Information processing method, information processing system, information processing device, and program

Abstract

Provided is a technique that is useful for predicting a price fluctuation in an investment brand.　This information processing method includes: a step for reading past price data pertaining to an investment brand according to a plurality of prediction indexes including a technical index or a chart pattern for predicting a price fluctuation in the investment brand; a determination step for determining, with respect to one or more prediction indexes for which the prediction of the price fluctuation has proven to be correct, whether the prediction satisfies a signal accuracy rate; and a step for selecting the prediction index satisfying the signal accuracy rate as an effective prediction index. The signal accuracy rate is an index indicating the relationship between the number of occurrences of a trading signal for each prediction index and the number of occurrences of proven-correct prediction in which a price fluctuation predicted by the trading signal has occurred.

Description

Information processing method, information processing system, information processing device, and program

[0001] The present disclosure relates to an information processing method, an information processing system, an information processing device, and a program.

[0002] As a method for predicting the price movements of financial products (referred to as "investment stocks" in this disclosure; further, "this disclosure" refers to the specification, claims, and drawings) whose prices are traded in the market, a method of predicting using technical indicators and chart patterns of investment stocks as predictive indicators is known. However, the technical indicators and chart patterns of investment stocks used to predict price movements are diverse and numerous, making it difficult even for experienced investors to find effective technical indicators and chart patterns for each investment stock. Patent Document 1 proposes a system that notifies a customer of stocks that meet the criteria for stock investment indicators requested by the customer.

[0003] Japanese Patent Application Laid-Open No. 2004-29882

[0004] Therefore, the present disclosure provides a new technology that is useful for predicting price movements of investment stocks.

[0005] A first aspect of the present disclosure is an information processing method performed by a computer device, the information processing method including the steps of: reading past price data of an investment stock using a plurality of predictive indicators, including technical indicators or chart patterns, that predict price movements of the investment stock; determining whether or not one or more of the predictive indicators that have correctly predicted price movements satisfy a signal accuracy rate, where the signal accuracy rate is an index indicating the relationship between the number of times a buy / sell signal is generated for each of the predictive indicators and the number of times that the predictions that have correctly predicted the price movements that have occurred by the buy / sell signals are generated; and selecting the predictive indicators that satisfy the signal accuracy rate as effective predictive indicators.

[0006] This can be useful in predicting the price movements of investment stocks.

[0007] In particular, in the first aspect, past price data of an investment stock is read using a plurality of predictive indicators, including technical indicators or chart patterns that predict price movements of the investment stock (reading step), thereby making it possible to confirm whether each predictive indicator can be used to predict price movements of the investment stock.

[0008] In addition, in the first aspect, for one or more of the predictive indicators whose price movement predictions have been successful, it is determined whether the predictions satisfy a signal accuracy rate (determination step). The signal accuracy rate is an index that indicates the relationship between the number of times a buy / sell signal occurs for each predictive indicator and the number of times that the predictions of the price movement predicted by the buy / sell signal have been successful. This makes it possible to determine the predictive indicators with higher prediction accuracy among the predictive indicators whose price movement predictions have been successful. The determination criterion can be the signal accuracy rate, which indicates the relationship between the number of times a buy / sell signal occurs and the number of times that the predictions have been successful, thereby enabling a clear determination.

[0009] Furthermore, in the first aspect, the predictive indicators that satisfy the signal accuracy rate are selected as effective predictive indicators, so that predictive indicators with high prediction accuracy can be selected as effective predictive indicators (selection step). The selected one or more effective predictive indicators are selected by reading past price data for each investment stock. Therefore, the effective predictive indicators are predictive indicators that reflect the factors and characteristics of price movements for each investment stock.

[0010] The determining step determines whether the signal accuracy rate is satisfied for a second retroactive period that is shorter than a first retroactive period for accumulating the price data retroactively from a prediction reference time for predicting price movements. This provides a new technology for predicting price movements of investment stocks. The first retroactive period is a period for accumulating price data. In contrast, the second retroactive period reflects the characteristics of the most recent price movements of the investment stocks. Therefore, it is possible to select an effective predictive indicator with higher prediction accuracy.

[0011] Predictions of price movements using technical indicators or chart patterns (referred to as "technical indicators" and "predictive indicators" in this disclosure) are based on the empirical rule that there is a certain regularity between buy / sell signals and subsequent price movements. However, there are no technical indicators that are always effective for all investment stocks. In other words, the price movements of investment stocks involve uncertainty based on various factors that cannot be captured by the empirical rule. Therefore, even if a technical indicator has been able to predict price movements in the past, it is preferable to confirm that it is still reliable at the forecast reference time and improve the accuracy of the prediction.

[0012] Therefore, in the information processing method, the satisfaction of the signal accuracy rate for the second retrospective period is determined, and an effective forecast indicator is selected. Therefore, according to this information processing method, it is possible to select a more accurate technical indicator that reflects a trend close to the forecast reference time.

[0013] Here, "forecast base time" means the time point at which a prediction of the price movement of the investment stock is made. The "forecast base time" may be, for example, the following times, but is not limited to these and may be other times. Furthermore, the "forecast base time" includes at least the year, month, and date, and may also include the time of day. The "forecast base time" described throughout this disclosure always has the above meaning, regardless of whether it is explicitly explained otherwise. (1) When the price of the investment stock is updated (e.g., any time point at which the price changes on the trading market, any time point at which price data available on the first computer device is updated) (2) When a prediction of the price movement of the investment stock is made (e.g., the present time, any time point in the past)

[0014] Furthermore, the information processing method may include a step of generating predictive evaluation data that predicts price movements based on the effective predictive indicator. This allows predictive evaluation data to be obtained based on accurate effective predictive indicators, and the predictive evaluation data can be used for information provision, etc. Here, the "predictive evaluation data" may be a predicted price of an investment stock. For example, the predicted price may be a price a predetermined period after a prediction reference time or a price a predetermined period after a buy / sell signal is generated, but is not limited to these and may be a price a predetermined period after a certain point in time.

[0015] The forecast valuation data may include forecast valuation information indicating whether the investment stock is undervalued (buy), neutral, or overvalued (sell). This allows the specific trend of the investment stock to be easily known. Here, the "forecast valuation information" is not limited to a specific form as long as it can directly or indirectly indicate whether the investment stock is undervalued, neutral, or overvalued. The information may be displayed in a recognizable form using characters, numbers, symbols, graphs, charts, etc.

[0016] The information processing method may include a step of generating an indicator list of the technical indicators or chart patterns selected as the effective predictive indicators. As is known, there are many predictive indicators based on technical indicators or chart patterns, and selecting effective predictive indicators is difficult and therefore a major concern for investors. Since the information processing method generates an indicator list of effective predictive indicators, the indicator list can be used for information provision, etc. By viewing the indicator list, investors can learn about the technical indicators that form the basis of the predictive evaluation data, which can serve as an opportunity for investment learning.

[0017] The indicator list may include the technical indicators or chart patterns as ineffective predictive indicators that are not selected as effective predictive indicators. While a large number of predictive indicators are known, there are not many effective predictive indicators for each investment stock. Therefore, knowing ineffective predictive indicators that should not be used for prediction is an important concern for investors. The indicator list including ineffective predictive indicators can be used for informational purposes, and can serve as an opportunity for investors to learn about investments.

[0018] Furthermore, the information processing method may include a step of generating a signal chart including buy and sell signals when price movements of the price data are predicted using the effective predictive indicators. This allows for confirmation of sell and buy signals that appear in past price data based on the effective predictive indicators. When two or more technical indicators are selected as effective predictive indicators, the buy and sell signals are predicted by combining them. Therefore, unlike, for example, displaying a single technical indicator on a chart, a chart can be created that includes buy and sell signals derived from a combination of multiple effective predictive indicators, which is difficult for average investors to create. The signal chart can be used for information provision, etc.

[0019] Furthermore, the information processing method may include a step of generating investment simulation data showing the performance results when the effective predictive indicator is applied to the price data and traded. This allows the effectiveness of the effective predictive indicator to be confirmed. Here, the "investment simulation data" may be data showing the return when an investment stock is managed based on the effective predictive indicator. For example, the data showing the return may be the amount of investment profit or loss, or the rate of increase or decrease of investment profit or loss, but is not limited to this and may be other data. For example, it may be the rate of change including dividends and distributions. Furthermore, the period of the investment simulation data may be in units of years, months, days, etc.

[0020] Furthermore, the information processing method may include a step of transmitting, to a second computer device capable of communicating with the first computer device, forecast information regarding a forecast of the price movement of the investment stock, the forecast information being output by the second computer device. This allows effective forecast indicators, forecast evaluation data, etc., generated by the first computer device to be provided to the second computer device. The first computer device may be an information providing server, and the second computer device may be a user terminal that receives information providing services from the information providing server.

[0021] The predictive information can be output by the second computer device in the form of at least one of text, audio, images, video, and character conversation. This allows the second computer device to output predictive information in the form of at least one of text, audio, images, video, and character conversation, making it possible to provide information according to the needs of a user of the second computer device.

[0022] A second aspect of the present disclosure is an information processing system, the information processing system including a first computer device and a second computer device, the first computer device including a reading unit, an accuracy rate satisfaction determination unit, and an effective predictive indicator selection unit, the reading unit being configured to be able to read past price data of an investment stock using a plurality of predictive indicators including technical indicators or chart patterns that predict price movements of the investment stock, the accuracy rate satisfaction determination unit being configured to determine whether or not one or more of the predictive indicators that have predicted the price movements have been successful satisfy a signal accuracy rate, the signal accuracy rate being an index indicating the relationship between the number of times a buy / sell signal has occurred for each of the predictive indicators and the number of times that the predictions have been successful when the price movements predicted by the buy / sell signals have occurred, and the effective predictive indicator selection unit being configured to select the predictive indicators that satisfy the signal accuracy rate as effective predictive indicators.

[0023] A third aspect of the present disclosure is an information processing device, the information processing device including a reading unit, an accuracy rate fulfillment determination unit, and an effective predictive indicator selection unit, wherein the reading unit is configured to read past price data of an investment stock using a plurality of predictive indicators including technical indicators or chart patterns that predict price movements of the investment stock, the accuracy rate fulfillment determination unit is configured to determine whether or not one or more of the predictive indicators that have correctly predicted the price movements satisfy a signal accuracy rate, wherein the signal accuracy rate is an index indicating the relationship between the number of times a buy / sell signal is generated for each of the predictive indicators and the number of times that the predictions have been successful in generating price movements predicted by the buy / sell signals, and the effective predictive indicator selection unit is configured to select the predictive indicators that satisfy the signal accuracy rate as effective predictive indicators.

[0024] A fourth aspect of the present disclosure is a program that causes a computer device to operate as a server that predicts price movements of an investment stock, the program including instructions to read past price data of the investment stock using a plurality of predictive indicators including technical indicators or chart patterns that predict price movements of the investment stock, and to determine whether or not one or more of the predictive indicators that have correctly predicted price movements satisfy a signal accuracy rate, where the signal accuracy rate is an indicator that indicates the relationship between the number of times a buy / sell signal has occurred for each predictive indicator and the number of times that predictions that have correctly predicted the price movements predicted by the buy / sell signals have occurred, and to select the predictive indicators that satisfy the signal accuracy rate as valid predictive indicators.

[0025] A fifth aspect of the present disclosure is a non-transitory computer-readable storage medium that stores a program for causing a computer device to operate as a server that predicts price movements of an investment stock, the program including instructions for: reading past price data of the investment stock using a plurality of predictive indicators including technical indicators or chart patterns that predict price movements of the investment stock; determining whether or not one or more of the predictive indicators that have correctly predicted price movements satisfy a signal accuracy rate, where the signal accuracy rate is an indicator that indicates the relationship between the number of times a buy / sell signal has occurred for each predictive indicator and the number of times that predictions that have correctly predicted the price movements predicted by the buy / sell signals have occurred; and selecting the predictive indicators that satisfy the signal accuracy rate as valid predictive indicators.

[0026] According to the second to fifth aspects, similar to the first aspect, it is possible to use the information to predict the price movements of investment stocks.

[0027] Furthermore, the second and third aspects may optionally include the following configuration: In this case, in the third aspect, the information processing device corresponds to a first computer device, and one or more other computer devices that can communicate with the information processing device correspond to a second computer device.

[0028] The accuracy rate sufficiency determination unit determines whether the signal accuracy rate is satisfied for a second retroactive period that is shorter than a first retroactive period for accumulating the price data retroactively from a prediction reference time for predicting price movements.

[0029] The first computer device includes a prediction evaluation data generation unit configured to generate prediction evaluation data that predicts price movements based on the effective prediction index, and the second computer device may be configured to output the prediction evaluation data acquired from the first computer device by screen display or audio.

[0030] The forecast valuation data may include forecast valuation information indicating whether the investment is undervalued (buy), neutral, or overvalued (sell).

[0031] The first computer device may include an indicator list generating unit configured to generate an indicator list of the technical indicators or chart patterns selected as the effective forecast indicators. The second computer device may be configured to output the indicator list acquired from the first computer device by screen display or audio.

[0032] The indicator list may include the technical indicators or chart patterns as invalid predictive indicators that are not selected as the valid predictive indicators.

[0033] The first computer device may include a chart generation unit, and the chart generation unit may include a step of generating a signal chart including buy / sell signals when a price movement of the price data is predicted by the valid predictive indicator. The second computer device may be configured to be able to display the signal chart obtained from the first computer device on a screen.

[0034] The first computer device may include an investment simulation data generation unit, and the investment simulation data generation unit may be configured to generate investment simulation data that indicates performance results when the effective forecasting indicator is applied to the price data and traded. The second computer device may be configured to display the investment simulation data acquired from the first computer device on a screen.

[0035] The first computer device may be configured to transmit forecast information regarding a forecast of price movements of the investment stock, which forecast information can be output by the second computer device, to a second computer device that can communicate with the first computer device. The second computer device may be configured to output the forecast information obtained from the first computer device.

[0036] The prediction information can be output by the second computer device in the form of at least one of text, audio, images, video, and character conversation.

[0037] According to one aspect of the present disclosure, a new technique for predicting price movements of investment securities is provided.

[0038] In order to clearly explain one aspect of the present disclosure based on the exemplary embodiments, the drawings necessary for the explanation will be briefly described. The drawings are merely for illustrating some embodiments and are not intended to limit the scope of the present disclosure. A person skilled in the art can obtain drawings of other related embodiments based on the drawings of the present disclosure without using any special inventive ability.

[0039] FIG. 1 is a configuration diagram of an information provision system according to one embodiment. FIG. 2 is a block configuration diagram of a computer device according to one embodiment. FIG. 3 is a functional configuration diagram of a server according to one embodiment. FIG. 4 is a functional configuration diagram of a user terminal according to one embodiment. FIG. 5 is an explanatory diagram of an information provision screen according to one embodiment. FIG. 6 is an explanatory diagram of an information provision screen according to one embodiment. FIG. 7 is an explanatory diagram of an information provision screen according to one embodiment. FIG. 8 is an explanatory diagram of an information provision process according to one embodiment. FIG. 9 is an explanatory diagram showing reading price data with a predictive index according to one embodiment. FIG. 10 is a flowchart showing selection of effective predictive indexes and price prediction according to one implementation.

[0040] One aspect of the present disclosure will be described based on an exemplary embodiment with reference to the drawings. The following embodiment does not unduly limit the content of the present invention described in the claims. Furthermore, not all of the configurations described in the present embodiment are necessarily essential as a solution to the present invention.

[0041] All embodiments and optional embodiments included in this disclosure may be combined with each other to form new embodiments, and all technical features and optional technical features included in this disclosure may be combined with each other to form new technical features.

[0042] The term "or" used in this disclosure is used as an inclusive term. For example, "A or B" means "A, B, or both A and B." "A," "B," and "both A and B" all respectively satisfy "A or B."

[0043] In this specification and claims, when terms such as "first," "second," and "nth (where n is a natural number)" are used to distinguish between different elements, they are not intended to indicate a particular order or superiority or inferiority. Configurations that are common to each embodiment are assigned the same reference numerals and redundant explanations will be omitted.

[0044] The directions or positional relationships indicated by terms such as "upper," "lower," "front," "rear," "left," and "right" and terms including these terms used in this specification and claims are based on the drawings and are merely for the purpose of conveniently and simply describing the embodiments. Therefore, unless expressly defined or limited, it is not intended to expressly or imply that a particular element or its use is configured in a particular direction, and it should be understood that this does not limit the scope of the claims and the embodiments.

[0045] When a numerical value or element is modified by the terms "about," "approximately," "nearly," or "substantially" as used in this specification and claims, it is understood to include the numerical value and any numerical values ​​before and after the numerical value, and to include the element and anything that can be said to be the same as the element.

[0046] All steps described in this specification and claims may be performed in order or randomly, as long as no inconsistency occurs in data processing. For example, if a method includes steps A and B, it may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. If a method is described as possibly including step C, step C may be added to the method in any order. For example, the method may include steps A, B, and C performed sequentially, or it may include steps A, C, and B, or it may include steps C, A, and B.

[0047] The term "** unit" used in this specification and claims may include, for example, a "functional unit" configured by combining one or more hardware resources and one or more software resources executed on the hardware resources. The hardware resources described in this specification and claims (for example, a computer device, a server, a user terminal, and the electronic devices that make up these) may include not only those configured as a single device, but also those configured by connecting two or more devices so that they can communicate with each other.

[0048] In the following embodiments, the information to be processed (programs, content, data, etc.) is represented by a high or low signal value as a binary bit collection consisting of 0 or 1, and calculations and communications are performed by a signal processing circuit comprising one or more pieces of hardware.

[0049] Here, the signal processing circuit refers to a circuit configured by combining one or more circuits, circuits, processors, memories, etc., and includes application specific integrated circuits (ASICs), programmable logic devices (for example, simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs)), etc.

[0050] An information providing system according to one aspect of the present disclosure will be described below. The information providing system will be described as an information processing method, an information processing system, an information processing device, a program, and a non-transitory computer-readable storage medium as set forth in the claims.

[0051] The "non-transitory computer-readable storage medium" referred to in the present disclosure is, for example, a flexible disk, an optical disk, a hard disk, a flash memory, a U-disk, a Secure Digital Memory Card (SD) card, a Multimedia Card (MMC) card, etc., and this non-transitory computer-readable storage medium stores one or more instructions, which can be executed by one or more processors to realize the information processing method of the embodiment.

[0052] As used herein, an "investment item" refers to a financial product whose price is determined by trading in the market as an investment target. Examples of investment items include domestic stocks, foreign stocks, foreign currencies (e.g., US dollar, US dollar / yen, Australian dollar / yen, British pound / yen, euro / yen, Canadian dollar, Swiss franc, Chinese yuan, Hong Kong dollar, New Zealand dollar, Indian rupee, etc.), commodity futures (e.g., gold, silver, palladium, platinum, copper, iron ore, crude oil, soybean oil, gasoline, natural gas, coffee, wheat, rice, sugar, cocoa, soybeans, etc.), bonds, and virtual currencies. The term "investment item" used throughout this disclosure, whether explicitly stated elsewhere in the specification, etc., is understood as described above and includes one or more financial products. Each investment item has its own appropriate predictive indicator.

[0053] To obtain capital gains through investment activities, it is necessary to timely recognize the trading signals contained in the price movements of investment stocks and make trades, and for this purpose a large number of predictive indicators are used. "Predictive indicators" include technical indicators and chart pattern analysis, but these are merely examples, and new predictive indicators are being developed, so the number of predictive indicators available to investors is ever increasing.

[0054] Known technical indicators as predictive indicators include trend analysis (examples include moving averages, parabolic, MACD, Bollinger bands, and DMI), oscillator analysis (examples include RSI, RCI, and stochastics), and candlestick analysis, but these are merely examples and it is not possible to list them all here. Furthermore, chart patterns (chart shapes) as predictive indicators include various formation analyses (examples include double bottoms and double tops, head and shoulders, and the like), but these are also merely examples and it is not possible to list them all here. The term "predictive indicator" used in various places in this disclosure may include all technical indicators and chart patterns used to predict the price movements of investment securities, regardless of whether or not explicitly explained elsewhere in the specification, etc.

[0055] There is no predictive indicator that is consistently effective for all investment stocks. Therefore, a problem that troubles many investors is selecting an effective predictive indicator for each investment stock. Selecting an appropriate predictive indicator from among the many predictive indicators for investment stocks whose prices may fluctuate daily is a time-consuming, labor-intensive task, and it is virtually impossible to do this consistently on a daily basis. A predictive indicator that was effective for a particular investment stock in the past may not be effective now, and vice versa. A predictive indicator that is effective for a particular investment stock may not be effective for other investment stocks. Even for a single investment stock, predictive indicators used in trending markets (e.g., trend indicators) differ from predictive indicators used in range markets (e.g., oscillator indicators). It is common for a combination of two or more predictive indicators to be used for individual investment stocks. It is virtually impossible for investors to perform such an analysis for multiple investment stocks that form an investment portfolio while taking these factors into consideration.

[0056] Therefore, the present embodiment aims to make it possible to select an effective predictive indicator for each individual investment stock.

[0057] [1] Information provision system configuration (Figure 1)

[0058] The information provision system 100 is realized by a server 200. The server 200 provides an information provision service to a large number of user terminals 300. Each user terminal 300 is configured to be able to communicate with the server 200 via a communication network N, and can use the information provision service provided by the server 200. The user terminals 300 can interact with each other using the information provision service of the server 200 as a communication platform. The server 200 constitutes the "first computer device" and "information processing device" of the present invention, and the user terminal 300 constitutes the "second computer device" of the present invention. In this specification and drawings, the symbol 300 is used for the "group of user terminals" and each "user terminal."

[0059] The server 200 can be configured with one or more computer devices 400 as hardware resources (FIG. 2). The server 200 provides an information provision service to the user terminal group 300 by executing a program (also referred to as an "information provision program" in this disclosure). The program executed by the server 200 constitutes the "program" of the present invention.

[0060] The information provision program 200A may be configured, for example, by a web application. A web application has a presentation layer, an application layer, and a data layer, and the hardware resources of the server 200 may be configured by one or more web servers that constitute the presentation layer, one or more application servers that constitute the application layer, and one or more database servers that constitute the data layer.

[0061] The presentation layer may be configured to include a client-side program executed on the user terminal 300 and a server-side program executed on the server 200. The client-side program serves as a user interface on the user terminal 300, performing input functions, display functions, etc. using, for example, a web browser or an application. The server-side program performs functions such as acquiring input data and commands from the user terminal 300 and outputting display data to the user terminal 300. The application layer is a function executed on the server 200, and performs functions such as executing commands from the user terminal 300, processing data, and generating display screens. The data layer is a function executed on the server 200, and performs functions such as extracting data from a storage device, updating data, and storing data.

[0062] The user terminal 300 is a computer device 400 capable of communicating with the server 200 ( FIG. 2 ), and can be configured, for example, as a laptop computer, desktop computer, tablet terminal, smartphone, smart watch, wearable device, in-vehicle navigation system, etc. However, the user terminal 300 is not limited to these and may be other devices. The server 200 can be connected to a large number of user terminals 300 that receive information provision services, and each user terminal 300 can receive provided information from the server 200.

[0063] [2] Explanation of hardware resources (Figure 2)

[0064] The server 200 and the user terminal 300 that make up the information providing system 100 may be configured with one or more computer devices 400. Fig. 2 is a block diagram of the hardware resources of one computer device 400. The computer device 400 may include, for example, a control device 401, a storage device 402, an input device 403, an output device 404, a communication device 405, and a data transmission path 406.

[0065] The control device 401 is one or more processors that execute an information provision program. The control device 401 operates to cause the computer device 400 to function as the server 200 or the user terminal 300.

[0066] The storage device 402 includes one or more ROMs, RAMs, external storage devices, etc. The storage device 402 stores various programs including an information provision program and data processed by the programs, but is not limited to these, and may also store other data.

[0067] The input device 403 may include one or more keyboards, mice, touch panels, etc. The output device 404 may include one or more display devices, speaker devices, etc. The communication device 405 controls communication with other computer devices 400 including the server 200 via the communication network N.

[0068] The data transmission path 406 is a signal line shared by the control device 401, the storage device 402, the input device 403, the output device 404, or the communication device 405 for transmitting and receiving data. The data transmission path 406 may be configured by circuit wiring on a circuit board, cable wiring, wireless communication, or the like.

[0069] [3] Functional configuration of server 200 (FIG. 3)

[0070] The server 200 includes a control unit 201, a storage unit 202, and an output unit 203 as a plurality of functional units. The control unit 201 is configured by the control device 401 executing an information provision program. The control unit 201 controls various operations performed by the server 200.

[0071] The storage unit 202 holds various programs including an information provision program and various data. The storage unit 202 can be configured using a storage device 402. Each of the storage units included in the storage unit 202, which will be described later, can be configured as a data table. Note that the term "storage" in the term "storage unit" used in this specification and claims is used as a synonym for "memory" and "accumulation."

[0072] The output unit 203 transmits various data generated by the server 200 to the user terminal 300 .

[0073] The control unit 201 includes multiple functional units shown in Figure 3, namely, an acquisition unit 210, a reading unit 211, an effective prediction indicator selection unit 212, a prediction evaluation data generation unit 213, an indicator list generation unit 214, a chart generation unit 215, an accuracy rate satisfaction determination unit 216, an investment simulation data generation unit 217, and a notification data generation unit 218, but may include other functional units without being limited to these.

[0074] The acquisition unit 210 acquires data transmitted from the user terminal 300 to the server 200 via the communication network N. The acquisition unit 210 also acquires price data, etc. of investment issues from an external server (issue data distribution server) via the communication network N. The acquisition unit 210 receives price data, etc. for each investment issue from one or more external servers that distribute the respective data, and stores the data in the storage unit 202.

[0075] The data acquired by the acquisition unit 210 includes, but is not limited to, the following data, and other data may also be acquired.

[0076] <User Data> User data is stored in the user data storage unit 220 of the storage unit 202. The user data storage unit 220 stores data (account data) related to an account registered by a user to use the information providing system 100. The account data stores data such as a user ID, a user name (nickname), an email address, and attribute information.

[0077] The "user ID" is an identification code for uniquely identifying each account in the information providing system 100. Information related to each user terminal 300 is associated with the user ID.

[0078] The “user name (nickname)” is information that is made public in the information providing system 100 in order to identify the user of the user terminal group 300 .

[0079] The "email address" is registered at the time of user registration, and is used as destination information when sending notifications by email, as described below.

[0080] The "attribute information" is a user profile and may include the user's self-introduction, occupation, area of ​​expertise in the occupation, investment history, investment areas of expertise, interests, etc. At least any of these may be made public to other user terminals 300 according to the user's settings.

[0081] <Stock Data> Stock data is data related to the attributes of individual investment stocks, and includes data such as the trading market, trading name, stock identification information, and fundamental information about the investment stock (organizational information, capital information, performance indicator information, financial information, etc.). The stock data may also include price data, which will be described later. The stock identification information is information that uniquely distinguishes individual investment stocks in the information providing system 100, and the price data, etc., which will be described later, are linked to this stock identification information for various processing. As mentioned above, the stock data may include all types of investment stocks, such as domestic stocks, foreign stocks, foreign currencies, commodity futures, bonds, and virtual currencies. The stock data is stored in the stock data storage unit 221 of the memory unit 202.

[0082] <Price Data> Price data is data related to the prices of individual investment issues formed in the trading market. Price data includes, but is not limited to, the year, month, day, hour, minute, and second (these are referred to as calendar data) when the price was formed, and the price at each point in time. Price data may also include other data, such as data related to price evaluations of the lowest price, highest price, opening price, and closing price, and data related to trading volume such as trading volume (trading volume). Price data exists for each individual investment issue, and can be provided from one or more external servers. As an example, the price data for each investment issue accumulated by the server 200 includes data for the past 30 years, for example. The price data is associated with issue identification data and stored in the price data storage unit 222 of the memory unit 202.

[0083] The reading unit 211 reads past price data of the investment stock using a plurality of forecasting indicators including technical indicators or chart patterns that predict price movements of the investment stock. The forecasting indicators used to read the price data are stored in the forecasting indicator storage unit 224 of the storage unit 202.

[0084] The effective forecasting indicator selection unit 212 selects, from among the forecasting indicators obtained by reading the price data of the investment stock, forecasting indicators that satisfy a predetermined signal accuracy rate as effective forecasting indicators. The selected effective forecasting indicators are associated with the stock identification data and price data and stored in the forecasting indicator storage unit 224 of the memory unit 202.

[0085] The forecast evaluation data generation unit 213 is configured to be able to generate forecast evaluation data that predicts price movements after a predetermined period has elapsed from a forecast reference time based on the effective forecast indicators. The forecast evaluation data may include forecast evaluation information 511a that indicates whether the investment stock is undervalued (buy), neutral, or overvalued (sell). The forecast evaluation data and forecast evaluation information are associated with stock identification data and price data and stored in the forecast evaluation data storage unit 225 of the memory unit 202.

[0086] The indicator list generation unit 214 generates an indicator list 514a of technical indicators or chart patterns selected as effective forecast indicators 514a1. The indicator list 514a can be configured to include technical indicators or chart patterns as ineffective forecast indicators 514a2 that are not selected as effective forecast indicators 514a1. The indicator list 514a is associated with stock identification data and price data and stored in the indicator list storage unit 226 of the storage unit 202.

[0087] The chart generating unit 215 generates a signal chart 512a including buy / sell signals when price movements are predicted using valid forecasting indicators for the price data. Data for forming the signal chart 512a is associated with the stock identification data and the price data and stored in the chart storage unit 227 of the memory unit 202.

[0088] The accuracy rate satisfaction determination unit 216 determines whether or not the prediction of one or more forecast indicators for which the price movement prediction has come true satisfies the signal accuracy rate 515b. The signal accuracy rate 515b is an index that indicates the relationship between the number of times a buy / sell signal has occurred for each forecast indicator and the number of times that the prediction of the price movement predicted by the buy / sell signal has come true. The signal accuracy rate 515b is associated with the forecast indicator, the stock identification data, and the price data, and is stored in the accuracy rate storage unit 228 of the memory unit 202.

[0089] The investment simulation data generation unit 217 generates investment simulation information 513a of the performance when trading is performed by applying the effective prediction indicator 514a1 to the price data. The investment simulation information 513a is associated with the issue identification data and the price data and stored in the investment simulation data storage unit 229 of the memory unit 202.

[0090] The notification data generation unit 218 selects or generates information about investment issues that can be output on the user terminal 300, and generates notification data to be notified by text, audio, images, videos, or character conversations. The information about investment issues generated by the notification data generation unit 218 can include at least any of predicted evaluation data, predicted evaluation information, an index list, a signal chart, a signal accuracy rate, and investment simulation information. The notification data is associated with various data stored in the storage unit 202 that is necessary for notification, and is stored in the notification data storage unit 230 of the storage unit 202.

[0091] The output unit 203 includes a transmission unit 231. The transmission unit 231 performs processing to output various data to the user terminal 300 via the communication network N. The data transmitted by the transmission unit 231 includes screen data displayed on the user terminal 300 and data related to notifications. The notifications transmitted by the transmission unit 231 may include at least one of audio notifications, notifications via email, and notifications via images or text messages on a website screen or app screen. This allows the user terminal 300 to reliably recognize receipt of a notification. Furthermore, notifications may include notifications via video or animation by a character displayed on a website screen, app screen, or the user terminal screen. Here, "character" may include a human character, an anthropomorphized character, or an animal, but may also include other characters. This may increase the user's attention to notifications. "Notification by a character" may include both one-way speech from the character to the user and dialogue between the character and the user.

[0092] [4] Functional configuration of user terminal 300 (FIG. 4)

[0093] The user terminal 300 includes a control unit 301, an input unit 302, a storage unit 303, a notification processing unit 304, an output processing unit 305, and a communication unit 306 as a plurality of functional units.

[0094] The control unit 301 controls various operations performed by the user terminal 300 by executing a client-side information provision program 300A.

[0095] The input unit 302 accepts data input by the user.

[0096] The storage unit 303 stores the client-side program, screen display data, and the like transmitted from the server 200 .

[0097] The notification processing unit 304 performs processing to display notifications received from the server 200 on the user terminal 300 in accordance with notification settings. Notifications can be sent on the information display screen 500 of the information provision program 300A, or can be configured to be received by email at the user terminal 300.

[0098] The output processing unit 305 outputs data to the output device 404 of the user terminal 300. The output device 404 is one or more display devices, speaker devices, etc. An information display screen 500 of the information provision program is displayed on the screen of the display device. Audio data including audio and video data are output from the speaker device.

[0099] The communication unit 306 performs data communication with the server 200 .

[0100] [5] Example of information display screen configuration (Figs. 5 to 8)

[0101] 5 shows an example of an information display screen 500 implemented by the information provision programs 200A and 300A. The information display screen 500 is displayed on the display device of the user terminal 300. The information display screen 500 is configured from a web page or an application screen.

[0102] <Information Display Screen 500> The information display screen 500 includes an investment brand chart display section 510, a forecast evaluation information display section 511, a signal chart display section 512, and an investment simulation display section 513, but is not limited to these and may include one or more of these or one or more display sections for other information. Furthermore, the display format of these is not limited to the format shown in FIG. 5 and may be displayed in other formats.

[0103] The forecast evaluation information display section 511 and the like display information related to the investment brand (Euro / US Dollar in the example of FIG. 5) displayed in the chart display section 510. The display contents of the chart display section 510, forecast evaluation information display section 511, signal chart display section 512, and investment simulation display section 513 change in accordance with the investment brand selected by the user terminal 300. The investment brand can be selected on an investment brand selection screen (not shown). Each time a different investment brand is selected, the display contents of the information display screen 500 change.

[0104] <Chart Display Section 510> The chart display section 510 is a display area including a chart 510a showing the price of an investment stock. FIG. 5 illustrates a chart 510a for the EUR / USD exchange rate. The chart 510a can be set as either a live chart or a static chart at a predetermined time. The display period of the chart 510a can be any display period, such as hours and minutes, days, months, or years, depending on the display settings configured on the user terminal 300. The chart 510a can display graphs of prices and technical indicators, such as price line graphs, candlestick charts, moving averages, Ichimoku Kinko Hyo, Bollinger Bands, Stochastics, MACD, and RSI. The chart 510a can display multiple data lines (price data lines and one or more technical indicator data lines) overlapping in a single graph drawing area, or multiple graph areas side by side. The chart 510a can be obtained from an external server.

[0105] <Prediction evaluation information display section 511> The prediction evaluation information display section 511 is a display area including prediction evaluation information 511a of the investment issue. The prediction evaluation information 511a is information indicating whether the investment issue will be undervalued (buy), neutral, or overvalued (sell) after a predetermined number of days have passed since the prediction reference time.

[0106] When the user terminal 300 requests the server 200 to provide a forecast valuation of an investment stock, the user terminal 300 can select a specific forecast base time. This allows the user to know the forecast valuation at any point in the past. In particular, by knowing the forecast valuation at any point in the past, the user can verify the accuracy of the forecast valuation in light of fluctuations in actual price data. "After a predetermined number of days have passed" refers to the number of days that have passed since the forecast base time. This number of days can be any number of days, and may include the number of days set by the server 200 or the number of days set by the user terminal 300.

[0107] FIG. 5 illustrates a meter graphic as the forecast evaluation information 511a. While the meter is illustrated as a sectorial ring (arc-shaped or arch-shaped), other shapes (e.g., a band-shaped) are also possible. Furthermore, other display forms (e.g., text only, symbols only, or a combination of text and symbols) are also possible instead of graphics. In the case of text, examples include "low," "medium," and "high." In the case of symbols, arrows indicating an upward, downward, or flat trend may be used. "Low" in the forecast evaluation information 511a indicates that the investment stock is in a trending market where it is undervalued (buying), "high" indicates that the investment stock is in a trending market where it is overvalued (selling), and "medium" indicates that the investment stock is in a range where it is neither undervalued nor overvalued. The characters "low," "medium," and "high" are merely examples, and other characters may be used without being limited to these. By displaying such forecast evaluation information 511a on the user terminal 300, the specific trends of the investment stock can be easily and visually ascertained.

[0108] The forecast evaluation information 511a is generated based on forecast evaluation data. The forecast evaluation data is generated by the forecast evaluation data generating unit 213 and is data obtained by predicting price movements after a predetermined period has elapsed from the forecast reference time based on the effective forecast indicators.

[0109] The prediction evaluation data generation unit 213 generates, as an example, a predicted price of the investment stock after a predetermined period has elapsed from the prediction reference time based on the valid prediction indicator. This generated predicted price of the investment stock becomes the prediction evaluation data. As will be described later, a valid prediction indicator is one or more prediction indicators from the group of prediction indicators that have correctly predicted price movements, and whose predictions satisfy the signal accuracy rate. Therefore, the predicted price is a prediction price based on a prediction indicator with a higher prediction accuracy among the prediction indicators that have correctly predicted price movements.

[0110] If the predicted price is higher than the price of the investment issue at the prediction reference time when the prediction evaluation is performed, the prediction evaluation information 511a indicates "low", if the predicted price is lower than said price, the prediction evaluation information 511a indicates "high", and if the predicted price is substantially unchanged from said price, the prediction evaluation information 511a indicates "medium". A case in which the predicted price is substantially unchanged from said price means that the predicted price falls within the range of increase or decrease in price set by the prediction evaluation data generation unit 213. The range of increase or decrease in price is set by the prediction evaluation data generation unit 213 as being different for each investment issue.

[0111] <Signal Chart Display Section 512> The signal chart display section 512 is a display area including a signal chart 512a. The signal chart 512a includes a data line 512b and buy / sell signals, i.e., a buy signal 512c and a sell signal 512d. The signal chart 512a can be displayed for any retroactive period (signal display period) that dates back from the forecast reference time. The signal display period can be set to coincide with the data accumulation period of price data stored in the price data storage section 222 for each investment brand, or can be any period within that data accumulation period. The signal display period can be set by the server 200, or any signal display period can be selected by the user terminal 300.

[0112] The signal chart display section 512 may include a display year selection tab 512e. The display year selection tab 512e is for selecting the signal chart 512a to be displayed on the user terminal 300. In the example of FIG. 5, the signal chart 512a for each year from 2023 to 2019 can be selected, and the signal chart 512a with 2023 as the forecast base time is illustrated. When 2022 to 2019 is selected, the signal chart 512a corresponding to the signal display period (e.g., the past five years) with each year as the forecast base time is displayed.

[0113] <Investment Simulation Display Unit 513> As shown in FIG. 5, the investment simulation display unit 513 is a display area that includes investment simulation information 513a. The investment simulation information 513a indicates the performance results when investment issues are traded based on effective forecasting indicators during a simulation period extending back from the forecast base time, and is generated by the investment simulation data generation unit 217. This allows the accuracy of the effective forecasting indicators to be confirmed. The simulation period is a predetermined period extending back from the forecast base time for which forecast evaluation is performed. The simulation period may coincide with the data accumulation period of price data stored in the price data storage unit 222 for each investment issue, or may be any period within that data accumulation period. The simulation period can be set by the server 200, or any period may be selectable by the user terminal 300.

[0114] The investment simulation display section 513 shown in FIG. 5 illustrates a cumulative return in the form of a graph as the investment simulation information 513a, but is not limited to this and other display formats may also be used.

[0115] FIG. 6 shows a second display mode of the investment simulation display section 513. The investment simulation information 513c included in this investment simulation display section 513 is in a table format including "period," "AI return," and "hold return." "AI return" indicates the investment performance for each year of the simulation period when a trading simulation is performed by predicting the price movements of an investment stock using an effective prediction index. This makes it possible to show the accuracy of the effective prediction index. "Hold return" indicates the return for each year of the simulation period when an investment stock is held without being traded. By displaying the AI ​​return and hold return in comparison in this way, it is possible to clearly show the accuracy of the effective prediction index.

[0116] The display target on the investment simulation display unit 513 may be, but is not limited to, the investment simulation information 513a in Fig. 5, the investment simulation information 513c in Fig. 6, or a combination of both. The selection of the display target may be set by the server 200, or may be arbitrarily selectable by the user terminal 300.

[0117] <Forecast Indicator Display Section 514> The information display screen 500 may further include a forecast indicator display section 514, shown in FIG. 7 as an example. The forecast indicator display section 514 is configured as an indicator list 514a that shows the technical indicators or chart patterns used as effective forecast indicators 514a1 at the time of the forecast reference. As is known, there are many forecast indicators, and selecting effective forecast indicators is difficult in itself, and therefore, is an important concern for investors. By looking at the forecast indicator display section 514, investors using the user terminal 300 can learn the technical indicators that served as the basis for the forecast, and can use this as an opportunity to learn about investing.

[0118] In FIG. 7 , among the listed example predictive indicators, "Hammer" and "Shooting star" are indicated as valid predictive indicators 514a1 in white letters on a black background. In addition to these, the indicator list 514a also indicates ineffective predictive indicators 514a2, which are technical indicators or chart patterns not selected as valid predictive indicators 514a1. For example, "Head & Shoulders" and "Bollinger band" fall into this category and are displayed as ineffective predictive indicators 514a2 in black letters on a white background. Although there are many predictive indicators, depending on the investment stock, there are not many that are actually useful for prediction. Therefore, knowing the ineffective predictive indicators 514a2 that do not need to be used is an important concern for investors, just like the valid predictive indicators 514a1. The indicator list 514a can be used for information provision, etc., and can be an opportunity for investors to learn about investments.

[0119] The indicator list 514a shown in FIG. 7 is a display format that lists the valid forecast indicators 514a1 and the invalid forecast indicators 514a2, but is not limited to this and other display formats may be used. For example, a display format that displays the valid forecast indicators 514a1 and the invalid forecast indicators 514a2 separately without mixing them may be used, or a display format that displays only the valid forecast indicators 514a1 and the invalid forecast indicators 514a2 in a separate window may be used. Alternatively, a display format that displays only the valid forecast indicators 514a1 and does not display the invalid forecast indicators 514a2 may be used. The types and number of hits of the valid forecast indicators 514a1 and the invalid forecast indicators 514a2 vary depending on the investment stock. All forecast indicators may be displayed as the indicator list 514a, or a display format that displays only a portion of them may be used. On the other hand, a display format that lists all or some of the technical indicators or chart patterns that the information providing system 100 uses to interpret price data using statistical models or machine learning may be used.

[0120] <Signal Accuracy Rate Display Section 515> The information display screen 500 may further include a signal accuracy rate display section 515 shown in Fig. 8 as an example. The signal accuracy rate display section 515 includes signal accuracy rate information 515a. The signal accuracy rate information 515a in Fig. 8 includes a signal accuracy rate 515b, and further includes, as examples, a period 515c and a return 515d.

[0121] As shown in FIG. 10 , the signal accuracy rate 515b is an index showing the relationship between the number of buy / sell signals generated for each predictive index and the number of times that the price movements predicted by the buy / sell signals have occurred when the price data of an investment stock is read using a predictive index. For example, if the price data of investment stock X is read using technical index A, and the number of buy / sell signals generated is 100, and the number of times that the prediction of the price movement normally predicted by technical index A has been correct is 80, the signal accuracy rate 515b is 80%. Next, if the price data of investment stock X is read using technical index B, and the number of buy / sell signals generated is 50 and the number of times that the prediction has been correct is 30, the signal accuracy rate 515b is 60%. In this way, the server 200 reads the price data of each investment stock using all technical indexes and chart patterns that can be used for price prediction and makes predictions, thereby ranking the signal accuracy rates. Technical indexes and chart patterns with a signal accuracy rate of, for example, 80% or higher are selected from the ranking as effective predictive indexes. The signal accuracy rate 515b can be used to make such a determination.

[0122] When generating the signal accuracy rate 515b, for example, the target period for reading price data using predictive indicators can be the entire accumulation period of price data. For example, if the data accumulation period is 30 years, all of that data can be read. In this case, predictive indicators that generated multiple buy / sell signals 20 years ago can be identified, but such predictive indicators that were effective in the past may not necessarily be effective at present. This is because price movements of investment stocks are influenced by various environmental factors, such as politics, economy, society, policy, corporate performance, business environment, and investor sentiment, and these environmental factors change.

[0123] Therefore, the target period for reading price data using a predictive indicator can be the past five years, three years, one year, etc. By limiting the target period to the most recent period going back from the forecast base date in this way, it becomes possible to select a predictive indicator that takes into account recent price movement trends as an effective predictive indicator. Specifically, it is preferable that the target period for reading is the past five years.

[0124] The signal accuracy rate display section 515 is not limited to that shown in Fig. 8 and may have other display forms. For example, the signal accuracy rate display section 515 may include the signal accuracy rate 515b, and may not include the period 515c or the return 515d, and may include other information. For example, if the accuracy of the prediction as a result of applying the technical indicator to a single year is simply to be displayed, only the signal accuracy rate 515b may be displayed.

[0125] 8 shows an example of the signal accuracy rate 515b displayed as a percentage (%), but is not limited to this and may be displayed in other forms. The signal accuracy rate 515b may be a qualitative display ranked based on the percentage generated by the accuracy rate sufficiency determination unit 216. For example, the signal accuracy rate 515b may be a multiple-stage evaluation using numbers or symbols, or a multiple-stage evaluation using letters such as "high, medium, low" or "top, medium, bottom." Any of these may be included in the signal accuracy rate 515b.

[0126] <Combination of Multiple Display Sections> Although an example has been shown in which the investment stock chart display section 510, the forecast evaluation information display section 511, the signal chart display section 512, the investment simulation display section 513, the forecast index display section 514, and the signal accuracy rate display section 515 are configured as individual display sections (display areas), the present invention is not limited to this, and a configuration in which multiple pieces of information are integrated and displayed on a single display section may be used, as exemplified below. Note that a combination of multiple pieces of information not exemplified below may also be used.

[0127] The display area of ​​the chart display section 510 may include buy signals 512c, sell signals 512d, and signal accuracy rates 515b of a signal chart 512a.

[0128] The display area of ​​the prediction evaluation information display section 511 may include an index list 514 a of the prediction index display section 514 .

[0129] The display area of ​​the signal chart display section 512 may include signal accuracy rate information 515a or signal accuracy rate 515b.

[0130] The display area of ​​the investment simulation display section 513 may include a signal accuracy rate 515b.

[0131] [6] Explanation of information provision (Figures 9 to 11)

[0132] An example of an information providing method (information processing method) by the information providing system 100 will be described below. The following description will focus on the prediction of price movements of investment brands by the server 200. The information providing method by the server 200 includes an information generation stage and an information providing stage.

[0133] <Information generation stage>

[0134] Data Acquisition (FIG. 9, S201) The acquisition unit 210 acquires stock data and price data for each investment stock from an external server and stores them in the stock data storage unit 221 and the price data storage unit 222 (S201). The price data is stored in association with the stock identification information of the stock data. The acquisition unit 210 acquires new price data sent from the external server in response to price fluctuations of the investment stock and stores it in the price data storage unit 222. Price data can include, but is not limited to, low prices, high prices, opening prices, closing prices, and trading volumes. The new price data can be accumulated in real time while the trading market is open or accumulated after the trading market closes. The price data can be for the entire data accumulation period on the external server or for a portion of the period, but price data for the entire period is usually acquired. This allows as many past prediction indicators as possible to be read from the price data of the investment stock, thereby improving the accuracy of price movement predictions.

[0135] The acquisition unit 210 acquires forecast index data from an external server and stores it in the forecast index storage unit 224. The forecast index data is forecast indexes used to analyze price data, and is the above-mentioned technical indexes and chart patterns. When new technical indexes and chart patterns are provided from the external server, the acquisition unit 210 acquires and stores them. The forecast index data is used as learning data when machine learning price data.

[0136] Selection of Effective Prediction Indicators (FIG. 9, S203) Data processing in the selection of effective prediction indices (S203) will be described with reference to FIGS.

[0137] 10, the selection of effective predictive indicators is performed by reading all technical indicators and chart patterns for a specific investment stock (S1003), and identifying technical indicators, etc. that have correctly predicted price movements from among them (S1005). It is determined whether each technical indicator, etc. that has correctly predicted has performance exceeding a predetermined signal accuracy rate (S1007), and technical indicators, etc. that exceed this rate are selected as effective predictive indicators (S1009). The server 200 predicts prices using the effective predictive indicators every time price data is updated (S1011).

[0138] On the other hand, if there are no technical indicators that have correctly predicted price movements (S1005), all technical indicators that have read price data become invalid prediction indicators (S1013) and are not used as price prediction indicators (S1015).

[0139] Furthermore, technical indicators that do not exceed a predetermined signal accuracy rate (S1007) are deemed to be invalid predictive indicators (S1013) and are not used as price predictive indicators (S1015).

[0140] The above-described selection of effective predictive indicators is performed for each investment brand, thereby enabling selection of effective predictive indicators specific to each investment brand.

[0141] After selecting effective and ineffective forecasting indicators, the server 200 can update the effective and ineffective forecasting indicators at any time by the steps shown in Fig. 11. In other words, after labeling specific technical indicators or chart patterns as effective and ineffective forecasting indicators, the server 200 can reset the labeling of effective and ineffective forecasting indicators and newly select effective and ineffective forecasting indicators again at any time. This update can be performed at any time, such as every time the server 200 obtains new price data, every predetermined number of days, every predetermined number of months, etc.

[0142] The selection of an effective predictive indicator for one investment brand will be described in detail below with reference to FIG.

[0143] After the price data is acquired, the selection of effective forecast indicators is started (S1001). The reading unit 211 reads the price data stored in the price data storage unit 222 for the investment brand using the technical indicators stored in the forecast indicator storage unit 224 (S1003, reading step).

[0144] Then, it is determined whether there are any technical indicators etc. whose predictions have come true (S1005). If there are any technical indicators etc. whose predictions have come true, the process proceeds to the next step (S1007, determination step), and if there are no technical indicators etc. whose predictions have come true, the technical indicators etc. that have been read are labeled as invalid prediction indicators (S1013), and the process ends without making a price prediction (S1015).

[0145] The reading performed by the reading unit 211 can be performed, for example, by a statistical model or machine learning. Technical indicators and chart patterns are based on a patterned relationship between the occurrence of a buy / sell signal and a price increase or decrease that occurs after a predetermined number of days have passed. For example, when performing machine learning, the reading unit 211 performs machine learning using at least the price fluctuation from the occurrence of a buy / sell signal until the price increase or decrease occurs and the predetermined number of days (trend days) as features, thereby improving the accuracy of the reading and making it possible to predict price fluctuations based on technical indicators, etc., corresponding to the investment stock. This can be used as a trained model to predict price movements.

[0146] The process proceeds from step (S1005) to the next step (S1007), where the accuracy rate satisfaction determination unit 216 determines whether the prediction of one or more forecast indicators that have correctly predicted price movements satisfies the signal accuracy rate 515b (S1007, determination step). If the prediction satisfies the signal accuracy rate 515b, the process proceeds to the next step (S1009). On the other hand, if the signal accuracy rate 515b is not satisfied, the technical indicator or the like is labeled as an invalid forecast indicator (S1013), and the process ends without making a price prediction (S1015).

[0147] Proceeding from step (S1007) to the next step, the effective predictive indicator selection unit 212 selects one or more predictive indicators that satisfy a predetermined signal accuracy rate as effective predictive indicators from among the predictive indicators that read the price data of the investment stock (S1009, selection step).

[0148] Subsequently, the forecast evaluation data generation unit 213 generates forecast evaluation data that predicts price movements after a predetermined period has elapsed from the forecast reference time based on the selected effective forecast indicators (S1011). The forecast evaluation data includes forecast evaluation information 511a that indicates whether the investment stock is undervalued (buy), neutral, or overvalued (sell). Furthermore, the forecast evaluation data generation unit 213 generates, as an example, a forecast price of the investment stock after a predetermined period has elapsed from the forecast reference time based on the effective forecast indicators (S1011).

[0149] Generally, after a buy / sell signal corresponding to a predetermined technical indicator is generated, a price movement contrary to the theory may occur. However, whether this is a so-called "false" or not cannot be determined without waiting for subsequent price movements. However, what kind of price movement is a false move can be predicted by setting feature quantities related to the price movement from the generation of a buy / sell signal until the price rises or falls (such as the range of price movement fluctuations, the number of rises or falls, and the amount of time (years, months, days, hours, minutes, and seconds) until the price movement in line with the theory occurs after the generation of a buy / sell signal) as parameters (stock attribute parameters) and machine learning past price data for each investment stock. Furthermore, as an example, since the characteristics of a false price movement that occurs in a certain investment stock may also occur as false price movements in other investment stocks belonging to the same industry, the feature quantities of the price movements of other investment stocks belonging to the same industry may be set as parameters (industry attribute parameters) in machine learning. Furthermore, as an example, between companies that make up an industry supply chain, there may be cases where the price movements of upstream investment stocks are linked to the price movements of downstream investment stocks, so in machine learning, the features of the price movements of other investment stocks that make up the supply chain may be set as parameters (industry attribute parameters).

[0150] The reading unit 211 performs machine learning of the predictive indicators each time the price data of an investment stock is updated. This allows for the extraction of effective predictive indicators that reflect fluctuations in the latest price data. Recent price movements are shaped by the influence of various environmental factors, such as politics, the economy, society, policy, corporate performance, the business environment, and investor sentiment, that occur on a daily basis. Therefore, early assessment of the effectiveness of predictive indicators each time price data is updated can be one factor in improving the accuracy of predictions. For this reason, the information providing system 100 performs machine learning each time new price data is acquired. Furthermore, the machine learning dataset includes stock data (including fundamental information for each investment stock), price data, and time data, and may further include investment environment information related to the valuation of the investment stock (fundamentals information for each country, political trend information, economic trend information, geopolitical risk information, policy information such as monetary policy, legal and regulatory information, industry information, market information, economic trend information, consumer trend information, etc.). The type, frequency, and degree of changes in the investment environment information are then set as feature parameters (investment environment attribute parameters) and can be used for machine learning of related investment stocks. This machine learning reveals that the impact of investment environment attribute parameters differs for each investment stock. Therefore, by using machine learning to determine the price movements of each investment stock and the types and weightings of investment environment attribute parameters that are highly correlated with the price movements of each investment stock, it is possible to improve the accuracy of price movement predictions, which will be described later.

[0151] The following are examples of learning models that can be used for machine learning in this information provision system 100, but since learning models are constantly evolving, other learning models can be used without being limited to these. Different learning models can be used for each investment stock depending on the characteristics of the investment stock. <Classification model> Random Forest, Convolutional Neural Network (CNN), Gradient Boosting, Support Vector Machine Bagging <Time series> Long Short Term Memory (LSTM), BiLSTM, Gated Recurrent Unit (GRU), DLinear, Transformer, AutoFormer <Optimization> Genetic Algorithm, Reinforcement Learning <Dynamic Sharpe Ratios and Pairs Trading> Bayesian ML <Sentiment Analysis> Large language models, FinBERT

[0152] The prediction indices (valid prediction indices, invalid prediction indices) read by the reading unit 211 as described above are stored in the prediction indices storage unit 224 (S207). In addition, machine learning related data including a trained model when machine learning is performed and various parameters set in the training stage are stored in the trained model storage unit 223.

[0153] The aforementioned forecast evaluation data generation unit 213 generates forecast evaluation data for the investment stock after a predetermined number of days have passed since the forecast reference time based on the effective forecast indicator (S1011). The forecast evaluation data may include, for example, a forecast price for the investment stock. For example, the forecast price may be a price after a predetermined period has passed since the forecast reference time or a price after a predetermined period has passed since a buy / sell signal was generated, but is not limited to these and may be a price after a predetermined period has passed since any given point in time.

[0154] The predicted evaluation data generation unit 213 can generate a predicted price by machine learning using the aforementioned learning model, for example. The predicted evaluation data generation unit 213 learns fluctuations in past price data that occurred after a valid prediction indicator was read. For example, the predicted evaluation data generation unit 213 can learn price fluctuations after a predetermined period has elapsed since the occurrence of a buy / sell signal using the price and the period elapsed since the occurrence of a buy / sell signal in past price data as feature quantities, and generate a price or price range after a predetermined period has elapsed from the prediction reference time using the learned model. As a result, when a specific buy / sell signal is currently read, that is, when a valid prediction indicator is currently read from price data, a specific predicted price or a predicted price range after a predetermined period has elapsed (after any time period such as years, months, days, hours, minutes, or seconds has elapsed) can be generated. The elapsed period and price as feature quantities described above are examples, and the feature quantities may also include other data. As mentioned above, qualitative data and quantitative data (including data in which qualitative data is replaced with quantitative data) related to investment environment information (political trend information, economic trend information, geopolitical risk information, policy information such as monetary policy, legal and regulatory information, industry information, market information, economic trend information, consumer trend information, etc.) can be included in the features, thereby improving the accuracy of predicting the predicted price or the range of predicted prices. Note that the prediction evaluation data generation unit 213 may generate effective prediction indicators using a statistical model instead of machine learning.

[0155] The predicted price generated by the predicted evaluation data generation unit 213 is accumulated in the predicted evaluation data storage unit 225 as a predicted price generated based on a technical indicator or chart pattern that has a track record of appearing in the past price data of the investment stock and that also matches the characteristics of the recent price movements of the investment stock (S205, S1015).

[0156] <Information provision stage>

[0157] An example in which the server 200 provides information to the user terminal 300 will be described.

[0158] The user terminal 300 accesses the information providing service provided by the server 200 (S301), and then requests the server 200 to provide information on a specific investment brand (S303).

[0159] The control unit 201 of the server 200 receives a prompt from the user terminal 300 (S211), obtains from the memory unit 202 the currently stored information that can be provided to the user terminal 300 regarding the investment stock, and generates, for example, the following information to be provided (S213):

[0160] The predicted rating data generating unit 213 generates a predicted rating information display unit 511 including the meter shown in FIG. 5 as predicted rating information 511a.

[0161] The chart generation unit 215 generates a signal chart display unit 512 including a signal chart 512a shown in FIG.

[0162] The investment simulation data generation unit 217 generates an investment simulation display unit 513 including investment simulation information 513a shown in Fig. 5. The investment simulation data generation unit 217 generates an investment simulation display unit 513 including investment simulation information 513c shown in Fig. 6.

[0163] The index list generating unit 214 generates a prediction index display unit 514 including an index list 514a shown in FIG.

[0164] The transmitting unit 231 of the output unit 203 of the server 200 transmits the information to be provided generated by the control unit 201 to the user terminal 300 (S215).

[0165] The user terminal 300 displays an information display screen 500 including the provided information received from the server 200 on a browser or app displayed on an output device 404 such as a display device (S305). The user terminal 300 also outputs audio including the provided information through the audio output of the output device 404. If information on other investment brands is required, the process returns to step S303 and the subsequent steps are repeated. On the other hand, if information on other investment brands is not required, the information provision ends (S307).

[0166] By executing the information provision method (information processing method) by the information provision system 100 in the manner described above, various information for analyzing the price movements of investment stocks requested from the user terminal 300 can be provided to the investor operating the user terminal 300.

[0167] <Notification information provision stage>

[0168] The server 200 can provide various information related to investment brands to the user terminal 300. The information provided by the server 200 may be provided without a request from the user terminal 300, or may be provided after receiving a request from the user terminal 300.

[0169] As one aspect of information provision, the notification data generation unit 218 generates notification data for delivering various information related to investment brands to the user terminal 300. The notification data may include, for example, at least one of a notification in the form of a video or animation in which a human character or an anthropomorphized character speaks to the user on a website screen, an app screen, or the screen of the user terminal. In this way, because the notification is a video or animation in which a human character or an anthropomorphized character speaks to the user, it is possible to increase the user's attention to the notification.

[0170] In this case, the notification content may include predicted evaluation information 511a for the investment stock. Specifically, the notification may be in the form of a character or the like talking to the user in a conversational format, informing the user whether the investment stock of the user's interest is undervalued, neutral, or overvalued. In this case, the notification data generation unit 218 may be configured to be able to accept requests from the user terminal 300 by transmitting to the user terminal 300 a notification setting input unit for allowing the user to pre-set the investment stock, day of the week for notification, time for notification, etc. for which the user requests notification of the predicted evaluation information 511a.

[0171] The user data storage unit 220 stores attribute information. The notification data generation unit 218 can be configured to extract the user's field of expertise, investment history, investment areas of expertise, interests, etc. from the attribute information, and automatically notify the user terminal 300 of predicted evaluation information 511a of related investment stocks. In addition, the notification data generation unit 218 can be configured to use the above-mentioned industry attribute parameters and investment environment attribute parameters to extract other investment stocks that are linked to the price movements of the investment stock, and similarly automatically notify the user terminal 300 of predicted evaluation information 511a for these investment stocks. This makes it possible to notify the user of a prediction of whether an investment stock that the user is interested in is undervalued, neutral, or overvalued, or to notify the user of a prediction of whether another investment stock that may be unexpected to the user and whose price movements are likely to be linked is undervalued, neutral, or overvalued.

[0172] [7] Modified Example

[0173] 7, the index list 514a displaying the valid forecasting index 514a1 and the invalid forecasting index 514a2 has been exemplified, but for example, this index list 514a may be configured as an index selection display unit that enables the user terminal 300 to select the forecasting index to be used for price prediction. This allows the user terminal 300 to select the forecasting index by itself and know the forecast price using the selected forecasting index, which can be used as a learning opportunity to improve the skill of selecting forecasting indexes.

[0174] 100 Information providing system (information processing system) 200 Server (first computer device, information processing device) 300 User terminal (second computer device)

Claims

1. An information processing method performed by a computer device, comprising: a step of reading past price data of a plurality of investment stocks using a plurality of predictive indicators including technical indicators or chart patterns that predict price movements of the investment stocks; a determination step of determining, for each investment stock, whether or not the prediction of one or more of the predictive indicators that have correctly predicted price movements for the investment stocks satisfies a signal accuracy rate, wherein the signal accuracy rate is an indicator that indicates the relationship between the number of times a buy / sell signal has occurred for each predictive indicator and the number of times that the prediction of the price movement predicted by the buy / sell signal has occurred has been correct; and a step of selecting, for each investment stock, the predictive indicators that satisfy the signal accuracy rate as valid predictive indicators.

2. The information processing method according to claim 1, wherein the determination step determines whether the signal accuracy rate is satisfied for a second retroactive period that is shorter than a first retroactive period for accumulating the price data retroactively from a forecast reference time for predicting price movements.

3. The information processing method according to claim 1, further comprising the step of generating, for each of the investment issues, forecast evaluation data that predicts price movements based on the effective forecast indicators.

4. The information processing method according to claim 3, wherein the forecast valuation data includes forecast valuation information indicating whether the investment stock is undervalued, neutral, or overvalued.

5. The information processing method according to claim 1, further comprising the step of generating an indicator list of the technical indicators or chart patterns selected as the effective predictive indicators for each investment brand.

6. The information processing method according to claim 5, wherein the indicator list includes the technical indicators or chart patterns as ineffective predictive indicators that are not selected as effective predictive indicators.

7. The information processing method according to claim 1, further comprising the step of generating a signal chart including buy / sell signals when price movements of the price data are predicted using the effective predictive indicator.

8. The information processing method according to claim 1, further comprising the step of generating investment simulation data showing the performance results when trading is carried out by applying the effective predictive indicator to the price data.

9. The information processing method according to claim 1, further comprising a step of transmitting, to a second computer device capable of communicating with the computer device as the first computer device, forecast information regarding a forecast of the price movement of the investment stock that can be output by the second computer device.

10. The information processing method according to claim 9, wherein the prediction information can be output by the second computer device in the form of at least one of text, audio, images, video, and character conversation.

11. An information processing system, the information processing system including a first computer device, the first computer device including a reading unit, an accuracy rate satisfaction determination unit, and an effective predictive indicator selection unit, the reading unit being configured to be able to read past price data of a plurality of investment stocks using a plurality of predictive indicators including technical indicators or chart patterns that predict price movements of the investment stocks, the accuracy rate satisfaction determination unit being configured to be able to determine for each investment stock whether or not the prediction of one or more of the predictive indicators that have correctly predicted the price movements for each investment stock satisfies a signal accuracy rate, wherein the signal accuracy rate is an indicator that indicates the relationship between the number of times a buy / sell signal has occurred for each predictive indicator and the number of times the prediction has been correct when the price movement predicted by the buy / sell signal has occurred, and the effective predictive indicator selection unit being configured to be able to select the predictive indicators that satisfy the signal accuracy rate as effective predictive indicators for each investment stock.

12. An information processing device comprising: a reading unit, an accuracy rate satisfaction determination unit, and an effective predictive indicator selection unit; the reading unit is configured to be able to read past price data of a plurality of investment stocks using a plurality of predictive indicators including technical indicators or chart patterns that predict price movements of the investment stocks; the accuracy rate satisfaction determination unit is configured to be able to determine, for each investment stock, whether or not the prediction of one or more predictive indicators that have correctly predicted the price movements for each investment stock satisfies a signal accuracy rate, wherein the signal accuracy rate is an index showing the relationship between the number of times a buy / sell signal has occurred for each predictive indicator and the number of times the prediction has been correct when the price movement predicted by the buy / sell signal has occurred; and the effective predictive indicator selection unit is configured to be able to select, for each investment stock, the predictive indicators that satisfy the signal accuracy rate as effective predictive indicators.

13. A program that causes a computer device to operate as a server that predicts price movements of investment stocks, the program including instructions for performing the following: reading past price data of multiple investment stocks using multiple predictive indicators including technical indicators or chart patterns that predict price movements of the investment stocks; determining for each investment stock whether or not the prediction of one or more predictive indicators that have correctly predicted price movements for each investment stock satisfies a signal accuracy rate, wherein the signal accuracy rate is an indicator that indicates the relationship between the number of times a buy / sell signal has occurred for each predictive indicator and the number of times that predictions that have correctly predicted the price movements predicted by the buy / sell signals have occurred; and selecting for each investment stock the predictive indicator that satisfies the signal accuracy rate as a valid predictive indicator.

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