing technology, specifically to a method for intelligent evaluation of fund products based on big data. Background Technology

[0002] A fund is a collective investment approach where investors share profits and risks. It pools funds from investors by issuing fund units, which are then managed and utilized by a fund manager and custodian to invest in financial instruments such as stocks and bonds. This collective investment approach leverages the scale of capital, reduces investment costs, and increases investment returns. Fund products include equity funds, bond funds, money market funds, and mixed funds. Equity funds primarily invest in stocks, aiming for long-term capital appreciation. Equity funds offer higher returns but also carry greater risk. Bond funds primarily invest in bonds, seeking stable returns. Bond funds have relatively lower risk and are suitable for investors with lower risk tolerance. Money market funds primarily invest in short-term money market instruments such as treasury bills and commercial paper, seeking low risk and stable returns. Money market funds typically feature high liquidity and security. Mixed funds invest in multiple assets, including stocks and bonds, to achieve a balance between returns and risks. Diversification helps reduce the overall risk of the investment portfolio; the risk and return of mixed funds fall between those of equity funds and bond funds. When choosing a fund, investors need to consider their own risk tolerance, investment goals and investment horizon, as well as the fund's historical performance, the fund manager's experience and ability, and the fund company's reputation. They should also pay close attention to market dynamics and policy changes and adjust their investment portfolio in a timely manner to cope with market changes.

[0003] Currently, traditional big data-based intelligent evaluation methods for fund products rely on a relatively simplistic approach of analyzing users' investment intentions based on economic levels. This approach neglects the importance users place on the duration of fund returns. For new users investing in fund products for the first time, this method requires a high level of expertise, which can easily lead to distrust and reduce users' willingness to invest in the long term. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a big data-based intelligent evaluation method for fund products. This method has advantages such as more accurate positioning of user investment intentions through multi-dimensional analysis, better user experience, and higher long-term investment intentions. It solves the problem that traditional big data-based intelligent evaluation methods for fund products have a relatively simple way of analyzing user intentions and low long-term investment intentions among first-time investors.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a big data-based intelligent evaluation method for fund products, comprising the following steps:

[0008] Step 1: Obtain fundraising information for currently offered fund products through a big data platform connected to the network, and compile the obtained data into a product dataset;

[0009] Step 2: Obtain basic information of all investment users through the online fund purchase platform, and compile the obtained data into a user dataset;

[0010] Step 3: Obtain all dynamic policies released in the investment market through a big data platform connected to the network, and compile the obtained data into a market dataset;

[0011] Step 4: Based on the user dataset, analyze and generate corresponding investment periods Q for different investment users, and then analyze the average total assets PZ and average return duration PS of different investment users based on the investment period Q to clarify the economic level and investment preferences of each investment user.

[0012] Step 5: Based on the average total assets PZ and average return duration PS of the investment users, analyze and generate the matching index Pizs for each fund product in the product dataset, and filter out a list of fund products that match the economic level and investment preferences of the investment users.

[0013] Step 6: Based on the market dataset, extract all dynamic policies released by investors within their investment period Q, and analyze and generate a trend data group Qssj to help investors select fund products with more stable returns.

[0014] Preferably, in step one, the expression for the product dataset is {C1}. s C2 s C3 s ..., Cp s}, C1 s To Cp s This corresponds to the fundraising information for each fund product currently being offered for sale. The fundraising information includes the required fundraising amount, investment strategy, and rate of return. s represents the contract duration of the fund product, and 1 to p represent the number of fund products currently being offered for sale.

[0015] Preferably, in step two, the expression for the user dataset is {Y1}. d Y2 d Y3 d ...,Yn d}, Y1 d To Yp dThe basic information corresponds to each investor, including total assets and historical investment records. d represents the time point when the investor's basic information was updated, and 1 to n represent the number of investors on the fund purchase platform.

[0016] Preferably, in step three, the expression for the market dataset is {S1}. t S2 t S3 t ...,Sc t}, S1 t To Sc t This corresponds to each dynamic policy released at a different point in time within the investment market. Dynamic policies include monetary policy and trade policy. Monetary policy is a means of regulating currency exchange rates, while trade policy is a means of regulating trade fees. t represents the time point at which the dynamic policy is released, and 1 to c represent the number of dynamic policies released in the investment market.

[0017] Preferably, in step four, the analysis process for the investment period Q is as follows:

[0018] Extract the basic information of the i-th investor from the user dataset and label it as Yi. d Mark the total assets of the i-th investor as The historical investment records of the i-th investor are marked as follows:

[0019] If the historical investment records of the i-th investor are... The data is not available in the table, and the corresponding investment period Q is assumed to be one year.

[0020] If the historical investment records of the i-th investor are... There is a single record, and the investment period Q is the contract duration of the fund product in that record.

[0021] If the historical investment records of the i-th investor are... There are multiple records in the data, and the maximum value of the fund product contract duration among the multiple records is taken as the corresponding investment period Q.

[0022] Preferably, in step four, the formula for calculating the average total assets PZ is as follows:

[0023]

[0024] In the formula, PZ represents the average total assets. This represents the total assets of the i-th investor at the end of their fund contract after a single investment period Q. The average total assets PZ of the i-th investor is the average of the total assets at the beginning of the fund contract and the total assets at the end of the fund contract within a single investment period Q. i , This indicates that the average total assets of n investment users are calculated simultaneously.

[0025] Preferably, in step four, the calculation process for the average contract duration PS is as follows:

[0026] Based on the historical investment records of the i-th investor Extract the maximum contract duration of the fund product in each record and mark it as... 1 to l represent that the i-th investor has l historical investment records;

[0027]

[0028] In the formula, PS represents the average contract duration. This represents the sum of the maximum contract durations of fund products in each record for the i-th investment user. The sum of the maximum contract durations divided by the total number of records yields the average contract duration (PS) for the i-th investor's purchase of a fund product. i , This indicates that the average contract duration for n investment users purchasing fund products is calculated simultaneously.

[0029] Preferably, in step five, the matching index Pixs is calculated as follows:

[0030] Extract the fundraising information of the kth fund currently being offered in the product dataset and label it as Ck. s , will Ck s The amount raised is marked as 's' represents the contract duration of the fund product;

[0031]

[0032] In the formula, Pizs represents the matching index. This represents the ratio of the total amount raised by p fund products to the average total assets of the i-th investor. This indicates that the ratio of the contract duration of p fund products to the average contract duration of fund products purchased by the i-th investor is calculated simultaneously. The matching index includes the ratio of the amount raised to the average total assets and the ratio of the contract duration to the average contract duration.

[0033] Preferably, in step five, p fund products are arranged from smallest to largest according to the ratio of the amount raised to the average total assets in the matching index Pizs, and p fund products are arranged from largest to smallest according to the ratio of the contract duration to the average contract duration in the matching index Pizs. The two arrangements are then overlapped, and fund products with the same order in the two arrangements are selected to form a list of fund products that meet the economic level and investment preferences of the investors.

[0034] Preferably, in step six, the calculation process for the trend data group Qssj is as follows:

[0035] Extract all dynamic policies published within the investment period Q from the market dataset, and label the currency exchange rates published within the investment period Q as follows: 1 to w represent w monetary policies issued during investment cycle Q, and trade policies issued during investment cycle Q are marked as... 1 to e indicates that there are e trade policies issued within the investment cycle Q, and t indicates the time point when dynamic policies are issued;

[0036]

[0037] In the formula, Qssj represents the trend data group. This indicates the latest monetary policy released at the current point in time. This indicates the monetary policy released at a point in time prior to the current point in time. This means calculating the exchange rate difference corresponding to monetary policy at two adjacent points in time within the investment period Q. This indicates the latest trade policy released at the current point in time. This indicates the trade policy released at a point in time prior to the current point in time. This indicates that within the investment cycle Q, the difference in transaction fees corresponding to two adjacent time points is calculated simultaneously.

[0038] Compared with existing technologies, this invention provides a big data-based intelligent evaluation method for fund products, which has the following beneficial effects:

[0039] 1. This invention obtains fundraising information for currently offered fund products, basic information of all investors, and all dynamic policies released in the investment market by connecting a big data platform and a fund purchase platform via the network. These are then categorized into product datasets, user datasets, and market datasets. Based on the user dataset, a corresponding investment period Q is generated for different investors. Then, based on the investment period Q, the average total assets PZ and average return duration PS of different investors are analyzed to clarify each investor's economic level and investment preferences. Based on the average total assets PZ and average return duration PS of investors, a matching index Pizs for each fund product in the product dataset is generated. According to the investor's economic level and investment preferences, each currently offered fund product is matched, resulting in more accurate positioning of user investment intentions through multi-dimensional analysis.

[0040] 2. This invention ranks p fund products from smallest to largest according to the ratio of fundraising amount to average total assets in the matching index Pizs, and simultaneously ranks p fund products from largest to smallest according to the ratio of contract duration to average contract duration in the matching index Pizs. The two ranking results are then overlapped, and fund products with the same order in the two ranking results are selected to form a list of fund products that meet the economic level and investment preferences of investors. Based on the market dataset, all dynamic policies released within the investment period Q of investors are extracted and analyzed to generate trend data group Qssj, which helps investors select fund products with more stable returns, resulting in a better user experience and a higher willingness to invest in the long term. Attached Figure Description

[0041] Figure 1 This is a diagram illustrating the steps of the method of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0043] Traditional big data-based intelligent evaluation methods for fund products rely on a simplistic approach of analyzing users' investment intentions through economic data. This approach neglects the importance users place on the duration of fund returns. For first-time fund investors, this method demands a high level of expertise and can easily breed distrust, thus reducing their willingness to invest long-term. Therefore, a big data-based intelligent evaluation method for fund products is provided. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0044] Step 1: Obtain fundraising information for currently offered fund products through a network connection to a big data platform, and compile the obtained data into a product dataset. The expression for the product dataset is {C1}. s C2 s C3 s ..., Cp s}, C1 s To Cp s This corresponds sequentially to the fundraising information of each fund product currently being offered for sale. The fundraising information includes the required fundraising amount, investment strategy, and rate of return. s represents the contract duration of the fund product, and 1 to p represent the number of fund products currently being offered for sale. Comprehensive collection of fundraising information for all fund products helps improve the accuracy of subsequent evaluations and supports personalized investment advice.

[0045] Step 2: Obtain basic information of all investment users through the online fund purchase platform, and compile the obtained data into a user dataset. The expression for the user dataset is {Y1}. d Y2 d Y3 d ...,Yn d}, Y1 d To Yp d Each investment user's basic information is listed in turn. The basic information includes total assets and historical investment records. d represents the time point when the investment user's basic information is updated. 1 to n represent the n investment users in the fund purchase platform. By understanding the total assets of investment users, more personalized investment advice and product recommendations can be provided to users to ensure that the recommended products or strategies are in line with the user's risk tolerance, investment goals and funding needs. Historical investment records can help the fund platform analyze the user's behavior patterns and preferences to improve the user experience.

[0046] Step 3: Obtain all dynamic policies released in the investment market through a network connection to a big data platform, and compile the obtained data into a market dataset. The expression for the market dataset is {S1}. t S2 t S3 t ...,Sc t}, S1 t To Sc t This corresponds to the dynamic policies released at different points in time within the investment market. The dynamic policies include monetary policy and trade policy. Monetary policy is a means of controlling currency exchange rates, and trade policy is a means of controlling trade fees. t represents the time point when the dynamic policy is released, and 1 to c represent that there are c dynamic policies released in the investment market. In the event of large exchange rate fluctuations or frequent changes in trade fees, real-time updates of dynamic policies can ensure that investors do not miss any favorable investment opportunities.

[0047] Step 4: Based on the user dataset, analyze and generate corresponding investment periods Q for different investment users. The analysis process is as follows:

[0048] Extract the basic information of the i-th investor from the user dataset and label it as Yi. d Mark the total assets of the i-th investor as The historical investment records of the i-th investor are marked as follows:

[0049] If the historical investment records of the i-th investor are... The absence of data indicates that the user is in a wait-and-see period, and is most likely a new user investing in fund products for the first time. The corresponding investment period Q is one year by default.

[0050] If the historical investment records of the i-th investor are... The presence of a single record indicates that the user has a high level of trust in the fund platform and a moderate investment intention. The contract duration of the fund product in that record is taken as the corresponding investment period Q.

[0051] If the historical investment records of the i-th investor are... The presence of multiple records indicates that the user highly trusts the fund platform, hopes for higher returns, and has a strong investment intention. The maximum contract duration of the fund products among the multiple records is taken as the corresponding investment period Q.

[0052] Based on the investment cycle Q, the average total assets PZ and average return duration PS of different investment users are analyzed, and the calculation formula is as follows:

[0053]

[0054] In the formula, PZ represents the average total assets. This represents the total assets of the i-th investor at the end of their fund contract after a single investment period Q. The average total assets PZ of the i-th investor is the average of the total assets at the beginning of the fund contract and the total assets at the end of the fund contract within a single investment period Q. i , This means that the average total assets of n investment users can be calculated at the same time, which can help us understand the overall investment scale of users, more accurately assess users' investment capabilities and risk tolerance, and facilitate the formulation of reasonable investment strategies and fund products in the future.

[0055] Based on the historical investment records of the i-th investor Extract the maximum contract duration of the fund product in each record and mark it as... 1 to l represent that the i-th investor has l historical investment records;

[0056]

[0057] In the formula, PS represents the average contract duration. This represents the sum of the maximum contract durations of fund products in each record for the i-th investment user. The sum of the maximum contract durations divided by the total number of records yields the average contract duration (PS) for the i-th investor's purchase of a fund product. i , This means simultaneously calculating the average contract duration of fund products purchased by n investment users, clarifying the economic level and investment preferences of each investment user;

[0058] Step 5: Based on the average total assets (PZ) and average return duration (PS) of investment users, analyze and generate the matching index (Pizs) for each fund product in the product dataset. The calculation process is as follows:

[0059] Extract the fundraising information of the kth fund currently being offered in the product dataset and label it as Ck. s , will Ck s The amount raised is marked as 's' represents the contract duration of the fund product;

[0060]

[0061] In the formula, Pizs represents the matching index. This represents the ratio of the total amount raised by p fund products to the average total assets of the i-th investor. This means that the ratio of the contract duration of p fund products to the average contract duration of fund products purchased by the i-th investor is calculated simultaneously. The matching index includes the ratio of the amount raised to the average total assets and the ratio of the contract duration to the average contract duration. Based on the investor's economic level and investment preferences, each currently offered fund product is matched, and the multi-dimensional analysis of the user's investment intentions is more accurate.

[0062] Based on the ratio of fundraising amount to average total assets in the matching index Pizs, p fund products are ranked from smallest to largest. At the same time, based on the ratio of contract duration to average contract duration in the matching index Pizs, p fund products are ranked from largest to smallest. The two ranking results are then overlapped, and fund products with the same order in the two ranking results are selected to form a list of fund products that meet the economic level and investment preferences of investors, resulting in a better user experience and a higher willingness to invest in the long term.

[0063] Step Six: Based on the market dataset, extract all dynamic policies released by investment users within their investment period Q, and analyze and generate a trend data group Qssj. The calculation process is as follows:

[0064] Extract all dynamic policies published within the investment period Q from the market dataset, and label the currency exchange rates published within the investment period Q as follows: 1 to w represent w monetary policies issued during investment cycle Q, and trade policies issued during investment cycle Q are marked as... 1 to e indicates that there are e trade policies issued within the investment cycle Q, and t indicates the time point when dynamic policies are issued;

[0065]

[0066] In the formula, Qssj represents the trend data group. This indicates the latest monetary policy released at the current point in time. This indicates the monetary policy released at a point in time prior to the current point in time. This means calculating the exchange rate difference corresponding to monetary policy at two adjacent points in time within the investment period Q. This indicates the latest trade policy released at the current point in time. This indicates the trade policy released at a point in time prior to the current point in time. This means that within the investment period Q, the difference in transaction fees corresponding to two adjacent time points in the trade policy is calculated simultaneously, which helps investors choose fund products with more stable returns, resulting in a better user experience and a higher willingness to invest in the long term.

[0067] Example 1: In this experiment, an investor who has only invested in one fund product within the past 6 months was selected as the experimental subject. This investor purchased 10,000 yuan of a 6-month fixed-term fund product on January 1, 2024. The contractual interest rate for this fund product is 1.2%. The formula for calculating the investor's average total assets PZ is as follows:

[0068]

[0069] In the formula, PZ represents the average total assets. This represents the total assets of the i-th investor at the end of the fund contract after a single investment period Q, 10000 × 1.8% = 180. This represents the average total assets of the i-th investment user at the beginning of the fund contract and at the end of the fund contract within a single investment period Q. 10090 is the average total assets of this investment user.

[0070] Example 2: In this experiment, three currently offered fund products were selected as experimental products. The first fund product raised 3,000 yuan and had a contract duration of 3 months. The second fund product raised 4,000 yuan and had a contract duration of 4 months. The third fund product raised 5,000 yuan and had a contract duration of 5 months. Investors with an average total asset value of 4,000 yuan and an average contract duration of 3 months were selected as experimental subjects. The calculation process for the matching index Pizs of the three currently offered fund products is as follows:

[0071]

[0072] In the formula, Pizs represents the matching index. These represent the ratios of the total amount raised by the three fund products to the average total assets of the investor. This represents the ratio of the contract duration of the three fund products to the average contract duration of the fund products purchased by the investor.

[0073] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A big data-based intelligent evaluation method for fund products, characterized by: Includes the following steps: Step 1: Obtain fundraising information for currently offered fund products through a big data platform connected to the network, and compile the obtained data into a product dataset; Step 2: Obtain basic information of all investment users through the online fund purchase platform, and compile the obtained data into a user dataset; Step 3: Obtain all dynamic policies released in the investment market through a big data platform connected to the network, and compile the obtained data into a market dataset; Step 4: Based on the user dataset, analyze and generate corresponding investment periods Q for different investment users, and then analyze the average total assets PZ and average return duration PS of different investment users based on the investment period Q to clarify the economic level and investment preferences of each investment user. Step 5: Based on the average total assets PZ and average return duration PS of the investment users, analyze and generate the matching index Pizs for each fund product in the product dataset, and filter out a list of fund products that match the economic level and investment preferences of the investment users. Step 6: Based on the market dataset, extract all dynamic policies released by investors within their investment period Q, and analyze and generate a trend data group Qssj to help investors select fund products with more stable returns.

2. The intelligent evaluation method for fund products based on big data according to claim 1, characterized in that: In step one, the expression for the product dataset is {C1}. s C2 s C3 s ..., Cp s }, C1 s To Cp s This corresponds to the fundraising information for each fund product currently being offered for sale. The fundraising information includes the required fundraising amount, investment strategy, and rate of return. s represents the contract duration of the fund product, and 1 to p represent the number of fund products currently being offered for sale.

3. The intelligent evaluation method for fund products based on big data according to claim 2, characterized in that: In step two, the expression for the user dataset is {Y1}. d Y2 d Y3 d ...,Yn d }, Y1 d To Yp d The basic information corresponds to each investor, including total assets and historical investment records. d represents the time point when the investor's basic information was updated, and 1 to n represent the number of investors on the fund purchase platform.

4. The intelligent evaluation method for fund products based on big data according to claim 3, characterized in that: In step three, the expression for the market dataset is {S1}. t S2 t S3 t ...,Sc t }, S1 t To Sc t This corresponds to each dynamic policy released at a different point in time within the investment market. Dynamic policies include monetary policy and trade policy. Monetary policy is a means of regulating currency exchange rates, while trade policy is a means of regulating trade fees. t represents the time point at which the dynamic policy is released, and 1 to c represent the number of dynamic policies released in the investment market.

5. The intelligent evaluation method for fund products based on big data according to claim 4, characterized in that: In step four, the analysis process for the investment period Q is as follows: Extract the basic information of the i-th investor from the user dataset and label it as Yi. d Mark the total assets of the i-th investor as The historical investment records of the i-th investor are marked as follows: If the historical investment records of the i-th investor are... The data is not available in the table, and the corresponding investment period Q is assumed to be one year. If the historical investment records of the i-th investor are... There is a single record, and the investment period Q is the contract duration of the fund product in that record. If the historical investment records of the i-th investor are... There are multiple records in the data, and the maximum value of the fund product contract duration among the multiple records is taken as the corresponding investment period Q.

6. The intelligent evaluation method for fund products based on big data according to claim 5, characterized in that: In step four, the formula for calculating the average total assets PZ is as follows: In the formula, PZ represents the average total assets. This represents the total assets of the i-th investor at the end of their fund contract after a single investment period Q. The average total assets PZ of the i-th investor is the average of the total assets at the beginning of the fund contract and the total assets at the end of the fund contract within a single investment period Q. i , This indicates that the average total assets of n investment users are calculated simultaneously.

7. The intelligent evaluation method for fund products based on big data according to claim 6, characterized in that: In step four, the calculation process for the average contract duration PS is as follows: Based on the historical investment records of the i-th investor Extract the maximum contract duration of the fund product in each record and mark it as... 1 to l represent that the i-th investor has l historical investment records; In the formula, PS represents the average contract duration. This represents the sum of the maximum contract durations of fund products in each record for the i-th investment user. The sum of the maximum contract durations divided by the total number of records yields the average contract duration (PS) for the i-th investor's purchase of a fund product. i , This indicates that the average contract duration for n investment users purchasing fund products is calculated simultaneously.

8. The intelligent evaluation method for fund products based on big data according to claim 7, characterized in that: In step five, the matching index Pizs is calculated as follows: Extract the fundraising information of the kth fund currently being offered in the product dataset and label it as Ck. s , will Ck s The amount raised is marked as 's' represents the contract duration of the fund product; In the formula, Pizs represents the matching index. This represents the ratio of the total amount raised by p fund products to the average total assets of the i-th investor. This indicates that the ratio of the contract duration of p fund products to the average contract duration of fund products purchased by the i-th investor is calculated simultaneously. The matching index includes the ratio of the amount raised to the average total assets and the ratio of the contract duration to the average contract duration.

9. The intelligent evaluation method for fund products based on big data according to claim 8, characterized in that: In step five, p fund products are arranged from smallest to largest according to the ratio of the amount raised to the average total assets in the matching index Pizs. At the same time, p fund products are arranged from largest to smallest according to the ratio of the contract duration to the average contract duration in the matching index Pizs. The two arrangements are then overlapped, and fund products with the same order in the two arrangements are selected to form a list of fund products that meet the economic level and investment preferences of investors.

10. The intelligent evaluation method for fund products based on big data according to claim 9, characterized in that: In step six, the calculation process for the trend data group Qssj is as follows: Extract all dynamic policies published within the investment period Q from the market dataset, and label the currency exchange rates published within the investment period Q as follows: 1 to w represent w monetary policies issued during investment cycle Q, and trade policies issued during investment cycle Q are marked as... 1 to e indicates that there are e trade policies issued within the investment cycle Q, and t indicates the time point when dynamic policies are issued; In the formula, Qssj represents the trend data group. This indicates the latest monetary policy released at the current point in time. This indicates the monetary policy released at a point in time prior to the current point in time. This means calculating the exchange rate difference corresponding to monetary policy at two adjacent points in time within the investment period Q. This indicates the latest trade policy released at the current point in time. This indicates the trade policy released at a point in time prior to the current point in time. This indicates that within the investment cycle Q, the difference in transaction fees corresponding to two adjacent time points is calculated simultaneously.

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