TWM642113U - Investment protfolio analysis system - Google Patents

Abstract

An investment portfolio analysis system includes a basic model training module, a SHAP value calculation module, an investment portfolio ratio calculation module, an integrated calculation module and a determination module. The investment portfolio ratio calculation module is configured to set environmental parameters, and calculate the environmental parameters and estimated return rate obtained by the basic model training module to obtain an investment portfolio ratio. The integrated calculation module is configured to obtain the estimated return rate of the investment portfolio, and calculate the corresponding SHAP values of the investment portfolio according to the proportion of the investment portfolio. The determination module is configured to determine the degree of influence of each of factors according to the SHAP values corresponding to factor data, so as to select a plurality of key factors.

Description

Portfolio Analysis System

This application relates to a portfolio analysis system, in particular to a system for judging key factors affecting the price of financial commodities.

There are many types of financial products, including various investment targets. For example, funds and stocks are the most common investment targets for most people. The price of financial commodities changes due to various factors, such as the rise or fall of interest rates, the cyclical supply and demand cycle of the business climate, company revenue, and even natural and man-made disasters and government policies, etc., which will cause market price fluctuations. How to predict the price trend is the most interesting topic for most investors. In order to assist investors in judging the price trend of financial commodities, various methods of predicting changes in financial commodities have also emerged accordingly, such as judging from information such as historical prices, price strength, technical aspects, fundamentals and/or chips.

However, the market of financial commodities is changing rapidly, especially the proportion of investment portfolio and potential risks are often a black box for investors. It is very difficult to accurately predict the future rise and fall of the market without knowing what changes will occur under what conditions. However, if we can grasp the key factors affecting the price, we can still judge the general direction of the rise and fall of the price of financial commodities and the current position of the boom or product cycle. Therefore, in order to find out the key factors that mainly affect the price trend among the many factors that affect the financial commodity market, and help investors to understand how the investment strategy is formulated simply and clearly, so that a relationship between investment information and investors can be established. The relationship between trust and transparency is an urgent issue that needs to be resolved.

In one technical aspect of this application, an investment portfolio analysis system is proposed, which includes a memory and a processor electrically connected to the memory, and the investment portfolio analysis system operates in an electronic device, and includes a basic model training The module is used to train a basic model with a machine learning method based on a plurality of factor data corresponding to each of a plurality of factors affecting an investment target and a historical rate of return in a preset period of time in historical prices, and obtain the corresponding The estimated return rate of the investment target. A SHAP value calculation module is used for substituting the factor data into the basic model, and executing the SHAP algorithm processing on the basic model to obtain the respective SHAP values corresponding to the factor data. An investment portfolio ratio calculation module is used to set an environmental parameter, and calculate the environmental parameter and the estimated rate of return to obtain the investment portfolio ratio of the investment portfolio including multiple investment targets. An integrated calculation module is used to calculate the estimated rate of return according to the proportion of the investment portfolio to obtain the estimated rate of return of the investment portfolio, and calculate the corresponding SHAP value of the investment portfolio according to the proportion of the investment portfolio. A judgment module, used for judging the degree of influence of each of these factors according to the SHAP value corresponding to each of the data of these factors, so as to select a plurality of key factors.

In some embodiments, the plurality of investment targets includes individual stocks, mutual funds, a first type of index stock funds (exchange-traded funds, ETFs) and/or bond ETFs, a second type of index stock funds and/or bonds Type ETF, and the listing period of the first type ETF is longer than the listing period of the second type ETF.

In some embodiments, the investment portfolio analysis system further includes a risk coefficient calculation module, which is used to calculate the risk coefficient of the investment target when the investment target is the individual stock or the second type ETF, and perform the following steps: setting The first type of ETF or weighted index is used as the reference target, and the reference return rate of the reference target is calculated. Substitute the reference rate of return into a pricing model to calculate the risk coefficient between the investment target and the reference target. Substitute the risk coefficient into the basic model to obtain the estimated rate of return corresponding to the investment target.

In some embodiments, the risk coefficient calculation module is also used to set the risk coefficient of the first type ETF to 1 when the investment target is the first type ETF.

In some embodiments, the environmental parameters set by the investment portfolio ratio calculation module are selected from the group consisting of the historical price, price volatility, estimated rate of return, and estimated confidence ratio of the investment target, and the The environmental parameters and the estimated rate of return are calculated through a default model.

In some embodiments, the investment portfolio analysis system further includes a data collection module for selecting these from the group consisting of overall economic indicators, fundamental indicators, raw material indicators, bargaining chip indicators, foreign exchange, and technical indicators. Factors and the data of the factors corresponding to the factors are collected for the basic model training module to access.

In some embodiments, the investment portfolio analysis system further includes a historical data calculation module, which is used to calculate the historical rate of return in the preset time period according to the historical price of the investment target for storage by the basic model training module. Pick.

In some embodiments, the investment portfolio analysis system further includes a database module, including an influencing factor database, a historical data database, a model database and a rate of return data database.

The following is a detailed description of the embodiments in conjunction with the accompanying drawings, but the described specific embodiments are only used to explain the present invention, not to limit the present invention, and the description of the structure and operation is not used to limit the order of its execution, any Devices with equivalent functions produced by the recombination of components are within the scope of the disclosure of the present invention.

In addition, terms such as "first" and "second" mentioned in the present invention do not represent any order, quantity or importance, but are only used to distinguish different parts, and the drawings are only used for schematic illustration.

The terms (terms) used throughout the specification and claims, unless otherwise noted, generally have the ordinary meaning of each term used in the field, in the disclosed content and in the special content. Certain terms used to describe the disclosure are discussed below or elsewhere in this specification to provide those skilled in the art with additional guidance in describing the disclosure.

The application provides an investment portfolio analysis system and method, which are used to determine one or more key factors among many factors affecting the investment portfolio of financial commodities. The investment portfolio includes multiple investment targets, and the multiple investment targets include individual stocks, mutual funds, the first type of index stock funds (exchange-traded funds, ETFs) and/or bond ETFs, and the second type of index stock funds And/or bond ETF, etc., where the first type ETF can also be called large ETF, the second type ETF can also be called small ETF, and the listing period of the first type ETF is longer than that of the second type ETF period.

First, the investment portfolio analysis method of the present application is used as an example for illustration. In some embodiments, the investment portfolio analysis method of the present application is executed by a processor. Please refer to FIG. 1 . FIG. 1 is a flowchart of an investment portfolio analysis method 100 according to an embodiment of the present application. The portfolio analysis method 100 of the present application includes steps S110-S180, etc. It should be noted that the order of the steps is not limited thereto, and the order can be changed according to actual needs. The specific steps of the portfolio analysis method 100 of the present application are as follows: Step S110: Collect multiple factor data corresponding to multiple factors affecting each investment target in the investment portfolio. Step S120: Calculate a historical rate of return in a preset period of time in the historical price of each investment target, and train a basic model for each investment target with a machine learning method. Step S130: Use the trained basic model to obtain the estimated rate of return of the corresponding investment target. Step S140: Substituting the factor data into the corresponding basic model, and performing SHAP (SHapley Additive exPlanations) algorithm processing on the basic model to obtain the respective SHAP values corresponding to the factor data. Step S150: Set environmental parameters, and calculate the environmental parameters and the estimated rate of return to obtain the investment portfolio ratio of the investment targets. Step S160: Calculate the estimated rate of return according to the proportion of the investment portfolio to obtain an estimated rate of return for the investment portfolio. Step S170: Calculate the SHAP value corresponding to the investment portfolio based on the investment portfolio ratio. Step S180: Determine the degree of influence of each of the factors according to the SHAP values corresponding to the factor data, so as to select a plurality of key factors.

In some embodiments, the investment portfolio in step S110 may be composed of multiple investment targets such as stocks and first-type ETFs. Separately collect multiple factor data corresponding to multiple factors affecting each investment target. For example, for stock product A, data corresponding to factors that may affect its price (stock price) at a certain point in time or a certain period of time are collected. Specifically, the factor data corresponding to the factors affecting the stock price can be collected automatically through the processor, and stored in the database. Alternatively, the user can also input factors and corresponding factor data to be stored in the database. The factors affecting the stock price are selected from the group consisting of overall economic indicators, fundamental indicators, raw material indicators, bargaining chip indicators, foreign exchange, and technical indicators, for example.

In some embodiments, aggregate economic indicators include, for example, the Fear Index, 10-Year Treasury Rate, 10-Year-2-Year Spread, U.S. 30-Year Mortgage Rate, U.S. High Yield Index Option-Adjusted Spread, U.S. Overall inventory, new orders for durable goods in the U.S. manufacturing sector, consumer spending on durable goods in the U.S., initial jobless claims in the U.S., non-farm payrolls in the U.S., and unemployment in the U.S. within five weeks, among others.

Fundamental indicators include, for example, announced or seasonally adjusted monthly revenue, ratio of monthly revenue to stock price, price-to-earnings ratio calculated from past financial reports, price-to-earnings ratio using announced financial reports to revenue estimates, stock price calculated from past financial reports Ratio to book value, price-to-book ratio using announced earnings and revenue estimates, cash yields on announced revenue estimates, past or upcoming dividend yields, and more.

Raw material indicators include, for example, Brent Crude Oil, WTI Crude Oil, Natural Gas, Gold, Silver, Copper, Iron, Tin, US Soybeans, US Wheat, US Corn, and the CRB Commodity Index, among others.

Chip indicators include, for example, the average number of shares held by shareholders, the proportion of shareholders holding more than 600 shares, the proportion of shareholders holding more than 1,000 shares, foreign capital buying and selling (including the average value of 5 days, 20 days, 60 days, etc.), credit trading exceeding (including 5-day, 20-day, 60-day average value), self-dealer trading super (including 5-day, 20-day, 60-day average value), financing balance (including 5-day, 20-day, 60-day average value), and Balance of securities lending (including 5-day, 20-day, 60-day average value) and so on.

Foreign exchange includes, for example, the U.S. dollar index, the U.S. dollar to Taiwan dollar exchange rate, and the Australian dollar to U.S. dollar exchange rate, etc.

Technical indicators include, for example, trading volume (including 5-day, 20-day, 60-day averages) and past prices. It should be understood that the factor data collected by the investment portfolio analysis method 100 of the present invention are not limited to the above examples, and any factors that may affect stock prices can be applied to the investment portfolio analysis method 100 disclosed in the present invention.

In some embodiments, the factor data corresponding to the factors affecting the investment target may be, for example, data such as the growth rate of each factor at a fixed time interval. For example, according to user needs, the growth rate of various factors related to the stock product A in any past period of time can be collected. In another embodiment, according to actual needs, the factor data corresponding to the factors affecting the stock price can be converted into numerical values such as log values, and the present invention does not impose any restrictions on this.

該簡單報酬率計算方式的值域為-1~+∞，數值偏重於正的方向。 In step S120, a historical rate of return in a preset period of time in the historical price of each investment target is calculated, and a basic model is trained for each investment target by a machine learning method. Specifically, the historical rate of return refers to the rate of return after buying a financial product and holding it for t days. Generally speaking, the rate of return can include simple rate of return and logarithmic rate of return. In some embodiments, the simple rate of return for entering the market on day d and holding for t days is: r =

The value range of this simple rate of return calculation method is -1~+∞, and the value is biased towards the positive direction.

該對數報酬率計算方式的值域為-∞~+∞，值域上為對稱。本申請以機器學習方法訓練該基礎模型是透過自動化機器學習。換句話說，在資料平衡的情況下，訓練效果較佳，故對數報酬率計算方式更適合使用在機器學習的報酬率預估上。例如：針對股票產品A，收集其於過去一段預設時間的報酬率。例如，投資組合分析方法100可收集股票產品A過去數年、數月、數周、或任意時間區間的歷史報酬率。較佳地，投資組合分析方法100在步驟S110中是收集前述因素資料的時間點之後一段預設時間的報酬率。舉例來說，當收集的因素資料為各影響因素於時間點T1的資料，則透過上述報酬率的計算方法計算收集時間點T1之後一段預設時間的歷史報酬率。 In some embodiments, the logarithmic return of entering the market on day d and holding for t days is: R =

The value range of the logarithmic rate of return calculation method is -∞~+∞, and the value range is symmetrical. This application uses machine learning methods to train the base model through automated machine learning. In other words, when the data is balanced, the training effect is better, so the logarithmic rate of return calculation method is more suitable for the rate of return estimation of machine learning. For example: for stock product A, collect its rate of return for a predetermined period of time in the past. For example, the investment portfolio analysis method 100 can collect the historical rate of return of the stock product A in the past years, months, weeks, or any time interval. Preferably, in the investment portfolio analysis method 100, in step S110, the rate of return for a predetermined period of time after the time point when the aforementioned factor data is collected. For example, when the collected factor data is the data of each influencing factor at time point T1, the historical rate of return for a preset period of time after the collection time point T1 is calculated through the above method of calculating the rate of return.

Next, the factor data collected in step S110 and the historical rate of return calculated in step S120 are used to train the basic model through a machine learning method. In detail, the factor data is used as the input of the training model, and the historical rate of return is used as the target of the training model, and the most suitable basic model is trained through machine learning.

In step S130, the estimated rate of return of the corresponding investment target is obtained by using the trained basic model. In some embodiments, in order to improve the accuracy of the estimated rate of return, the estimated rate of return has different calculation conditions corresponding to different types of investment targets. In some embodiments, if it is the first type of EFT (large ETF) and the listing period is longer, generally speaking, it is at least 5 years, since the training data of this type of investment target is sufficient, the historical rate of return in the above step S120 It can be used as the estimated rate of return of this large ETF. In some embodiments, if the investment target is individual stocks, mutual funds, or the second type of ETF (small ETF), because it is more likely to be affected by short-term bargaining chips, or when the listing time is short, there are few historical data and cannot be effectively trained It is more difficult to estimate the compensation when the model is produced. At this time, the estimated return rate of this type of investment target can be changed to a reference target as an indicator, such as the rate of return estimated by the market (such as the market) or a large ETF, and then based on past investment targets (such as individual stocks, mutual funds) The risk coefficient between the market and the market further predicts the estimated return rate of individual stocks and mutual funds in the future.

Please refer to FIG. 2 . FIG. 2 is a flow chart of calculating the estimated rate of return of a single investment target according to an embodiment of the present application. In some embodiments, when the investment target is individual stocks, mutual funds or second-type ETFs, the step of obtaining the estimated rate of return of the corresponding investment target using the trained basic model (S130) includes the following steps: Step S131: Set the first type of ETF or weighted index as the reference target, and calculate the reference rate of return of the reference target; Step S132: Substituting the reference rate of return into a pricing model to calculate the risk coefficient between the investment target and the reference target; and Step S134: Substituting the risk coefficient into the basic model to obtain an estimated rate of return corresponding to the investment target. Preferably, the pricing model can be a capital asset pricing model, but it is not limited thereto.

See Figure 2 continued. In some embodiments, when the investment target is a first-type ETF, since the historical data of a large ETF is sufficient, step S133 may be included in step S130: set the risk coefficient of the first-type ETF to 1. By increasing the evaluation of the above risk coefficient, the accuracy of the estimated return rate of the investment target can be effectively improved.

After the training of the basic model is completed, in step S140, the current or specific time points to be analyzed are substituted into the trained basic model, and the SHAP (SHapley Additive exPlanations) algorithm is executed with the basic model. To obtain the SHAP value corresponding to each factor data, the SHAP value indicates the degree to which each feature (ie factor data) affects the estimated rate of return, so that the predictive analysis of the basic model can achieve the effect of interpretation.

In some embodiments, the basic model trained in step S120 is substituted into the factor data corresponding to the plurality of time intervals in the preset time period corresponding to the historical rate of return stored in step S110, and based on each The time interval model executes the SHAP algorithm, so the SHAP value corresponding to each factor data in each time interval can be obtained. For example, assuming that the preset time period corresponding to the historical rate of return is several years, the preset time period can be divided into a plurality of time intervals based on annual, monthly, weekly, or daily units, for example. It should be understood that the preset time period and time interval may be adjusted according to actual needs, which are not limited in this application.

In some embodiments, the multiple SHAP values corresponding to each factor data are summed up to obtain the SHAP important value corresponding to each factor data. Preferably, the absolute values of the plurality of SHAP values corresponding to each factor data can be summed up to obtain the SHAP important value corresponding to each factor data. In some embodiments, the SHAP important value may also be further subjected to average calculation or other numerical conversion, which is not limited in the present application.

It should be noted that, from past experience, it can be found that if there is a lack of appropriate investment ratio for the investment portfolio of multiple investment targets, even if the estimated rate of return of a single investment target is obtained, the investment portfolio will still not be able to achieve the best investment effect. The investment portfolio analysis method 100 of the present application provides an investment portfolio ratio that can improve investment effects. In step S150, by setting environmental parameters and calculating the environmental parameters and the estimated rate of return, the investment portfolio ratios of the investment targets are obtained. In some embodiments, the environmental parameter is selected from the group consisting of the historical stock price, stock price volatility, estimated rate of return and estimated confidence ratio of the investment target, and the environmental parameter and the estimated rate of return are obtained through a Preset models for calculations. Preferably, the preset model may be a Black-Litterman model, but it is not limited thereto.

In step S160, the estimated rate of return of each investment target is calculated according to the proportion of the investment portfolio to obtain the estimated rate of return of the investment portfolio. It should be noted that if the estimated rate of return of a single investment target is calculated using the logarithmic rate of return, when calculating the estimated rate of return of the investment portfolio, it must first be converted back to the simple rate of return, and then the combination of weighted additions.

In step S170, after the investment portfolio ratio is obtained, the SHAP value corresponding to the investment portfolio is calculated based on the investment portfolio ratio. It should be noted that the calculation of the SHAP value corresponding to the investment portfolio is differentiated according to whether the rate of return of each investment target is simple rate of return or logarithmic rate of return. In addition, the SHAP value corresponding to this investment portfolio is the SHAP value obtained after evaluating the aforementioned risk coefficients, so it can greatly reduce the short-term chips or the short listing period of investment targets such as individual stocks, mutual funds or small ETFs that may affect the overall The degree to which a portfolio is projected to return.

Finally, in step S180, the influence degree of each of these factors is judged according to the SHAP value corresponding to each of the factor data, so as to select a plurality of key factors. Please refer to Figure 3 and Figures 4A to AE. Fig. 3 is a flow chart of selecting a plurality of key factors in one embodiment of the present application, Fig. 4A is an example diagram of the sorting results of key factors in one embodiment of the present application, and Fig. 4B is a key factor in Fig. 4A and a portfolio for one year The relationship diagram of the rate of return, Figure 4C is an example figure of the key factor sorting results of one embodiment of the present application, Figure 4D is a relationship diagram of one of the key factors of Figure 4C and a portfolio's annual rate of return, and Figure 4E is the application An example diagram of ranking results of key factors in an embodiment. The specific content of step S180 includes: Step S181: find out the factors corresponding to the highest number of accumulated SHAP values in the preset time period; Step S182: find out the factors corresponding to the SHAP values being positive at the last time point of the preset time period; and Step S183: find out the factors corresponding to negative values at the last time point of the preset time period among the SHAP values.

FIG. 4A shows an example diagram of ranking results of the top ten key factors in history according to an embodiment of the present application. In the example in FIG. 4A , the factors corresponding to the highest accumulated SHAP values in history can be found. Taking the analysis of the key factors affecting the return rate of the investment portfolio on the annual growth rate of the CRB commodity index in Figure 4A as an example, the relationship between the annual growth rate of the CRB commodity index and the return rate of this investment portfolio (Figure 4B) finds that when the annual growth rate of the CRB commodity index When it is high, the return on this investment portfolio is low. Therefore, when the investment portfolio analysis method 100 is used as an auxiliary judgment basis, it can be judged that the investment portfolio cannot resist inflation.

It should be understood that FIG. 4A is only an example of the application of the investment portfolio analysis method 100 in the embodiment of the present application. It only ranks the top ten key factors in a certain period, and does not list the current ranking of all factors, and the selection of factors , the number of key factors and the setting of time can be designed according to the actual needs of users.

In particular, the SHAP algorithm measures the degree to which each feature in the model (ie factor data) contributes positively or negatively to each forecast (ie historical rate of return). For example, when SHAP is positive, the larger the value, the greater the positive contribution, and when the SHAP is negative, the smaller the value, the greater the negative contribution. Simply put, the greater the absolute value of the SHAP value, the greater the contribution. FIG. 4C shows an example diagram of the ranking results of the top five bullish key factors according to an embodiment of the present application. Taking the factors corresponding to the positive annual growth rate of U.S. durable goods consumption expenditure in Figure 4C as an example, it is found from the relationship diagram (Figure 4D) between the annual growth rate of U.S. durable goods consumption expenditure and the return rate of this investment portfolio that when the U.S. durable goods When the annual growth rate of financial consumption expenditure is low, the return rate of this investment portfolio is high. However, due to the slowdown in the current economy, the annual growth rate of US durable goods consumption expenditure is -11%. This investment portfolio has a higher probability under the allocation of stocks and bonds rise.

FIG. 4E shows an example diagram of the ranking results of the top five bearish key factors according to an embodiment of the present application. Take the factors corresponding to the negative annual growth rate of the CRB commodity index in Figure 4E as an example. It can be seen that the annual growth rate of copper and the annual growth rate of the CRB commodity index are both factors related to inflation, and they are also factors that lead to the current investment The portfolio has a relatively negative impact. In addition, the SHAP value of the recent negative impact is smaller than the SHAP value of the positive impact, which can also indicate that this portfolio has a greater chance of rising in the near future.

As mentioned above, the investment portfolio analysis method of this application uses machine learning to obtain the estimated return rate of the investment portfolio based on the risk coefficient evaluation and the optimal investment portfolio ratio, and provides an interpretable model, which can clearly and simply show The key leading indicator factors that affect the investment portfolio provide investors with information on the advantages and disadvantages of the current investment portfolio, thereby enabling the establishment of a trustful and transparent relationship between investment information and investors.

Please refer to Figure 5, Figure 6 and Figure 7. Fig. 5 is a schematic block diagram of an investment portfolio analysis system according to an embodiment of the present application, Fig. 6 is a structure diagram of a basic model training module of one embodiment of the present application, and Fig. 7 is one of one embodiment of the present application Architecture diagram of a portfolio analysis system. As shown in Figure 5, the portfolio analysis system 200 of the present application includes a memory 201 and a processor 202 electrically connected to the memory 201, and the portfolio analysis system 200 operates on an electronic device 300 (as shown in Figure 9 , which will be described in detail later), is used to execute the investment portfolio analysis method 100 . In some embodiments, the memory 201 is used to store computer programs running on the processor 202 , that is, the processor 202 is used to execute the investment portfolio analysis method 100 . As shown in Figure 7, the investment portfolio analysis system 200 at least includes a basic model training module 210, a SHAP value calculation module 220, a portfolio ratio calculation module 230, an integrated calculation module 240 and a judgment module 250 .

In some embodiments, as shown in Figure 6, the investment portfolio analysis system 200 also includes a data collection module 211, a historical data calculation module 212, an influencing factor database 213, a historical data database 214 and a model Database 215. Specifically, the data collection module 211 is used to select these factors from the group consisting of overall economic indicators, fundamental indicators, raw material indicators, bargaining chip indicators, foreign exchange, and technical indicators and collect the respective corresponding data of these factors. These factor data are for the influencing factor database 213 to access. The historical data calculation module 212 is used for calculating the historical rate of return in the preset time period according to the historical price of the investment target, for the historical data database 214 to access. In addition, the basic model trained by the basic model training module 210 can be stored in the model database 215, wherein the model database 215 is also used to store the basic models trained according to different investment targets, or the basic model training module 210 for each One or more models trained at a point in time for future use when needed.

In some embodiments, as shown in FIG. 7 , the basic model training module 210 is used to execute, for example, steps S120 and S130 of the investment portfolio analysis method 100, based on the multiple factors corresponding to each of the multiple factors affecting an investment target Factor data and a historical rate of return for a preset period of time in historical prices, use machine learning methods to train a basic model, and obtain the estimated rate of return of the corresponding investment target, wherein the estimated rate of return is stored in a rate of return Data repository 216 .

The SHAP value calculation module 220 is used to execute, for example, step S140 of the investment portfolio analysis method 100 . Specifically, the SHAP value calculation module 220 is used to substitute these factor data into the basic model, and execute the SHAP algorithm processing on the basic model, so as to obtain the corresponding SHAP values of the respective factor data that affect the estimated rate of return. value.

Please refer to FIG. 7 , the investment portfolio ratio calculation module 230 is used to execute, for example, step S150 of the investment portfolio analysis method 100 . Specifically, the investment portfolio ratio calculation module 230 is used to set an environmental parameter, and calculate the environmental parameter and the estimated rate of return to obtain the investment portfolio ratio of the investment portfolio including multiple investment targets. In some embodiments, the environmental parameter set by the investment portfolio ratio calculation module 230 is selected from the group consisting of the historical stock price, stock price volatility, estimated rate of return, and estimated confidence ratio of the investment target, and the The environmental parameters and the estimated rate of return are calculated through a default model. Preferably, the preset model may be a Black-Litterman model, but it is not limited thereto.

Please refer to FIG. 8 . FIG. 8 shows a structure diagram of the investment portfolio analysis system 200 between the SHAP value calculation module 220 and the integration calculation module 240 corresponding to multiple investment targets. As shown in FIG. 8 , the investment portfolio analysis system 200 further includes a risk coefficient calculation module 241 for executing steps S131 , S132 , S133 and S134 of the investment portfolio analysis method 100 . In some embodiments, after obtaining the SHAP value of each investment target through the SHAP value calculation module 220, the investment portfolio analysis system 200 also applies the investment portfolio ratio obtained by the investment portfolio ratio calculation module 230 to each investment The SHAP value of the target, and increase the calculation of the risk coefficient of each investment target. In detail, the risk coefficient calculation module 241 is used to calculate the risk coefficient of the investment target when the investment target is the individual stock, mutual fund or the second type ETF, and perform the following steps: set the first type ETF or weighted Index as the reference target, and calculate the reference rate of return of the reference target; then, substitute the reference rate of return into a pricing model to calculate the risk coefficient between the investment target and the reference target; finally, substitute the risk factor into The basic model is used to obtain the estimated rate of return corresponding to the investment target. Preferably, the pricing model can be a capital asset pricing model. In particular, when the investment target is the first-type ETF, the risk coefficient calculation module 241 sets the risk coefficient of the investment target of the first-type ETF to 1.

As shown in FIG. 7 and FIG. 8 , the integrated computing module 240 is used to execute, for example, steps S160 and S170 of the investment portfolio analysis method 100 . Specifically, the integrated calculation module 240 is used to calculate the estimated rate of return according to the proportion of the investment portfolio to obtain the estimated rate of return of the investment portfolio, and calculate the corresponding SHAP value of the investment portfolio according to the proportion of the investment portfolio.

Please refer to FIG. 7 , the judgment module 250 is used to execute the step S180 of the investment portfolio analysis method 100 . Specifically, the judging module 250 is used for judging the degree of influence of each of these factors according to the SHAP values corresponding to the respective factor data, so as to select a plurality of key factors. Specifically, the judging module 250 judges the degree of influence of each of these factors according to the SHAP value corresponding to the investment portfolio calculated by the integrated computing module 240, so as to select a plurality of key factors at present or at a specific time point, or, According to the corresponding SHAP value of the investment portfolio, the degree of positive or negative contribution of each feature (ie factor data) to the estimated return rate of the investment portfolio is judged, so as to select a plurality of key factors in a specific period. For the detailed steps and flow executed by the judging module 250 , please refer to the description of the investment portfolio analysis method 100 mentioned above.

Please refer to FIG. 9 . FIG. 9 is a usage scenario diagram of the investment portfolio analysis system 200 according to an embodiment of the present application. As shown in FIG. 9 , the investment portfolio analysis system 200 can operate in an electronic device 300 (ie, a server), for example. The server 300 can be accessed by one or more user equipments. In some embodiments, the server 300 can be connected to the user equipment 310a through wired access technology, so that it can further use the investment portfolio analysis system 200 . In addition, the server 300 can also be connected to another user equipment 310b through wireless access technology, so that it can further use the investment portfolio analysis system 200 . In some embodiments, the user equipment may be a computer, a mobile phone, or various non-mobile or portable smart devices, which are not limited in this application.

In particular, the embodiments of the investment portfolio analysis system described above can be implemented as analog processors by means of programming (such as using computers, processors, etc.). In other embodiments, specialized or dedicated circuitry may be used to implement one or more of the elements, functions or elements. The term "module" or "element" as used herein is intended to include any hardware, software, logic or combination of the foregoing for implementing the functionality attributed to the module or element.

Although the embodiment of the present invention has been disclosed as above, it is not intended to limit the present invention. Anyone who is familiar with this technology can make some changes and modifications without departing from the scope of the present invention. Therefore, the protection scope of the present invention should be The scope of the attached patent application shall prevail.

100: Portfolio Analysis Methods S110, S120, S130, S140, S150, S160, S170, S180: steps S131, S132, S133, S134: steps S181, S182, S183: steps 200: Portfolio Analysis System 201: memory 202: Processor 210:Basic model training module 211: Data collection module 212:Historical data calculation module 213: Influencing Factor Database 214: Historical data database 215:Model database 216: Rate of return data database 220: SHAP value calculation module 230: Portfolio ratio calculation module 240: Integrated Computing Module 241:Risk coefficient calculation module 250: Judgment module 300: electronic device 310a, 310b: user equipment

FIG. 1 is a flowchart of a portfolio analysis method according to an embodiment of the present application. FIG. 2 is a flow chart of calculating the estimated rate of return of a single investment target according to an embodiment of the present application. FIG. 3 is a flow chart of selecting a plurality of key factors according to an embodiment of the present application. Fig. 4A is an example diagram of ranking results of key factors according to an embodiment of the present application. FIG. 4B is a graph showing the relationship between one of the key factors in FIG. 4A and the annual rate of return of an investment portfolio. Fig. 4C is an example diagram of the ranking results of key factors according to an embodiment of the present application. FIG. 4D is a relationship diagram between one of the key factors in FIG. 4C and the annual rate of return of an investment portfolio. Fig. 4E is an example diagram of the ranking results of key factors according to an embodiment of the present application. FIG. 5 is a schematic block diagram of an investment portfolio analysis system according to an embodiment of the present application. FIG. 6 is an architecture diagram of a basic model training module according to an embodiment of the present application. FIG. 7 is an architecture diagram of an investment portfolio analysis system according to an embodiment of the present application. FIG. 8 is another architecture diagram of an investment portfolio analysis system according to an embodiment of the present application. FIG. 9 is a usage scenario diagram of an investment portfolio analysis system according to an embodiment of the present application.

200: Portfolio Analysis System

210:Basic model training module

216: Rate of return data database

220: SHAP value calculation module

230: Portfolio ratio calculation module

240: Integrated Computing Module

250: Judgment module

Claims (8)

An investment portfolio analysis system includes a memory and a processor electrically connected to the memory, and operates in an electronic device, and the investment portfolio analysis system includes: A basic model training module, which is used to train a basic model with a machine learning method based on a plurality of factor data corresponding to each of a plurality of factors affecting an investment target and a historical rate of return in a preset period of time in historical prices, And obtain the estimated rate of return of the corresponding investment target; A SHAP value calculation module, which is used to substitute the factor data into the basic model, and execute the SHAP algorithm processing on the basic model to obtain the respective SHAP values corresponding to the factor data; An investment portfolio ratio calculation module, which is used to set an environmental parameter, and calculate the environmental parameter and the estimated rate of return, so as to obtain the investment portfolio ratio of the investment portfolio containing multiple investment targets; An integrated calculation module, used to calculate the estimated rate of return according to the proportion of the investment portfolio to obtain the estimated rate of return of the investment portfolio, and calculate the corresponding SHAP value of the investment portfolio according to the proportion of the investment portfolio; and A judgment module, used for judging the degree of influence of each of these factors according to the SHAP value corresponding to each of the data of these factors, so as to select a plurality of key factors. The investment portfolio analysis system as described in Claim 1, wherein the plurality of investment targets include individual stocks, mutual funds, the first type of index stock funds (exchange-traded funds, ETFs) and/or bond ETFs, and the second type of index Stock funds and/or bond ETFs, and the listing period of the first type ETF is longer than the listing period of the second type ETF. The investment portfolio analysis system as described in claim 2 further includes a risk coefficient calculation module, which is used to calculate the risk coefficient of the investment target when the investment target is the individual stock, mutual fund or the second-type ETF, and Perform the following steps: Set the first type of ETF or weighted index as the reference target, and calculate the reference return rate of the reference target; Substituting the reference rate of return into a pricing model to calculate the risk coefficient between the investment target and the reference target; and Substitute the risk coefficient into the basic model to obtain the estimated rate of return corresponding to the investment target. The investment portfolio analysis system as described in Claim 3, wherein the risk coefficient calculation module is also used to set the risk coefficient of the first type ETF to 1 when the investment target is the first type ETF. The investment portfolio analysis system as described in Claim 1, wherein the environmental parameter set by the investment portfolio ratio calculation module is selected from the historical stock price, stock price volatility, estimated rate of return and estimated confidence ratio of the investment target. The group formed, and the environmental parameter and the estimated rate of return are calculated through a default model. The investment portfolio analysis system as described in claim 1 further includes a data collection module for selecting from the group consisting of overall economic indicators, fundamental indicators, raw material indicators, bargaining chip indicators, foreign exchange, and technical indicators The factors and the factor data corresponding to the factors are collected for the basic model training module to access. The investment portfolio analysis system as described in Claim 1 further includes a historical data calculation module, which is used to calculate the historical rate of return in the preset time period according to the historical price of the investment target for the basic model training module to access. The investment portfolio analysis system as described in Claim 1 further includes a database module, including an influencing factor database, a historical data database, a model database and a rate of return data database.

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