TWI747421B - Investment portfolio selection system - Google Patents

Abstract

An investment portfolio selection system includes a data storage device and a data processing device. The data storage device includes an ESG level database, a technical database and a fundamental database. The ESG level database stores ESG level data of multiple financial commodity stocks, and the technical database stores technical data of financial commodity stocks. The fundamental database stores fundamental data of financial commodity stocks. By analyzing the ESG degree evaluation, technical data and fundamental data, the portfolio list is selected to achieve the purpose of stable investment and risk control.

Description

Portfolio Selection System

An investment system, especially a system that filters the list of investment portfolios through analysis.

Investment has become a habit, leisure or financial management behavior in today's society. The traditional investment and financial management system is complicated and has many shortcomings, such as the inability to effectively screen to provide users with a suitable investment list and investment ratio, ineffective or limited profitability, slow crisis management or even no warning, resulting in serious financial losses for users etc.

Therefore, users mostly follow the suggestions of managers and put their own subjective intuition into the market. Under the inefficient investment operation and risk control, users suffer serious losses. Even if there is a profit, the profit margin is quite limited.

In view of this, the present invention provides an embodiment of an investment portfolio selection system, which includes a data storage device and a data processing device.

The data storage device includes ESG level database, technical data database and fundamental database. The ESG level database stores ESG level data of multiple financial commodity stocks, and the technical database stores technical data of financial commodity stocks. The face database stores the fundamental information of financial commodity stocks.

The data processing device is connected to the data storage device and includes a model scoring module, a first preselection module, a first screening module, and a processing module.

The model scoring module is based on the technical data of financial commodity stocks to obtain the scoring level of each financial commodity stock through weighted calculation and comparison; the first pre-selection module is connected to the model scoring module, and selects and stores the scoring level above the preset level and ESG The level rating is the financial commodity stocks above the preset rating; the first screening module is connected to the first pre-selection module, and the first pre-selection module screens and stores financial commodity stocks with a rating level above the limited level, and the limited level is greater than or equal to the preset rating. Set the level; the processing module is respectively connected to the model scoring module, the first preselection module and the first screening module. For the financial commodity stocks stored in the first screening module, search for a technical line data and data in the corresponding technical data Fundamental data is analyzed and compared to determine the eligible financial commodity stocks to be included in the portfolio list.

As in the aforementioned portfolio selection system, in one embodiment, the portfolio list includes the shareholding weight of each financial commodity stock.

As in the aforementioned portfolio selection system, in one embodiment, the first pre-selection module also selects and stores the ESG level evaluation based on the fundamental data of financial commodity stocks, an estimated profit data and ESG level evaluation data. Set up financial commodity stocks above the rating; the first screening module selects and stores financial commodity stocks that meet the preset conditions in the first pre-selection module based on fundamental data and a preset condition.

As in the aforementioned portfolio selection system, in one embodiment, the data processing device further includes a second preselection module and a second screening module, the second preselection module is connected to the model scoring module, and the second preselection module is based on technology Face data and fundamental data, select and store financial commodity stocks whose ESG level rating is above the default rating; the second screening module is connected to the second pre-selection module, and the second pre-selection module is screened and stored as the limit Financial commodity stocks above the level, and do not repeat the financial commodity stocks selected by the first screening module.

As in the aforementioned investment portfolio selection system, in one embodiment, the financial commodity stocks include multiple index component stocks and multiple non-index component stocks, and the number of multiple index component stocks is greater than that of non-index component stocks.

As in the aforementioned investment portfolio selection system, in one embodiment, the model scoring module scores each financial product stock after weighted calculation based on the stock price of each financial product stock for at least one period.

Like the aforementioned portfolio selection system, in one embodiment, the data processing device further includes a matching module, which is connected to the processing module, and allocates the portfolio list based on the fundamental data and technical data of the financial commodity stocks in the portfolio list The shareholding ratio of each financial commodity stock in the country.

As in the above-mentioned portfolio selection system, in one embodiment, the processing module also analyzes the stock prices and market conditions of each financial commodity stock in the portfolio list, and the stock price of the financial commodity stocks in the portfolio list has increased by a preset value greater than the market increase. When determining the range, further analyze the technical data of the financial commodity stocks in the portfolio list to decide whether to send a sell message.

As in the aforementioned portfolio selection system, in one embodiment, the processing module also analyzes the stock prices and market conditions of each financial commodity stock in the portfolio list. When one of the following conditions occurs, the processing module sends a sell message: 1 The return on financial commodity stocks in the portfolio list fell by more than 10%; 2. The fundamental information of the financial commodity stocks in the portfolio list has undergone major changes; 3. The financial commodity stocks in the portfolio list have a medium and long-term technical aspect The data showed a downward trend.

As in the aforementioned portfolio selection system, in one embodiment, when one of the following conditions occurs, the processing module again searches for the corresponding fundamental data and technical data for the financial commodity stocks in the quasi-investment portfolio database, and analyzes and compares them. To determine another portfolio list: 1. The model scoring module prompts a weak signal, which means that the score of financial commodity stocks is lower than the preset score level; 2. The stock price of financial commodity stocks in the portfolio list is in a forecast After the set period, the trend lags behind the broader market by more than 5%; 3. The medium and long-term technical data of the financial commodity stocks in the portfolio list shows a downward trend.

Through one or more embodiments of the present invention, a stable investment portfolio list can be pursued, and the investment portfolio list established by systematic analysis and adjustment of holdings can achieve an investment benefit that has a gain that is better than that of the market. In one embodiment, attention is paid to risk control. When the profit has reached a preset level (for example, the rise of financial commodity stocks in the portfolio list after a period of time is greater than 15% of the market), the system will list the portfolio After re-examining these financial commodity stocks, based on the analysis of fundamental data and technical data, the system judges that it should be sold for profit, and the system directly executes the selling step. In one embodiment, the system informs the customer (or system user) of the sale profit closing message in a reminder or suggestion way, and the customer (or system user) decides whether to sell the financial commodity stocks. . On the other hand, if the market shows a downward trend, the system will also recommend selling stop losses. On the whole, the investment made by the investment portfolio selection system of the present invention has better profits than the market. When the market shows a downward trend, its losses are less than the market. Therefore, it can effectively achieve the purpose of stable investment and risk control. .

Please refer to FIG. 1 and FIG. 2. FIG. 1 is a schematic diagram of the structure of an investment portfolio selection system 1 according to an embodiment of the present invention. FIG. 2 is a schematic diagram of the operation flow of the portfolio selection system 1 according to an embodiment of the present invention.

The portfolio selection system 1 includes a data storage device 11 and a data processing device 12. The data storage device 11 includes an ESG level database 111, a technical database 112, and a fundamental database 113. The ESG level database 111 stores ESG level data of multiple financial commodity stocks, and the technical database 112 stores financial commodity stocks. Technical data, such as K-line and moving average (five-day line, ten-day line) and other data. The fundamental database 113 stores fundamental information of financial commodity stocks, such as historical financial statements, price-to-net value ratio, yield rate, ROE, and price-to-earnings ratio.

The data processing device 12 is connected to the data storage device 11, and includes a model scoring module 121, a first preselection module 122a, a first screening module 123a, and a processing module 124.

The model scoring module 121 obtains the scoring level of each financial commodity stock based on the technical data of the financial commodity stock through weighted calculation, such as the 1st, 5th, 1 month, and 3 month price fluctuations of the financial commodity stock Perform a weighted calculation of a preset weight, and compare to obtain a scoring level. The rating scale ranges from -10 to +10 points, with a score below -7 as a weak score. +7 points or more is a strong score, the higher the score indicates that the financial commodity stock is a better investment target. In some embodiments, the model scoring module 121 scores each financial commodity stock after weighted calculation based on the stock price of each financial commodity stock for at least one period. The period is, for example, calculated in weeks, months, or in short days, such as 1 day, 5 days, and the present invention is not limited.

The first pre-selection module 122a is connected to the model scoring module 121 to perform the following step S11: select and store financial commodity stocks whose scoring level is above a preset level and whose ESG level rating is above the preset rating. The preset level is, for example, 7 points, and the preset rating is, for example, C. That is to say, in the illustrated embodiment, the first preselection module 122a will select a level of 7 or more and an ESG level rating of C or more. Stocks of financial commodities, and store them up. The ESG level evaluation is different from the past companies only assessing financial performance, but also incorporates environmental, social and corporate governance factors into investment decision-making or corporate management considerations. E (environment) refers to care for the environment, S (social responsibilty) refers to the consideration of social responsibility, and G (Corporate Governance) refers to corporate governance. At the environmental level, consideration includes aspects such as biodiversity and environmental pollution prevention and control; at the social consideration level, it may include such aspects as labor conditions, work safety, community health and safety, and relationships with stakeholders affected by the industry. Maintenance, land occupation and involuntary migration, compensation and care for local aborigines, preservation of cultural heritage, etc., and emphasize the transparency and openness of corporate governance. If the rating is A+, it can indicate that the company (financial commodity stock) is an excellent student in terms of environmental, social, and corporate governance governance conditions.

The first screening module 123a is connected to the first pre-selection module 122a, and the first screening module 123a performs the following step S12: screening and storing financial commodity stocks with a rating level above a limited level in the first pre-selecting module 122a. The level is equal to or greater than the preset level. In the above embodiment, the limit level is 9 points, then the first screening module 123a selects those financial commodity stocks selected by the first pre-selection module 122a with a score of 9 or more and an ESG level rating of C or more. Financial commodity stocks. The financial commodity stocks with a score of 7 to 8 and an ESG rating of C or higher are also stored as an observation group.

The processing module 124 is respectively connected to the model scoring module 121, the first preselection module 122a, and the first screening module 123a. The processing module 124 executes the following step S13: For the financial commodity stocks stored in the first screening module 123a (the above Financial commodity stocks with a score of 9 or more and an ESG rating of C or higher), look for the technical line data and fundamental data in the corresponding technical data, and analyze and compare to determine the eligible financial commodity stocks to be placed in the investment portfolio List L, the conditions described can be preset by the user, such as stock price, yield rate, shareholder equity (ROE), capital, price-to-earnings ratio and moving average (five-day line, ten-day line) and other conditions. After analysis and comparison, the processing module 124 lists financial commodity stocks that meet the above-mentioned preset conditions as the investment portfolio list L. Further, in some embodiments, the processing module 124 also provides suggestions on the shareholding ratio of each financial commodity stock based on the technical data and fundamental data and the conditions preset by the user, for example, in the portfolio list L Financial commodity stocks that are not constituent stocks of the index can only hold up to 1%. For example, in the portfolio list L, review the original weight of the financial commodity stocks of the index constituent stocks, based on the fundamental information and technical aspects of the financial commodity stocks. Information, and adjust its shareholding ratio in the portfolio list L. If the original weight of an A-share is 0.05%, then based on its fundamental data and technical data, the processing module 124 recommends that the A-share holding ratio in the portfolio list L be adjusted to 0.9%. In one embodiment, if the user thinks that he wants to add financial commodity stocks in the portfolio list L, he can pass the preset conditions (as described above) in the above-mentioned observation group to the processing module 124 for analysis, and select the better ones. Of financial commodity stocks are included in the portfolio list L.

Please refer to FIG. 2 again. In some embodiments, the first preselection module 122a further performs the following step S21: select and store the ESG level based on the fundamental data of the financial commodity stocks, the estimated profit data, and the evaluation data of the ESG level The evaluation is financial commodity stocks above the preset evaluation. For example, looking for financial commodity stocks that the company predicts that its profit will continue to grow, and its real value is underestimated. The estimated profit data such as the company's revenue estimated by financial analysts Growth data. The first screening module 123a also executes the following step S22: According to the fundamental data and a preset condition, the first preselection module 122a screens and stores financial commodity stocks that meet the preset conditions. The preset conditions are, for example, The selected financial commodity stocks have a clear upward trend in profit growth in the medium to long term, or are targets that have opportunities or turnarounds in response to current events. For example, when the epidemic occurs, aviation and tourism stocks are in a downturn. The above-mentioned aviation stocks and tourism stocks may be the subject of opportunities or transfers. Similarly, the processing module 124 also analyzes and compares these financial commodity stocks to classify strong scores, intermediate scores, and weak scores (please refer to the above for strong and weak scores). With technical data and fundamental data, they will match Financial commodity stocks under preset conditions are put into the portfolio list L. In this embodiment, the combination of financial commodity stocks in the investment portfolio list L can be expanded, and the user can select the real target investment according to the investment portfolio list L.

In addition, please refer to FIG. 2 again. The data processing device 12 further includes a second preselection module 122b and a second screening module 123b (as shown in FIG. 1). The second preselection module 122b is connected to the model scoring module 121, and the second The screening module 123b is connected to the second preselection module 122b. In this embodiment, the second preselection module 122b executes the following step S31: According to technical data and fundamental data, select and store financial commodity stocks whose ESG level rating is above the preset rating. The second screening module 123b executes the following step S32: screening and storing financial commodity stocks with a scoring level above a limited level in the second pre-selection module 122b, and the selected financial commodity stocks are not repeated in the first screening module 123a Selected financial commodity stocks. According to the above-mentioned selection method of steps S11-S22, some potential financial commodity stocks may be missed. In this case, the second pre-selection module 122b and the second screening module 123b have the function of supplementing the portfolio list L, for example, in the remaining Among the financial commodity stocks, the financial commodity stocks that meet the set conditions and have an ESG rating of C or higher are selected, and then the processing module 124 analyzes and compares based on fundamental data and technical data to classify financial commodities with strong and weak scores. Find suitable financial commodity stocks and include them in the portfolio list L, allowing users to have more diversified investment portfolios.

In the above, financial commodity stocks include multiple index constituent stocks and multiple non-index constituent stocks, and the number of multiple index constituent stocks is greater than that of non-index constituent stocks. In some embodiments, in step S11, the financial commodity stocks selected by the first preselection module 122a are index constituent stocks; in step S21, the financial commodity stocks selected by the first preselection module 122a are index constituent stocks and non-index constituent stocks. Constituent stocks. However, the present invention is not limited. In other embodiments, in step S11, the financial commodity stocks selected by the first preselection module 122a include index component stocks and non-index component stocks.

In one embodiment, in the portfolio list L, the ratio of the financial commodity stocks selected in steps S11-S13 to the financial commodity stocks selected in steps S21-S13 and S31-S13 is about 8:2.

Please refer to Figure 1 again. In this embodiment, the data processing device 12 further includes a matching module 125, a connection processing module 124, and allocates investment based on the technical data and fundamental data of the financial commodity stocks in the portfolio list L The shareholding ratio of each financial commodity stock in the portfolio list L. That is to say, one of the risk control functions in an embodiment of the portfolio selection system 1 of the present invention is based on the fundamental data and technical data of the financial commodity stocks in the portfolio list L, and based on the current investment Combining the comprehensive benefits of L investment in the portfolio list, users are advised to adjust the proportions so as to obtain greater benefits, or to quickly stop losses and reduce losses. In some embodiments, the user can set the shareholding ratio by himself. After the setting, the ratio module 125 performs analysis according to the user's setting, and then gives adjustment suggestions. In other words, the proportioning module 125 has the function of optimizing the shareholding ratio and can flexibly adjust the shareholding ratio to achieve the purpose of stable investment. For example, in the above, for example, the proportioning module 125 limits the holding of the financial commodity stocks of the non-index constituent stocks in the portfolio list L to a maximum of 1%, and the original weight of the financial commodity stocks of the index constituent stocks is viewed in the portfolio list L According to the fundamental data and technical data of the financial commodity stock, adjust its shareholding ratio in the portfolio list L. If the original weight of a B-share is 0.06%, based on its fundamental data and technical data, the matching module 125 recommends that the B-share holding ratio in the portfolio list L be adjusted to 0.1%. In some embodiments, there is an adjustable threshold for increasing the shareholding ratio suggested by the ratio module 125.

In addition, one of the risk control functions in an embodiment of the portfolio selection system 1 of the present invention is that the processing module 124 analyzes the stock prices and market conditions of each financial commodity stock in the portfolio list L, and treats them as the portfolio When the stock price of the financial commodity stocks in list L has increased by a predetermined rate greater than the market gain, for example, it has reached 10% or 15%, further analyze the technical data of the financial commodity stocks in the portfolio list L and decide whether to issue after cross-comparison A sell message prompts the user to make a profit. In some cases, the portfolio selection system 1 will force the sale of these financial commodity stocks that meet the above-mentioned profitability conditions.

Under the risk control of loss, the processing module 124 also analyzes the stock prices and market conditions of each financial commodity stock in the portfolio list L. When one of the following conditions occurs, the processing module 124 sends a sell message: 1. Portfolio list The return on financial commodity stocks in L has dropped by more than 10%; 2. There are major news about the financial commodity stocks in the portfolio list L, such as the company's financial report loss, or changes in company personnel, or changes in company shareholder holdings, etc. News; 3. The medium and long-term technical data of the financial commodity stocks in the portfolio list L show a downward trend. At this time, the processing module 124 will remind the user that the financial commodity stock should be sold to stop the loss. In some cases, the portfolio selection system 1 will force the sale of the financial commodity stocks to prevent losses from continuing to expand.

On the other hand, when one of the following conditions occurs, the processing module 124 searches for the corresponding technical data and the fundamental data for the financial commodity stocks in the quasi-investment portfolio database again, and performs analysis and comparison to determine another portfolio list L: 1. The model scoring module 121 prompts a weak signal, which means that the score of the financial commodity stock is lower than the preset score level, for example, when it is lower than -7 minutes; 2. The stock price of the financial commodity stock in the portfolio list L After a preset period (for example, 30 days), the trend lags behind the broader market by more than 5%; 3. The medium and long-term technical data of the financial commodity stocks in the portfolio list L show a downward trend. In other words, the processing module 124 will adjust the list in the investment portfolio, which may include buying new financial commodity stocks, selling loss-making financial commodity stocks, or adjusting the shareholding ratio.

The portfolio selection system 1 of the present invention, in one embodiment, can be applied to personal computer devices, such as desktop computers, notebook computers, or tablet computers. For example, the data storage device 11 is a physical hard disk or a cloud hard disk, and the data processing device 12 is a computing device that executes software to implement the aforementioned model scoring module 121, the first preselection module 122a, and the second preselection module 122b. , The first screening module 123a, the second screening module 123b, the processing module 124 and the proportioning module 125. The data processing device 12 and the data storage device 11 can be integrated into a body or separately arranged. In another embodiment, the portfolio selection system 1 can be applied to a mobile device, such as a smart phone. The data processing device 12 is arranged in the body, and the data storage device 11 can be connected in a cloud remote connection mode, and is operated by an application of a smart phone to execute the portfolio selection system 1. In another embodiment, the data storage device 11 and the data processing device 12 can also be applied to a cloud server, which is connected by a user's computer equipment, such as a desktop computer, a notebook computer, a tablet computer, or a smart phone. Cloud server use. In other words, the present invention does not limit the application field.

Through one or more embodiments of the present invention, a stable investment portfolio list L can be pursued, and the investment portfolio list L established by the analysis of the portfolio selection system and adjustment of the holdings can achieve an investment benefit that has a growth rate that is better than that of the market. In one embodiment, attention is paid to risk control. When the profit has reached a predetermined level (for example, the increase of financial commodity stocks in the portfolio list after a period of time is greater than 15% of the market), the portfolio selection system will After re-examining these financial commodity stocks in the portfolio list, based on the analysis of fundamental data and technical data, the portfolio selection system determines that it should be sold for profit, and the portfolio selection system directly executes the selling step. In one embodiment, the portfolio selection system informs the customer (or system user) of the sale profit closing message in a reminder or suggestion way, and the customer (or system user) decides whether to sell the products. Financial commodity stocks. On the other hand, when the market is showing a downward trend, the portfolio selection system will also recommend a sell stop loss. On the whole, the investment made by the investment portfolio selection system of the present invention has better profits than the market. When the market shows a downward trend, its losses are less than the market. Therefore, it can effectively achieve the purpose of stable investment and risk control. .

1: Portfolio selection system 11: Data storage device 111: ESG Level Database 112: Technical database 113: Fundamental Database 12: Data processing device 121: Model Scoring Module 122a: The first preselected module 122b: The second preselection module 123a: The first screening module 123b: The second screening module 124: Processing Module 125: Proportioning module S11-S31: steps S12-S32: steps S13: steps L: Portfolio list

[Figure 1] is a schematic diagram of the architecture of an investment portfolio selection system according to an embodiment of the present invention. [Figure 2] is a schematic diagram of the operation flow of the portfolio selection system of an embodiment of the present invention.

1: Portfolio selection system

11: Data storage device

111: ESG Level Database

112: Technical database

113: Fundamental Database

12: Data processing device

121: Model Scoring Module

122a: The first preselected module

122b: The second preselection module

123a: The first screening module

123b: The second screening module

124: Processing Module

125: Proportioning module

Claims (10)

A portfolio selection system that includes: A data storage device, including an ESG level database, a technical database, and a fundamental database. The ESG level database stores ESG level data of multiple financial commodity stocks, and the technical database stores these A technical aspect of financial commodity stocks, and the fundamental database stores a fundamental aspect of such financial commodity stocks; and A data processing device, connected to the data storage device, and containing: A model scoring module, based on the technical data of the financial commodity stocks, to obtain a scoring level for each financial commodity stock through weighted calculation and comparison; A first pre-selection module, connected to the model scoring module, selects and stores financial commodity stocks whose scoring level is above a preset level and the ESG level rating is above a preset rating; A first screening module, connected to the first pre-selection module, in the first pre-selection module, to screen and store financial commodity stocks with a rating level above a limited level, and the limited level is greater than or equal to the preset level; and A processing module is respectively connected to the model scoring module, the first preselection module and the first screening module, and for the financial commodity stocks stored in the first screening module, find the corresponding technical data in the A technical line data and the fundamental data are analyzed and compared to determine the eligible financial commodity stocks to be included in a portfolio list. The investment portfolio selection system according to claim 1, wherein the list of investment portfolios includes the shareholding weight of each of the financial commodity stocks. The portfolio selection system according to claim 1, wherein the first pre-selection module also selects and stores ESG levels based on the fundamental data of the financial commodity stocks, an estimated profit data and ESG level evaluation data The rating is financial commodity stocks above the preset rating; the first screening module screens and stores financial commodity stocks that meet the preset condition in the first preselection module based on the fundamental data and a preset condition. The portfolio selection system according to claim 3, wherein the data processing device further includes a second preselection module and a second screening module, the second preselection module is connected to the model scoring module, and the second preselection Based on the technical data and the fundamental data, the module selects and stores financial commodity stocks whose ESG level rating is higher than the preset rating; the second screening module is connected to the second pre-selected module, and the second pre-selected model The group screens and stores financial commodity stocks whose scoring grade is above the limited grade, and does not repeat the financial commodity stocks screened out by the first screening module. The investment portfolio selection system according to claim 1, wherein the financial commodity stocks include multiple index constituent stocks and multiple non-index constituent stocks, and the number of the multiple index constituent stocks is greater than the non-index constituent stocks. The investment portfolio selection system according to claim 1, wherein the model scoring module scores each financial commodity stock based on the stock price of each financial commodity stock for at least one period day after weighted calculation. The portfolio selection system of claim 1, wherein the data processing device further includes a matching module connected to the processing module, based on the fundamental data and the technical data of the financial commodity stocks in the portfolio list , Allocate the shareholding ratio of each financial commodity stock in the investment portfolio list. The investment portfolio selection system according to claim 1, wherein the processing module also analyzes the stock price and market conditions of each financial commodity stock in the investment portfolio list, and the stock price of the financial commodity stock in the investment portfolio list has increased more than the market When the increase is a preset range, further analyze the technical data of the financial commodity stocks in the portfolio list to decide whether to send a sell message. The investment portfolio selection system according to claim 1, wherein the processing module also analyzes the stock price and market conditions of each financial commodity stock in the investment portfolio list, and when one of the following conditions occurs, the processing module issues a sell message: 1. The return rate of the financial commodity stocks in the investment portfolio list has fallen by more than 10%; 2. There are important news on the fundamental information of the financial commodity stock in the portfolio list; and 3. The medium and long-term technical data of the financial commodity stock in the investment portfolio list shows a downward trend. The investment portfolio selection system according to claim 1, wherein the processing module, when one of the following conditions occurs, again searches for the corresponding technical data and the fundamentals of the financial commodity stocks screened by the first screening module Data, analyze and compare to determine another portfolio list: 1. The model scoring module prompts a weak signal, which means that the score of the financial commodity stock is lower than a preset score level; 2. The stock price of the financial commodity stock in the portfolio list lags behind the market by more than 5% after a predetermined period; and 3. The medium and long-term technical data of the financial commodity stock in the portfolio list shows a downward trend.

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