JP2005141694A - Portfolio creation device and method, and program for creating portfolio - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To allow an investor to easily select an actual fund (investment destination commodity) so as to satisfy a fund distribution rate following a portfolio and to allow the investor to grasp an investment amount. <P>SOLUTION: In steps 31 and 32, inputs (investment stance) of an operation period, an initial investment amount, an investment amount for every month, a target amount and risk tolerance are received. In a step 33, one of model portfolios (recommended portfolio) is selected out of a plurality of previously prepared model portfolios based on the investment stance. A transfer rate to the fund (investment destination commodity) satisfying the investment distribution rate to the investment object of the recommended model portfolio is calculated. In step 34, a custom portfolio including an investment distribution rate for every fund is created. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a portfolio creation apparatus and method, and a portfolio creation program.

  Uncertainty is generally associated with future returns from investments. For this reason, instead of making all investments in a single investment target (asset class), diversification of investment targets is used to diversify risks.

  In Patent Document 1, asset allocation is performed based on a plurality of questions prepared in advance and their answers (in Patent Document 1, the respective allocation rates for the four categories of cashability, stability, long-term asset formation, and profitability) And a portfolio construction support device that distributes the input asset management amount based on the determined asset allocation.

  The target of investor (investor) asset management may be the future profit, that is, the investment result (achieved amount) at a certain time in the future. The portfolio construction support device described in Patent Document 1 can determine the allocation destination (classification) of the investor's on-hand amount (asset management amount), but realizes future profits (target, target achievement amount) To determine what investment targets (asset classes) should be allocated to assets (eg domestic stocks, domestic bonds, foreign stocks, foreign bonds, money (deposits, savings)) Can not.

Even if the allocation rate of funds for the investment target (asset class) is determined, it is difficult to purchase (invest funds) according to the determined allocation rate. This is because an actual fund (invested product) may invest by distributing the invested amount among a plurality of investment targets. For this reason, even if the allocation rate of funds for multiple investment targets is determined, the actual fund (investment product) is selected to match the determined allocation rate (preferably matched), and the investment amount is determined. It is not easy for investors to do.
JP 2003-223555 A

  An object of the present invention is to determine a fund allocation rate for a plurality of investment objects by paying attention to an operation result (achieved amount) for an investment at a certain future time (time).

  In addition, the present invention allows an investor to easily select a fund (investment product) so as to follow a fund allocation ratio according to the portfolio, and to easily grasp the investment amount for each fund. The purpose is to do.

  The portfolio creation device according to the present invention comprises a plurality of model portfolio data storage means for storing a plurality of model portfolio data in which an investment distribution ratio for each of a plurality of investment objects is determined for each of a plurality of model portfolios. Fund data storage means for storing a plurality of fund data in which an investment distribution ratio is determined for each of a plurality of investment objects, an investment stance data input means for receiving input of data representing an investment stance, Based on the investment stance data input from the investment stance data input means from among the plurality of model portfolio data stored in the model portfolio data storage means, any one of the model portfolio data Data The above-mentioned fund data so as to approach the investment allocation rate for each of a plurality of investment objects included in the model portfolio data selection means to be determined and the model portfolio data selected by the model portfolio data selection means. A fund investment distribution rate correcting means for correcting an investment distribution rate for each of a plurality of investment objects for each of a plurality of fund data stored in the storage means is provided.

  According to the portfolio creation method of the present invention, for each of a plurality of model portfolios, a plurality of model portfolio data in which an investment allocation ratio for each of a plurality of investment targets is determined is stored in a model portfolio data storage device. In addition, for each of a plurality of funds, a plurality of fund data in which an investment allocation ratio for each of a plurality of investment targets is determined is stored in a fund data storage device, and data indicating an investment stance is input as an investment stance. -Accept from the data input device, and based on the data representing the investment stance accepted by the model portfolio data selection device, select one of the above model portfolio data from the model portfolio data. Select and fund investment Investment to each of the plurality of investment objects for each of the plurality of fund data so as to approach the investment distribution ratio for each of the plurality of investment objects included in the selected model portfolio data by the allocation rate correction device. The distribution rate is corrected.

  The plurality of model portfolio data includes data each representing a fund allocation rate for a plurality of investment objects (asset classes). An investment target (asset class) is an investment management target such as Japanese equities, Japanese bonds, foreign equities, foreign bonds, or money (deposits, savings). Different model portfolio data have different funding ratios. The fund allocation ratio for a plurality of investment objects included in the model portfolio data is, for example, indicated by any numerical value from 0% to 100%, and the sum is 100%.

  The plurality of fund data includes data each representing a fund allocation rate for a plurality of investment objects (asset classes). A fund means an investee product. The fund allocation rate for multiple investment targets (asset classes) included in the fund data is the funds (investment money) that the fund (invested product) has received from the investor. This indicates the ratio (investment) (for example, special account) in which investment target (asset class) is invested. The fund allocation rate for a plurality of investment objects included in the fund data is also indicated by any numerical value from 0% to 100%, for example, and the sum is 100%.

  One of the model portfolio data is selected from a plurality of model portfolio data based on the data representing the investment stance that is input and accepted (the model having the selected model portfolio data). (The portfolio is hereinafter referred to as the recommended model portfolio.)

  The selection of the recommended model portfolio is performed based on data representing the investment stance that is input and accepted as described above.

  In one embodiment, the model portfolio data storage means includes data representing a standard deviation and an expected rate of return for each of the model portfolios, and represents the investment stance input from the investment stance data input means. The data includes data representing risk tolerance for investment, and the model portfolio data selection means calculates the standard deviation and the expected rate of return of the model portfolio stored in the model portfolio data storage means. The model portfolio to be selected is selected based on the probability for the rate of return for each model portfolio obtained based on the data to represent and the risk tolerance data input from the investment stance data input means. Narrow down candidates. A recommended model portfolio is selected from the narrowed model portfolio.

  The risk tolerance is input, for example, by displaying a question sentence and a plurality of selected answer branches for the question sentence on the display screen and accepting an answer selected from the displayed plurality of selected answer branches. can do.

  Further, the data representing the investment stance inputted from the investment stance data input means further includes data representing an operation period, an initial investment amount, a monthly investment amount and a target amount. For each model portfolio, based on data representing the monthly investment amount, data representing the standard deviation and expected return for each model portfolio stored in the model portfolio data storage means, and a random number generated. In addition, data representing the expected profit amount during the operation period may be calculated. The model / portfolio selection means determines the investment stance based on the data representing the expected earnings during the investment period for each model portfolio calculated by the first expected earnings calculation means and the data representing the target price. Based on this, one of the model portfolio data is selected. In any case, the selected model portfolio data is selected in accordance with the investor's investment stance that is input and accepted.

  For the recommended model portfolio, as described above, the investment allocation ratio for each of the plurality of investment targets is determined. The investment allocation ratio for the investment object for each of the plurality of fund data stored in the fund data storage means is modified so as to approach the asset allocation ratio for the plurality of investment objects for the recommended model portfolio.

  Each of the plurality of fund data has an investment allocation ratio for each of the plurality of investment objects as described above. The investment allocation ratio (provision rate) for each of the multiple investment targets for each of the revised fund data allows the sum of the investment allocation ratios for each of the multiple fund data investment targets to be calculated for the recommended model portfolio. If the investment allocation rate for each corresponding investment target is the same, the recommended model is to allocate (invest) investment funds to the fund according to the investment allocation rate for multiple investment targets for each such fund data. • Investment allocation that matches the portfolio (this is the closest approach).

  To modify the investment allocation ratio (proportion ratio) for each of multiple investment targets for each of the fund data to approximate the asset allocation ratio for multiple investment targets for the recommended model portfolio, for example, the fund -The investment allocation rate (transfer rate: fund allocation rate) for each piece of data is provisionally determined so that the total is 100%, and the investment allocation rate (fund allocation rate) for each provisionally determined fund data ), The investment distribution ratio for each investment object for each fund stored in the fund data storage means is corrected while maintaining the ratio, and the investment for each investment object for each revised fund is corrected. Calculate the sum of the allocation ratio for each investment target (this is called a custom portfolio) To calculate the magnitude of the difference between the non-portfolio with the recommended model portfolio (distance) (the sum of the squares of the differences). Furthermore, a custom portfolio based on other transfer ratios (fund allocation ratio) is created, and the magnitude (distance) of the difference from the recommended model portfolio is calculated. This process can be repeated by a predetermined number of times, and a transfer ratio (fund allocation ratio) according to a custom portfolio with the smallest difference (distance) can be obtained.

  The transfer rate (fund allocation rate) is repeatedly determined so that the magnitude (distance) (sum of the squares of the differences) of the difference between the custom portfolio and the recommended model portfolio gradually approaches. By repeating the above process multiple times (many times), the custom portfolio has an investment allocation ratio close to (or consistent with) the recommended model portfolio.

  As described above, according to the present invention, any one model portfolio (recommended portfolio) is selected from a plurality of model portfolios in line with the investor's stance toward investment, and is determined as the selected recommended portfolio. The investment allocation ratio for each investment object for each of the funds is modified so as to conform to the investment allocation ratio for each of the plurality of investment objects. Since the fund allocation rate for multiple investment targets for multiple funds (invested products) is determined to be in line with the recommended portfolio, it will be decided to invest along any of the recommended portfolios. It is possible to show to investors in an easy-to-understand manner whether the amount will be allocated.

  Preferably, based on the investment distribution ratio for each investment object for each of the data representing the initial investment amount and the monthly investment amount and the plurality of fund data corrected by the fund investment distribution ratio correcting means, Fund allocation amount calculating means for calculating the initial investment amount allocation amount and the monthly investment amount allocation amount for each of the funds, and the initial investment amount for the plurality of funds calculated by the fund allocation amount calculating means. Display data creating means for creating data for displaying the allocated amount and the allocated amount for the monthly investment amount on the display screen of the display device is further provided. Since the initial investment amount allocation to the fund and the monthly investment amount allocation are shown, it is easy to show the investor how and how much the fund (investment product) should be purchased. it can.

  In another embodiment, a custom represented by the sum of each investment target for each of a plurality of investment objects for each of a plurality of fund data corrected by the fund investment distribution ratio correcting means. -For the portfolio, the standard deviation and expected rate of return calculation means for calculating the standard deviation and expected rate of return, and the standard deviation and expected rate of return of the custom portfolio calculated by the standard deviation and expected rate of return calculation means are input. And a second expected income calculation means for calculating data representing the expected income during the operation period based on the data representing the operation period, initial investment amount and monthly investment amount, and the generated random number. It is done.

  Preferably, display data for displaying a graph representing the expected earnings during the management period is generated based on the data representing the expected earnings during the management period calculated by the second expected earnings calculation means. A graph display data creation means is provided. It is possible to show the expected amount of revenue during the management period based on the custom portfolio in an easy-to-understand manner for investors.

  In another embodiment, the calculation of the data representing the expected profit amount during the operation period by the second expected profit amount calculating means is repeated at least three times, and the operation is successfully performed from the data representing a plurality of expected profit amounts. In the case of the average, the case where the operation fails, the determination means for determining the data representing the expected revenue during the operation period, and the average if the operation is successful determined by the determination means. The expected revenue during the operation period for each successful case, average case, and unsuccessful operation based on data representing the expected return when the operation fails and when the operation fails Is further provided with graph display data creation means for creating data for displaying a graph representing the above on the display screen of the display device. Since the second expected earnings calculation means is calculated by using the generated random number, the calculated expected earnings will be different for each calculation. From the different expected earnings, the expected earnings for successful operations, average cases, and failed operations are determined. This makes it easy to understand investment decisions made by investors.

  The present invention also provides a program suitable for controlling the portfolio creating apparatus.

  Another portfolio creation device according to the present invention is a model portfolio data storage means for storing a plurality of model portfolio data in which an investment allocation ratio for each of a plurality of investment objects is determined for each of a plurality of model portfolios. , Fund data storage means for storing a plurality of fund data for each of a plurality of funds, each of which has an investment allocation ratio for each of a plurality of investment objects, data representing an investor's risk tolerance, and an investor's investment Investment stance data input means for receiving input of data representing an investment stance including desired data, and the investment stance data input means from among a plurality of model portfolio data stored in the model portfolio data storage means Investment input from Model portfolio data selection means for selecting any one model portfolio data based on risk tolerance data and investment desire data included in the transaction data, and the above model portfolio data selection means Investment allocation ratio for each of the plurality of investment objects included in the selected model portfolio data and investment for each of the plurality of investment objects for each of the plurality of fund data stored in the fund data storage means Fund investment allocation that corrects the investment allocation ratio for each of the plurality of investment objects for each of the plurality of fund data stored in the fund data storage means so that the distance of the sum of the allocation ratios for each investment object approaches Specially equipped with rate correction means To.

  In a portfolio creation device according to another invention, a single model portfolio is selected from a plurality of model portfolios according to the investor's investment stance, and a plurality of investment targets included in the selected model portfolio (recommended portfolio). The investment allocation ratio for each of the plurality of investment objects included in the fund data is adjusted so as to match the investment allocation ratio for each of the above. Since the fund allocation rate for multiple investment targets for multiple funds (invested products) is determined to be in line with the recommended portfolio, it will be decided to invest along any of the recommended portfolios. It is possible to show to investors in an easy-to-understand manner whether the amount will be allocated.

(I) Hardware Configuration FIG. 1 is a block diagram showing the overall configuration of a custom portfolio creation system.

  The custom portfolio creation system includes a center computer 2 connected to the network 1 and a plurality of client computers 3 (hereinafter referred to as clients 3). The center computer 2 is placed under the control of an administrator (management company or the like) of the custom portfolio creation system. The client 3 is placed under the control of a user who uses the custom portfolio creation system (such as an employee who presents a custom portfolio, which will be described later to investors).

  FIG. 2 is a block diagram showing a hardware configuration of the center computer 2.

  The center computer 2 is a so-called computer system (work station or the like), and includes a CPU 11 that controls the entire center computer 2 in an integrated manner. Input device 12 (keyboard, mouse, etc.) for inputting instructions, commands, characters, etc. to CPU 11, display device 13 (LCD display device, etc.) for displaying characters, images, etc., buffer area, work area And the like, a hard disk 15 for storing programs and data, and a communication device 16 for data communication with the client 3 via the network 1 are connected.

  The hard disk 15 of the center computer 2 is required for transmission / reception of an OS (Operating System) for controlling the overall operation of the center computer 2 and data between the center computer 2 and the client 3. Programs and data such as a communication program, a custom portfolio creation program, a model portfolio data table, a fund management data table, a correlation coefficient table, and an investment object data table, which will be described later. In this embodiment, the center computer 2 also serves as a web server. In response to a request from the client 3, the center computer 2 obtains web data (for example, HTML (Hyper Text Markup Language) data) stored in the hard disk 15 in advance or a custom portfolio creation program described above. The web data including the processed result is transmitted to the client 3. Details of the custom portfolio creation program, model portfolio data table, fund management data table, correlation coefficient table, and investment target data table will be described later.

  The client 3 is also a so-called computer system (such as a personal computer) and basically has the same hardware configuration as that of the center computer 2. In FIG. 2, the devices constituting the client 3 are specified by the reference numerals shown in parentheses.

  The hard disk 25 of the client 3 stores an OS (operating system) and a browser program (communication program) (hereinafter referred to as a browser). An image (window) based on the web data transmitted from the center computer 2 to the client 3 is displayed on the display screen of the display device 23 by the browser. A character string, a request for web data, and the like input using the input device 22 are transmitted from the client 3 to the center computer 2 by the browser.

(II) Data Table FIG. 3 shows an example of the model portfolio data table 17. As described above, the model portfolio data table 17 is stored in the hard disk 15 of the center computer 2.

  The model portfolio data table 17 stores data related to 10 model portfolios from model portfolio A to model portfolio J in this embodiment. The data on each model portfolio A to J includes standard deviation, expected rate of return and investment target (asset class) (in this example, five investment targets: domestic stocks, domestic bonds, overseas stocks, overseas bonds and money). (Asset class)) includes data on the fund allocation rate.

  The standard deviation indicates a variation in the amount of profit (expected amount of achievement of investment) when funds are managed according to the fund allocation rate for each investment target described later. It is a numerical value representing the so-called risk level.

  Expected rate of return: The rate of return expected when funds are managed according to the fund allocation rate for each investment target described below. It is a numerical value representing the so-called return level.

  Fund allocation rate for each investment target: The allocation rate of funds for each investment target (domestic stocks, domestic bonds, foreign stocks, foreign bonds and money). For example, model portfolio A shown in Fig. 3 has an allocation rate of 20% for domestic stocks, 7% for domestic bonds, 49% for foreign stocks, 0% for foreign bonds, and 24% for money (deposits and savings) (total) Indicates that the investment should be allocated to each investment target.

  The fund allocation rate for each investment target for each model portfolio A to J stored in the model portfolio data table 17 is the so-called “efficient frontier” (expected rate of return determined for each investment target ( Average) and standard deviation, and a curve obtained based on the correlation coefficient between each investment object. In this example, the model portfolios AJ are arranged in alphabetical order from low risk and low return to high risk and high return.

  FIG. 4 shows an example of a fund management data table. A fund management data table is also stored in the hard disk 15 of the center computer 2.

  The fund management data table 18 stores a fund allocation rate (special account) for each investment object for each of a plurality of funds (investment products) specified by the fund name. For example, the fund (investment product) with the fund name “Japan Stock Type A” has a 100% allocation rate for domestic stocks, and the allocation rate for other investment targets (domestic bonds, overseas stocks, overseas bonds and money) is 0% (total is 100%). That is, the fund with the fund name “Japanese stock type A” indicates that 100% of the investment money deposited from the investor is managed by buying and selling domestic stocks. The fund (investment product) with the fund name “World Stock Type A” has an allocation rate of 10% for domestic stocks, 80% for overseas stocks, and 10% for money. The distribution ratio for domestic bonds and overseas bonds is 0% (total is 100%). That is, the fund with the fund name “World Stock Type A” uses 10% of the investment deposited from investors by buying and selling domestic stocks, 80% by buying and selling foreign stocks, and 10% by money (deposits). , Savings).

  FIG. 5 shows an example of the correlation coefficient table. As will be described later, the correlation coefficient table 19 is used for calculating the standard deviation of the portfolio (more specifically, the model portfolio and the custom portfolio) (for calculating the covariance used for calculating the standard deviation of the portfolio). ) (Details will be described later).

  The correlation coefficient table 19 stores correlation coefficients with other investment objects for each of the five investment objects. For example, focusing on the investment target “domestic stock”, the correlation coefficients between domestic stock and other investment targets (domestic bonds, foreign stocks, foreign bonds and money) are 0.705, 0.711, 0.110, and 0.018, respectively. (The correlation coefficient between domestic stocks and domestic stocks is 1.000).

  FIG. 6 shows an example of the investment object data table. The investment target data table 20 stores a standard deviation and an expected rate of return determined for each investment target, and a covariance between the investment targets. The covariance is determined based on the standard deviation for each investment target stored in the investment target data table 20 and the correlation coefficient (see FIG. 5) described above (details will be described later).

(III) Calculation of portfolio standard deviation and expected rate of return The standard deviation and expected rate of return of the model portfolio described above (Fig. 3) and the standard deviation and expected rate of return of the custom portfolio described later are obtained by the following equations. It is done.

(i) Portfolio standard deviation

  In Equation 1, matrix A is a one-column matrix whose element is the fund allocation rate for each investment target (asset class) determined for each portfolio (see FIG. 3 for the model portfolio). The matrix B is a one-row matrix having the fund allocation rate for each investment object (asset class) determined for each portfolio as an element. Covariance (i, j) represents each of covariances (see FIG. 6) (25 covariances) determined for each investment target.

  Covariance (i, j) is expressed by the following equation.

  In Equation 2, the matrix C is a one-column matrix having the standard deviation for each investment object stored in the investment object data table 20 (FIG. 6) as an element. The matrix D is a one-row matrix having the standard deviation for each investment object stored in the investment object data table 20 (FIG. 6) as an element. The correlation coefficient (i, j) represents each of correlation coefficients (see FIG. 5) (25 correlation coefficients) determined for each investment target.

(ii) Expected rate of return of the portfolio The expected rate of return of the portfolio is calculated by the following formula.

  In Equation 3, the allocation rate of investment object i (i is domestic stock, domestic bond, overseas stock, overseas bond, money) is stored in model portfolio data table 17 (FIG. 3) for the model portfolio. What is used is used. The expected rate of return of the investment target i is the one stored in the investment target data table 20 (FIG. 6).

  Although details will be described later, in the custom portfolio creation system, a custom portfolio is created for each investor (user) separately from the model portfolio. The custom portfolio also has a fund allocation ratio for each investment target (domestic stocks, domestic bonds, overseas stocks, overseas bonds, money). In calculating the standard deviation and expected rate of return for a custom portfolio (Equation 1 and Equation 3), the standard deviation and expected rate of return for each investment target for the custom portfolio obtained by the custom portfolio creation system are used. It is done.

(IV) Processing of Custom Portfolio Creation Program FIG. 7 is a flowchart showing an outline of the processing procedure of the custom portfolio creation program. As described above, the custom portfolio creation program is stored in the hard disk 15 of the center computer 2. The center computer 2 operates according to the custom portfolio creation program. First, I will explain the outline of the custom portfolio creation program processing.

  Input of basic information of the investor (user) is accepted (step 31). The basic information includes the operation period, target amount, initial investment amount (initial investment amount), and monthly investment amount.

  Next, input of the risk tolerance of the investor (user) is accepted (step 32). The risk tolerance is input by the investor selecting one of the options (question options) displayed on the display screen of the display device 23 of the client 3.

  The recommended model portfolio is determined (step 33). In the recommended model portfolio determination process, the model portfolio A to J stored in the model portfolio data table 17 is selected from the ten model portfolios A to J based on the input risk tolerance. Thus, one of the model portfolios is determined as the recommended model portfolio. Also, based on the expected rate of return and standard deviation determined for the recommended model portfolio, and the basic information entered (operation period, target amount, initial investment amount (initial investment amount) and monthly investment amount), Expected achievement amount after the operation period when successful, expected achievement amount after the operation period when average, expected achievement amount after the operation period when operation fails, probability of reaching the target amount, original The probability of a main crack is calculated. These calculated predicted achievement amounts, probabilities, and the like are displayed as numerical values and graphs on the display screen of the display device 23 of the client 3.

  Next, custom portfolio creation processing is performed (step 34). In the custom portfolio creation process, the fund allocation ratio (special account) for each investment object for each of the plurality of funds (investment products) stored in the fund management data table 18 described above, and each of the recommended model portfolio Each investment in the recommended model portfolio is based on how much (how much) and how much (investment) of which funds (investment products) out of multiple funds (investment products) based on the fund allocation ratio for the investment target. A process is performed to determine whether the funds allocation rate for the object is met (or as close as possible). In the custom portfolio creation process, the expected rate of return and standard deviation are calculated based on the fund allocation rate for each investment target in the fund (generally multiple) funds to be purchased (invested). Based on the rate of return and standard deviation, and basic information entered (operation period, target amount, initial investment amount (initial investment amount) and monthly investment amount) The predicted amount of achievement, the expected amount of money after the operation period has elapsed in the average case, the predicted amount of achievement after the lapse of the operation period when the operation has failed, the probability of reaching the target amount, the probability of breaking the principal, etc. are calculated. These calculated predicted achievement amounts, probabilities, and the like are displayed as numerical values and graphs on the display screen of the display device 23 of the client 3.

  The calculated achievement predicted amount, probability, and the like are stored and stored in the hard disk 15 of the center computer 2 together with the name of the investor (user) and the like (step 35).

  FIG. 8 shows an example of the basic information input window W1 displayed on the display screen of the display device 23 of the client 3, and FIG. 9 shows the risk tolerance input window W1 displayed on the display screen of the display device 23 of the client 3. An example of each is shown. FIG. 10 is a flowchart showing a detailed processing procedure of the recommended model portfolio determination processing (FIG. 7; step 33). FIG. 11 shows an example of a simulation result display window W3 displayed on the display screen of the display device 23 of the client 3 in accordance with the recommended model / portfolio determination process. The details of the custom portfolio creation process (FIG. 7; step 34) and storage process (FIG. 7; step 35) will be described later, and the basic information input process (step 31) will be described with reference to FIGS. The risk tolerance input process (step 32) and the recommended model portfolio determination process (step 33) will be described in detail.

  When the user of the client 3 inputs a browser activation command using the input device 23 of the client 3, the browser is read from the hard disk 25 and read into the CPU 21. The browser starts.

  When a user of the client 3 inputs a URL (Uniform Resource Locator) of the center computer 2, data (HTML data) representing a basic information input window is transmitted from the center computer 2 to the client 3 through the network 1. On the display screen of the display device 23 of the client 3, a basic information input window W1 (FIG. 8) is displayed. Other windows to be described later are similarly displayed on the display screen of the display device 23 of the client 3 based on the data representing the window transmitted from the center computer 2.

  The basic information input window W1 (FIG. 8) includes an input box W1a for inputting an operation period, an input box W1b for inputting a target amount, and an input box W1c for inputting an initial investment amount (initial investment amount). And an input box W1d for inputting a monthly investment amount and a clickable character string W1e “next step”.

  Using the input device 22 of the client 3, the investor (or the operator of the client 3) uses the input device 22 of the client 3 to indicate the number of years of operation desired by the investor in the operation period input box W1a, and the above operation years have elapsed in the target amount input box W1b. A number that represents the amount that is desired to be achieved later (target target amount), the initial investment amount (initial cost) in the first investment amount input box W1c, and the monthly investment amount in the monthly investment amount input box W1d Enter (Running Cost).

  When “next step” W1e is clicked, data representing the operation period, data representing the target amount, data representing the initial investment amount, and monthly investment entered in each of the input boxes W1a, W1b, W1c and W1d Data representing the forehead is transmitted from the client 3 to the center computer 2 via the network 1. The center computer 2 receives the data representing the operation period, the data representing the target amount, the data representing the initial investment amount, and the data representing the monthly investment amount transmitted from the client 3, and temporarily stores them in the memory 14. (FIG. 7: Step 31).

  When “next step” W1e is clicked, a risk tolerance input window W2 (FIG. 9) is displayed on the display screen of the display device 23 of the client 3 instead of the basic information input window W1.

  The risk tolerance window W2 includes four answer selection branches for the three question items W2a, W2b, and W2c, Q1, Q2, and Q3. A check button is provided on the left side of each answer selection branch. For each of the three questions Q1, Q2 and Q3, one answer selection branch is selected from the four answer selection branches by the investor. The selection of the answer selection branch is performed by the investor (or the operator of the client 3) clicking the check box using the input device 12 (mouse or the like). A black dot appears in the clicked check box.

  Three answers to the questions Q1, Q2 and Q3 selected by the investor using the risk tolerance window W2 are used to measure (measure) the risk tolerance of the investor. Based on the measured (measured) risk tolerance, a model portfolio (recommended portfolio) that any portfolio from among multiple model portfolios A to J should recommend to investors as described later (The details will be clarified in the description of the recommended model portfolio decision process).

  When “next step” W2d is clicked, three answers (data indicating answer options) to each question item Q1, Q2 and Q3 selected by the investor are transmitted from the client 3 to the center computer 2. The center computer 2 receives the data representing the three answer selections transmitted from the client 3 and temporarily stores them in the memory 14 (FIG. 7; step 32).

  The center computer 2 that has received the data representing the three answer options executes the recommended model portfolio determination process (FIG. 7; step 33, FIG. 10).

  In the recommended model portfolio determination process, as described below, the standard deviation and the expected rate of return for each of the model portfolios A to J stored in the model portfolio data table 17 (FIG. 3) are used. . The standard deviation and the expected rate of return for each of the model portfolios A to J are calculated and obtained by the above-described equations 1 to 3. Of course, the standard deviation and the expected rate of return for each of the model portfolios A to J may be calculated in advance and stored in the model portfolio data table 17 (see FIG. 3).

  The following processing is performed for all model portfolios A to J stored in the model portfolio data table 17 (see FIG. 3). Here, it is assumed that the processing is performed in the order of model portfolio A, model portfolio B,..., Model portfolio J (alphabetical order).

  Referring to FIG. 10, data representing the standard deviation and expected rate of return for model portfolio A is read from model portfolio data table 17 and temporarily stored in memory 14 (step 41).

  In the memory 14 of the center computer 2, as described above, the data indicating the operation period, the data indicating the initial investment amount, and the monthly investment amount input using the client 3 (basic information input window W1) are stored. Representing data is temporarily stored.

  First, data representing the standard deviation and expected rate of return for model portfolio A, as well as the data representing the entered investment period, the data representing the initial investment amount, and the data representing the monthly investment amount are used for scenario scenario. Data is calculated (step 42).

  Scenario data is calculated using standard deviation and expected rate of return for model portfolios, data for the entered investment period, data for initial investment, and data for monthly investment. The amount of assets that will be generated in the future (the expected amount of achievement). FIG. 12 shows an example of a calculation formula for calculating scenario data.

For example, the operation period is 10 years (120 months). In this case, the scenario data represents the monthly asset amount (achievement expected asset amount) from the asset amount after _January (predicted asset amount) (D ₁ ) to the asset amount after 120 months (D ₁₂₀ ). It will contain data.

Referring to FIG. 12, for example, the asset values (D ₁ , D ₂ , D ₃ , D ₁₂₀ ) after 1 month, 2 months, 3 months and 120 months are calculated by the following equation.

Assets after January (D ₁₎ = first investment × (1+ expected rate of return + standard deviation × random number (n ₁₎₎
Asset amount after February (D ₂ ) = (Asset amount after _January (D ₁ ) + Monthly investment amount) × (1 + Expected return
Rate + standard deviation x random number (n ₂ ))
Asset amount after March (D ₃ ) = (Asset amount after _February (D ₂ ) + Monthly investment amount) × (1 + Expected return
Rate + standard deviation x random number (n ₃ ))
Assets, net of 120 May (D ₁₂₀₎ = (assets after 119 May (D ₁₁₉₎ + monthly investment) × (1+ period
Waiting profit rate + standard deviation x random number (n ₂₀₀ ))

Here, the random number (n ₁ ), the random number (n ₂ ),..., The random number (n ₂₀₀ ) are random numbers according to a normal distribution that is generated (calculated) every time the asset amount of each month is calculated. A value Z obtained by a Gaussian function expressed by the following equation (average is 0, standard deviation is 1) is used as a random number (n ₁ ), a random number (n ₂ )..., A random number (n ₂₀₀ ).

  Here, the uniform random number is an arbitrary number between 0 and 1 generated by the custom portfolio creation program. A normal random number is an arbitrary number generated by a custom portfolio creation program according to a normal distribution.

The value Z (random number (n ₁ ), random number (n ₂ )... Random number (n ₂₀₀ )) obtained by the above-described Gaussian function may be a positive value or a negative value. . For example, if the random number (n ₁ ) is a positive value, the investment amount (D ₁ ) after one month is higher (amount) than the initial investment amount. If the random number (n ₁ ) is a negative value, if the result of adding “1 + expected rate of return” and “standard deviation × random number” (negative value) is greater than 1, 1 The investment amount after month (D ₁ ) is higher than the initial investment amount (amount), and if it is less than 1, the asset amount after _January (D ₁ ) is lower than the initial investment amount It becomes.

  Scenario data obtained by the calculation formula shown in FIG. 12 is temporarily stored in the memory 14 of the host computer 2.

  Similar processing is repeated a predetermined number of times (for example, 1000 times) (NO in step 43, step 42). A predetermined number (for example, 1000) of scenario data is temporarily stored in the memory 14.

The predetermined number of scenario data stored in the memory 14 is sorted in descending order of the asset amount (asset amount after the operation period) in the last month (step 44). FIG. 13 shows how 1000 scenario data items are sorted in descending order of the asset amount (D ₁₂₀ ) in the last month.

  The sorted scenario data is used to determine the expected asset value for each successful operation, average operation, and operation failure. In addition, a predetermined number of scenario data is used to calculate the probability of reaching the target amount, the probability of breaking principal, and the expected shortage (step 45).

  For example, the asset amount of the last month included in the 50th scenario data from the top of the sorted scenario data is used as the predicted asset amount when the operation is successful. For example, the asset value for the last month included in the scenario data in the middle of the sorted scenario data (500th in the case of 1000 scenario data) is used as the predicted achievement amount in the average case. . For example, the asset amount of the last month included in the 50th scenario data from the bottom of the sorted scenario data is used as the predicted asset amount when the operation fails (see FIG. 13).

  The probability of achieving the target amount is 100 by dividing the number of scenario data whose asset amount in the final month exceeds the target amount (previously entered amount) by the total number of scenario data. Determined by multiplying by.

  The probability of a loss of principal is multiplied by 100 by dividing the number of scenario data whose asset amount in the final month is less than the target amount (the amount entered in advance) by the total number of scenario data. To be determined.

  The expected deficit is calculated by subtracting the last month's asset amount from the target amount for a given number of scenario data for which the last month's asset amount is less than the target amount. Determined by dividing by the total number of scenario data.

  With the above processing, when the model portfolio A is applied, the expected achievement amount of the asset, the probability of reaching the target amount, the loss of principal, when the operation is successful, the average case and the operation fails And the expected shortfall. These determined values (amount and probability) are temporarily stored in the memory 14 (step 46).

  The same processing is performed for each of the remaining model portfolios B to J (NO in step 47, step 48, steps 41 to 46). That is, for each of the model portfolios A to J, the expected achievement amount, probability of reaching the target amount, probability of breakage of principal in case of successful operation, average case, and unsuccessful operation And the expected shortfall.

  For each of the model portfolios A to J, the expected achievement amount, probability of reaching the target amount, probability of breakage of principal, and expectation when the operation is successful, the average case and when the operation fails When the shortage amount to be determined is determined and stored in the memory 14, the process proceeds to a process of determining which portfolio of the model portfolios A to J is the recommended portfolio (YES in step 47, step 49). ).

  Refer to the risk tolerance input window W2 of FIG. In the risk tolerance input window W2, answers to the question items Q1 (reference W2a) and the question items Q2 (reference W2b) are stored in the memory 14 of the center computer 2 as described above. Used to narrow down the 10 model portfolios A to J to a smaller number of model portfolios. For example, suppose that in question Q1, “the asset decreases at the end of the year compared to the beginning of the year has no resistance until about once in 10” is selected. In this case, among the model portfolios A to J, the probability that the rate of return obtained from the expected rate of return and standard deviation determined for each model portfolio is negative is 10% or less. Candidate for portfolio.

  Assume that in question Q2, “it is not acceptable for the asset to fall by 20% in a certain year” is selected. In this case, among the model portfolios A to J, the probability that the rate of return calculated from the expected rate of return and the standard deviation exceeds -20% is 90% or more. Is done.

  Of the answers to the question item Q1 and the answer to the question item Q2, those with more severe conditions are adopted, and recommended model portfolio candidates are selected from the model portfolios A to J.

  When “I don't care” is selected for both the question Q1 and the question Q2, all the model portfolios A to J are candidates for the recommended portfolio.

  Next, the answer to the question item Q3 is used, and one of the model portfolios selected by the answer to the question item Q1 and the question item Q2 is selected. If “I want to get as high a return as possible” was selected, the “average expected asset value” obtained for each of the selected model portfolios (at the end of the investment period) The model portfolio having the highest value (numerical value) is determined as the recommended model portfolio. If “I want to increase the target price” is selected, the recommended model portfolio with the highest “probability to reach the target price” is selected from the selected model portfolios. The If “I want to avoid breaking the principal as much as possible even if I cannot achieve the target amount” is selected, the one with the lowest “probability of breaking principal” among the narrowed model portfolios Determined as a recommended model portfolio. When “I want to make the expected value of the amount that is insufficient for the target as small as possible” has been selected, the recommended model portfolio with the lowest “expected amount of shortage” among the selected model portfolios To be determined.

  When the recommended model portfolio is determined, window data representing the simulation result display window W3 (FIG. 11) is created and transmitted to the client 3 (step 50).

  On the display screen of the display device 23 of the client 3, a simulation result display window W3 is displayed.

  The simulation result display window W3 shows the estimated asset amount of each of the determined recommended model portfolio and the other two model portfolios when the management is successful, the average case, and when the management fails. , As well as the column W3a for displaying the probability of reaching the target amount, the probability of breaking principal, and the expected shortfall, and the fund allocation rate for each investment target defined for the recommended model portfolio (model portfolio Data table 17 (stored in FIG. 3) is a column W3b for displaying a pie chart, the total paid premiums (bar graph), the expected achievement amount when the operation is successful (dashed line), the average Predicted amount of money (broken line), predicted amount of money when operation fails (broken line) Point chain line), shortfall expected (short dashed line), and shows the target amount (solid line), respectively, including years a horizontal axis, a simulation graph display field W3c the vertical axis was the amount. The graph shown in the simulation graph display field W3c is based on scenario data (see FIG. 12) created based on the determined recommended model portfolio (any one of the model portfolios A to J). Needless to say.

  If the investment amount (initial investment amount and monthly investment amount) is managed according to the recommended model portfolio determined according to the risk tolerance of the investor in the simulation result display window W3, what will happen at the end of the investment period? It is displayed clearly whether you can make a profit.

  When the character string W3d “next step” is clicked, the process proceeds to the custom portfolio creation process (FIG. 7; step 34). In the custom portfolio creation process, as described above, the fund allocation ratio (special account) for each investment target for each of a plurality of funds (investment products) stored in the fund management data table 18 (FIG. 4). And how much (how much) to buy (invest in) which fund (investment product) out of multiple funds (investment product) based on the fund allocation ratio for each investment target in the recommended model portfolio , A process is performed to determine whether the recommended model portfolio meets (or is as close as possible) to the funding ratio for each investment target.

  14 and 15 are flowcharts showing the detailed procedure of the custom portfolio creation process. 16, FIG. 17 and FIG. 18 show the windows (fund allocation input window W4 (FIG. 16), custom simulation result display window W5 displayed on the display screen of the display device 23 of the client 3 in the custom portfolio creation processing. (FIGS. 17 and 18)).

  When the character string W3d “to next step” in the simulation result display window W3 is clicked, the fund distribution input window W4 (FIG. 16) is displayed on the display screen of the client 3.

  The fund allocation input window W4 is a column W4a for displaying the fund allocation rate for each investment target (asset class) in the recommended model portfolio, and the fund allocation rate for each investment target (asset class) for each fund (invested product) Is included, a holding condition input field W4c, and a clickable character string W4d.

  In the recommended model portfolio fund allocation rate display field W4a, the name of the recommended model portfolio (model portfolio C in FIG. 16) determined in the above-described recommended portfolio determination process, and model portfolio data The fund allocation ratio for each investment target of the recommended portfolio stored in the table 17 (FIG. 3) is displayed.

  The fund allocation rate display field W4b of the fund (investment product) includes a plurality of fund names (investment product names) and a fund specified by each fund name based on the fund management data table 18 (FIG. 4). The fund allocation rate for each investment target is displayed.

  The holding condition input field W4c includes an input box for inputting the lower limit and the upper limit of the holding condition (investment condition) according to the ratio or the amount corresponding to each of the plurality of fund names. As will be described later, in the custom portfolio creation process, the initial investment amount and monthly investment amount should be invested (distributed) in any fund (investment product) in any proportion (ratio) (amount). It is determined whether it should be purchased or not. In the input box included in the holding condition input field W4c, when there is a fund that an investor wants to hold (investment, purchase) (or does not want to hold), enter a lower limit value and / or an upper limit value (ratio or amount) To do. When the lower limit value or the upper limit value is input, the investment ratio (amount) to the fund (invested product) is determined within the range.

  When “next step” W4d is clicked, the upper limit value and / or the lower limit value input using the fund allocation input window W4 is transmitted from the client 3 to the host computer 2. The holding conditions (the upper limit / lower limit of the ratio or amount) are stored in the memory 14 of the host computer 2 (step 61).

  An agent is created (steps 62 and 63). With reference to FIG. 19, the process of creating an agent when the holding condition (the upper limit / lower limit of the ratio or amount) is not input will be described first.

  A random number (any integer from 0 to 100) is generated for each fund (investment product). The generated random number is used as a temporary allocation rate for each fund (step 62). Next, by dividing each temporary allocation rate for each fund by the total of the temporary allocation rate, the total allocation rate for each fund is adjusted to 100%, and the adjusted fund allocation rate Based on the above, the fund distribution ratio for the investment target of each fund is corrected (step 63).

  Specifically, referring to FIG. 19, for example, the provisional allocation rate (random number) for the fund name “Japanese stock type A” is “50”, and the total of the provisional allocation rates is “453”. In this case, the adjusted fund allocation ratio for the fund name “Japanese stock type A” is 11% (50/453 = 0.1103... = 0.11, 0.11 multiplied by 100).

  According to the adjusted fund allocation ratio, the fund allocation ratio for the investment target set for each fund is revised. For example, the fund allocation rate for each investment target of Japanese stock type A is 100%, 0%, 0%, 0%, 0% for Japanese stocks, domestic bonds, overseas stocks, overseas bonds, and money, respectively ( See left side of Figure 19; total is 100%). Based on the fund allocation rate for each investment target of Japanese stock type A, the total of the fund allocation rate for each investment target is the above-mentioned adjusted fund allocation rate (11% for Japanese stock type A). The fund allocation ratio for each investment target of Japanese stock type A is revised. In other words, for the Japanese equity type A with an adjusted fund allocation ratio of 11%, the fund allocation ratio for the investment target “Japan equities” is 11% (the total is 11%). As another example, if the adjusted fund allocation rate for the fund name “World Stock Type A” is 20%, the fund allocation rate for each investment target of World Stock Type A is Japanese equities, domestic bonds, overseas Since each of stocks, overseas bonds and money is 10%, 0%, 80%, 0% and 10% (total is 100%), 2%, 0%, 16%, 0% and 2% for Japanese stocks, domestic bonds, overseas stocks, overseas bonds and money, respectively (total is 20%).

  In this way, for each fund, the agent is the one that corrects the fund allocation ratio for each investment target set for each fund based on the adjusted fund allocation ratio for each fund (right side of FIG. 19). And surrounded by a one-dot chain line).

  For example, when the lower limit and / or the upper limit is entered as a ratio, if the temporary allocation rate determined by random numbers is smaller than the lower limit, the temporary allocation rate is increased to the lower limit. When the allocation rate is larger than the upper limit value, the temporary allocation rate is lowered to the upper limit value. For example, referring to FIG. 19, the provisional allocation rate for fund name “Japanese stock type A” is determined as “50%”, and the upper limit (ratio) for fund name “Japanese stock type A” is 30%. Suppose the lower limit (ratio) is 10%. The temporary allocation rate (50%) determined by random numbers is larger than the upper limit (ratio) (30%). In this case, the temporary allocation rate is reduced from “50%” to “30%”.

  The provisional allocation rate determined in consideration of the lower limit value and / or the upper limit value (hereinafter referred to as the second temporary allocation rate) is used to determine the adjusted fund allocation rate. Specifically, first, an adjustment factor described below is calculated, and an adjusted fund allocation ratio for each fund is determined using the calculated adjustment factor.

  The adjustment factor is calculated by the following equation.

  (1−total value of lower limit value) / total value of second provisional distribution ratio) −total value of lower limit value

  The fund allocation ratio of each fund after adjustment is calculated by the following formula.

  Lower limit of fund i + (second temporary allocation rate of fund i-lower limit of fund i) x adjustment factor

  Even when the upper limit value and / or the lower limit value are input in this way, the adjusted fund allocation ratio for each fund is calculated. Based on the calculated fund allocation rate after adjustment, the fund allocation rate for each investment target set for each fund is modified (agent creation).

  Whether the holding condition (upper limit value and / or lower limit value) is not input or not, five agents are created in the same manner as described above (NO in step 64, steps 62 to 63).

  The processing shown in FIG. 15 is performed for each of the five created agents (first to fifth agents) (steps 65 to 69).

  The fund allocation rate (adjusted fund allocation rate) of each fund specified by the agent (see Fig. 19) is corrected using the following formula to obtain a new fund allocation rate.

  New fund allocation rate = fund allocation rate + random number x α-α / 2 Equation 5

  In Equation 5, an integer between 0 and 100 is used as the random number. The coefficient α is a coefficient (for example, a numerical value such as 0.1) indicating the degree of separation between the calculated new fund allocation rate and the fund allocation rate.

  Based on the new fund allocation rate obtained by Equation 5, the same processing as in step 63, that is, satisfying the ownership conditions for each fund, and the total of the allocation rate of each fund (new fund allocation rate) is 100% The new fund allocation rate for each fund will be revised. Based on the revised fund allocation rate, the fund allocation rate for each fund's investment target is revised (step 81). Data similar to that of the above-mentioned agent obtained based on the new fund allocation rate obtained by Equation 5 is called “child” for the agent.

  Similarly, five children are created (NO in step 82, step 81).

  The distance between the agent and the recommended portfolio is calculated for each of the five children (step 83). FIG. 20 shows a method for calculating the distance between the agent shown in FIG. 19 and the recommended model portfolio (model portfolio C).

  Based on the agent, the sum of the investment distribution ratio is calculated for each investment target (for example, the total sum for the investment target “domestic stock” is 19%).

  Next, for each investment target, the difference between the sum of the investment allocation ratios obtained from the above agent and the investment allocation ratio for the corresponding investment target of the recommended model portfolio (here, model portfolio C) is calculated. (For example, the investment allocation rate of “domestic equities” for investment in the recommended model portfolio is 17%, so the difference is 2%).

  The sum of the squares of the differences in the investment allocation ratios for each investment target is calculated (in the example shown in FIG. 20, the sum is 510). This calculation result represents the distance between the agent and the recommended model portfolio.

  Similar processing is performed for each of the five children. The distance between each of the five children and the recommended model portfolio is calculated.

  Of the distance between the agent and the recommended model portfolio, and the distance between each of the five children and the recommended model portfolio, the closest distance (the sum of the squares of the differences in the fund allocation ratio for each investment target is the smallest) ) Is a new agent (step 84).

  Similar processing is repeated a predetermined number of times, for example, 10,000 times (NO in step 85, steps 81 to 84). With each processing, the distance between the agent and the recommended model portfolio will gradually approach.

  After the above-described processing is repeated 10,000 times, the same processing as step 83 is performed for five agents as processing targets (step 70). The agent closest to the recommended model portfolio is set as the custom portfolio (step 71).

  As described above, a custom portfolio can be obtained through many times (in the above case, about 50,000 times) processing (reducing the distance between the recommended model portfolio and the agent (child)). The investment allocation ratio for each investment target in the portfolio is the same as or nearly equal to the recommended model portfolio. In addition, the recommended model portfolio has only the fund allocation rate for each investment target, but the custom portfolio has not only the fund allocation rate for each investment target but also each fund (investment product). This includes the fund allocation ratio for each investment target.

  As with the model portfolio described above, when the created custom portfolio is used, scenario data is created and the operation is successful. The amount achieved, the probability of reaching the target, the probability of breaking the principal, and the expected shortage are calculated. The standard deviation and expected rate of return used to create scenario data related to the custom portfolio are calculated based on the above-mentioned formula 1, formula 2 and formula 3, using the fund allocation rate for each investment target in the custom portfolio. Is done.

  Custom simulation result display window data is created and transmitted to the client 3 (step 72). 17 and 18 show an example of the custom simulation result display window W5 displayed on the display screen of the client 3. FIG.

  The custom simulation result display window W5 includes a column W5a for displaying a fund allocation ratio for each investment target of the recommended model portfolio (here, model portfolio C), and each investment for each fund based on the custom portfolio. Column W5b for displaying the fund allocation ratio for the target, Column W5c for displaying the fund allocation ratio for each investment target based on the custom portfolio, the fund allocation ratio for each investment target and the initial investment amount entered for each fund And the field W5d that displays the initial investment amount and the monthly investment amount for each fund, which is obtained based on the entered monthly investment amount, each of the recommended model portfolio and the custom portfolio managed successfully Case, average case Expected achievement amount when operation fails, and the probability of reaching the target, the probability of breaking the principal and the expected shortage amount, a column W5d, a graph display column W5e based on the recommended model portfolio, a graph based on the custom portfolio A display field 5f and a character string W5g “to the next step” are included.

  In the field W5d for displaying the initial investment amount and the monthly investment amount for each fund, the amount obtained by multiplying the input initial investment amount by the fund allocation ratio (ratio) / 100 for each fund. (The initial investment amount for each fund) and the amount obtained by multiplying the input monthly investment amount by the fund allocation ratio (ratio) / 100 for each fund (monthly investment amount for each fund) Is done.

  When “next step” W5g is clicked, a window (not shown) for inputting the name, date of birth, sex, etc. (investor information) of the investor (user) is displayed on the display screen of the client 3 Is displayed. The entered investor information is transmitted from the client 3 to the center computer 2. The data related to the custom portfolio and the data related to the recommended portfolio are stored (associated) with the investor information on the hard disk 15 of the center computer 2.

  In the embodiment described above, a custom portfolio creation program, model portfolio data table 17, fund management data table 18, correlation coefficient table 19 and investment target data table 20 are stored in the hard disk 15 of the center computer 2. When the window data including the processing result of the custom portfolio creation program is transmitted from the center computer 2 to the client 3, the simulation result display window W3 and the custom simulation result display window W5 are displayed on the display screen of the client 3. Are displayed in the custom portfolio creation program, model portfolio data table 17, fund management data table 18, correlation coefficient table 19, and investment target data The table 19 may be stored in the hard disk 25 of the client 3. In this case, the client 3 constitutes a so-called stand-alone custom portfolio creation system.

  FIG. 21 shows a functional block diagram of the custom portfolio creation system.

  Based on the operation period, the initial investment amount, the monthly investment amount, the target amount, and the risk tolerance of the investor inputted from the investment stance information input device 101, it is stored in the model portfolio data storage device 102. One model portfolio data is selected as a recommended model portfolio from a plurality of model portfolio data (recommended model portfolio selection function 103). The selected recommended model portfolio (recommended model portfolio data) includes data representing an investment allocation ratio for a plurality of investment targets.

  The distance between the recommended model portfolio and the custom portfolio is calculated (function block 105). The custom portfolio used for calculating the distance is the fund data read from the fund data storage device 106 in which the fund data in which the fund allocation ratio for the plurality of investment targets is determined is stored for each of the plurality of funds. For each fund, the sum of each investment target is obtained for the fund with the fund allocation rate corrected for the investment target (function block 107). Multiple custom portfolios are created, and the distance between the recommended model portfolio and each of the multiple custom portfolios is calculated.

  Of the multiple custom portfolios, the custom portfolio with the shortest distance is adopted, and the contents of the custom portfolio (fund allocation ratio to the investment target for each fund after adjustment) are used to The initial investment amount for each fund and the monthly investment amount are calculated (function block 108). Graph data is created based on the calculated expected future earnings. A graph representing the expected amount of future earnings, the initial investment amount for each fund, and the monthly investment amount are displayed on the display screen of the display device 109.

  22 and 23 are other examples of functional block diagrams of the custom portfolio creation system.

  The first storage device 121 includes data defining a fund allocation rate for a plurality of investment targets for each of a plurality of model portfolios, and data representing a standard deviation and an expected rate of return for each of a plurality of model portfolios. Is remembered.

  The first input device 122 is used to input data representing the operation period, initial investment amount, monthly investment amount, and target amount.

  The initial investment amount and the monthly investment amount input from the first input device 122, the standard deviation and the expected rate of return of the model portfolio stored in the first storage device 121, and the generated random number are used. Thus, the expected asset amount of each month during the operation period input from the first input device 122 is calculated (function block 123). The calculation of the monthly expected asset amount during the operation period is repeated a predetermined number of times. Since random numbers are used for calculating the monthly expected asset amount during the operation period, the monthly expected asset amounts during a plurality of operation periods obtained by being repeated a predetermined number of times are different.

  Data representing the monthly expected asset amount during a plurality of operation periods is sorted in descending order of the expected asset amount at the end of the operation period (function block 124). Data that represents the estimated asset amount for each month during multiple operation periods is used, and the expected asset amount for each successful operation, average operation, and operation failure (during the operation period) (Expected asset amount of each month) is determined (function block 125). For example, in the simplest case, the calculation of the expected asset amount for each month during the operation period is repeated three times, and the data representing the expected asset amount for each month during the three operation periods is high in the expected asset amount at the end of the operation period. Sorted in order, the asset value with the highest expected asset amount at the end of the operation period (first) is the expected asset amount when the operation is successful, the second is the expected asset amount when the average is expected, the third Is determined as the expected asset amount when the operation fails.

  Further, the probability of reaching the target amount, the probability of breaking the principal amount, and the amount (expected value) deficient in the target amount are calculated based on data representing the monthly expected asset amount during a plurality of operation periods (functional block 126). ). The probability of reaching the target amount is the number of data that represents the expected asset amount for each month during a plurality of operation periods, but the expected asset amount for the last month of the operation period exceeds the target amount input from the first input device 122. Is divided by the number of data (the number of repetitions) representing the expected asset amount for each month during multiple operation periods and multiplied by 100. The probability of a loss of principal is the data representing the expected asset amount for each month during multiple operation periods, although the expected asset amount for the last month of the operation period is less than the target amount input from the first input device 122. Calculated by dividing the number by the number of data representing the expected asset amount for each month during multiple operating periods (number of repetitions) and multiplying by 100. The amount (expected value) deficient in the target amount is the target in which the expected asset amount for the last month of the operation period is input from the first input device 122 among the data representing the expected asset amount for each month during a plurality of operation periods. For items that are less than the amount, subtract the expected asset amount for the last month of the operation period from the target amount, and then add the subtraction value to the number of data representing the expected asset amount for each month during multiple operation periods (the number of repetitions) ) Divided by.

  The processing from the function block 123 to the function block 126 is executed for each of a plurality of model portfolios.

  From the second input device 128, data (including data representing the investor's wishes) representing the magnitude of the risk allowed by the investor (risk tolerance) is input. The data representing the risk tolerance is determined based on, for example, a question sentence and a plurality of selected answer branches corresponding to the question sentence to the investor and the choice answer branch selected by the investor.

  The data input from the first input device 122 and the second input device 128 can be said to be data representing the investment stance of the investor. The input data is used to select a model portfolio that meets the investor's investment stance from a plurality of model portfolios.

  A standard deviation (representing the degree of risk) and an expected rate of return (representing the degree of return) are determined and stored in the first storage device 121 for each of the plurality of model portfolios. This standard deviation and expected rate of return can be used to select a model portfolio. Further, the expected achievement amount for each model portfolio obtained in the function block 125 and the probabilities and expected values obtained in the function block 126 can also be used for selection of the model portfolio. Of course, only one of these may be used to select the model portfolio.

  In any case, a model portfolio that meets the investor's investment stance is selected from a plurality of model portfolios. A model portfolio that meets the investment stance of the selected investors is the recommended model portfolio.

  The second storage device 130 stores data defining investment distribution ratios for a plurality of investment targets for each of a plurality of funds (investment products) (an ID (fund name) that identifies a fund for each fund) There is). The fund ID (fund name) is read from the second storage device 130, and the fund allocation rate is provisionally determined for each fund (for each fund ID and for each fund name). The tentatively determined fund allocation ratio is adjusted so that the sum is 100% (function block 129).

  As described above, each fund has an investment allocation ratio for a plurality of investment targets. Each of the investment allocation ratios for a plurality of investment targets determined for each fund is revised so that the total is the provisionally determined fund allocation ratio while maintaining the ratio (function block 131). . For example, each of the investment allocation ratios for a plurality of investment targets for a certain fund is 10%, 50%, 0%, 20%, and 20% (when there are five investment targets). Assume that the fund allocation rate provisionally determined for the fund is 20%. In this case, the value obtained by multiplying the fund allocation rate “20%” by 10% / 100%, 50% / 100%, 0% / 100%, 20% / 100%, 20% / 100% ( 2%, 10%, 0%, 4%, 4%) is the investment allocation ratio for the investment target after adjustment for the fund.

  After the investment allocation ratio for the revised investment target is determined for each fund, the investment allocation ratio for the revised investment target for each fund is summed for each investment target. The total value of the investment allocation ratio for each investment target (herein referred to as a custom portfolio) and the investment allocation ratio for each investment target included in the recommended model portfolio data are used to create a custom portfolio. And the distance (degree of separation) between the recommended model portfolio. The distance (degree of separation) between the custom portfolio and the recommended model portfolio is quantified by calculating the sum of the squares of the difference in investment allocation ratios for each investment target of the custom portfolio and the recommended model portfolio. (Function block 132).

  The distance (degree of separation) between the custom portfolio and the recommended model portfolio is temporarily stored in the memory (function block 133).

  The processing from the function block 129 to the function block 133 is repeated a predetermined number of times. A plurality of distance data is stored in the memory. The standard deviation and the expected rate of return are calculated by using the investment allocation rate for the investment target after the correction for each fund that has obtained the closest distance (function block 134).

  The same processing as in the function blocks 123 to 126 is performed using the newly obtained standard deviation and expected rate of return (function block 135). If the operation is successful, the average, the expected asset amount when the operation fails, the probability of reaching the target amount, the probability of breaking the principal, the amount of money that is insufficient for the target amount (expected value) is calculated . In addition, the investment allocation ratio for the investment target after correction for each fund that has obtained the closest distance, the initial investment amount input from the first input device 122, and the monthly investment amount are used. The initial investment amount and the monthly investment amount are calculated (function block 135). Data representing the amount (numerical value) and data representing the probability (numerical value) are used to create data for displaying numerical values and graphs on the display screen of the display device, and are displayed on the display screen of the display device 136. The

  Each functional block shown in FIGS. 21 to 23 can also be configured by a hardware circuit. Of course, each functional block can also be realized by the cooperation of the hardware circuit and the software.

It is a block diagram which shows the whole structure of a custom portfolio creation system. It is a block diagram which shows the hardware constitutions of a center computer. An example of a model portfolio data table is shown. An example of a fund management data table is shown. An example of a correlation coefficient table is shown. An example of an investment object data table is shown. It is a flowchart which shows the process sequence of a custom portfolio creation program. An example of a basic information input window is shown. An example of a risk tolerance input window is shown. It is a flowchart which shows the process sequence of a recommendation model portfolio determination process. An example of a simulation result display window is shown. The calculation formula of scenario data is shown. Shows how scenario data is arranged in descending order of assets in the last month. It is a flowchart which shows the process sequence of a custom portfolio creation process. It is a flowchart which shows the process sequence of a custom portfolio creation process. An example of a fund allocation input window is shown. An example of a custom simulation result display window is shown. An example of a custom simulation result display window is shown. The state of agent creation is shown. The calculation of the distance between the recommended portfolio and the agent is shown. The functional block diagram of a custom portfolio creation system is shown. The other example of a functional block diagram of a custom portfolio creation system is shown. The other example of a functional block diagram of a custom portfolio creation system is shown.

Explanation of symbols

1 Network 2 Center computer 3 Client computer
11,21 CPU
12, 22 input device
13, 23, 109, 136 Display device
14, 24 memory
15, 25 hard disk
16, 26 Communication device
17 Model portfolio data table
18 Fund management data table
19 Correlation coefficient table
20 Investment data table
101 Investment stance information input device
102 Model portfolio data storage
106 Fund data storage
121 First storage device
122 First input device
128 Second input device
130 Second storage device

Claims (12)

Model portfolio data storage means for storing a plurality of model portfolio data in which an investment allocation ratio for each of a plurality of investment targets is determined for each of a plurality of model portfolios,
Fund data storage means for storing a plurality of fund data in which an investment distribution ratio for each of a plurality of investment objects is determined for each of a plurality of funds,
Investment stance data input means for accepting input of data representing investment stance,
Based on the investment stance data input from the investment stance data input means from among the plurality of model portfolio data stored in the model portfolio data storage means, any one of the model portfolio data The model portfolio data selection means for selecting data, and the fund so as to approach the investment allocation ratio for each of a plurality of investment objects included in the model portfolio data selected by the model portfolio data selection means. A fund investment distribution ratio correcting means for correcting an investment distribution ratio for each of a plurality of investment objects for each of a plurality of fund data stored in the data storage means;
Portfolio creation device with The model portfolio data storage means includes data representing the standard deviation and expected rate of return for each model portfolio,
The data representing the investment stance input from the investment stance data input means includes data representing a risk tolerance for investment,
The above model portfolio data selection means are:
Probability with respect to the return of each model portfolio obtained based on the standard deviation and the expected return of each of the model portfolio stored in the model portfolio data storage means, and the investment stance -Narrow down the model portfolio candidates to be selected based on the risk tolerance data input from the data input means.
The portfolio creation device according to claim 1. The data representing the investment stance input from the investment stance data input means further includes data representing an operation period, initial investment amount, monthly investment amount and target amount,
Data representing the investment period, initial investment amount and monthly investment amount, data representing the standard deviation and expected rate of return for each model portfolio stored in the model portfolio data storage means, and generated random numbers And a first expected revenue calculation means for calculating data representing the expected revenue during the management period for each model portfolio.
The model portfolio selection method is as follows:
One model portfolio data is selected based on the data representing the expected revenue during the management period for each model portfolio calculated by the first expected revenue calculation means and the data representing the target amount. To do,
The portfolio creation device according to claim 1 or 2. The fund investment distribution rate correction means is
Fund allocation rate determining means for determining a fund allocation rate for each fund in correspondence with each of a plurality of fund data stored in the fund data storage means,
Maintaining the ratio of the investment allocation ratio to each of the plurality of investment objects included in each fund data, so that the sum of the investment allocation ratios matches the fund allocation ratio determined by the fund allocation ratio determining means. Correction means for correcting the investment distribution ratio of each fund data, and the investment distribution ratios for a plurality of investment objects included in each fund data corrected by the above correction means are summed for each investment object, and each investment object Calculate the sum of the squares of the differences between each of the investment allocation ratios and the corresponding investment allocation ratio of the investment target included in the model portfolio data selected by the model portfolio selection means. Including calculation means,
The determination of the fund allocation ratio for each fund by the fund allocation ratio determining means, the correction of the investment distribution ratio of each fund data by the correcting means, and the calculation of the sum of the squares of the differences by the calculating means are repeated several times, and the smallest difference The portfolio creation device according to any one of claims 1 to 3, wherein an investment distribution ratio of each fund data from which a sum of squares of the two is obtained is adopted. Based on the investment allocation rate for each investment object for each of the initial investment amount and the data representing the monthly investment amount and the plurality of fund data corrected by the fund investment allocation rate correction means, Fund allocation amount calculation means for calculating the initial investment amount allocation amount and the monthly investment amount allocation amount for each of the above, and the initial investment amount allocation amount for a plurality of funds calculated by the fund allocation amount calculation means, and Display data creation means for creating data for displaying the amount allocated to the monthly investment amount on the display screen of the display device;
The portfolio creation device according to claim 3 or 4, further comprising: Standard deviation for a custom portfolio represented by the sum of each investment target of the investment allocation ratio for each of the plurality of investment targets for each of the plurality of fund data corrected by the above-mentioned fund investment allocation ratio correction means And standard deviation and expected rate of return calculation means for calculating expected rate of return, and standard deviation and expected rate of return of custom portfolio calculated by the above standard deviation and expected rate of return calculation means, entered investment period, initial investment Provided with a second expected income amount calculation means for calculating data representing the expected income amount during the operation period based on data representing the amount of money and monthly investment amount, and a generated random number;
The portfolio creation device according to any one of claims 3 to 5. Graph display data for creating display data for displaying a graph representing the expected earnings during the management period based on the data representing the expected earnings during the management period calculated by the second expected earnings calculation means With creation means,
The portfolio creation device according to claim 6. Repeat the calculation of the data representing the expected earnings during the investment period by the second expected earnings calculation means at least three times, and if the operation is successful from the data representing multiple expected earnings, the average A decision means for determining data representing the expected revenue during the operation period, and a case where the operation is successful, an average case, and an operation Based on the data representing the expected revenue when the operation fails, a graph that shows the expected revenue during the operation period for each of the successful operation, average operation, and operation failure Equipped with graph display data creation means for creating data to be displayed on the display screen
The portfolio creation device according to claim 6. The calculation of the data representing the expected earnings during the operation period by the second expected earnings calculation means is repeated a plurality of times,
A means for calculating at least one of the probability of reaching the target amount, the probability of breaking the principal, and the expected shortage based on the data representing the plurality of expected earnings and the data representing the target amount A means for creating data for displaying on the display screen of the display device at least one of a probability of reaching the target amount, a probability of breaking principal, and an expected shortage amount,
The portfolio creation device according to claim 6. Computer
A model portfolio data storage means for storing a plurality of model portfolio data in which an investment allocation ratio for each of a plurality of investment objects is determined;
Fund data storage means for storing a plurality of fund data in which an investment distribution ratio for each of a plurality of investment objects is determined,
Investment stance data input means for accepting input of data representing investment stance,
Based on the investment stance data input from the investment stance data input means from among the plurality of model portfolio data stored in the model portfolio data storage means, any one of the model portfolio data The model portfolio data selection means for selecting data, and the fund so as to approach the investment allocation ratio for each of a plurality of investment objects included in the model portfolio data selected by the model portfolio data selection means. A fund investment distribution ratio correcting means for correcting an investment distribution ratio for each of a plurality of investment objects for each of a plurality of fund data stored in the data storage means;
Program to function as. For each of a plurality of model portfolios, a plurality of model portfolio data in which an investment allocation ratio for each of a plurality of investment targets is determined is stored in a model portfolio data storage device,
For each of a plurality of funds, a plurality of fund data in which an investment distribution ratio for each of a plurality of investment objects is determined is stored in a fund data storage device,
Accepting the input of data representing investment stance from the investment stance data input device,
Based on the data representing the investment stance accepted by the model portfolio data selection device, one of the plurality of model portfolio data is selected, and the model portfolio data is selected.
A plurality of investment targets for each of the plurality of fund data are arranged so as to approach the investment distribution ratio for each of the plurality of investment targets included in the selected model portfolio data by the fund investment distribution ratio correction device. Modify the investment allocation ratio for each,
Portfolio creation method. Model portfolio data storage means for storing a plurality of model portfolio data in which an investment allocation ratio for each of a plurality of investment targets is determined for each of a plurality of model portfolios,
Fund data storage means for storing a plurality of fund data in which an investment distribution ratio for each of a plurality of investment objects is determined for each of a plurality of funds,
Investment stance data input means for receiving input of data representing an investor's risk tolerance and data representing an investment stance including investor's investment desire data;
Of the plurality of model portfolio data stored in the model portfolio data storage means, data representing risk tolerance included in the investment stance data input from the investment stance data input means and investment wishes A model portfolio data selection means for selecting any one model portfolio data based on the data, and a plurality of investment targets included in the model portfolio data selected by the model portfolio data selection means. The investment allocation rate for each of the plurality of investment data for each of the plurality of investment objects for each of the plurality of fund data stored in the fund data storage means is close to each other. Fund data storage A fund investment distribution ratio correcting means for correcting an investment distribution ratio for each of a plurality of investment objects for each of a plurality of fund data stored in the means;
Portfolio creation device.

Images