TW202347230A - Method for estimating portfolio by quasi-efficient allocation and non-transitory computer-readable recording medium performing steps of setting a restriction, setting a portfolio, generating an expected payoff data set, and executing quasi-efficient allocation with an executable code - Google Patents

Abstract

This invention provides a method of estimating a portfolio by quasi-efficient allocation. An electronic apparatus uses an executable code to perform steps of setting a restriction, setting a portfolio, generating an expected payoff data set, and performing quasi-efficient allocation. A plurality of portfolios are randomly generated by an investment amount of an asset as a set in a case where a restriction condition is met, and an investment portfolio whose estimated annualized Sharpe value in the set meets an estimation condition and which is evaluated as the quasi-efficient allocation is used as a reference basis for selecting the investment portfolios. This invention further provides a non-transitory computer-readable recording medium capable of being read to perform the above method.

Description

Method for quasi-efficiency allocation estimation of investment portfolio and non-transitory computer-readable recording medium

The present invention relates to a method for estimating an investment portfolio, in particular to a method for estimating an investment portfolio with efficiency allocation, and a non-transitory computer-readable recording medium capable of executing the method.

The efficient allocation of a conventional investment portfolio is that an investment portfolio contains multiple assets (such as stocks, bonds...), assuming that the sum of the investment proportions of these multiple assets is 1 (and short selling is not used as an operation), through uniform optimization (Mean-Variance Optimization) Find the investment portfolio with the same expected return rate and the smallest risk among multiple investment portfolios. Each portfolio found has the following two characteristics: 1. When the expected return rate is fixed, Minimize risk (standard deviation). 2. Under the fixed risk (standard deviation), the expected rate of return can be maximized.

However, the proportion of each asset obtained by the uniformly optimized efficient allocation may not be an integer (that is, an odd zero number with a decimal point), which in practice results in the estimated investment amount of the investment portfolio being inconsistent with the transaction time. Due to the transaction restrictions on the incremental amount, it is impossible to directly put the efficiently configured investment portfolio into the trading market for subscription.

In addition, due to the complexity of the uniform optimization calculation process, the system's software and hardware must also meet high standards in order to execute smoothly.

Therefore, how to solve the above-mentioned problems of conventional efficiency configuration is the focus of the present invention.

In order to achieve the above purpose, the inventor devoted his mind to careful research and developed a method of estimating investment portfolios with a kind of efficient allocation and a non-transitory computer-readable recording medium. The estimated investment amount can meet the transaction conditions. The incremental amount allows the estimated investment portfolio to be directly put into the linked trading market for subscription.

The present invention provides a method for quasi-efficiency allocation estimation of investment portfolios, which is executed by an electronic device with a plurality of executable codes. The method includes setting constraints, setting investment portfolios, generating expected return data, aggregation and quasi-efficiency Configuration steps. First, select multiple assets that you want to invest in, and set a restriction condition for each asset that meets the incremental unit of amount; then set up an investment portfolio with a number of the assets, and the investment portfolio includes the investment amount of the certain assets, the investment amount of the certain assets The amount is set to comply with the restriction; and in the investment portfolio, the individual investment amounts of the certain assets correspond to a historical return information retrieved from a database to generate an expected return information, and the individual investment amounts of the certain assets are The investment weight is calculated as the proportion of the amount to the total investment. The expected return information includes at least an estimated annualized rate of return, an estimated annualized standard deviation and an estimated annualized Sharpe value; repeated through changes in an incremental amount Execute the steps of setting up the investment portfolio and generating expected return data to generate a set including a plurality of the investment portfolios; finally, the investment portfolio that meets an estimation condition based on the estimated annualized Sharpe value in the set is estimated to be Class efficiency configurator.

The invention also provides a non-transitory computer-readable recording medium that stores a plurality of executable codes so that an electronic device can execute the above method after reading and executing the executable codes.

In one embodiment, after the aggregation step, a step of calculating the slope is further included, which is to sort the multiple investment portfolios according to their estimated annualized standard deviations, so that the estimated annualized standard deviations meet The investment portfolio of the person with the first condition is an initial base point, and the estimated annualized rate of return and the estimated annualized standard deviation of the remaining investment portfolios are compared with the estimated annualized rate of return and the estimated annualized standard deviation corresponding to the initial base point. The slope calculated by normalizing the standard deviation is then sorted by magnitude to obtain an investment portfolio whose slope meets a second condition from the multiple investment portfolios, which is deemed to meet the estimation condition.

In one embodiment, after the step of calculating the slope, a loop operation step is further included, in which the slopes of the multiple investment portfolios that are lower than the second condition are eliminated to update the set, and the slopes are consistent with the second condition. The investment portfolio of the second condition is an updated base point that replaces the initial base point, and then returns to the step of calculating the slope to obtain the next investment portfolio whose slope meets the second condition.

In one embodiment, in the efficiency configuration step, the investment groups of the slopes corresponding to the initial reference point and at least one updated reference point are sorted in a high-low order. The one with the highest estimated annualized Sharpe value is deemed to meet the estimation conditions. In one embodiment, in the step of efficiency configuration, the initial reference point and at least one updated reference point are drawn to draw an efficiency frontier curve, and the estimated annualized value in the efficiency frontier curve is The one with the highest Sharpe value is deemed to meet the estimation conditions.

In one embodiment, in the loop operation step, if both the estimated annualized standard deviation and the estimated annualized rate of return are lower than the slope and meet the second condition, they are removed from the set. investment portfolio.

In one embodiment, the investment amount is an upper limit of investment amount, a lower limit of investment amount, and an incremental amount for each asset in the investment portfolio.

In one embodiment, the upper limit of the investment amount is set by input from an input interface; the lower limit of the investment amount and the incremental amount are automatically brought out based on the subscription restrictions and transaction restrictions known to each asset.

In one embodiment, the historical return data is obtained from the database by retrieving a historical return rate corresponding to each asset in the same time period in which listing times overlap.

In one embodiment, the historical return data of each asset includes annualized return rate and annualized standard deviation, as well as a correlation coefficient and covariance matrix between each asset.

In one embodiment, among the multiple investment portfolios, the investment portfolio with the lowest estimated annualized standard deviation is the one that meets the first condition, and the investment portfolio with the highest slope is the one that meets the second condition. By.

Thus, the method of estimating investment portfolios with efficiency-like allocation and the non-transitory computer-readable recording medium of the present invention can produce investment portfolio results similar to efficiency allocation, and because the investment amount matches the incremental amount at the time of transaction, the resulting The investment portfolio can be directly put into the trading market for subscription, so as to achieve more intuitive and efficient estimation results of the investment portfolio.

In order to fully understand the purpose, characteristics and effects of the present invention, the present invention is described in detail through the following specific embodiments and the accompanying drawings, as follows:

The present invention provides a method 100 for estimating investment portfolios with efficiency allocation, which is executed by an electronic device (not shown in the figure) using multiple executable codes. Please refer to Figure 1. The method 100 includes the setting of restriction conditions. 101. Portfolio setting 102, generating expected return data 103, aggregation 104, calculating slope 105, loop operation 106, and executing the steps of efficiency-like allocation 107, among which:

The step of setting the restriction 101 is to select a plurality of assets to be invested and set a restriction for each asset that meets the incremental unit of amount. The assets mentioned may be funds, stocks or bonds, but are not limited to this. The above-mentioned incremental amount unit refers to the incremental amount unit of the investment amount of the asset, which does not need to be converted or processed (such as rounding, or unconditional rounding or other integer processing), and can be directly invested in the market for subscription. In this embodiment, the multiple assets to be invested are selected from three funds, namely Fund A, Fund B and Fund C as shown in Table 1. However, the present invention is not limited to the above-mentioned funds and three funds. Table 1 (restriction setting) Fund name Maximum investment amount Minimum investment amount incremental amount Fund A 5000 1000 1000 Fund B 5000 1000 1000 Fund C 5000 1000 1000 Calculate data interval start date 2021/01/01 end date 2021/12/31 risk-free rate 1.5%

In one embodiment, the investment amount is an upper limit of investment amount, a lower limit of investment amount, and an incremental amount for each asset in the investment portfolio. In one embodiment, the upper limit of the investment amount is set by input from an input interface; the lower limit of the investment amount and the incremental amount are based on the subscription restrictions and transaction restrictions known to each asset (such as those announced by the securities exchange unit). restrictions) and automatically brought out. The upper limit of the investment amount is used to determine the maximum amount of each asset in the investment portfolio that is to be invested in the transaction (corresponding to the weight of each asset).

The step of setting up the investment portfolio 102 is to set up an investment portfolio with a number of the assets. The investment portfolio includes the investment amount of the certain assets, and the investment amount of each asset is set to comply with the restriction. For example, the investment portfolio shown in Table 1 is a combination of three funds: Fund A, Fund B, and Fund C. The investment amount includes an upper limit of investment amount of 5,000 yuan (NTD, the same below) and a lower limit of investment amount. Both are 1,000 yuan, and the incremental amount is in units of 1,000 yuan (thousand yuan). The difference in the incremental amount is used to generate a range that meets the lower limit of the investment amount and the upper limit of the investment amount, including Fund A, Fund B and Multiple investment portfolios of Fund C. For example, in an investment portfolio, the investment amounts of Fund A, Fund B, and Fund C are set to 1,000 yuan, 5,000 yuan, and 3,000 yuan respectively. At this time, the total investment is 9,000 yuan, and so on (and See group 1) in Table 5.

The step of generating expected return data 103 is to generate an expected return data by corresponding the certain assets in the investment portfolio with individual investment amounts to a historical return data retrieved from a database. The database may be Cloud server, or local server of the electronic device.

In one embodiment, the expected return information is as shown in Table 2, including an estimated annualized standard deviation, an estimated annualized Sharpe value, and an estimated annualized rate of return, where the estimated annualized Sharpe value It is calculated based on the corresponding estimated annualized standard deviation and estimated annualized rate of return. The calculation process also takes into account the risk-free interest rate, which is preset to 1.5% in this embodiment (calculation formula: Sharpe value = (rate of return - risk-free rate)/standard deviation). In this step, the investment weight is calculated based on the proportion of the individual investment amount of certain assets to the total investment.

Following the previous example, the total investment of Fund A, Fund B and Fund C is 9,000 yuan, and the investment weights of Fund A, Fund B and Fund C can be calculated as 11.11%, 55.56% and 33.33%. The calculated estimated annual The standardized deviation is 7.323, the estimated annualized return rate is 11.781, and the estimated annualized Sharpe value is 1.404 (see Group 1 in Table 2). Table 2 (expected return information) Fund A Fund B Fund C Estimated Annualized Standard Deviation Estimated annualized return Estimated Annualized Sharpe Value Group investment weight 1 11.11% 55.56% 33.33% 7.323 11.781 1.404 2 22.22% 33.33% 44.44% 5.013 8.362 1.369 3 50.00% 16.67% 33.33% 6.145 6.133 0.754 4 20.00% 40.00% 40.00% 5.739 9.407 1.378 5 45.45% 27.27% 27.27% 7.013 7.778 0.895 6 33.33% 11.11% 55.56% 3.528 4.943 0.976 7 23.08% 38.46% 38.46% 5.846 9.210 1.319 8 33.33% 33.33% 33.33% 6.297 8.553 1.120 9 71.43% 14.29% 14.29% 9.218 6.115 0.501 10 33.33% 44.44% 22.22% 8.134 10.359 1.089 11 9.09% 45.45% 45.45% 5.461 10.105 1.576 12 14.29% 14.29% 71.43% 2.266 ( minimum standard deviation) 5.130 1.602 13 71.43% 14.29% 14.29% 9.218 6.115 0.501 14 45.45% 45.45% 9.09% 10.190 10.732 0.906 15 45.45% 9.09% 45.45% 4.645 4.823 0.715 16 14.29% 71.43% 14.29% 10.875 14.415 1.188 17 44.44% 22.22% 33.33% 6.137 6.939 0.886 18 55.56% 33.33% 11.11% 9.614 8.936 0.773 19 33.33% 50.00% 16.67% 9.166 11.262 1.065 20 12.50% 25.00% 62.50% 3.137 6.840 1.702

In one embodiment, the historical return data is obtained from the database by retrieving a historical return rate corresponding to each asset in the same time period in which listing times overlap. As shown in Table 1, the same time period in which the listed listing times overlap is based on 2021/1/1~2021/12/31 as the data calculation interval, but is not limited to the time period in this example. The same time period Can be longer or shorter, as long as the time to market overlaps. As shown in Table 3, the historical return data includes the historical daily return rates of Fund A, Fund B and Fund C, but the present invention is not limited to this. Table 3 (Historical daily return rate) date Fund A Fund B Fund C 2021/1/1 0 0 0 2021/1/4 -0.106846 -1.663181 -0.26647 2021/1/5 1.215025 0.456926 -0.161113 2021/1/6 1.09799 -0.280763 0.122431 2021/1/7 0.936944 1.698374 0.109785 2021/1/8 0.854009 0.945725 0.106633 2021/1/11 -1.084632 -0.652207 -0.171646 2021/1/12 1.000864 -0.314253 -0.075836 2021/1/13 -0.548619 0.044175 0.012986 2021/1/14 0.851837 -0.624576 0.234335 2021/1/15 -1.679984 -0.487478 0.027154 2021/1/18 0.016032 0.016032 0.016032 2021/1/19 0.854172 1.03977 0.037539 2021/1/20 0.811446 1.640245 0.16065 (omitted) 2021/12/27 0.591084 1.243687 -0.106496 2021/12/28 -0.095572 -0.484715 -0.095572 2021/12/29 -0.309873 0.093317 0.151419 2021/12/30 0.010989 -0.101554 0.041611 2021/12/31 0.138944 -0.291721 -0.000138

As shown in Table 4, the historical return data of Fund A, Fund B and Fund C are based on individual historical daily returns (as shown in Table 3) and further include return rate, standard deviation, annualized return rate and annualized return rate. standard deviation. Table 4 (historical compensation data) Fund name Fund A Fund B Fund C rate of return 0.0170 0.0746 0.0102 standard deviation 0.8944 1.0007 0.2404 annualized rate of return 4.2859 18.8117 2.5627 annualized standard deviation 14.1978 15.8862 3.8158

The step of randomly generating the set 104 is to repeatedly execute the steps of setting the investment portfolio by changing the incremental amount to randomly generate a set including a plurality of the investment portfolios. In one embodiment, a computer is used to generate the plurality of investment portfolios randomly using an application program, but the invention is not limited to this. As shown in Table 5, a set of 20 investment portfolios including groups 1-20 is randomly generated. Fund A, Fund B and Fund C of each investment portfolio all meet the aforementioned upper limit of investment amount (5,000 yuan) and investment amount. The lower limit is (1,000 yuan), and the incremental amount is in units of 1,000 yuan. Table 5 (Collection of Portfolios) Fund A Fund B Fund C total investment Group Investment amount 1 1000 5000 3000 9000 2 2000 3000 4000 9000 3 3000 1000 2000 6000 4 2000 4000 4000 10000 5 5000 3000 3000 11000 6 3000 1000 5000 9000 7 3000 5000 5000 13000 8 4000 4000 4000 12000 9 5000 1000 1000 7000 10 3000 4000 2000 9000 11 1000 5000 5000 11000 12 1000 1000 5000 7000 13 5000 1000 1000 7000 14 5000 5000 1000 11000 15 5000 1000 5000 11000 16 1000 5000 1000 7000 17 4000 2000 3000 9000 18 5000 3000 1000 9000 19 2000 3000 1000 6000 20 1000 2000 5000 8000

The step of calculating the slope 105 is to sort the multiple investment portfolios according to their estimated annualized standard deviations, and use the investment portfolio whose estimated annualized standard deviation meets a first condition as an initial benchmark point. (Marked as (X1, Y1) in the coordinates in Figure 2). The investment portfolio that meets the first condition, in one embodiment, may be the investment portfolio with the lowest estimated annualized standard deviation among the multiple investment portfolios, such as the investment portfolio of Group 12 in Table 2 , its "estimated annualized standard deviation" is 2.266 and is the lowest in the set.

Continuing on from the above, and then based on the estimated annualized return and estimated annualized standard deviation of the remaining investment portfolios, the slope calculated from the estimated annualized return and estimated annualized standard deviation corresponding to the initial base point, and then Sort the slopes in order to obtain the one with the highest slope from the multiple investment portfolios, which meets a second condition. For example, in the coordinates of Figure 2, the investment portfolio to which (X2, Y2) belongs is marked. Group 12 has the highest slope of 1.963 (see Table 6 for the current estimated annualized standard deviation of 2.266, the current estimated annualized return of 5.130, and the current group's slope of 1.963).

The step of the loop operation 106 is to use the investment portfolio with a slope higher than the second condition among the plurality of investment portfolios as an updated base point (using this updated base point to replace the initial base point and become a new base point) ), and eliminate those with lower returns to update the set, and then return to the step of calculating the slope to obtain the portfolio condition with the next highest slope. Those that meet the second condition, in addition to the investment portfolio with the highest slope mentioned above, may also be those whose estimated annualized standard deviation is lower than the slope that meets the second condition, and the estimated annualized return Those whose rate is higher than the slope and meet the second condition are those with a lower return rate and will be eliminated from the set. The "current" refers to the initial benchmark point or the group corresponding to the updated benchmark point, the estimated annualized standard deviation, and the estimated annualized rate of return that serve as the basis for comparison.

For example, as shown in Table 6, based on the investment portfolios of groups 1-20, a total of five loop calculation steps of 106 are performed, and the results are respectively represented in Table 6 by point numbers 1 to 5 of the leading edge line. The point number 1 of the front line is based on the minimum estimated annualized standard deviation of group 12 as the initial reference point. The slope is then compared with other groups. Among them, group 20 has the largest slope of 1.963. Next, among groups 1 to 19, those whose estimated annualized standard deviation and estimated annualized return rate are both lower than group 20 are eliminated (that is, group 6, group 12, and group 15 are eliminated), Group 20 has the largest slope and is used as the base point for the first update. Then the slope is compared with the remaining groups after elimination. By analogy, group 11 has the largest slope and is the second update point. The base point, group 1 has the largest slope and is the third updated base point, and group 16 has the largest slope and is the fourth updated base point, thereby obtaining group 12, group 20, group The points of the five leading edge lines are numbered 1 to 5, including Category 11, Group 1, and Group 16. Table 6 (Maximum slope and group of loop operation) Point number of leading edge line 1 2 3 4 5 Current group 12 20 11 1 16 Current estimated annualized standard deviation 2.266 3.137 5.461 7.323 10.875 Current estimated annualized return 5.130 6.840 10.105 11.781 14.415 Group slope with current group 1.315 1.180 0.900 0.000 0.000 1 1.177 0.811 0.000 0.000 0.000 2 0.258 0.000 0.000 0.000 0.000 3 1.232 0.987 0.000 0.000 0.000 4 0.558 0.242 0.000 0.000 0.000 5 0.000 0.000 0.000 0.000 0.000 6 1.140 0.875 0.000 0.000 0.000 7 0.849 0.542 0.000 0.000 0.000 8 0.142 0.000 0.000 0.000 0.000 9 0.891 0.704 0.095 0.000 0.000 10 1.557 1.405 0.000 0.000 0.000 11 0.000 0.000 0.000 0.000 0.000 12 0.142 0.000 0.000 0.000 0.000 13 0.707 0.552 0.133 0.000 0.000 14 0.000 0.000 0.000 0.000 0.000 15 1.079 0.979 0.796 0.742 0.000 16 0.467 0.033 0.000 0.000 0.000 17 0.518 0.324 0.000 0.000 0.000 18 0.889 0.733 0.312 0.000 0.000 19 1.963 0.000 0.000 0.000 0.000 20 maximum slope 1.963 1.405 0.900 0.742 Maximum slope group 20 11 1 16

The step of quasi-efficiency allocation 107 is to estimate the investment portfolio as a quasi-efficiency allocator based on the estimated annualized Sharpe value in the set that meets an estimation condition. In one embodiment, in the step of efficiency configuration, the slopes corresponding to the initial reference point and at least one updated reference point are sorted, and the one with the highest estimated annualized Sharpe value is regarded as To comply with this estimation condition. According to the initial base point and the four updated base points, the corresponding group/estimated annualized Sharpe values as shown in Table 7 are group 20/1.702, group 12/1.602, group 11/1.576, respectively. Group 1/1.404, and Group 16/1.188.

In one embodiment, in the step of efficiency configuration 107, the initial reference points and four updated reference points corresponding to the aforementioned group 1, group 11, group 12, group 16, and group 20 are Based on this, a type of efficiency frontier curve is drawn (as shown in Figure 3), and the one with the highest annualized Sharpe value in this type of efficiency frontier curve, in this case, is the investment portfolio of group 20. To comply with this estimate. Table 7 (data corresponding to each group point in the class efficiency front curve) Group Estimated Annualized Standard Deviation Estimated annualized return Estimated Annualized Sharpe Value 12 2.266 5.130 1.602 20 3.137 6.840 1.702 11 5.461 10.105 1.576 1 7.323 11.781 1.404 16 10.875 14.415 1.188 risk free interest rate point 0 1.5

The present invention also provides a non-transitory computer-readable recording medium that stores a plurality of executable codes so that the electronic device can execute the above method after reading and executing the executable codes.

The algorithm of the above embodiment is listed and explained as follows: " begin //Start Initialize FundsSet.InvAmt; //Set the upper limit, lower limit, and incremental amount of the investment amount of each fund in the investment portfolio Initialize FundsSet.HisReturn; //Get the historical return data of each fund in the investment portfolio Function calFundsExpectReturnRisk(FundsSet ){ //Get the same or longer/shorter time period of the historical return data in each fund above GetCommonHisReturnLength(FundsSet.HisReturn); //Calculate the estimated annualized return and estimated annualized standard deviation of each fund CalExpectReturn (FundsSet); CalRisk(FundsSet); } for I = 0 & I < N;//Loop operation N times RandmAmtSet = RandomAmtAllocation(FundsSet); //Randomly obtain a set of investment amount allocations within the upper and lower limits of each fund's investment amount AllocationInPercentSet = AllocationInPercent(RandmAmtSet); //Based on the allocation amount of each fund mentioned above, calculate the total investment portfolio and the individual weight of each fund in the investment portfolio Portolio.ExpectReturnRisk = CalPortfolio ExpectReturnRisk(AllocationInPercentSet);//Calculate the estimated annual return of the investment portfolio based on the weight of each fund Rate and estimated annualized standard deviation PortfolioSet = AddPortofolio(Portolio);//Add the set of portfolios end forLowestRisk = SortPortfolioRisk(PortfolioSet);//Sort the estimated annualized standard deviation of each portfolio and obtain the smallest one as the initial Base point; CalPortfolioSharpRatio; (PortfolioSet);//Calculate the estimated annualized Sharpe value of all portfoliosSortPortfolioSharpRatio; (PortfolioSet);//Get the estimated annualized Sharpe value sorting through sorting and obtain the largest point IniPoint = LowestRisk;/ /The portfolio with the smallest estimated annualized standard deviation in the portfolio as the initial benchmark point N = PortfolioSet; //The number of portfolios included for I = 0 & I < N; //Loop operation N times HighSlopPortfolio = CalHighSlop(IniPoint, PortfolioSet); //Get the points of all other investment portfolios where the estimated annualized return and estimated annualized standard deviation are greater than the initial base point, calculate the slopes of these points, sort the slope values, and obtain the slopes The largest portfolio IniPoint = HighSlopPortfolio; //The point with the largest slope above is the updated base point of the portfolio PortfolioSet = PickHigherSlopExpectReturnLowerRisk(PortfolioSet); //Exclude all other lower slopes, as well as the estimated annualized standard deviation and the estimated year Convert all portfolio points with lower returns and create a new set of portfolios end forPortfolioSet = SortPortfolio (PortfolioSet); // Combine the investment portfolios corresponding to the initial base point and at least one updated base point, and annualize them according to individual estimates Sort the values of standard deviation and estimated annualized return from small to large DrawQuasiEfficientFrontier(PortfolioSet); //Draw the efficiency frontier curve FinalPortfolio = SortPortfolioSharpRatio (PortfolioSet); //Get the investment with the highest estimated annualized Sharpe value The combination becomes the optimized efficiency configuration end //End"

From the above description, it is easy to find that the characteristics of the present invention lie in the efficiency-like allocation estimation method of the investment portfolio of the present invention and the non-transitory computer-readable recording medium. Through the investment portfolio setting, the investment amount of the assets must comply with The incremental unit of amount is used as a restriction, and the expected return data is generated with reference to historical return data as the basis for estimation, and through repeated execution, the annualized Sharpe value of the investment that meets the estimation conditions is estimated in a set of randomly generated investment portfolios. The portfolio is a quasi-efficiency allocator, thereby producing an investment portfolio result that is close to efficient allocation, and since the investment amount matches the incremental amount at the time of transaction, the resulting investment portfolio can be directly put into the trading market for subscription to achieve the investment portfolio's The estimation results are more intuitive and more efficient.

The present invention has been disclosed above with preferred embodiments. However, those skilled in the art should understand that the embodiments are only used to illustrate the present invention and should not be interpreted as limiting the scope of the present invention. It should be noted that any changes and substitutions that are equivalent to this embodiment should be considered to be within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope of the patent application.

100:Method 101: Restriction setting 102: Portfolio Settings 103: Generate expected return information 104:Gathering 105: Calculate slope 106: Loop operation 107: Class efficiency configuration

Figure 1 is a method flow chart of a specific embodiment of the present invention. Figure 2 is a coordinate diagram of an investment portfolio based on an initial benchmark point according to a specific embodiment of the present invention. Figure 3 is a quasi-efficiency front curve diagram of a specific embodiment of the present invention.

100:Method

101: Restriction setting

102: Portfolio Settings

103: Generate expected return information

104:Gathering

105: Calculate slope

106: Loop operation

107: Class efficiency configuration

Claims (12)

A method for estimating investment portfolios with efficiency allocation, which is executed by an electronic device using a plurality of executable codes. The method includes: Restriction setting: Select multiple assets to invest in, and set a restriction for each asset that meets the incremental unit of amount; Investment portfolio setting: Set up an investment portfolio with a number of the assets, the investment portfolio includes the investment amount of the certain assets, and the investment amount of the certain assets is set to comply with the restriction; Generate expected return data: In the investment portfolio, the investment weight is calculated based on the proportion of the individual investment amount of the certain assets to the total investment, and the individual investment amount of the certain assets corresponds to a historical return data extracted from a database To generate an expected return information and calculate the investment weight based on the proportion of the individual investment amount of certain assets to the total investment. The expected return information at least includes an estimated annualized rate of return, an estimated annualized standard deviation and an estimated annualized standard deviation. Estimated annualized Sharpe value; Collection: Randomly generate a collection of multiple investment portfolios by repeatedly executing the steps of setting up the investment portfolio and generating expected return data by changing an incremental amount; and Class-efficiency allocation: A portfolio whose estimated annualized Sharpe value in the set meets an estimation condition is estimated to be a class-efficiency allocator. A method for estimating the efficiency allocation of investment portfolios as described in claim 1, wherein after the aggregation step, a step of calculating the slope is further included, which is sorted according to the estimated annualized standard deviation of the multiple investment portfolios. , taking the investment portfolio whose estimated annualized standard deviation meets a first condition as an initial benchmark point, and based on the estimated annualized return rate and estimated annualized standard deviation of the remaining investment portfolios corresponding to the initial benchmark point The slope calculated by the estimated annualized return rate and the estimated annualized standard deviation is then sorted by magnitude to obtain an investment portfolio whose slope meets a second condition from the multiple investment portfolios, which is regarded as meet the conditions for this estimate. The method of estimating investment portfolios with efficiency allocation as described in claim 2, wherein the step of calculating the slope further includes a step of loop calculation, if among the multiple investment portfolios, the return rate is eliminated is lower than the rate that meets the requirement. The slope of the second condition is updated by updating the set, replacing the initial base point with an investment portfolio whose slope meets the second condition, and then returning to the step of calculating the slope to obtain the next slope that meets the slope. Second condition portfolio. A method for estimating investment portfolios with efficiency allocation as described in claim 3, wherein in the step of efficiency allocation, the investment groups with slopes corresponding to the initial base point and at least one updated base point are compared with each other. Sort them, and the one with the highest estimated annualized Sharpe value is deemed to meet the estimation conditions. A method for estimating an investment portfolio with efficiency allocation as described in claim 4, wherein in the step of efficiency allocation, the initial base point and at least one updated base point are used to draw a type of efficiency front curve, with the The one with the highest estimated annualized Sharpe value in the efficiency-like front curve is deemed to meet the estimation conditions. A method for estimating investment portfolios with efficiency allocation as described in claim 5, wherein in the step of loop calculation, both the estimated annualized standard deviation and the estimated annualized rate of return are lower than the slope Those that meet the second condition are the investment portfolios that are removed from the set. The method of efficiency allocation estimation investment portfolio as described in claim 4, wherein the investment amount is an upper limit of investment amount, a lower limit of investment amount, and the incremental amount for each asset in the investment portfolio. A method for estimating investment portfolios with efficiency allocation as described in claim 7, wherein the upper limit of the investment amount is set by input from an input interface; the lower limit of the investment amount and the incremental amount are based on the knowledge of each asset. The subscription restrictions and transaction restrictions are automatically brought out. A method for estimating investment portfolios with efficiency allocation as described in claim 4, wherein the historical return data is to extract a historical return rate corresponding to each asset individually from the database in the same time period in which the listing time overlaps. obtained. A method for estimating investment portfolios with efficiency allocation as described in claim 9, wherein the historical return data of each asset includes annualized return rate and annualized standard deviation, and a correlation coefficient between each asset and Covariable matrix. A method for efficiently allocating estimated investment portfolios as described in claim 10, wherein among the multiple investment portfolios, the investment portfolio with the lowest estimated annualized standard deviation is the one that meets the first condition, and The investment portfolio with the highest slope is the one that meets the second condition. A non-transitory computer-readable recording medium that stores a plurality of executable codes so that an electronic device can perform the following steps after reading and executing the executable codes, including: Restriction setting: Select multiple assets to invest in, and set a restriction for each asset that meets the incremental unit of amount; Investment portfolio setting: Set up an investment portfolio with a number of the assets, the investment portfolio includes the investment amount of the certain assets, and the investment amount of the certain assets is set to comply with the restriction; Generate expected return data: In the investment portfolio, the investment weight is calculated based on the proportion of the individual investment amount of the certain assets to the total investment, and the individual investment amount of the certain assets corresponds to a historical return data extracted from a database To generate an expected return information and calculate the investment weight based on the proportion of the individual investment amount of certain assets to the total investment. The expected return information at least includes an estimated annualized rate of return, an estimated annualized standard deviation and an estimated annualized standard deviation. Estimated annualized Sharpe value; Collection: Randomly generate a collection of multiple investment portfolios by repeatedly executing the steps of setting up the investment portfolio and generating expected return data by changing an incremental amount; and Class-efficiency allocation: A portfolio whose estimated annualized Sharpe value in the set meets an estimation condition is estimated to be a class-efficiency allocator.

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