Intelligent investment advisor system based on big data

Abstract

The invention discloses an intelligent investment adviser system based on big data, and particularly relates to the technical field of financial science and technology, which comprises an intelligent investment adviser product database and an intelligent investment adviser area division module, an intelligent investment adviser service data calling module, an intelligent investment adviser service data analysis module, an intelligent investment adviser strategy generation module, an intelligent investment adviser strategy evaluation module and an intelligent investment adviser man-machine interaction module; the intelligent investment adviser area division module takes requested investors as a target area through an intelligent investment adviser request end, and divides each investor into a plurality of service sub-areas; through the big data technology, the historical investment adviser product data of investors in each service subarea can be quickly processed, the difference between individuals can be objectively captured, customized investment adviser product information meeting the user requirements is provided, the diversified requirements of the investors are met, manual intervention is reduced, and the investment adviser service cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of financial technology, and in particular to an intelligent investment advisory system based on big data. Background Art

[0002] In today's fast-paced and complex financial environment, investment has become an important way for people to increase their wealth. However, traditional personal investment advisory services have many shortcomings in meeting the diverse needs of investors.

[0003] When it comes to analyzing investors' risk preferences, traditional methods usually rely on simple questionnaires and subjective judgments, which are unable to objectively capture investors' true and dynamic risk tolerance. In traditional models, the analysis of investment period preferences is generally based only on the short-term, medium-term or long-term investment intentions initially expressed by investors, but fails to conduct an in-depth and objective analysis of the investment periods of historical investment advisory products, making it impossible to accurately match the periods of investment products with investors' actual demand time. Regarding investment capital preferences, traditional services often only focus on the amount of funds that investors can currently invest, while ignoring their potential sources of funds and liquidity needs, which may lead to a gap in the supply of funds for investment plans or affect investors' daily life funding needs.

[0004] With the explosive growth of financial market data and the rise of big data technology, it is possible to more accurately analyze investors' risk preferences, investment period preferences, and investment capital preferences. However, there is currently a lack of effective systems that can fully utilize the advantages of big data to comprehensively, deeply, and dynamically analyze these preferences and provide customized investment advice to investors. Therefore, developing a personal intelligent investment advisory system based on big data to achieve accurate analysis of investors' multi-dimensional preferences has become an urgent need to improve investment efficiency and satisfaction. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent investment advisory system based on big data to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a big data-based intelligent investment advisory system, comprising: Smart investment advisory product database: Through big data technology, the existing public product information of the smart investment advisory server is received and updated in real time to form a smart investment advisory product database; Smart investment advisory region division module: through the smart investment advisory request end, the requested investor is taken as the target region, and each investor is divided into several service sub-regions, which are recorded as 1, 2, 3, ...i..., n in sequence, and several service sub-regions are transmitted to the target data import module; Smart investment advisory service data retrieval module: through big data technology, retrieve the data of various investment advisory products within the historical preset evaluation period of each service sub-area, and transmit the data of various investment advisory products to the smart investment advisory service data analysis module; The smart investment advisory service data analysis module is used to analyze the various investment advisory product data obtained by the data retrieval module, obtain various preference indexes within the historical preset evaluation period of investors, and transmit the various preference indexes to the smart investment advisory strategy generation module. The smart investment advisory service data analysis module includes an investment risk tolerance preference analysis unit, an investment period preference analysis unit, and an investment fund preference analysis unit; Smart investment advisory strategy generation module: used to screen investment advisory products according to the various preference indexes obtained by the data analysis module, use the final screened products as target investment advisory products, generate preset smart investment advisory information, and transmit the investment advisory results confirmed by the service sub-area according to the generated preset smart investment advisory information to the smart investment advisory strategy evaluation module; Smart investment advisor strategy evaluation module: used to evaluate the profit results after investment consulting based on the investment consulting results obtained by the strategy generation module, and transmit the evaluation results of smart investment advisor profit evaluation indicators to the smart investment advisor human-computer interaction module; Smart investment advisory human-computer interaction module: used to receive the evaluation result of the evaluation module and the smart investment advisory income evaluation index for human-computer interaction. If the evaluation result of the smart investment advisory income evaluation index is less than 0, the smart investment advisory product database will prompt investors to adjust the investment advisory product; otherwise, there will be no prompt.

[0007] Technical effects and advantages of the present invention: 1. The present invention uses big data technology to quickly process the historical investment advisory product data of investors in each service sub-region, objectively capture the differences between individuals, provide customized investment advisory product information that meets user needs, meet the diverse needs of investors, reduce manual intervention, and reduce the cost of investment advisory services; 2. The present invention uses big data analysis technology to analyze the risk tolerance preferences, investment period preferences and investment amount preferences of sub-regions, and conducts multi-dimensional analysis, which can significantly improve the accuracy of investment recommendations, enhance the dynamic optimization and adaptability of investment strategies, and improve capital allocation efficiency and risk management capabilities; 3. The present invention uses an intelligent investment advisory strategy evaluation module to quickly generate investment strategy information based on big data analysis and intelligent algorithms, monitor profit results in real time, increase the analysis of influencing factors such as investment amount and profit, adjust investment strategies in a timely manner, and reduce investment risks. Compared with traditional manual investment advisory services, it reduces service costs and benefits more investors. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0010] See also Figure 1 As shown, the present invention provides a smart investment advisory system based on big data, including a smart investment advisory product database, a smart investment advisory region division module, a smart investment advisory service data retrieval module, a smart investment advisory service data analysis module, a smart investment advisory strategy generation module, a smart investment advisory strategy evaluation module and a smart investment advisory human-computer interaction module.

[0011] The smart investment advisory area division module is connected to the smart investment advisory service data retrieval module, the smart investment advisory service data analysis module is respectively connected to the smart investment advisory service data retrieval module and the smart investment advisory strategy generation module, the smart investment advisory product database is respectively connected to the smart investment advisory service data analysis module, the smart investment advisory strategy generation module, and the smart investment advisory human-computer interaction module, and the smart investment advisory strategy evaluation module is respectively connected to the smart investment advisory strategy generation module and the smart investment advisory human-computer interaction module.

[0012] Smart investment advisory product database: Through big data technology, the existing public product information of the smart investment advisory server is received and updated in real time to form a smart investment advisory product database; What needs to be specifically explained in this implementation is that product information includes but is not limited to product category information (such as stocks, bonds, funds, etc.), key information such as the risk level, expected return, investment period, capital threshold, etc. of each type of product; and regular data maintenance and updates, including adding new product information, updating existing product information, deleting delisted products, etc. At the same time, data is regularly backed up and restored through encryption technology to ensure data security and reliability.

[0013] Smart investment advisory region division module: through the smart investment advisory request end, the requested investor is taken as the target region, and each investor is divided into several service sub-regions, which are recorded as 1, 2, 3, ...i..., n in sequence, and several service sub-regions are transmitted to the target data import module; Smart investment advisory service data retrieval module: through big data technology, retrieve the data of various investment advisory products within the historical preset evaluation period of each service sub-area, and transmit the data of various investment advisory products to the smart investment advisory service data analysis module; What needs to be specifically explained in this embodiment is that data encryption technology is used to ensure the security of various investment advisory product data within the historical preset evaluation period of each service sub-area retrieved by the smart investment advisory service data retrieval module, and prevent them from being stolen, leaked, tampered with, etc., and to ensure the authenticity and accuracy of the data.

[0014] What needs to be specifically explained in this embodiment is that various investment advisory product data include sub-regional risk tolerance preference analysis parameters, sub-regional investment period preference analysis parameters and sub-regional investment funds preference analysis parameters, wherein the sub-regional risk tolerance preference analysis parameters include a risk level type data set, the total investment amount under each risk level and the average investment amount of investment advisory products under all risk levels; the sub-regional investment period preference analysis parameters include an investment period type data set and the amount of all historical investment advisory products under the period type; the sub-regional investment funds preference analysis parameters include the investment amount of each investment advisory product under each risk level, and the average investment amount of all investment advisory products under each risk level.

[0015] The smart investment advisory service data analysis module is used to analyze the various investment advisory product data obtained by the data retrieval module, obtain various preference indexes of investors in the historical preset evaluation period, and transmit the various preference indexes to the smart investment advisory strategy generation module. The smart investment advisory service data analysis module includes an investment risk tolerance preference analysis unit, an investment period preference analysis unit, and an investment fund preference analysis unit. Obtaining various preference indexes of investors in the historical preset evaluation period includes the following steps: What needs to be specifically explained in this embodiment is that the investment risk tolerance preference analysis unit is used to process the risk tolerance preference analysis parameters, the investment period preference analysis unit is used to process the investment period preference analysis parameters, and the investment capital preference analysis unit is used to process the investment capital preference analysis parameters.

[0016] Step 1: Investment risk tolerance preference analysis unit: First, through natural language processing technology, the name of each investment advisory product in the sub-region purchase record is matched with the product name in the smart investment advisory product database, and the risk level of each investment advisory product in the purchase record is identified, and the risk level category data set RD is obtained by statistics. , Rj represents the jth risk level, k represents the number of risk levels, for example, the risk levels are R1 (low risk), R2 (medium-low risk), R3 (medium risk), R4 (medium-high risk), R5 (high risk), etc.; then, through big data technology, the total investment amount under the jth risk level is obtained and the average investment amount of investment advisory products at all risk levels , the investment risk tolerance preference index model for each risk level in each service sub-area is obtained as follows: , represents the investment risk tolerance preference index under the j-th risk level of the ith service sub-region; What needs to be specifically explained in this embodiment is that by identifying the risk level, the system can help investors better manage risks. For example, for investors with low risk tolerance, the system can recommend low-risk or medium-low-risk products to reduce potential investment losses; obtaining the investment amount at each risk level is helpful to analyze the popularity of products at different risk levels and the risk preference distribution of investors.

[0017] What needs to be specifically explained in this embodiment is R1 (low risk): the expected rate of return is relatively low, and the general annualized rate of return is around 1.5% to 2.5%, which is suitable for investors with low risk tolerance; R2 (medium-low risk): the expected rate of return is medium to low, and the general annualized rate of return is around 2.5% to 3.5%, which is suitable for investors pursuing stable returns, most conservative investors; R3 (medium risk): the expected rate of return is medium, and the annualized rate of return is generally in the range of 4% to 7%, which is suitable for investors with a certain risk tolerance; R4 (medium-high risk): the expected rate of return fluctuates widely, and is generally advertised between 6% and 15%, which is suitable for investors who are willing to bear higher risks in pursuit of higher returns; R5 (high risk): expected rate of return: the highest but also the greatest risk, the rate of return is often advertised at more than 10%, which is suitable for investors with rich investment experience and high risk tolerance.

[0018] Step 2: Investment term preference analysis unit: First, through natural language processing technology, match the name of each investment advisory product from the sub-region purchase record with the product name in the smart investment advisory product database, and identify the recommended investment term of each investment advisory product in the purchase record, and obtain the investment term type data set TD. , TI represents the first investment period, N represents the number of investment period types, such as short-term (less than 1 year), medium-term (1-5 years), long-term (more than 5 years), etc.; then, through big data technology, the amount of all historical investment advisory products under the first investment period type is obtained. , the investment term preference index model for each service sub-region and each investment term type is: , represents the investment term preference index under the I-th investment term type in the ith service sub-region, AIR represents the investment preference adjustment coefficient, which is used to measure whether investors tend to hold products longer or shorter than the expected term. represents the recommended investment period for each investment advisory product under the I-th investment period, m represents the number of historical investment advisory products under the I-th investment period, Indicates the historical investment period of each investment advisory product under the I investment period; What needs to be specifically explained in this embodiment is that based on historical data, the average investment period of all historical investment advisory products at the same risk level and the sum of the investment periods of all historical investment advisory products at all risk levels are calculated, which helps the system to build a more balanced and optimized investment portfolio for investors to achieve the best balance between risk and return; by analyzing the investment period and recommended investment period of each historical investment advisory product at the same risk level, the system can recommend investment products that better meet the needs of investors.

[0019] Step 3: Investment fund preference analysis unit: First, use big data technology to obtain the investment amount of each investment advisory product under the jth risk level in the sub-region ; Then the investment fund preference index model for each risk level in each service sub-area is obtained as follows: , represents the investment capital preference index of the jth risk level in the ith service sub-region, represents the total investment amount under the j-th risk level, and m represents the number of historical investment advisory products under the j-th risk level; What needs to be specifically explained in this embodiment is that a more reasonable asset allocation plan is formulated for the customer based on the customer's total investment amount, risk level preference and the average investment amount at each risk level.

[0020] Smart investment advisory strategy generation module: used to screen investment advisory products according to the various preference indexes obtained by the data analysis module, use the final screened products as target investment advisory products, generate preset smart investment advisory information, and transmit the investment advisory results confirmed by the service sub-area according to the generated preset smart investment advisory information to the smart investment advisory strategy evaluation module. The smart investment advisory strategy generation module includes the following steps: Step 1: Use the investment risk tolerance index as the first screening condition, select the risk level corresponding to the maximum value of the investment risk tolerance index as the target risk level of the sub-region investment advisory products, and select investment advisory products that meet the target risk tolerance level from the smart investment advisory product database and mark them as the first screening results; Step 2: First, use the investment period preference index as the second screening condition, select the recommended investment period H(t) under the investment period type corresponding to the maximum value of the investment period preference index as the target investment period, and obtain the upper limit of the investment period of the sub-region investment advisory product as H(t), and the lower limit of the investment period of the sub-region investment advisory product as L(t). , It represents the standard deviation of the investment period of all historical investment advisory products under the investment period category corresponding to the maximum value of the investment period preference index. The average investment period of all historical investment advisory products under the investment period type corresponding to the maximum investment period preference index is obtained to obtain the investment period range ; Then select the products that meet the investment period range from the first screening results Investment advisory products and marked as the second screening result; Step 3: Use the investment fund preference index as the third screening condition and select the average investment amount of all investment advisory products at the risk level corresponding to the maximum value of the investment fund preference index. is the target investment amount of the sub-region investment advisory product, and the upper limit of the investment amount of the sub-region investment advisory product is , , the lower limit of the investment amount of the sub-region investment advisory product is , , It indicates the investment amount marked as abnormal in the investor's historical purchase record at the risk level corresponding to the maximum value of the investment fund preference index. The sum of the investment amounts of all investment advisory products at the risk level corresponding to the maximum value of the investment fund preference index. Abnormal investment amounts, such as redemptions before the investment period due to emergency situations (such as emergency medical expenses, children's education expenses, etc.), are used to obtain the investment amount range. ,,Select the funds threshold that meets the investment amount range from the second screening results The investment advisory products are marked as the third screening results; Step 4: First, use big data technology to obtain the total investment amount of all risk levels in the sub-region , get the upper limit of the number of recommended smart investment advisory products If the decimal part of H(n) is greater than or equal to 0.5, round it up, otherwise round it down. For example, if the actual value of H(n) is 1.5, then H(n)=2; if the actual value of H(n) is 1.2, then H(n)=1, and the lower limit of the number of recommended smart investment advisory products L(n) is obtained. , if the decimal part of L(n) is greater than or equal to 0.5, then round up, otherwise round down; then the range of the number of recommended smart investment advisory products is , at least one recommendation number is required; finally, the third screening results are sorted according to the expected rate of return, investment advisory products that meet the recommended number range are screened out, and preset smart investment advisory information is generated. The preset smart investment advisory information includes all information disclosed by each investment advisory product in the smart investment advisory product database, such as investment advisory product name, risk level, investment period, expected return, capital threshold, etc.; Step 5: The investor receives the preset smart investment advisory information generated by the smart investment advisory server, confirms the investment advisory product, and completes the investment advisory operation; Smart investment advisor strategy evaluation module: used to evaluate the investment advisory results obtained by the strategy generation module, and transmit the evaluation result, the smart investment advisory income evaluation index RCP, to the smart investment advisory human-computer interaction module. The evaluation model is: represents the initial investment amount, represents the amount of investment income, and λ represents the factors affecting investment income, such as market fluctuations, the impact of new policies, etc.; Smart investment advisory human-computer interaction module: used to receive the evaluation result of the evaluation module and the smart investment advisory income evaluation index for human-computer interaction. If the evaluation result smart investment advisory income evaluation index is less than 0, the smart investment advisory product database will prompt investors to adjust the investment advisory product information to respond to market changes in a timely manner. The information on the adjustment of the investment advisory product includes the redemption amount, detailed public information on the replacement of the investment advisory product, the reason for the adjustment, etc.; otherwise there will be no prompt.

[0021] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart investment advisory system based on big data, characterized by: include: Smart investment advisory product database: Through big data technology, the existing public product information of the smart investment advisory server is received and updated in real time to form a smart investment advisory product database; Smart investment advisory region division module: through the smart investment advisory request end, the requested investor is taken as the target region, and each investor is divided into several service sub-regions, which are recorded as 1, 2, 3, ...i..., n in sequence, and several service sub-regions are transmitted to the target data import module; Smart investment advisory service data retrieval module: through big data technology, retrieve the data of various investment advisory products within the historical preset evaluation period of each service sub-area, and transmit the data of various investment advisory products to the smart investment advisory service data analysis module; The smart investment advisory service data analysis module is used to analyze the various investment advisory product data obtained by the data retrieval module, obtain various preference indexes within the historical preset evaluation period of investors, and transmit the various preference indexes to the smart investment advisory strategy generation module. The smart investment advisory service data analysis module includes an investment risk tolerance preference analysis unit, an investment period preference analysis unit, and an investment fund preference analysis unit; Smart investment advisory strategy generation module: used to screen investment advisory products according to the various preference indexes obtained by the data analysis module, use the final screened products as target investment advisory products, generate preset smart investment advisory information, and transmit the investment advisory results confirmed by the service sub-area according to the generated preset smart investment advisory information to the smart investment advisory strategy evaluation module; Smart investment advisor strategy evaluation module: used to evaluate the profit results after investment consulting based on the investment consulting results obtained by the strategy generation module, and transmit the evaluation results of smart investment advisor profit evaluation indicators to the smart investment advisor human-computer interaction module; Smart investment advisory human-computer interaction module: used to receive the evaluation result of the evaluation module and the smart investment advisory income evaluation index for human-computer interaction. If the evaluation result of the smart investment advisory income evaluation index is less than 0, the smart investment advisory product database will prompt investors to adjust the investment advisory product; otherwise, there will be no prompt.

2. The big data-based smart investment advisory system according to claim 1, characterized in that: The investment advisory product data in the smart investment advisory service data retrieval module includes sub-regional risk tolerance preference analysis parameters, sub-regional investment period preference analysis parameters and sub-regional investment funds preference analysis parameters, wherein the sub-regional risk tolerance preference analysis parameters include a risk level type data set, the total investment amount under each risk level and the average investment amount of investment advisory products under all risk levels; the sub-regional investment period preference analysis parameters include an investment period type data set and the amount of all historical investment advisory products under the period type; the sub-regional investment funds preference analysis parameters include the investment amount of each investment advisory product under each risk level and the average investment amount of all investment advisory products under each risk level.

3. The big data-based smart investment advisory system according to claim 1, characterized in that: The investment risk tolerance preference analysis unit in the smart investment advisory service data analysis module first matches the name of each investment advisory product from the sub-region purchase record with the product name in the smart investment advisory product database through natural language processing technology, and identifies the risk level of each investment advisory product in the purchase record, and statistically obtains the risk level category data set RD, , Rj represents the jth risk level; Then, through big data technology, the total investment amount under the jth risk level is obtained. and the average investment amount of investment advisory products at all risk levels , the investment risk tolerance preference index model for each risk level in each service sub-area is obtained as follows: , It represents the investment risk tolerance preference index under the j-th risk level of the ith service sub-area.

4. The big data-based smart investment advisory system according to claim 1, characterized in that: The smart investment advisory service data analysis module first matches the name of each investment advisory product from the sub-region purchase record with the product name in the smart investment advisory product database through natural language processing technology, and identifies the recommended investment period of each investment advisory product in the purchase record, and obtains the investment period category data set TD by statistics. , TI represents the first investment period, N represents the number of investment period types; then, through big data technology, the amount of all historical investment advisory products under the first investment period type is obtained. , the investment term preference index model for each service sub-region and each investment term type is: , represents the investment term preference index under the I-th investment term type in the ith service sub-area, AIR represents the investment term preference adjustment coefficient, , represents the recommended investment period for each investment advisory product under the I-th investment period, m represents the number of historical investment advisory products under the I-th investment period, It indicates the historical investment period of each investment advisory product under the Ith investment period.

5. The big data-based smart investment advisory system according to claim 1, characterized in that: The investment fund preference analysis unit in the smart investment advisory service data analysis module first obtains the investment amount of each investment advisory product under the jth risk level in the sub-region through big data technology. , the average investment amount of all investment advisory products under the jth risk level ; Then the investment fund preference index model for each risk level in each service sub-area is obtained as follows: , represents the investment capital preference index of the jth risk level in the ith service sub-region, represents the investment amount under the j-th risk level, and m represents the number of historical investment advisory products under the j-th risk level.

6. The big data-based smart investment advisory system according to claim 1, characterized in that: The smart investment advisor strategy generation module includes the following steps: Step 1: Use the investment risk tolerance index as the first screening condition, select the risk level corresponding to the maximum value of the investment risk tolerance index as the target risk level of the sub-region investment advisory products, and select investment advisory products that meet the target risk tolerance level from the smart investment advisory product database and mark them as the first screening results; Step 2: First, use the investment period preference index as the second screening condition, select the recommended investment period H(t) under the investment period type corresponding to the maximum value of the investment period preference index as the target investment period, and obtain the upper limit of the investment period of the sub-region investment advisory product as H(t), and the lower limit of the investment period of the sub-region investment advisory product as L(t). , It represents the standard deviation of the investment period of all historical investment advisory products under the investment period category corresponding to the maximum value of the investment period preference index. The average investment period of all historical investment advisory products under the investment period type corresponding to the maximum investment period preference index is obtained to obtain the investment period range ; Then, the investment advisory products that meet the investment period range [H(t), L(t)] are screened out from the first screening results and marked as the second screening results; Step 3: Use the investment fund preference index as the third screening condition and select the average investment amount of all investment advisory products at the risk level corresponding to the maximum value of the investment fund preference index. is the target investment amount of the sub-region investment advisory product, and the upper limit of the investment amount of the sub-region investment advisory product is , , the lower limit of the investment amount of the sub-region investment advisory product is , , It indicates the investment amount marked as abnormal in the investor's historical purchase record at the risk level corresponding to the maximum value of the investment fund preference index. The sum of the investment amounts of all investment advisory products at the risk level corresponding to the maximum value of the investment fund preference index is used to obtain the investment amount range. , select the funds threshold that meets the investment amount range from the second screening results The investment advisory products are marked as the third screening results; Step 4: First, use big data technology to obtain the total investment amount of all risk levels in the sub-region , get the upper limit of the number of recommended smart investment advisory products If the decimal part of H(n) is greater than or equal to 0.5, round it up, otherwise round it down to get the lower limit of the number of smart investment advisory product recommendations L(n). , if the decimal part of L(n) is greater than or equal to 0.5, then round up, otherwise round down; then the range of the number of recommended smart investment advisory products is , the number of recommendations is at least 1; finally, the third screening results are sorted according to the size of the expected rate of return, the investment advisory products that meet the range of the recommended number are screened out, and the preset smart investment advisory information is generated; Step 5: The investor receives the preset smart investment advisory information generated by the smart investment advisory server, confirms the investment advisory product, and completes the investment advisory operation.

7. The big data-based smart investment advisory system according to claim 1, characterized in that: The evaluation model in the smart investment advisory strategy evaluation module is: , represents the smart investment advisory income evaluation index, represents the initial investment amount, Indicates the amount of money after the investment generates income. Represents the factor affecting investment returns.

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