Processing method and program

Abstract

To provide a retrospective means and opportunity relating to transaction of an investment product.SOLUTION: A program disclosed herein causes a computer to execute a process of acquiring first data indicating a time-series change in the price of an investment product, a process of acquiring second data indicating a time-series change based on the valuation profit and loss of a plurality of users, and a process of displaying the first data and the second data side by side.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information providing server, a data processing device, an information providing method, and a program. [Background technology]

[0002] Techniques related to the present invention are disclosed in Patent Documents 1 to 4.

[0003] Patent Document 1 discloses a technology that defines a trading style by clustering the trading orientation of a user based on the past trading information and market price information of all users, and then searches for a path to the desired trading style and provides advice.

[0004] Patent Document 2 discloses a technology for generating and presenting advice to customers who buy and sell investment products such as stocks, investment trusts, exchange-traded funds (ETFs), foreign exchange margin trading (FX), etc. This technology generates diagnostic results including information on the user's trading trends, information on the reasons for the user's trading trends, information on social aspects of the user's trading trends, and information for improving the user's trading trends, and generates advice based on the diagnostic results.

[0005] Patent Document 3 discloses a technology that performs the steps of aggregating investment data and real-time trade data of multiple investors, ranking the multiple investors according to investment performance derived from the investment data, generating security ratings for securities held by the multiple investors using the rankings and trade data, and providing customized recommendations.

[0006] Patent Document 4 discloses a learning means for a decision list, which is one of rule-based models that combines a plurality of simple conditions. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] International Publication No. 2019 / 087552 [Patent Document 2] Patent Publication No. 2019-74863 [Patent Document 3] Special table number 2010-501909 [Patent Document 4] International Publication No. 2020 / 059136 Summary of the Invention [Problem to be solved by the invention]

[0008] When trading investment products, it is difficult to judge the timing of buying and selling. It is also not easy to review the success or failure of an investment product transaction.

[0009] The present invention aims to provide a means and opportunity for reviewing transactions of investment products. [Means for solving the problem]

[0010] According to the present invention, An information providing server is provided that has an output means for outputting a screen that displays, side by side, reference target customer trading tendency time series data showing the trading tendencies of multiple reference target customers over time, calculated for each stock based on the past investment product trading data of the multiple reference target customers who meet the reference criteria, and a price chart showing the time-series price changes of the investment products.

[0011] Further, according to the present invention, The computer An information provision method is provided that outputs a screen that displays, side by side, reference target customer trading tendency time series data showing the trading tendencies of multiple reference target customers over time, calculated for each stock based on the past investment product trading data of the multiple reference target customers who meet the reference criteria, and a price chart showing the time-series price changes of the investment products.

[0012] Further, according to the present invention, Computer, A program is provided that functions as an output means for outputting a screen that displays, side by side, reference target customer trading tendency time series data showing the trading tendencies of multiple reference target customers over time, calculated for each stock based on the past investment product trading data of multiple reference target customers who meet the reference criteria, and a price chart showing the time-series price changes of investment products.

[0013] Further, according to the present invention, an identification means for identifying a plurality of reference target customers that satisfy a reference criterion from among a plurality of customers; a calculation means for calculating reference target customer trading tendency time series data that indicates the trading tendency of the plurality of reference target customers for each issue in a time series based on past investment product trading data of the plurality of reference target customers; A data processing apparatus is provided having: [Effects of the Invention]

[0014] According to the present invention, a technology is realized that can provide a means and opportunity for reviewing transactions of investment products. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 10 is a diagram showing an example of a screen provided by the information providing server of the present embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information providing server and a data processing device according to the present embodiment. [Figure 3] FIG. 2 is a functional block diagram illustrating an example of a data processing device according to the present embodiment. [Figure 4] FIG. 2 is a diagram for explaining the concept of calculations of the data processing device of the present embodiment. [Figure 5] FIG. 2 is a diagram for explaining the concept of calculations of the data processing device of the present embodiment. [Figure 6] FIG. 2 is a functional block diagram of an information providing server according to the present embodiment; DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In all the drawings, like components are designated by like reference numerals, and the description thereof will be omitted as appropriate.

[0017] <Summary> First, an overview of the technology of this embodiment will be described. The technology of this embodiment is a technology that allows for obtaining means and opportunities for reviewing transactions of investment products such as stocks. The technology of this embodiment is used, for example, by business entities (such as securities companies) that act as intermediaries for the buying and selling of investment products. The business entities use the technology of this embodiment to provide customers with information that allows for obtaining means and opportunities for reviewing transactions of investment products. For example, the information is provided to customers via the business entity's web page or application. Note that this use example is merely an example and is not limited to this.

[0018] The technology of this embodiment is realized by a data processing device that generates data to be provided to a customer, and an information providing server that transmits predetermined information to a customer's terminal in response to a customer's request.

[0019] The data processing device identifies, from among the business entity's customers, customers whose buying and selling timing is useful as a reference (e.g., customers who are making profits) as reference customers.The data processing device then generates data showing the buying and selling trends of the reference customers over time based on the past investment product transaction data of the identified reference customers.The information providing server then provides the customer with a screen that displays the generated data showing the buying and selling trends of the reference customers over time alongside data showing changes in the prices of investment products over time.

[0020] An example of this information is shown in Figure 1 (4). Details will be explained below, but Figure 1 (4-1) is data showing the time-series price changes of investment products, and Figure 1 (4-2) is data showing the time-series buying and selling tendencies of reference customers. Figure 1 (4-2) shows the time-series buying tendencies of reference customers. A larger value indicates a stronger buying tendency, and a smaller value indicates a weaker buying tendency. Note that by operating the screen, it is possible to switch between graphs showing the buying tendencies of reference customers and graphs showing the selling tendencies. The selling tendencies of reference customers are shown in the same way as the buying tendencies. This screen allows customers to check the relationship between price changes of investment products and the buying and selling tendencies of reference customers.

[0021] The data processing device also estimates the causes of the trading trends of the reference target client at each timing based on the past investment product transaction data of the identified reference target client and the past state values ​​of each of a plurality of decision-making factors (which may affect the decision to buy or sell investment products, such as the number of days since the previous settlement).The information providing server then provides the client with a screen showing the estimation results, i.e., the causes of the trading trends of the reference target client at each timing.

[0022] An example of this information is shown in Figure 1 (5). Details will be explained below, but Figure 1 (5) shows the factors and their status values ​​that are presumed to be the cause of the reference customer's buying tendency on June 3, 2019. This screen allows customers to check what factors the reference customer is using to decide the timing of buying and selling.

[0023] "Configuration of data processing device" Next, the configuration of the data processing device will be described. As described above, the data processing device is a device that generates data to be provided to customers.

[0024] <Hardware configuration> An example of the hardware configuration of a data processing device will be described below. FIG. 2 is a diagram showing an example of the hardware configuration of a data processing device. Each functional unit of the data processing device is realized by any combination of hardware and software, centered around a CPU (Central Processing Unit) of any computer, memory, programs loaded into the memory, a storage unit such as a hard disk that stores the programs (this can store programs that are pre-loaded when the device is shipped, as well as programs downloaded from storage media such as CDs (Compact Discs) or servers on the Internet), and a network connection interface. Those skilled in the art will understand that there are many variations in the methods and devices for realizing this.

[0025] As shown in FIG. 2, the data processing device has a processor 1A, a memory 2A, an input / output interface 3A, a peripheral circuit 4A, and a bus 5A. The peripheral circuit 4A includes various modules. The data processing device does not necessarily have to have the peripheral circuit 4A. The data processing device may be composed of multiple physically and / or logically separated devices, or may be composed of a single device that is physically and logically integrated. In the former case, each of the multiple devices that make up the data processing device may have the above hardware configuration.

[0026] The bus 5A is a data transmission path for the processor 1A, memory 2A, peripheral circuit 4A, and input / output interface 3A to transmit and receive data to and from each other. The processor 1A is an arithmetic processing device such as a CPU or a GPU (Graphics Processing Unit). The memory 2A is a memory such as a RAM (Random Access Memory) or a ROM (Read Only Memory). The input / output interface 3A includes an interface for acquiring information from an input device, an external device, an external server, an external sensor, etc., and an interface for outputting information to an output device, an external device, an external server, etc. Examples of input devices include a keyboard, a mouse, a microphone, etc. Examples of output devices include a display, a speaker, a printer, a mailer, etc. The processor 1A can issue commands to each module and perform calculations based on the results of those calculations.

[0027] <Functional configuration> Next, the functional configuration of the data processing device will be described. Fig. 3 shows an example of a functional block diagram of the data processing device 20. As shown in the figure, the data processing device 20 has an identification unit 21, a calculation unit 22, and a second storage unit 23.

[0028] The identification unit 21 identifies a plurality of reference target customers who satisfy the reference criteria from among a plurality of customers. The identification result is used in the processing by the calculation unit 22, which will be described below.

[0029] The "customer" is a customer of a business entity that uses the technology of this embodiment.

[0030] The "entity that uses the technology of this embodiment" is, for example, an entity that acts as an intermediary for buying and selling investment products.

[0031] Examples of "investment products" include, but are not limited to, stocks, investment trusts, exchange-traded funds (ETFs), foreign exchange margin trading (FX), gold, virtual currencies, bonds, and real estate investment trusts (REITs).

[0032] The "reference standard" is set so that it is met by customers for whom the timing of buying and selling is useful, and is not met by customers for whom the timing of buying and selling is not useful.

[0033] For example, the reference standard is defined using the valuation profit and loss during the reference period. An example of a standard using the valuation profit and loss during the reference period is "the valuation profit and loss during the reference period corresponds to the top M% of all customers." According to such a reference standard, customers who are generating particularly excellent profits among all customers are identified as reference customers.

[0034] In addition, the reference standard may be defined using at least one of the number of trades per day during the reference period, the total number of trades during the reference period, and the number of stocks traded during the reference period, in addition to the valuation profit / loss during the reference period.

[0035] An example of a criterion using the number of trades per day during the reference period is "the statistical value (maximum, average, mode, etc.) of the number of trades per day during the reference period is equal to or less than a first reference value." Using such a reference criterion, day traders who frequently trade can be excluded from reference customers.

[0036] An example of a criterion using the total number of transactions during the reference period is "the total number of transactions during the reference period is equal to or greater than a second reference value." Using such a reference criterion, customers with an extremely low trading frequency can be excluded from the reference target customers.

[0037] An example of a criterion using the number of issues traded during the reference period is "the number of issues traded during the reference period is equal to or greater than a third threshold." Using such a reference criterion, customers who trade very infrequently can be excluded from the list of reference customers.

[0038] For example, the reference standard can be a standard that uses the valuation gain or loss during the reference period and at least one of the following: a standard that uses the number of trades per day during the reference period, a standard that uses the total number of trades during the reference period, and a standard that uses the number of stocks traded during the reference period, connected by a logical operator (e.g., logical AND).

[0039] The "reference period" is the period used for identifying reference customers. There are various ways to define the reference period. For example, the reference period may be the most recent specified period (e.g., the most recent year, the most recent six months, etc.). When the reference period is defined in this way, the reference period is updated daily. As a result, the customers identified as reference customers may also change daily. Note that the reference period may also be defined in other ways, such as the previous month, the previous year, or the previous fiscal year.

[0040] The reference period may be a predetermined fixed value or may be freely set by the client. In the latter case, the client can specify a desired period and learn the timing of buying and selling from reference clients who have made good profits during that period. For example, by setting a long reference period, clients who have made good profits over a long period of time can be designated as reference clients. Furthermore, by setting a period during which the price of an investment product is falling as the reference period, clients who have made good results during that period can be designated as reference clients.

[0041] The second storage unit 23 stores past investment product transaction data (trading history, profits and losses, earnings, etc.) of each of a plurality of clients. The identification unit 21 identifies a plurality of reference target clients who meet the reference criteria based on the investment product transaction data stored in the second storage unit 23.

[0042] The identifying unit 21 may further identify a plurality of comparison target customers who satisfy the comparison criteria from among the plurality of customers. The identification result is used in the processing by the calculation unit 22, which will be described below.

[0043] The "comparison standard" is set so that it is met by customers for whom the timing of buying and selling is not useful, and is not met by customers for whom the timing of buying and selling is useful.

[0044] For example, the comparison standard is defined using the valuation profit and loss during the reference period. An example of a standard using the valuation profit and loss during the reference period is "the valuation profit and loss during the reference period corresponds to the bottom N% of all customers." According to such a comparison standard, customers who are not particularly profitable among all customers are identified as comparison target customers.

[0045] As with the reference standard, the comparison standard may be defined using at least one of the number of transactions per day during the reference period, the total number of transactions during the reference period, and the number of stocks traded during the reference period, in addition to the unrealized profit or loss during the reference period. Details are the same as for the reference standard. By defining the comparison standard using such items, it is possible to exclude day traders who trade frequently and customers who trade very infrequently from the comparison target customers.

[0046] The second storage unit 23 stores past investment product transaction data (trading history, profits and losses, earnings, etc.) of each of a plurality of clients. The identification unit 21 identifies a plurality of comparison target clients who meet the comparison criteria based on the investment product transaction data stored in the second storage unit 23.

[0047] The calculation unit 22 executes a process of calculating the trading tendency time series data of the reference target customer and a process of estimating the cause of the trading tendency at each timing indicated by the trading tendency time series data of the reference target customer. Each process will be described in detail below.

[0048] -Processing to calculate time series data on trading trends of reference customers- The calculation unit 22 calculates reference target client trading tendency time series data for each issue based on past investment product trading data of the multiple reference target clients identified by the identification unit 21.

[0049] "Reference customer trading tendency time series data" is data that shows the trading tendency of multiple reference customers over time. The data in (4-2) in Figure 1 is the reference customer trading tendency time series data.

[0050] The calculation unit 22 separately generates reference target customer buying and selling tendency time series data that indicates the buying tendency of multiple reference target customers over time, and reference target customer buying and selling tendency time series data that indicates the selling tendency of multiple reference target customers over time. There are cases where the buying tendency and the selling tendency simultaneously become stronger or weaker. For this reason, data indicating the buying tendency and data indicating the selling tendency are generated separately.

[0051] The reference target customer buying and selling tendency time series data, which shows the buying tendency of multiple reference target customers over time, is data that indicates the strength of the buying tendency as a numerical value for each predetermined unit period. The calculation unit 22 calculates a numerical value indicating the strength of the buying tendency for each unit period based on a predetermined calculation formula. In (4-2) of FIG. 1, the unit period is "1 day," and the strength of the buying tendency is indicated by a "value in the range of maximum value +5, minimum value -5." The larger the value, the stronger the buying tendency. Note that this example is merely an example and is not limiting.

[0052] Furthermore, the reference target customer trading tendency time series data, which shows the selling tendency of multiple reference target customers over time, is data that numerically indicates the strength of the selling tendency for each predetermined unit period. The calculation unit 22 calculates a numerical value indicating the strength of the selling tendency for each unit period based on a predetermined arithmetic formula. As in the example of (4-2) in FIG. 1, for example, the unit period is "1 day," and the strength of the selling tendency is indicated by "a value in the range of a maximum value +5 to a minimum value -5." The larger the value, the stronger the selling tendency. Note that this example is merely an example and is not limited to this.

[0053] There are various methods for calculating the strength of the buying trend and the strength of the selling trend, and various methods can be adopted in this embodiment. An example will be described below.

[0054] -Calculation example 1- In this example, the strength of the buying trend for each unit period is calculated based on the results of a comparison with the buying trend for the specified period, i.e., "to what extent have the reference customers increased / decreased their purchases compared to the specified period?"

[0055] The greater the degree of increase in purchases compared to a predetermined period, the larger the numerical value indicating the strength of the purchasing trend is set, and the greater the degree of decrease in purchases compared to a predetermined period, the smaller the numerical value indicating the strength of the purchasing trend is set.

[0056] The predetermined period may be the most recent few days, the most recent few months, the most recent year, or any other period.

[0057] This will be explained in more detail using Figure 4. Figure 4 is a diagram for explaining the concept of the process for calculating the strength of the buying trend on March 5th. In the figure, the five days from February 27th to March 4th correspond to the specified period. The bar graph and the numerical values ​​(number of shares purchased) displayed above it show the buying status of the reference target customers on each day. The number of shares purchased, which indicates the buying status of the reference target customers, is a statistical value (total value, average value, etc.) of the number of shares purchased by each of multiple reference target customers. Note that the method of showing the buying status using the number of shares purchased is an example when the investment target is stocks. The buying status of the reference target customers can be expressed numerically using an appropriate method depending on the type of investment target.

[0058] In the example of Figure 4, the buying situation on March 5th is greater than the buying situation over the specified period (the average value over the specified period). Therefore, the numerical value indicating the strength of the buying trend on March 5th is a positive value. The greater the deviation between the buying situation on March 5th and the buying situation over the specified period (the average value over the specified period), the greater the numerical value indicating the strength of the buying trend on March 5th.

[0059] Although not shown, if the buying situation on March 5th is smaller than the buying situation for a specified period (the average value for the specified period), the numerical value indicating the strength of the buying trend for March 5th will be a negative value. The greater the deviation between the buying situation on March 5th and the buying situation for a specified period (the average value for the specified period), the smaller the numerical value indicating the strength of the buying trend for March 5th will be.

[0060] -Calculation example 2- In this example, the strength of the reference customer's purchasing tendency for each unit period is calculated based on "the extent to which the reference customer has increased / decreased purchases compared to a specified period" and "the extent to which the comparison customer has increased / decreased purchases compared to a specified period."

[0061] The greater the degree to which the reference target customer has increased purchases compared to the specified period, the larger the numerical value indicating the strength of the reference target customer's purchasing tendency is set.In this case, the smaller the degree to which the comparison target customer has increased purchases compared to the specified period, the larger the numerical value indicating the strength of the reference target customer's purchasing tendency is set.

[0062] The greater the degree to which the reference customer has reduced purchases compared to the predetermined period, the smaller the numerical value indicating the strength of the reference customer's purchasing tendency is made.In this case, the smaller the degree to which the comparison customer has reduced purchases compared to the predetermined period, the smaller the numerical value indicating the strength of the reference customer's purchasing tendency is made.

[0063] The predetermined period may be the most recent few days, the most recent few months, the most recent year, or any other period.

[0064] This will be explained in more detail using Figure 5. Figure 5 is a diagram for explaining the concept of the process for calculating the strength of the buying tendency of the reference target customer on March 5th. The bar graph shows the buying status of each of the reference target customer and the comparison target customer on March 5th, as well as the buying status over a specified period (average value for the specified period). The buying status of the reference target customer is a statistical value (total value, average value, etc.) of the number of shares purchased by each of multiple reference target customers. Similarly, the buying status of the comparison target customer is a statistical value (total value, average value, etc.) of the number of shares purchased by each of multiple comparison target customers. Note that the method of showing the buying status using the number of shares purchased is an example when the investment target is stocks. The buying status of the reference target customer can be expressed numerically using an appropriate method depending on the type of investment target.

[0065] In the example of Figure 5, the purchasing behavior of the reference target customer on March 5th is greater than the purchasing behavior of the reference target customer in the specified period (average value for the specified period). Therefore, the numerical value indicating the strength of the purchasing tendency of the reference target customer on March 5th is a positive value. The greater the deviation between the purchasing behavior of the reference target customer on March 5th and the purchasing behavior of the reference target customer in the specified period (average value for the specified period), the greater the numerical value indicating the strength of the purchasing tendency of the reference target customer on March 5th. Furthermore, the smaller the purchasing behavior of the comparison target customer on March 5th is than the purchasing behavior of the comparison target customer in the specified period (average value for the specified period), the greater the numerical value indicating the strength of the purchasing tendency of the reference target customer on March 5th.

[0066] Although not shown, if the buying activity of the reference customer on March 5th is smaller than the buying activity of the reference customer in a specified period (average value for the specified period), the numerical value indicating the strength of the buying tendency of the reference customer on March 5th will be a negative value. The greater the deviation between the buying activity of the reference customer on March 5th and the buying activity of the reference customer in a specified period (average value for the specified period), the smaller the numerical value indicating the strength of the buying tendency of the reference customer on March 5th will be. Furthermore, the greater the buying activity of the comparison customer on March 5th is than the buying activity of the comparison customer in a specified period (average value for the specified period), the smaller the numerical value indicating the strength of the buying tendency of the reference customer on March 5th will be.

[0067] By using the data of the comparison target customers in this way, it is possible to highlight the timing of the reference target customers' trading trends that you want to pay particular attention to, that is, the numerical values ​​of the timing that shows a different trend from the comparison target customers.

[0068] -Calculation example 3- In this example, the strength of the selling trend for each unit period is calculated based on the results of a comparison with the selling trend for the specified period, i.e., "to what extent has the reference customer increased / decreased their selling compared to the specified period?"

[0069] The greater the degree of increase in selling compared to the predetermined period, the larger the numerical value indicating the strength of the selling tendency is set, and the greater the degree of decrease in selling compared to the predetermined period, the smaller the numerical value indicating the strength of the selling tendency is set.

[0070] The predetermined period may be the most recent few days, the most recent few months, the most recent year, or any other period. The details are the same as in the first calculation example.

[0071] -Calculation example 4- In this example, the strength of the selling tendency of the reference customer is calculated based on "the extent to which the reference customer has increased / decreased sales compared to a specified period" and "the extent to which the comparison customer has increased / decreased sales compared to a specified period."

[0072] The greater the degree to which the reference target customer has increased sales compared to the specified period, the larger the numerical value indicating the strength of the reference target customer's selling tendency is made.In this case, the smaller the degree to which the comparison target customer has increased sales compared to the specified period, the larger the numerical value indicating the strength of the reference target customer's selling tendency is made.

[0073] The greater the degree to which the reference target customer has reduced sales compared to the predetermined period, the smaller the numerical value indicating the strength of the reference target customer's selling tendency is made.In this case, the smaller the degree to which the comparison target customer has reduced sales compared to the predetermined period, the smaller the numerical value indicating the strength of the reference target customer's selling tendency is made.

[0074] The predetermined period may be the most recent few days, the most recent few months, the most recent year, or any other period. Details are the same as in Calculation Example 2.

[0075] By using the data of the comparison target customers in this way, it is possible to highlight the timing of the reference target customers' trading trends that you want to pay particular attention to, that is, the numerical values ​​of the timing that shows a different trend from the comparison target customers.

[0076] -Processing to estimate the causes of trading trends at each timing indicated by time-series data on reference customer trading trends- The calculation unit 22 estimates the cause of the trading tendency at each timing indicated in the trading tendency time series data of the reference target customers based on the past investment product trading data of the multiple reference target customers identified by the identification unit 21 and the past state values ​​of each of the multiple decision-making items.

[0077] Factors that can influence the decision to buy or sell an investment product are factors that can affect the decision to buy or sell an investment product. Factors that can influence the decision to buy or sell an investment product vary depending on the investment product. For example, when the investment product is a stock, factors that can be determined from the stock price chart include the moving average deviation (5 days), moving average deviation (25 days), moving average deviation (75 days), golden cross (5-day and 25-day moving averages), and death cross (5-day and 25-day moving averages). Other items to consider when the investment product is stocks include various information about the company, such as years since establishment, market, days since listing, industry, full-year sales, full-year operating profit, full-year ordinary profit, full-year net profit, full-year sales compared to the previous year, full-year operating profit compared to the previous year, full-year ordinary profit compared to the previous year, full-year net profit compared to the previous year, number of news items, number of weekly news items, number of timely disclosures, number of weekly timely disclosures, expected PER (price-earnings ratio), expected EPS (earnings per share), expected ROE (return on equity ratio), expected dividend yield, actual dividend yield, actual dividend payout ratio, actual PBR (price-to-book ratio), actual BPS (book value per share), etc. Note that the examples given here are merely examples and are not limited to these.

[0078] Here, the process of estimating the causes of the buying and selling trends at each timing will be described. The calculation unit 22 estimates the causes of the buying and selling trends at each timing from these "past state values ​​of the decision-making items" using a model that regresses the above-mentioned "reference target customer buying and selling trend time-series data." When calculating values ​​indicating the strength of the buying and selling trends of the reference target customer on a daily basis as in (4-2) of FIG. 1, the calculation unit 22 estimates the causes of the strength of the buying trend and the strength of the selling trend on a daily basis.

[0079] The model is realized using the learning means disclosed in Patent Document 4. The model is generated by learning using the past investment product trading data (objective variable) of multiple reference clients and the past state values ​​(explanatory variables) of multiple decision-making items as training data. According to the model, it is possible to identify a rule that contributes well to the regression of the "reference client trading tendency time-series data" from among a large number of rules that are generated in advance by combining one or more of the decision-making items. Examples of the rule include, but are not limited to, "full-year ordinary profit is 5% or more compared to the previous year" and "full-year ordinary profit is 5% or more compared to the previous year and the industry is the service industry."

[0080] "Configuration of information provider server" Next, the configuration of the information providing server will be described. As described above, the information providing server is a device that transmits predetermined information to a customer's terminal in response to a customer's request.

[0081] <Hardware configuration> An example of the hardware configuration of an information providing server will be described below. FIG. 2 is a diagram showing an example of the hardware configuration of an information providing server. Each functional unit of the information providing server is realized by any combination of hardware and software, centered around a CPU (Central Processing Unit) of any computer, memory, programs loaded into the memory, a storage unit such as a hard disk that stores the programs (this can store programs that are pre-loaded when the device is shipped, as well as programs downloaded from storage media such as CDs (Compact Discs) or servers on the Internet), and a network connection interface. Those skilled in the art will understand that there are many variations in the methods and devices for realizing this.

[0082] As shown in FIG. 2, the information providing server has a processor 1A, a memory 2A, an input / output interface 3A, a peripheral circuit 4A, and a bus 5A. The peripheral circuit 4A includes various modules. The information providing server does not have to have the peripheral circuit 4A. The information providing server may be composed of multiple physically and / or logically separated devices, or may be composed of a single device that is physically and logically integrated. In the former case, each of the multiple devices that make up the information providing server can have the above hardware configuration.

[0083] <Functional configuration> The functional configuration of the information providing server will be described. Fig. 6 shows an example of a functional block diagram of the information providing server 10. As shown in the figure, the information providing server 10 has a communication unit 11, an output unit 12, a screen generation unit 13, and a first storage unit 14.

[0084] The communication unit 11 communicates with customer terminals via a communication network such as the Internet. The customer terminals include, but are not limited to, smartphones, mobile phones, tablet terminals, personal computers, smart watches, and the like.

[0085] The first storage unit 14 stores the calculation results of the calculation unit 22. In response to a request from a customer, the screen generation unit 13 generates a screen including predetermined information using the data stored in the first storage unit 14. The output unit 12 transmits (outputs) the screen generated by the screen generation unit 13 to the customer terminal via the communication unit 11. As a result, the screen is displayed on the customer terminal. The transmission and reception of the screen is realized, for example, via a web page or an application.

[0086] An example of a screen displayed on a client terminal is shown in Figure 1. Figure 1 shows an example of a screen when the investment product is a stock.

[0087] (1) in Figure 1 displays the name of the stock specified by the customer, the current stock price, the price change from the previous day, the number of shares held by the customer, the total valuation amount, and the total valuation profit / loss.

[0088] (2) in Figure 1 shows a UI (user interface) component that allows a customer to select a desired stock from stocks that they have traded in the past.

[0089] (3) in Figure 1 shows the past trading history for the stock specified by the customer.

[0090] In (4) of FIG. 1, a price chart ((4-1) of FIG. 1) showing the time-series price changes of a stock (investment product) specified by a customer is displayed side by side, along with reference target customer trading tendency time-series data ((4-2) of FIG. 1) showing the trading tendency of the stock of multiple reference target customers over time. The graph display shown in (4-2) of FIG. 1 is realized based on data calculated by the data processing device 20. In the illustrated example, the price chart and the reference target customer trading tendency time-series data are displayed in the same time series. That is, the displayed period, memory unit, memory interval, value of each memory, etc. are the same. The price chart and the reference target customer trading tendency time-series data are displayed vertically so that data of the same date and time are aligned vertically.

[0091] In addition, in (4-2) of Figure 1, a UI component is displayed that allows the selection of "Sell" or "Buy." In addition, in (4-2) of Figure 1, when "Buy" is selected, reference target customer trading tendency time series data that shows the buying tendencies of multiple reference target customers over time is displayed. When "Sell" is selected, the content of the graph in (4-2) of Figure 1 switches to reference target customer trading tendency time series data that shows the selling tendencies of multiple reference target customers over time. In this way, the reference target customer trading tendency time series data that shows the buying tendencies of multiple reference target customers over time and the reference target customer trading tendency time series data that shows the selling tendencies of multiple reference target customers over time are displayed separately.

[0092] (5) in Fig. 1 shows the factors and their status values ​​that are presumed to be the cause of the purchase tendency of the reference customer at the timing specified by the customer. The display shown in (5) in Fig. 1 is realized based on the data calculated by the data processing device 20.

[0093] In the figure, June 3, 2019 is specified. For example, the timing may be specified by selecting one bar graph on the graph in (4-2) of FIG.

[0094] In Figure 1 (5), the rules that contributed most to the regression of the "reference customer trading trend time series data" identified from among many rules are displayed in pairs, along with the contribution rate, which indicates the degree to which each rule contributed to the regression. The figure shows three rules and their respective contribution rates. The larger the contribution rate value, the greater the degree to which each rule contributed to the regression of the reference customer trading trend time series data.

[0095] Although three rules are shown in the figure, the number of rules to be displayed here is a design matter. From the viewpoint of ease of viewing, the screen generation unit 13 may be provided with means for appropriately selecting a rule to be displayed on the screen from among the multiple rules that contributed to the regression.

[0096] For example, the screen generation unit 13 may select rules to be displayed on the screen based on the condition that "a predetermined number of rules are selected in descending order of their degree of contribution."

[0097] Additionally, the screen generation unit 13 may be provided with a means for preventing the selection of overlapping rules with similar content. For example, the screen generation unit 13 may select rules to be displayed on the screen based on the condition that "rules that match some or all of the decision-making items are not selected overlappingly." Examples of rules that match some or all of the decision-making items include "full-year ordinary profit compared to the previous year is 5% or more" and "full-year ordinary profit compared to the previous year is 10% or more." These two rules both have the decision-making item "full-year ordinary history compared to the previous year," and are therefore completely identical.

[0098] Additionally, the screen generating unit 13 may select a rule that is not considered to be of interest to the comparison target customer from among a plurality of rules that contributed to the regression of the reference target customer trading tendency time series data.

[0099] In this example, the calculation unit 22 of the data processing device 20 generates the trading trend time series data of the comparison target customer using a method similar to the method for generating the trading trend time series data of the reference target customer. The trading trend time series data of the reference target customer is generated using the past investment product trading data of the reference target customer, while the trading trend time series data of the comparison target customer is generated using the past investment product trading data of the comparison target customer.

[0100] Then, the calculation unit 22 of the data processing device 20 estimates the cause of the trading tendency at each timing indicated in the comparison target customer trading tendency time series data using a method similar to the process of estimating the cause of the trading tendency at each timing indicated in the reference target customer trading tendency time series data.

[0101] Then, the screen generation unit 13 selects, from among the multiple rules that contributed to the regression of the reference target customer trading tendency time series data, rules that are not included in the multiple rules that contributed to the regression of the comparison target customer trading tendency time series data.

[0102] <Action and effect> According to the information providing server 10 and the data processing device 20 of this embodiment, as shown in (4) of Fig. 1, it is possible to provide a customer with a screen that displays data ((4-1) of Fig. 1) showing the time-series price changes of investment products and data ((4-2) of Fig. 1) showing the time-series buying and selling trends of reference customers, which are useful for determining the timing of buying and selling. This screen allows the customer to learn about the relationship between the price changes of investment products and the buying and selling trends of reference customers.

[0103] Furthermore, the information providing server 10 and the data processing device 20 can provide the customer with a screen displaying the factors presumed to be the cause of the buying and selling tendency of the reference customer at the timing designated by the customer, and their status values, as shown in (5) of Fig. 1. This screen allows the customer to learn what factors the reference customer uses to decide the timing of buying and selling.

[0104] Furthermore, the information providing server 10 and the data processing device 20 can generate the above screen by using not only the investment product trading data of the reference client, but also the investment product trading data of the comparison client whose buying and selling timing is not useful. By comparing with the comparison client, the particularly distinctive features of the reference client (buying and selling trends and decision-making factors) become more prominent, and it becomes possible to present these prominent details to the client.

[0105] In this specification, "acquisition" includes at least one of the following: "the device retrieves data stored in another device or storage medium (active acquisition)" based on user input or program instructions, such as receiving data by making a request or inquiry to another device, or accessing and reading out another device or storage medium; "the device inputs data output from another device (passive acquisition)" based on user input or program instructions, such as receiving data that is distributed (or transmitted, push notification, etc.), and selecting and acquiring data from received data or information; and "the device generates new data by editing data (converting it to text, rearranging data, extracting some data, changing the file format, etc.), and then acquires the new data."

[0106] Some or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes. 1. An information providing server having an output means for outputting a screen displaying side-by-side reference target customer trading tendency time series data showing the trading tendencies of multiple reference target customers over time, calculated for each stock based on the past investment product trading data of the multiple reference target customers who meet the reference criteria, and a price chart showing the time-series price changes of investment products. 2. The output means the reference target customer buying and selling tendency time series data showing the buying tendency of the plurality of reference target customers in a time series; the reference target customer trading tendency time series data showing the selling tendency of the plurality of reference target customers in a time series; The information providing server according to 1 outputs a screen that displays the above items separately. 3. The information providing server according to 1 or 2, wherein the reference standard is defined using valuation profit and loss within a reference period. 4. An information providing server as described in 3, wherein the reference standard is further defined using at least one of the number of trades per day during the reference period, the total number of trades during the reference period, and the number of stocks traded during the reference period. 5. An information providing server according to any one of 1 to 4, wherein the reference target customer trading tendency time series data is calculated based on the past investment product trading data of the plurality of reference target customers as well as the past investment product trading data of a plurality of comparison target customers who meet the comparison criteria. 6. The output means An information providing server as described in any one of 1 to 5, which outputs a screen showing the decision-making items and the status values ​​that are estimated to be the causes of the trading trends at each timing indicated in the trading trend time series data of the reference target customers based on the past investment product trading data of the plurality of reference target customers and the past status values ​​of each of the plurality of decision-making items. 7. The output means 7. The information providing server according to any one of 1 to 6, which outputs the screen on which the reference target customer trading tendency time series data and the price chart are displayed in the same time series. 8. The computer An information provision method that outputs a screen that displays, side by side, reference target customer trading tendency time series data that shows the trading tendencies of multiple reference target customers over time, calculated for each stock based on the past investment product trading data of the multiple reference target customers who meet the reference criteria, and a price chart that shows the time-series price changes of investment products. 9. Computer A program that functions as an output means for outputting a screen that displays, side by side, reference target customer trading tendency time series data showing the trading tendencies of multiple reference target customers over time, calculated for each stock based on the past investment product trading data of multiple reference target customers who meet the reference criteria, and a price chart showing the time-series price changes of investment products. 10. A means for identifying a plurality of reference customers that meet a reference criterion from among a plurality of customers; a calculation means for calculating reference target customer trading tendency time series data that indicates the trading tendency of the plurality of reference target customers for each issue in a time series based on past investment product trading data of the plurality of reference target customers; A data processing device having: 11. The identification means further identifies a plurality of comparison target customers who satisfy the comparison criteria from among the plurality of customers; The calculation means calculates the reference target customer trading tendency time series data based on the past investment product trading data of the plurality of reference target customers and the past investment product trading data of the plurality of comparison target customers.

[0107] This application claims priority based on Japanese Patent Application No. 2021-024931, filed on February 19, 2021, the disclosure of which is incorporated herein in its entirety. [Explanation of symbols]

[0108] 10 Information Server 11 Communications Department 12 Output section 13 Screen generation section 14 First memory unit 20 Data processing device 21 Specific section 22 Calculation section 23 Second memory section 1A processor 2A Memory 3A input / output I / F 4A peripheral circuit 5A Bus

Claims

1. A process of obtaining first data showing time-series price changes of investment products; A process of identifying users whose valuation profit and loss within a reference period meets a condition; A process of acquiring second data that indicates the strength of the user's buying tendency or the strength of the user's selling tendency in a time series for each unit period; A process of estimating the cause of the buying and selling tendency at each timing from the past state values ​​of the decision-making items using a model that regresses the second data; a process of displaying the first data, the second data, and the cause side by side; A program that causes a computer to execute the following.

2. The program of claim 1 , wherein the investment product includes stocks.

3. The program according to claim 2 , wherein the second data is acquired for each stock brand.

4. The process of displaying the first data and the second data side by side includes:

2. The program according to claim 1, wherein, in response to receiving a request from a user, the program causes the first data and the second data to be displayed side by side on a terminal of the user who sent the request.

5. 2. The program according to claim 1, wherein the first data and the second data each represent data from the same period.

6. The process of displaying the first data and the second data side by side includes:

2. The program according to claim 1, wherein the second data is displayed below the first data.

7. The program of claim 1, wherein the criteria for judgment include deviation from the 5-day moving average, deviation from the 25-day moving average, deviation from the 75-day moving average, golden cross between the 5-day moving average and the 25-day moving average, dead cross between the 5-day moving average and the 25-day moving average, number of years since establishment, type of market, number of days since listing, industry, full-year sales, full-year operating profit, full-year ordinary profit, full-year net profit, full-year sales compared to last year, full-year operating profit compared to last year, full-year ordinary profit compared to last year, full-year net profit compared to last year, number of news items, number of weekly news items, number of timely disclosures, number of weekly timely disclosures, expected price-earnings ratio (PER), expected earnings per share (EPS), expected ROE (return on sales), expected dividend yield, actual dividend yield, actual dividend payout ratio, actual PBR (price-to-book ratio), or actual BPS (book values ​​per share).

8. One or more computers A process of obtaining first data showing time-series price changes of investment products; A process of identifying users whose valuation profit and loss within a reference period meets a condition; A process of acquiring second data that indicates the strength of the user's buying tendency or the strength of the user's selling tendency in a time series for each unit period; A process of estimating the cause of the buying and selling tendency at each timing from the past state values ​​of the decision-making items using a model that regresses the second data; a process of displaying the first data, the second data, and the cause side by side; How to process.

9. The method of claim 8 , wherein the investment product includes stocks.

10. The processing method according to claim 9 , wherein the second data is acquired for each stock brand.

11. The process of displaying the first data and the second data side by side includes: The processing method according to claim 8, wherein, in response to receiving a request from a user, the first data and the second data are displayed side by side on a terminal of the user who sent the request.

12. 9. The processing method according to claim 8, wherein the first data and the second data each represent data from the same period.

13. The process of displaying the first data and the second data side by side includes: The processing method according to claim 8, wherein the second data is displayed below the first data.

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