JP2021125212A - Stock transaction information display program - Google Patents

Abstract

To give advice about an issue for stock transaction.SOLUTION: A stock transaction information display program for displaying information on an issue for stock transaction causes a computer to perform: an information acquisition step of acquiring fundamental information on the issue for which a stock transaction is newly performed; and a display step of using three or more level degrees of relation between reference fundamental information on the issue in the past acquired in advance and increase/decrease data on the stock price of the issue at the moment after acquiring the reference fundamental information, and displaying increase/decrease data on the stock price of each issue while prioritizing the higher of the three or more level degrees of relation between the reference fundamental information according to the fundamental information acquired in the information acquisition step and the increase/decrease data on the stock price of each issue.SELECTED DRAWING: Figure 35

Description

The present invention relates to a stock trading information display program that displays information on stocks of stock trading.

In recent years, applications that provide various advice when conducting stock trading and systems that automate stock trading, that is, perform automatic trading, have been used. When using such an app or system, it is possible to increase the winning rate and increase profits by receiving advice based on a huge amount of data analysis. However, even if the enormous amount of data can be acquired, it takes considerable effort to analyze this and compile an output solution that can give accurate advice to the user. In addition, the current situation is that a system capable of automatically performing these operations has not been proposed in the past.

Therefore, the present invention was devised in view of the above-mentioned problems, and the purpose of the present invention is to acquire a huge amount of data in a stock trading information display program that gives advice on stocks of stock trading. It is an object of the present invention to provide a stock trading information display program that displays information on stocks of stock trading that can collectively display output solutions that can analyze and give accurate advice to the user.

In order to solve the above-mentioned problems, the stock trading information display program according to the present invention is a stock trading information display program that displays stock price increase / decrease data of each stock to be traded. Three or more stages of the information acquisition step to acquire the fundamental information of the stock, the reference fundamental information of each stock acquired in advance, and the stock price increase / decrease data of each stock at the time after the acquisition of the reference fundamental information. Using the degree of association, priority is given to those with a higher degree of association of three or more levels of the reference fundamental information according to the fundamental information acquired in the above information acquisition step and the stock price increase / decrease data of each stock. It is characterized by having a computer execute a display step of displaying stock price increase / decrease data.

Even if you do not have any special skills or experience, you will be able to provide useful advice for trading stocks.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the stock transaction information display program to which the present invention is applied will be described in detail with reference to the drawings.

The first embodiment FIG . 1 is a block diagram showing an overall configuration of a stock transaction information display system 1 in which a stock transaction information display program to which the present invention is applied is implemented. The stock transaction information display system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. The information acquisition unit 9 also includes a temperature sensor, a humidity sensor, a flow rate sensor, and other sensors capable of identifying substances and physical properties. The information acquisition unit 9 may be configured by means for automatically fetching character strings and data posted on a site on the Internet.

The database 3 stores various information necessary for displaying stock transaction information. Information necessary for displaying stock price information includes reference market information regarding past market conditions, reference event information that reflects events that occurred during the detection period of past market conditions, and external information regarding the detection period of past market conditions. External environmental information for reference that reflects the environment, household information for reference that reflects statistical data about households at the time of detection of past market conditions, and reference that reflects statistical data about real estate at the time of detection of past market conditions Real estate information, reference expert opinion information that reflects the opinions of experts announced at the time of past market condition detection, reference natural environment information that reflects information on the natural environment at the time of past market condition detection, A data set with stock price increase / decrease data of each stock in the past market conditions is stored.

That is, in addition to such reference market condition information, the database 3 contains reference event information, reference external environment information, reference household information, reference real estate information, reference expert opinion information, and reference natural environment information. Any one or more of the above and the increase / decrease data of the stock price of each stock in the past market conditions are stored in association with each other.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation of the stock transaction information display system 1 having the above-described configuration will be described.

In the stock transaction information display system 1, for example, as shown in FIG. 3, it is premised that three or more levels of linkage between the reference market information and the stock price increase / decrease data of each stock are set and acquired in advance. Become. Reference market information is various information related to market conditions. Examples of this reference market information include interest rates, exchange rates, stock prices of each stock, crude oil, futures, precious metals, and price movements of Bitcoin. This reference market information may be displayed as a time-series chart, a line graph, or the like for these targets. In addition, information such as Bollinger band, volume, MACD, and moving average line may be attached. In addition, this market condition information may be accompanied by information such as a chart of each brand, Bollinger Bands, MACD, and a moving average line. Information such as a chart showing price movements between currencies, Bollinger Bands, MACD, and moving average lines may be attached to the exchange rate.

The stock price increase / decrease data of each issue is data showing how much the stock price of each issue has increased / decreased at the time after the reference market condition information was acquired. This increase / decrease data may be counted by the actual increase / decrease price range, or may be expressed by the increase / decrease rate. This increase / decrease data is represented by an increase / decrease in the stock price at the measurement time point (later time point) with respect to the stock price at the previous time point (that is, the time point when the reference market information is acquired). The time point before here is 10 seconds before, 1 minute before, 30 minutes before, 1 hour before, 4 hours before, 1 day before, 10 days ago, 1 month ago, 1 year ago, 5 years ago, etc. As in the above, it may be configured with any time width with respect to the measurement time point. That is, the stock price increase / decrease data shows the increase / decrease of the stock price at the measurement time point with respect to the stock price at the previous time point when a certain point in the chart is set as the measurement time point. Alternatively, the stock price increase / decrease data may represent the foot itself in the stock price chart.

In other words, through this reference market information and the data set of stock price increase / decrease data, various technical events that occurred in the reference market information (for example, the chart has risen for 3 consecutive days, temporarily high price) You can see how the stock price increased or decreased at a later point (such as when a beard appeared on the chart). In other words, the data set is the result of stock price increase / decrease for technical events. Therefore, by collecting a data set of reference market information and stock price increase / decrease data, it is possible to know what kind of market conditions have occurred in the past and how the stock price has increased / decreased. ..

In the example of FIG. 3, it is assumed that the input data is, for example, reference market condition information P01 to P03. The reference market information as such input data is linked to the output. In this output, stock price increase / decrease data as an output solution is displayed.

The reference market information is linked to each other through three or more levels of linkage to the stock price increase / decrease data as this output solution. The reference market information is arranged on the left side via this degree of association, and the increase / decrease data of each stock price is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which stock price increase / decrease data is highly relevant to the reference market information arranged on the left side. In other words, this degree of association is an index showing what kind of stock price increase / decrease data is likely to be associated with each reference market condition information, and the most accurate stock price increase / decrease data from the reference market condition information is used. It shows the accuracy of selection. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the stock price increase / decrease data as an output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node with the stock price increase / decrease data as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to whether the reference market condition information or the stock price increase / decrease data in that case is adopted in determining the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 3 is created.

For example, it is assumed that a certain reference market information is when the preceding MACD pulls out the lagging average (SIGNAL) from the bottom to the top in the stock price chart of a certain stock. Under such market conditions, it is assumed that the stock price of the stock in question often increased by 30% at a later point in time. In such a case, the degree of association with a 30% increase in the stock price of the stock becomes stronger. On the other hand, in exactly the same market conditions, it is assumed that the stock price of the stock has decreased by 25% at a subsequent point in time, and the stock price of the stock has increased by 30% in few cases. In such a case, the degree of association of the stock price down by 25% becomes stronger, and the degree of association of the stock price of the issue up by 30% becomes low.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference market information P01, analysis is performed from the price movement data of the stock price of each stock in the past. This may be extracted from, for example, electronic data of past stock price charts. In the case of reference market information P01, if there are many cases of stock price increase / decrease data A1 (stock 〇 × company 75% increase) of each issue, set a higher degree of association leading to this increase / decrease data A1 and increase / decrease. If there are many cases of data A3 (stock price △ □ company 25% down), set a higher degree of association that leads to this increase / decrease data A3. For example, in the example of the reference market information P01, the increase / decrease data A1 and the increase / decrease data A3 are linked. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, in order to actually give new advice to customers regarding stock trading issues, the above-mentioned learned data will be used to predict changes in stock prices. In such a case, the market information regarding the market conditions at the time of the actual new stock trading will be acquired. This market condition information is composed of the same kind of data as the above-mentioned reference market condition information.

The newly acquired market condition information is input by the above-mentioned information acquisition unit 9. The information acquisition unit 9 may acquire charts, price movement data, and the like as electronic data.

Based on the market information newly acquired in this way, the future stock price (that is, future stock price increase / decrease data) that is likely to occur is predicted with respect to the market information. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02, the increase / decrease data A2 is associated with w15 and the increase / decrease data A3 is associated with the association degree w16 via the degree of association. In such a case, the increase / decrease data A2 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A3 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, it is possible to search the stock price status of each stock that may occur in the future from the newly acquired market condition information through the stock price increase / decrease data and display it to the user (consultant). By looking at the search result, the user (consultant) can obtain a guideline for the stock to be bought and sold based on the searched stock price increase / decrease data. It is possible to give useful advice to the user just by showing the search result of the stock price increase / decrease data. By the way, in constructing this advice, in addition to simply displaying the searched stock price increase / decrease data, based on this increase / decrease data, specifically which stock and how much stock should be purchased or sold. The advice may be configured by displaying.

In the example of FIG. 4, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference event information P14 to 17. The intermediate node shown in FIG. 4 is a combination of reference event information and reference market information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

In the example of FIG. 4, it is premised that a combination of the reference market condition information and the reference event information is formed. Reference event information is a concept that includes various social news, events, incidents, celebrations, celebrations, etc. that occurred in Japan or abroad, as well as news, events, incidents, celebrations, celebrations, etc. that occurred about each company. be. This reference event information can be obtained from blogs, analyst reports, securities reports, advertisements, press releases, news articles, etc. regarding each company or society as a whole. These reference event information may be extracted through a character string, a dependency, or the like obtained by analyzing a news article through text mining.

In the example of FIG. 4, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference event information P14 to 17. The intermediate node shown in FIG. 4 is a combination of reference event information and reference market information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference market information and the reference event information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. Reference market information and reference event information are arranged on the left side via this degree of association, and stock price increase / decrease data of each stock is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the stock price increase / decrease data of each stock with respect to the reference market condition information and the reference event information arranged on the left side. In other words, this degree of association is an index showing what kind of stock price increase / decrease data is likely to be associated with each reference market information and reference event information, and is a reference market information and reference. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from the event information. In addition to market data, the stock price increase / decrease data for each stock at a later point in time will differ depending on various events that actually occurred in society as a whole or in each company. Therefore, by combining these reference market condition information and reference event information, it is possible to search for the optimum stock price increase / decrease data of the stock.

In the example of FIG. 4, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 obtains past data as to which of the reference market condition information, the reference event information, and the stock price increase / decrease data of each stock in that case was suitable for determining the actual search solution. By accumulating and analyzing and analyzing these, the degree of association shown in FIG. 4 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference event information P16, the increase / decrease data of the stock price of each stock is analyzed from the past data. If there are many cases where the increase / decrease data of the stock price of each issue is increase / decrease data A1 (stock 〇 × company 75% up), set a higher degree of association leading to this increase / decrease data A1 and increase / decrease data A2 (stock 〇 × company). When there are many cases of 50% down) and few cases of increase / decrease data A1, the degree of association connected to the increase / decrease data A2 is set high, and the degree of association connected to the increase / decrease data A1 is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree is set to 2 points.

Further, the degree of association shown in FIG. 4 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 4, the node 61b is a node in which the reference event information P14 is combined with the reference market condition information P01, the degree of association of the increase / decrease data A3 is w15, and the degree of association of the increase / decrease data A5. Is w16. The node 61c is a node that is a combination of the reference event information P15 and P17 with respect to the reference market condition information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for increase / decrease data for displaying stock transaction information, the above-mentioned learned data will be used. In such a case, the market information regarding the market conditions at the time of the new stock trading is acquired, and the event information reflecting the events that occurred at the time of the new stock trading is acquired. This event information corresponds to the above-mentioned reference event information, and data such as news, newspapers, and blogs may be taken in or directly input.

Based on the market information and event information newly acquired in this way, the optimum stock price increase / decrease data for each stock is searched for. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the event information is P17, the node 61d is associated through the degree of association, and this node In 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

FIG. 5 shows an example in which a combination of the above-mentioned reference market condition information and reference external environment information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. ..

External environmental information for reference is politics and economy outside the company, such as GDP, employment statistics, mining and manufacturing production index, capital investment, labor force survey, business conditions index, consumption expenditure, new car sales volume, consumer price index, etc. Includes various data on, society, technology, etc.

In the example of FIG. 5, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference external environment information P18 to 21. The intermediate node shown in FIG. 5 is a combination of the reference market condition information and the reference external environment information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference external environment information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. The reference market information and the reference external environment information are arranged on the left side via this degree of association, and the increase / decrease data is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference market condition information and the reference external environment information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference market condition information and reference external environment information, and the reference market information and reference external environment information. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock. In addition to market data, stock prices change depending on the actual external environment. Therefore, by combining these reference market condition information and reference external environment information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference external environment information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference external environment information P20, the increase / decrease data is analyzed from the past data. For example, in the example of the intermediate node 61a, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 5, the node 61b is a node that is a combination of the reference market condition information P01 and the reference external environment information P18, and the degree of association of the increase / decrease data A3 is w15 and the association of the increase / decrease data A5. The degree is w16. The node 61c is a node that is a combination of the reference external environment information P19 and P21 with respect to the reference market condition information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned market condition data, external environment information that reflects the external environment at the time of new stock trading is acquired. As the external environment information, for example, if it is employment statistics information, the data may be directly taken in. If it is other statistical data, the data may be acquired directly.

Based on the market information and external environmental information newly acquired in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the external environment information is P21, the node 61d is associated with the node 61d through the degree of association. In the node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 6 shows an example in which a combination of the above-mentioned reference market condition information and reference household budget information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set.

The reference household information includes various data related to household consumption status survey, household data, average working hours per week, statistical data of savings amount, statistical data of annual income, household budget, and the like.

In the example of FIG. 6, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference household information P22 to 25. The intermediate node shown in FIG. 6 is a combination of reference market information and reference household information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference market information and the reference household information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. Reference market information and reference household information are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference market information and the reference household information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference market condition information and reference household budget information, and is the most from the reference market condition information and the reference household budget information. It shows the accuracy in selecting the stock price increase / decrease data of each stock that is likely to be. In addition to market data, there are stocks whose stock prices fluctuate depending on the actual state of the household budget. Therefore, by combining these reference market condition information and reference household budget information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

In the example of FIG. 6, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference household information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing the above, the degree of association shown in FIG. 6 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market information P01 and the reference household information P24, the increase / decrease data is analyzed from the past data. For example, in the example of the intermediate node 61a, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node that is a combination of the reference market information P01 and the reference household information P22, and the degree of association of the increase / decrease data A3 is w15 and the degree of association of the increase / decrease data A5. Is w16. The node 61c is a node that is a combination of the reference household information P23 and P25 with respect to the reference market information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned market data, household information that reflects statistical data on the household budget at the time of new stock trading will be acquired. If the household information is data published by each government agency, such as statistical data on the amount of savings, the data may be directly taken in. If it is other statistical data, the data may be acquired directly.

Based on the market information and household information newly acquired in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the household information is P25, the node 61d is associated through the degree of association, and this node In 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

As an alternative to the reference household information shown in FIG. 6, reference real estate information reflecting statistical data on real estate at the time of detection of the last market condition may be used as input data. In such a case, the detailed description will be omitted by replacing the reference household information with the reference real estate information and replacing the household information with the real estate information.

In such a case, the combination of the reference market condition information and the reference real estate information, and the degree of association of the stock price increase / decrease data of each of the above stocks with three or more levels will be used. The node 61 defines the degree of association between the combination of the reference market condition information and the reference real estate information and the increase / decrease data of the stock price of each of the above three levels or more.

The reference real estate information includes all information related to real estate such as office vacancy rate, tsubo unit price, rent market price, land price, and statistical data on vacant houses.

In forming such a degree of association and giving advice on stock trading, obtain real estate information that reflects the statistical day regarding real estate at the time of new stock trading, and refer to the same or similar. We provide advice on stock trading stocks based on the combination of real estate information for reference and market condition information for reference, and the degree of association with the stock price increase / decrease data of each stock at three or more levels.

FIG. 7 shows an example in which a combination of the above-mentioned reference market condition information and reference expert opinion information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. There is.

Reference expert opinion information means any information that gives an expert opinion on the increase or decrease in stocks, and is specialized in stock price forecasts and reasons for increase or decrease in stocks published in analyst reports and newspaper articles. Home comments, opinions, etc. In addition, the reference expert opinion information may simply be the prediction itself as to whether the stock price of each stock will rise, fall, or remain unchanged. This reference expert opinion information includes opinions on the Nikkei Stock Average as a whole, opinions on specific segments and industries, and opinions on individual stocks. In addition, the reference expert opinion information may include comments by experts (analysts) posted on the Internet and forecasts of increase or decrease.

In the example of FIG. 7, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference expert opinion information P26 to 29. The intermediate node shown in FIG. 7 is a combination of reference market condition information and reference expert opinion information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference market information and the reference expert opinion information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. .. Reference market information and reference expert opinion information are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference market condition information and the reference expert opinion information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference market condition information and reference expert opinion information, and the reference market condition information and reference expert opinion information. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from the opinion information. Stock price fluctuations may correlate with actual expert opinion in addition to market data. Therefore, by combining these reference market condition information and reference expert opinion information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

In the example of FIG. 7, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference expert opinion information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market information P01 and the reference household information P28, the increase / decrease data is analyzed from the past data. For example, in the example of the intermediate node 61a, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference market condition information P01 is combined with the reference expert opinion information P26, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node that is a combination of the reference expert opinion information P27 and P29 with respect to the reference market information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned market conditions data, expert opinion information that reflects the expert opinions announced at the time of new stock trading will be acquired. As for the expert opinion information, for example, if there is an expert opinion expressed in a newspaper article, the data may be directly taken in.

Based on the market information and expert opinion information newly acquired in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the expert opinion information is P29, the node 61d is associated through the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 8 shows an example in which a combination of the above-mentioned reference market condition information and the reference natural environment information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. ..

Reference natural environment information means all information related to the natural environment such as disaster data, temperature data, precipitation data, wind direction data, humidity data, etc., and data on the past natural environment released by the Meteorological Agency, or private companies and private companies. Data on the past natural environment released by individuals.

In the example of FIG. 8, it is assumed that the input data is, for example, reference market condition information P01 to P03 and reference natural environment information P30 to 33. The intermediate node shown in FIG. 8 is a combination of the reference market condition information and the reference natural environment information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference market condition information and the reference natural environment information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. The reference market information and the reference natural environment information are arranged on the left side via this degree of association, and the increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference market condition information and the reference natural environment information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference market condition information and reference natural environment information, and the reference market condition information and reference natural environment information. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock. Stock price fluctuations may correlate with actual expert opinion in addition to market data. Therefore, by combining these reference market condition information and reference natural environment information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

In the example of FIG. 8, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference market condition information, the reference natural environment information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference market condition information P01 and the reference natural environment information P32, the increase / decrease data is analyzed from the past data. For example, in the reference market condition information P01, in the example of the intermediate node 61a to which the reference natural environment information P32 is linked, it is linked to the output of the increase / decrease data A1 and the increase / decrease data A2. The degree of association of w14 is set to 7 points, and the degree of association of w14 connected to the increase / decrease data A2 is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 8, the node 61b is a node that is a combination of the reference market condition information P01 and the reference natural environment information P30, and the degree of association of the increase / decrease data A3 is w15 and the association of the increase / decrease data A5. The degree is w16. The node 61c is a node that is a combination of the reference expert opinion information P31 and P33 with respect to the reference market information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned market condition data, the natural environment information that reflects the information of the natural environment at the time of new stock trading is acquired. The natural environment information may be taken in directly from, for example, data and information on the natural environment published by the Japan Meteorological Agency, a private company, or an individual, or a site containing these.

Based on the market information and natural environment information newly acquired in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired market condition information is the same as or similar to P02 and the natural environment information is P33, the node 61d is associated through the degree of association, and this In the node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In FIG. 9, in addition to the above-mentioned reference market condition information and reference event information, the combination of the reference external environment information and the stock price increase / decrease data of each stock with respect to the combination have three or more levels of association. An example of the settings is shown.

In such a case, as shown in FIG. 9, the degree of association is such that the set of combinations of the reference market condition information, the reference event information, and the reference external environment information is set as the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 9, in the node 61c, the reference market condition information P02 is associated with the association degree w3, the reference event information P15 is associated with the association degree w7, and the reference external environment information P19 is associated with the association degree w11. Similarly, in the node 61e, the reference market condition information P03 is associated with the association degree w5, the reference event information P15 is associated with the association degree w8, and the reference external environment information P18 is associated with the association degree w10.

Similarly, when such a degree of association is set, the search solution is determined based on the newly acquired market condition information, the event information, and the external environment information.

In determining the search solution, the degree of association shown in FIG. 9 acquired in advance is referred to. For example, when the acquired market condition information is the same as or similar to the reference market condition information P02, the acquired event information corresponds to the reference event information P15, and the acquired external environment information corresponds to the reference external environment information P19. In the combination, the node 61c is associated, and in this node 61c, the increase / decrease data A2 is associated with the association degree w17, and the increase / decrease data A4 is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

When combining three or more types of such input parameters, in addition to the reference market condition information, the reference event information, the reference external environment information, the reference household information, the reference real estate information, and the reference expert opinion It is applicable even if the combination is composed of any two or more of the information and the reference natural environment information.

In addition to the stock price increase / decrease data of each stock, as the output data, as shown in FIG. 10, advice on actual stock purchasing behavior (for example, 〇 × company buying, XX company holding), etc. is directly displayed. You may do so. This advice may be given not only to specify the stock to be advised to buy or sell, but also to give advice on the specific number of shares. Such advice may be generated based on the increase / decrease data described above. In such a case, it may be advised that if the stock price rises in the future, it should be bought, and if the stock price falls in the future, it should be sold. In addition to the possibility of return of stock trading, the advice may also indicate the risk. At this time, as the input data and the data to be trained, it is of course possible to directly include this advice content in the data set instead of the increase / decrease data and train it.

Further, the present invention may be embodied as an automatic stock trading program that automatically trades stocks. In such a case, after searching the increase / decrease data based on the above procedure, each stock of the stock price is automatically bought and sold based on the increase / decrease data. In such a case, the system side buys and sells the stock itself based on the advice regarding the purchasing behavior of the stock (for example, buying XX company, holding XX company). In such a case, if it is determined that the stock price of a certain stock will rise as a result of searching the increase / decrease data of each stock, the process of automatically purchasing the stock of that stock is performed. On the other hand, when it is determined that the stock price of a certain stock will fall, the stock of that stock is automatically sold or short-sold.

Second Embodiment Hereinafter, the second embodiment will be described. In executing this second embodiment, the stock transaction information display system 1, the information acquisition unit 9, the search device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by quoting the description of the first embodiment.

In the second embodiment, for example, as shown in FIG. 11, it is premised that three or more levels of association between the reference event information and the stock price increase / decrease data of each issue are set and acquired in advance. The reference event information, the event information, and the stock price increase / decrease data are the same as those in the first embodiment described above.

Through the reference event information and the dataset of stock price increase / decrease data, it is possible to know how the stock price has increased / decreased at a later time point after various events that occurred in the reference event information. In other words, the result of stock price increase / decrease with respect to the event is a data set. Therefore, by collecting a data set of reference event information and stock price increase / decrease data, it is possible to know what kind of event has occurred in the past and how the stock price has increased / decreased. ..

In the example of FIG. 11, it is assumed that the input data is, for example, reference event information P01 to P03. The reference event information as such input data is linked to the output. In this output, stock price increase / decrease data as an output solution is displayed.

The reference event information is associated with each other through three or more levels of association with the stock price increase / decrease data as the output solution. The reference event information is arranged on the left side via this degree of association, and the increase / decrease data of each stock price is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which stock price increase / decrease data is highly related to the reference event information arranged on the left side. In other words, this degree of association is an index showing what kind of stock price increase / decrease data is likely to be associated with each reference event information, and the most probable stock price increase / decrease data from the reference event information is used. It shows the accuracy of selection. In the example of FIG. 11, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the stock price increase / decrease data as an output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node with the stock price increase / decrease data as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to whether the reference event information or the stock price increase / decrease data in that case is adopted in determining the actual search solution, and analyzes these. By analyzing, the degree of association shown in FIG. 11 is created.

For example, suppose a reference event information is an event in which an influenza pandemic occurs. At this time, it is assumed that the stock prices of the stocks of the companies that produce and sell the masks increased by 70% at that time. In such a case, the degree of association with a 37% increase in the stock price of the stock becomes stronger. This analysis and analysis may be performed by artificial intelligence.

The reference event information may be one in which a sentence is morphologically analyzed through text mining, and is parsed and semantically analyzed. Similarly, the event information may be taken in as text data, and then morphological analysis, syntactic analysis, and semantic analysis may be performed and linked to the above-mentioned reference event information.

Further, the degree of association shown in FIG. 11 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 12, reference event information is input as input data, increase / decrease data of the stock price of each issue is output as output data, and at least one or more hidden data is hidden between the input node and the output node. Layers may be provided for machine learning. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when the degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, in order to actually give new advice to customers regarding stock trading issues, the above-mentioned learned data will be used to predict changes in stock prices. In such a case, event information at the time of actual new stock trading is acquired. This event information is composed of the same type of data as the above-mentioned reference event information.

The newly acquired event information is input by the information acquisition unit 9 described above. The information acquisition unit 9 may acquire charts, price movement data, and the like as electronic data.

Based on the event information newly acquired in this way, the future stock price (that is, future stock price increase / decrease data) that is likely to occur is predicted for the event information. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired event information is the same as or similar to P02, the increase / decrease data A2 is associated with w15 and the increase / decrease data A3 is associated with the association degree w16 via the association degree. In such a case, the increase / decrease data A2 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A3 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, from the newly acquired event information, the stock price status of each stock that may occur in the future can be searched through the stock price increase / decrease data and displayed to the user (consultant). By looking at the search result, the user (consultant) can obtain a guideline for the stock to be bought and sold based on the searched stock price increase / decrease data. It is possible to give useful advice to the user just by showing the search result of the stock price increase / decrease data. By the way, in constructing this advice, in addition to simply displaying the searched stock price increase / decrease data, based on this increase / decrease data, specifically which stock and how much stock should be purchased or sold. The advice may be configured by displaying.

Further, in the second embodiment as well, as in the first embodiment, the system side should buy and sell the stocks by itself based on the advice regarding the stock purchasing behavior (for example, XX company buying, XX company holding) and the like. It may be. In such a case, if it is determined that the stock price of a certain stock will rise as a result of searching the increase / decrease data of each stock, the process of automatically purchasing the stock of that stock is performed. On the other hand, when it is determined that the stock price of a certain stock will fall, the stock of that stock is automatically sold or short-sold.

FIG. 13 shows an example in which a combination of the above-mentioned reference event information and reference external environment information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. ..

The reference external environment information and the external environment information are the same as those in the first embodiment described above.

In the example of FIG. 13, it is assumed that the input data is, for example, reference event information P01 to P03 and reference external environment information P18 to 21. The intermediate node shown in FIG. 13 is a combination of the reference event information and the reference external environment information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference event information and the reference external environment information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. The reference event information and the reference external environment information are arranged on the left side via this degree of association, and the increase / decrease data is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference event information and the reference external environment information arranged on the left side. In other words, this degree of association is an index indicating what kind of increase / decrease data is likely to be associated with each reference event information and reference external environment information, and the reference event information and reference external environment information. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock. In addition to event information, stock prices change depending on the actual state of the external environment. Therefore, by combining the reference event information and the reference external environment information, the optimum stock price increase / decrease data of each stock is searched for.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference event information, the reference external environment information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 13 is created.

In the example of the degree of association shown in FIG. 13, the node 61b is a node that is a combination of the reference event information P01 and the reference external environment information P18, and the degree of association of the increase / decrease data A3 is w15 and the association of the increase / decrease data A5. The degree is w16. The node 61c is a node that is a combination of the reference external environment information P19 and P21 with respect to the reference event information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned event information, external environment information that reflects the external environment at the time of new stock trading is acquired. As the external environment information, for example, if it is employment statistics information, the data may be directly taken in. If it is other statistical data, the data may be acquired directly.

Based on the newly acquired event information and external environment information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 13 (Table 1) acquired in advance is referred to. For example, when the newly acquired event information is the same as or similar to P02 and the external environment information is P21, the node 61d is associated via the degree of association, and this In the node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.
As an alternative to the above-mentioned reference external environment information, the above-mentioned degree of association with the reference expert opinion information may be configured. The reference expert opinion information referred to here is the same as that of the first embodiment described above.

By substituting such reference expert opinion information with the reference external environment information shown in FIG. 13, the increase / decrease data of the stock price of each stock and the degree of association can be obtained via the combination node 61 with the reference event information. Form it. Then, when you want to actually predict the increase / decrease in the stock price of each stock, acquire the current event information and the external environment information, and search the increase / decrease data of the stock price of each stock based on the degree of association of these combinations. become.

As an alternative to the above-mentioned reference external environment information, the above-mentioned degree of association with the reference natural environment information may be configured. The reference natural environment information referred to here is the same as that of the first embodiment described above.

Such reference natural environment information is replaced with the reference external environment information shown in FIG. 13, and the degree of association with the stock price increase / decrease data of each stock is formed through the combination node 61 with the reference event information. I will do it. Then, if you want to actually predict the increase / decrease of the stock price of each stock, acquire the current event information and the natural environment information, and search the increase / decrease data of the stock price of each stock based on the degree of association of these combinations. become.

FIG. 14 shows an example in which a combination of the above-mentioned reference event information and reference fundamental information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set.

The reference fundamental information and the fundamental information are financial indicators, management indicators, etc. of each stock for performing so-called fundamental analysis in stock trading, for example, capital adequacy ratio, current ratio, liquidity on hand, cash flow, etc. These are indicators such as aging of EPS, sales ratio, ROE, ROA, PER, PBR, and BPS. In addition to this, ordinary financial statements (balance sheet, income statement, cash flow statement, etc. are also included in the reference fundamental information and fundamental information. Furthermore, this reference fundamental information and fundamental information are included in the company quarterly report. Information such as posted information, shareholder composition, capital, capital surplus, etc. is also included. The reference fundamental information and fundamental information may be composed of the above-mentioned indicators and changing trends of data, for example. , Difference, increase / decrease in sales ratio, ROE, ROA, PER, PBR, BPS, etc. at one point in time and each index, ROE, ROA, PER, PBR, BPS, etc. It may be used as reference fundamental information and fundamental information.

In the example of FIG. 14, it is assumed that the input data is, for example, reference event information P01 to P03 and reference fundamental information P18 to 21. The intermediate node shown in FIG. 14 is a combination of reference event information and reference fundamental information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference event information and the reference fundamental information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. Even if a very unfavorable event occurs in a stock, if the fundamental financial condition of the stock is good, it may not be regarded as a big risk and the stock price decline may not be so large. Judgment is made through the degree of association shown in FIG. The reference event information and the reference fundamental information are arranged on the left side via this degree of association, and the increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference event information and the reference fundamental information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference event information and reference fundamental information, and is the most from the reference event information and the reference fundamental information. It shows the accuracy in selecting the stock price increase / decrease data of each stock that is likely to be. In addition to event information, stock prices change depending on the state of actual fundamental information. Therefore, by combining these reference event information and reference fundamental information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference event information, the reference fundamental information, and the increase / decrease data in that case was suitable for determining the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 14 is created.

In the example of the degree of association shown in FIG. 14, the node 61b is a node in which the reference event information P01 is combined with the reference fundamental information P18, the degree of association of the increase / decrease data A3 is w15, and the degree of association of the increase / decrease data A5. Is w16. The node 61c is a node that is a combination of the reference fundamental information P19 and P21 with respect to the reference event information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned event information, the fundamental information of each stock at the time of stock trading is acquired.

Based on the newly acquired event information and fundamental information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 14 (Table 1) acquired in advance is referred to. For example, when the newly acquired event information is the same as or similar to P02 and the fundamental information is P21, the node 61d is associated via the degree of association, and this node In 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 15 shows an example in which a combination of the above-mentioned reference event information and reference-related stock market information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. There is.

The related issue market information and the related issue market information for reference are composed of the market information of other related issues previously associated with the trading candidate issue for which the stock is traded. The other related brand may be another rival automobile manufacturer, or a parts manufacturer that supplies parts to the automobile manufacturer, as long as the transaction candidate brand is an automobile manufacturer. Other related stocks include not only rival companies in the same industry, but also companies in other industries that cooperate with each other to build an ecosystem. This related issue may be preset for a transaction candidate issue in the system, or may be freely set on the user side. Further, for this related issue, when the access tendency via the Internet is analyzed and one issue is accessed, another issue that is frequently accessed may be set as the related issue.

The details of the market information are the same as those in the first embodiment described above. Therefore, the reference-related stock market information and the related stock market information are composed of information such as a time-series chart, a line graph, a Bollinger band, MACD, and a moving average line for the related stocks.

In the example of FIG. 15, it is assumed that the input data is, for example, reference event information P01 to P03 and reference related brand market condition information P18 to 21. The intermediate node shown in FIG. 15 is a combination of reference-related event information and reference-related brand market information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each trading candidate stock as an output solution is displayed. In other words, it predicts the increase or decrease in the stock price of trading candidate stocks from the market conditions of related stocks.

Each combination (intermediate node) of the reference event information and the reference-related stock market information is linked to each other through three or more levels of linkage to the stock price increase / decrease data of each trading candidate stock as this output solution. ing. If a very unfavorable event occurs in a certain stock and the price movement of the related stock is on a downward trend, it can be judged that the stock price of the trading candidate stock is more likely to fall. Judgment is made through the degree of association shown in FIG. 15 including such cases. Reference event information and reference-related stock market information are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference event information and the reference-related stock market information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference event information and reference-related stock market information, and the reference event information and reference-related stocks. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from market information. In addition to event information, stock prices change depending on the actual price movements of related stocks. Therefore, by combining the reference event information and the reference-related stock market information, the optimum stock price increase / decrease data of each stock will be searched for.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference event information, the reference-related brand market information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 15 is created.

In the example of the degree of association shown in FIG. 15, the node 61b is a node of the combination of the reference related issue market condition information P18 with respect to the reference event information P01, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node that is a combination of the reference-related stock market information P19 and P21 with respect to the reference event information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned event information, the market information of related stocks of each stock at the time of stock trading is acquired.

Based on the newly acquired event information and market information of related stocks in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 15 (Table 1) acquired in advance is referred to. For example, when the newly acquired event information is the same as or similar to P02 and the related issue market information is P21, the node 61d is associated via the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 16 shows an example in which a combination of the above-mentioned reference event information and reference statistical information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set.

The reference statistical information and statistical information include various statistical information related to household consumption survey, household data, average working hours per week, statistical data of savings amount, statistical data of annual income, household budget, and the like. In addition, the reference statistical information and statistical information include all statistical information on real estate such as office vacancy rate, tsubo unit price, rent market price, land price, and statistical data on vacant houses. In addition, the reference statistical information and statistical information include data such as GDP, business conditions index, consumption expenditure, new car sales volume, and consumer price index as statistical information on the economy. Reference statistics and statistics include statistical information about any of households, malfunctions, and economies.

In the example of FIG. 16, it is assumed that the input data is, for example, reference event information P01 to P03 and reference statistical information P18 to 21. The intermediate node shown in FIG. 16 is a combination of reference event information and reference statistical information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed. In other words, it predicts an increase or decrease in the stock price of a stock from the market conditions of the related stock.

Each combination (intermediate node) of the reference event information and the reference statistical information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. If a very unfavorable event occurs in a certain stock, while the above-mentioned statistical information is upward, it can be judged that the stock price of the trading candidate stock does not fall so much. Judgment is made through the degree of association shown in FIG. 16 including such cases. The reference event information and the reference statistical information are arranged on the left side via this degree of association, and the increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference event information and the reference statistical information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference event information and reference statistical information, and is the most from the reference event information and the reference statistical information. It shows the accuracy in selecting the stock price increase / decrease data of each stock that is likely to be. In addition to event information, stock prices change depending on the state of actual household, real estate, and economic statistics. Therefore, by combining these reference event information and reference statistical information, it is possible to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference event information, the reference statistical information, and the increase / decrease data in that case was suitable for determining the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 16 is created.

In the example of the degree of association shown in FIG. 16, the node 61b is a node in which the reference statistical information P18 is combined with the reference event information P01, the degree of association of the increase / decrease data A3 is w15, and the degree of association of the increase / decrease data A5. Is w16. The node 61c is a node in which the reference statistical information P19 and P21 are combined with respect to the reference event information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned event information, statistical information of each stock at the time of stock trading is acquired. It is desirable that the type of statistical information to be acquired corresponds to the statistical information for reference. For example, if the statistical information for reference is a statistic related to real estate, the newly acquired statistical information is also a statistic related to real estate. Is desirable.

Based on the event information and statistical information newly acquired in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 16 (Table 1) acquired in advance is referred to. For example, when the newly acquired event information is the same as or similar to P02 and the statistical information is P21, the node 61d is associated via the degree of association, and this node In 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

Further, also in the second embodiment, as shown in FIG. 9, three or more types of input parameters may be combined. In such a case, in addition to the reference event information, the reference external environment information and the reference specialty It is applicable even if the degree of association of the combination is composed of any two or more of the house opinion information, the natural environment information for reference, the fundamental information for reference, the market information of related stocks for reference, and the statistical information for reference. In addition to this, in addition to the reference event information, any of the reference external environment information, the reference expert opinion information, the reference natural environment information, the reference fundamental information, the reference related brand market information, and the reference statistical information. The degree of association of a combination of 1 or more and other parameters may be configured. That is, in addition to the reference event information, the association of the combination having the reference external environment information, the reference expert opinion information, the reference natural environment information, the reference fundamental information, the reference related brand market information, and the reference statistical information. Degrees may be configured.

Further, the degree of association of the combination of any one or more of the input parameters in the first embodiment and any one or more of the input parameters in the second embodiment may be configured. Then, when information corresponding to the input parameter of the degree of association of the combination is input, the solution search may be performed in the same manner as described above via the degree of association.

The present invention is not limited to the above-described embodiment. For example, as shown in FIG. 17, instead of the stock price increase / decrease data of each issue, the degree of association may be set by learning the increase / decrease data of each exchange in association with the reference event information. .. The increase / decrease data of each exchange shows the degree of increase / decrease for each exchange such as Japanese yen-US dollar, Japanese yen-renminbi, and British pound-US dollar.

In the example shown in FIG. 17, the method of searching for the increase / decrease data of each exchange is described by replacing the output solution with the increase / decrease data of each exchange from the increase / decrease data of the stock price of each stock in the above-mentioned explanation of FIG. The following description will be omitted.

When event information that reflects the event that occurred at the time of new foreign exchange trading is acquired, the reference event information shown in FIG. 17 and the increase / decrease data of each foreign exchange at the time after the event occurred 3 Use more than one level of association. Then, the above-mentioned increase / decrease data of each exchange is displayed by giving priority to the event information for reference according to the acquired event information and the increase / decrease data of each exchange with a higher degree of association of three or more stages.

FIG. 18 shows an example in which a combination of the above-mentioned reference event information and reference external environment information, and three or more levels of association with each exchange increase / decrease data for the combination are set. In the example shown in FIG. 18, the method of searching for the increase / decrease data of each exchange is described by replacing the output solution with the increase / decrease data of each exchange from the increase / decrease data of the stock price of each stock in the above description of FIG. The following description will be omitted.

When the event information that reflects the event that occurred at the time of the new exchange transaction and the external environment information that reflects the external environment are acquired, the reference event information and the reference external environment information shown in FIG. 18 are obtained. Use the degree of association of the increase / decrease data of each exchange for the combination of. Then, the one with a higher degree of association of three or more levels of the increase / decrease data of each exchange for the combination of the reference event information according to the acquired event information and the reference external environment information according to the acquired external environment information. Display the increase / decrease data of each exchange with priority.

In addition to the reference event information, as an alternative to this reference external environment information, any of the above-mentioned reference expert opinion information, reference natural environment information, reference fundamental information, reference related brand market information, and reference statistical information The degree of association with the combination of 1 or more may be created with the increase / decrease data of each exchange. As a result, in addition to the event information, when the above-mentioned expert opinion information, natural environment information, fundamental information, related stock market information, and statistical information are input as a substitute for this external environmental information, this degree of association is used. This makes it possible to search for and predict changes in exchange rates as well.

Similarly, when searching for and predicting an increase or decrease in exchange rate, the system side may buy and sell the exchange rate by itself. In such a case, if it is determined that one currency will rise in a certain currency (combination of currencies) as a result of searching the increase / decrease data of each currency, the process of automatically purchasing the currency of that currency. I do. On the other hand, when it is determined that one currency will depreciate in a certain currency combination, the currency may be automatically sold or short-sold.

Third Embodiment Hereinafter, the third embodiment will be described. In executing this third embodiment, the stock transaction information display system 1, the information acquisition unit 9, the search device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by quoting the description of the first embodiment.

In the third embodiment, for example, as shown in FIG. 19, it is premised that the degree of association between the external environment information and the stock price increase / decrease data of each issue is set in advance and acquired. The reference external environment information, the external environment information, and the stock price increase / decrease data are the same as those in the first embodiment described above.

After the acquisition of the reference external environment information through the dataset of the reference external environment information that reflects the past external environment acquired in advance and the stock price increase / decrease data of each stock at the time after the acquisition of the reference external environment. You can see how the stock price increased or decreased at that time. In other words, the result of stock price increase / decrease with respect to the event is a data set. Therefore, by collecting a data set of reference external environment information and stock price increase / decrease data, it is possible to know what kind of external environment has occurred in the past and how the stock price has increased / decreased. It becomes.

In the example of FIG. 19, it is assumed that the input data is, for example, reference external environment information P01 to P03. The reference external environment information as such input data is linked to the output. In this output, stock price increase / decrease data as an output solution is displayed.

The reference external environment information is associated with each other through three or more levels of association with the stock price increase / decrease data as this output solution. The external environment information for reference is arranged on the left side via this degree of association, and the increase / decrease data of each stock price is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which stock price increase / decrease data is highly relevant to the reference external environment information arranged on the left side. In other words, this degree of association is an index showing what kind of stock price increase / decrease data is likely to be associated with each reference external environment information, and is the most probable stock price increase / decrease data from the reference external environment information. It shows the accuracy in selecting. In the example of FIG. 19, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the stock price increase / decrease data as an output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node with the stock price increase / decrease data as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to whether the reference external environment information or the stock price increase / decrease data in that case is adopted in determining the actual search solution, and analyzes these. , The degree of association shown in FIG. 19 is created by analysis.

For example, suppose that some reference external environmental information is an event in which the consumer price index is down by 〇〇 points. At this time, it is assumed that the stock prices of the stocks of the companies whose main business is retail have decreased by 20% at that time. In such a case, the degree of association of the stock price down by 20% becomes stronger. This analysis and analysis may be performed by artificial intelligence.

When the external environment information for reference is news provided as text data, the sentence may be morphologically analyzed, syntactically analyzed, and semantically analyzed through text mining. Similarly, the external environment information may be taken in as text data, and then morphological analysis, syntactic analysis, and semantic analysis may be performed and linked to the above-mentioned reference external environment information.

Further, the degree of association shown in FIG. 19 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 20, external environment information for reference is input as input data, increase / decrease data of the stock price of each brand is output as output data, and at least one or more between the input node and the output node. A hidden layer may be provided for machine learning. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when the degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, in order to actually give new advice to customers regarding stock trading issues, the above-mentioned learned data will be used to predict changes in stock prices. In such a case, the external environment information at the time of the actual stock trading is acquired. This external environment information is composed of the same type of data as the above-mentioned reference external environment information.

The newly acquired external environment information is input by the above-mentioned information acquisition unit 9.

Based on the newly acquired external environment information in this way, the future stock price (that is, future stock price increase / decrease data) that is likely to actually occur is predicted with respect to the external environment information. In such a case, the degree of association shown in FIG. 19 (Table 1) acquired in advance is referred to. For example, when the newly acquired external environment information is the same as or similar to P02, the increase / decrease data A2 is associated with w15 and the increase / decrease data A3 is associated with the association degree w16 via the degree of association. In such a case, the increase / decrease data A2 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A3 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, it is possible to search the stock price status of each stock that may occur in the future from the newly acquired external environment information through the stock price increase / decrease data and display it to the user (consultant). By looking at the search result, the user (consultant) can obtain a guideline for the stock to be bought and sold based on the searched stock price increase / decrease data. It is possible to give useful advice to the user just by showing the search result of the stock price increase / decrease data. By the way, in constructing this advice, in addition to simply displaying the searched stock price increase / decrease data, based on this increase / decrease data, specifically which stock and how much stock should be purchased or sold. The advice may be configured by displaying.

Also in the third embodiment, as in the first and second embodiments, the system side buys and sells shares by itself based on advice on stock purchasing behavior (for example, buying XX companies, holding XX companies). You may do so. In such a case, if it is determined that the stock price of a certain stock will rise as a result of searching the increase / decrease data of each stock, the process of automatically purchasing the stock of that stock is performed. On the other hand, when it is determined that the stock price of a certain stock will fall, the stock of that stock is automatically sold or short-sold.

FIG. 21 shows an example in which a combination of the above-mentioned reference external environment information and reference expert opinion information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. ing.

The reference expert opinion information and the expert opinion information are the same as those in the first embodiment described above.

In the example of FIG. 21, it is assumed that the input data is, for example, reference external environment information P01 to P03 and reference expert opinion information P18 to 21. The intermediate node shown in FIG. 21 is a combination of the reference external environment information and the reference expert opinion information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the external environment information for reference and the expert opinion information for reference is related to each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. There is. External environment information for reference and expert opinion information for reference are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference external environment information and the reference expert opinion information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference external environment information and reference expert opinion information, and is a reference external environment information and reference. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from expert opinion information. In addition to external environmental information, stock prices change depending on the state of the actual expert opinion. Therefore, by combining these reference external environment information and reference expert opinion information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 21. That is, the search device 2 accumulates past data as to which of the reference external environment information, the reference expert opinion information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 21 is created.

In the example of the degree of association shown in FIG. 21, the node 61b is a node in which the reference external environment information P01 is combined with the reference expert opinion information P18, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node that is a combination of the reference expert opinion information P19 and P21 with respect to the reference external environment information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned external environmental information, expert opinion information that reflects the expert's opinion at the time of new stock trading is acquired. As the expert opinion information, for example, the expert's comment may be acquired as text data, and the opinion information may be extracted through morphological analysis, syntactic analysis, and semantic analysis. The same applies when extracting expert opinion information for reference.

Based on the newly acquired external environment information and expert opinion information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 21 (Table 1) acquired in advance is referred to. For example, when the newly acquired external environment information is the same as or similar to P02 and the expert opinion information is P21, the node 61d is associated through the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.
As an alternative to the above-mentioned reference expert opinion information, the above-mentioned degree of association with the reference natural environment information may be configured. The reference natural environment information referred to here is the same as that of the first embodiment described above.

By substituting such reference natural environment information with the reference external environment information shown in FIG. 21, the stock price increase / decrease data of each stock and the degree of association can be obtained via the combination node 61 with the reference external environment information. Form it. Then, when it is desired to actually predict the increase / decrease in the stock price of each stock, the current external environment information and the natural environment information are acquired, and the increase / decrease data of the stock price of each stock is searched based on the degree of association between these combinations. It will be.

FIG. 22 shows an example in which a combination of the above-mentioned reference external environment information and reference fundamental information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. ..

The reference fundamental information and the fundamental information are financial indicators, management indicators, etc. of each stock for performing so-called fundamental analysis in stock trading, for example, capital adequacy ratio, current ratio, liquidity on hand, cash flow, etc. These are indicators such as aging of EPS, sales ratio, ROE, ROA, PER, PBR, and BPS. In addition to this, ordinary financial statements (balance sheet, income statement, cash flow statement, etc. are also included in the reference fundamental information and fundamental information. Furthermore, this reference fundamental information and fundamental information are included in the company quarterly report. It also includes information such as posted information, shareholder composition, capital stock, and capital surplus.

In the example of FIG. 22, it is assumed that the input data is, for example, reference external environment information P01 to P03, and reference fundamental information P18 to 21 of each brand at the time of acquisition of the reference external environment information. The intermediate node shown in FIG. 22 is a combination of the reference external environment information and the reference fundamental information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference external environment information and the reference fundamental information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. Even if very unfavorable external environmental information occurs for a stock, if the stock's fundamental financial condition is good, it may not be regarded as a big risk and the stock price decline may not be so large. Judgment is made through the degree of association shown in FIG. 22 including such cases. The reference external environment information and the reference fundamental information are arranged on the left side via this degree of association, and the increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference external environment information and the reference fundamental information arranged on the left side. In other words, this degree of association is an index indicating what kind of increase / decrease data is likely to be associated with each reference external environment information and reference fundamental information, and is a reference external environment information and reference fundamental information. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock. The stock price changes depending on the state of the actual fundamental information in addition to the external environmental information. Therefore, by combining these reference external environment information and reference fundamental information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 22. That is, the search device 2 accumulates past data as to which of the reference external environment information, the reference fundamental information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 22 is created.

In the example of the degree of association shown in FIG. 22, the node 61b is a node in which the reference external environment information P01 is combined with the reference fundamental information P18, the degree of association of the increase / decrease data A3 is w15, and the association of the increase / decrease data A5. The degree is w16. The node 61c is a node that is a combination of the reference fundamental information P19 and P21 with respect to the reference external environment information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned external environmental information, the fundamental information of each stock at the time of stock trading is acquired.

Based on the newly acquired external environmental information and fundamental information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 22 (Table 1) acquired in advance is referred to. For example, when the newly acquired external environment information is the same as or similar to P02 and the fundamental information is P21, the node 61d is associated with the node 61d through the degree of association. In the node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 23 shows an example in which the combination of the above-mentioned reference external environment information, the reference-related stock market information, and the stock price increase / decrease data of each stock for the combination are set to three or more levels of association. ing.

The related issue market information and the related issue market information for reference are composed of the market information of other related issues previously associated with the trading candidate issue for which the stock is traded. The other related brand may be another rival automobile manufacturer, or a parts manufacturer that supplies parts to the automobile manufacturer, as long as the transaction candidate brand is an automobile manufacturer. Other related stocks include not only rival companies in the same industry, but also companies in other industries that cooperate with each other to build an ecosystem. This related issue may be preset for a transaction candidate issue in the system, or may be freely set on the user side. Further, for this related issue, when the access tendency via the Internet is analyzed and one issue is accessed, another issue that is frequently accessed may be set as the related issue.

The details of the market information are the same as those in the first embodiment described above. Therefore, the reference-related stock market information and the related stock market information are composed of information such as a time-series chart, a line graph, a Bollinger band, MACD, and a moving average line for the related stocks.

In the example of FIG. 23, it is assumed that the input data is, for example, reference external environment information P01 to P03 and reference related brand market condition information P18 to 21. The intermediate node shown in FIG. 23 is a combination of the reference-related external environment information and the reference-related brand market information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each trading candidate stock as an output solution is displayed. In other words, it predicts the increase or decrease in the stock price of trading candidate stocks from the market conditions of related stocks.

Each combination (intermediate node) of the reference external environment information and the reference-related stock market information is linked to each other through three or more levels of linkage to the stock price increase / decrease data of each trading candidate stock as this output solution. It matches. If a very unfavorable event occurs in a certain stock and the price movement of the related stock is on a downward trend, it can be judged that the stock price of the trading candidate stock is more likely to fall. Judgment is made through the degree of association shown in FIG. 23 including such cases. External environment information for reference and market information of related stocks for reference are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference external environment information and the reference-related stock market information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference external environment information and reference related stock market information, and is a reference external environment information and reference. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from the related stock market information. In addition to external environmental information, stock prices change depending on the actual price movements of related stocks. Therefore, by combining these reference external environment information and reference-related stock market information, the optimum stock price increase / decrease data for each stock will be searched for.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 23. That is, the search device 2 accumulates past data as to which of the reference external environment information, the reference-related brand market information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 23 is created.

In the example of the degree of association shown in FIG. 23, the node 61b is a node in which the reference related brand market information P18 is combined with the reference external environment information P01, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node that is a combination of the reference-related brand market information P19 and P21 with respect to the reference external environment information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned external environment information, the market information of the related stocks of the trading candidate stocks at the time of stock trading is acquired.

Based on the newly acquired external environment information and market information of related stocks in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 23 (Table 1) acquired in advance is referred to. For example, when the newly acquired external environment information is the same as or similar to P02 and the related brand market information is P21, the node 61d is associated via the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 24 shows an example in which a combination of the above-mentioned reference external environment information and reference statistical information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. ..

The reference statistical information and statistical information include various statistical information related to household consumption survey, household data, average working hours per week, statistical data of savings amount, statistical data of annual income, household budget, and the like. In addition, the reference statistical information and statistical information include all statistical information on real estate such as office vacancy rate, tsubo unit price, rent market price, land price, and statistical data on vacant houses. In addition, the reference statistical information and statistical information include data such as GDP, business conditions index, consumption expenditure, new car sales volume, and consumer price index as statistical information on the economy. Reference statistics and statistics include statistical information about any of households, malfunctions, and economies.

In the example of FIG. 24, it is assumed that the input data is, for example, reference external environment information P01 to P03 and reference statistical information P18 to 21. The intermediate node shown in FIG. 24 is a combination of reference statistical information and reference external environment information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed. In other words, it predicts an increase or decrease in the stock price of a stock from the market conditions of the related stock.

Each combination (intermediate node) of the reference external environment information and the reference statistical information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. If a very unfavorable event occurs in a certain stock, while the above-mentioned statistical information is upward, it can be judged that the stock price of the trading candidate stock does not fall so much. Judgment is made through the degree of association shown in FIG. 24 including such cases. External environment information for reference and statistical information for reference are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference external environment information and the reference statistical information arranged on the left side. In other words, this degree of association is an index indicating what kind of increase / decrease data each reference external environment information and reference statistical information are likely to be associated with, and is a reference external environment information and reference statistical information. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock. In addition to external environmental information, stock prices change depending on the state of actual household, real estate, and economic statistics. Therefore, by combining these reference external environment information and reference statistical information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 24. That is, the search device 2 accumulates past data as to which of the reference external environment information, the reference statistical information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 24 is created.

In the example of the degree of association shown in FIG. 24, the node 61b is a node in which the reference statistical information P18 is combined with the reference external environment information P01, the degree of association of the increase / decrease data A3 is w15, and the association of the increase / decrease data A5. The degree is w16. The node 61c is a node that is a combination of the reference statistical information P19 and P21 with respect to the reference external environment information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned external environment information, statistical information of each stock at the time of stock trading is acquired. It is desirable that the type of statistical information to be acquired corresponds to the statistical information for reference. For example, if the statistical information for reference is a statistic related to real estate, the newly acquired statistical information is also a statistic related to real estate. Is desirable.

Based on the newly acquired external environment information and statistical information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 24 (Table 1) acquired in advance is referred to. For example, when the newly acquired external environment information is the same as or similar to P02 and the statistical information is P21, the node 61d is associated with the node 61d through the degree of association. In the node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

Further, also in the third embodiment, as shown in FIG. 9, three or more types of input parameters may be combined, and in such a case, in addition to the reference external environment information, the reference expert opinion information and the reference. It is applicable even if the degree of association of the combination is composed of any two or more of the natural environment information for reference, the fundamental information for reference, the market condition information of related stocks for reference, and the statistical information for reference. In addition to this, in addition to the reference external environment information, any one or more of the reference expert opinion information, the reference natural environment information, the reference fundamental information, the reference related brand market information, the reference statistical information, etc. The degree of association with the combination with the parameters of may be configured. That is, in addition to the reference external environment information, the degree of association of the combination having the reference expert opinion information, the reference natural environment information, the reference fundamental information, the reference related brand market information, and the reference statistical information is configured. You may.

Further, the degree of association between the combination of any one or more of the input parameters in the third embodiment and any one or more of the input parameters in the first embodiment or any one or more of the input parameters in the second embodiment is It may be configured. Further, the degree of association of the combination of any one or more of the input parameters in the third embodiment, any one or more of the input parameters in the first embodiment, and any one or more of the input parameters in the second embodiment is configured. It may be what is done. Then, when information corresponding to the input parameter of the degree of association of the combination is input, the solution search may be performed in the same manner as described above via the degree of association.

The present invention is not limited to the above-described embodiment. For example, as shown in FIG. 25, instead of the stock price increase / decrease data of each stock, the degree of association can be set by learning the increase / decrease data of each exchange in association with the reference external environment information. good. The increase / decrease data of each exchange shows the degree of increase / decrease for each exchange such as Japanese yen-US dollar, Japanese yen-renminbi, and British pound-US dollar.

In the example shown in FIG. 25, the method of searching for the increase / decrease data of each exchange is described by replacing the output solution with the increase / decrease data of each exchange from the increase / decrease data of the stock price of each stock in the above-mentioned explanation of FIG. The following description will be omitted.

When the external environment information that reflects the event that occurred at the time of the new exchange transaction is acquired, the reference external environment information shown in FIG. 25 and the increase / decrease data of each exchange at the time after the event occurs. Use the degree of association of 3 or more levels. Then, the above-mentioned increase / decrease data of each exchange is displayed by giving priority to the data having a higher degree of association between the external environment information for reference and the increase / decrease data of each exchange according to the acquired external environment information.

FIG. 26 shows an example in which the combination of the above-mentioned external environment information for reference, the expert opinion information for reference, and the increase / decrease data of each exchange for the combination are set to three or more levels of association. .. In the example shown in FIG. 26, the method of searching for the increase / decrease data of each exchange is described by replacing the output solution with the increase / decrease data of each exchange from the increase / decrease data of the stock price of each stock in the explanation of FIG. 21 described above. The following description will be omitted.

When the external environment information reflecting the event that occurred at the time of the new exchange transaction and the expert opinion information reflecting the expert opinion are acquired, the reference external environment information shown in FIG. 26 and the reference are obtained. Use the degree of association of each exchange increase / decrease data with respect to the combination with expert opinion information. Then, the degree of association between the external environment information for reference according to the acquired external environment information and the increase / decrease data of each exchange for the combination of the expert opinion information for reference according to the acquired expert opinion information is three or more levels. The higher one is prioritized and the increase / decrease data of each exchange is displayed.

In addition to the reference external environment information, as an alternative to this reference expert opinion information, any one or more of the above-mentioned reference natural environment information, reference fundamental information, reference related brand market information, and reference statistical information The degree of association of the combination may be created with the increase / decrease data of each exchange. As a result, in addition to the external environmental information, when the above-mentioned natural environment information, fundamental information, related brand market information, and statistical information are input as a substitute for this expert opinion information, the same can be done by using this degree of association. It is possible to search for and predict changes in exchange rates.

Similarly, when searching for and predicting an increase or decrease in exchange rate, the system side may buy and sell the exchange rate by itself. In such a case, if it is determined that one currency will rise in a certain currency (combination of currencies) as a result of searching the increase / decrease data of each currency, the process of automatically purchasing the currency of that currency. I do. On the other hand, when it is determined that one currency will depreciate in a certain currency combination, the currency may be automatically sold or short-sold.

Fourth Embodiment Hereinafter, the fourth embodiment will be described. In executing this fourth embodiment, the stock transaction information display system 1, the information acquisition unit 9, the search device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by quoting the description of the first embodiment.

In the fourth embodiment, for example, as shown in FIG. 27, it is premised that three or more levels of association between the expert opinion information and the stock price increase / decrease data of each issue are set and acquired in advance. The reference expert opinion information, the expert opinion information, and the stock price increase / decrease data are the same as those in the first embodiment described above.

How at the time after the acquisition of the reference expert opinion information through the dataset of the reference expert opinion information acquired in advance and the stock price increase / decrease data of each stock at the time after the acquisition of the reference external environment. You can see if the stock price has increased or decreased. In other words, the data set is the result of stock price increase / decrease in response to expert opinions and comments. For this reason, by collecting a dataset of expert opinion information for reference and stock price increase / decrease data, how the stock price will increase / decrease after the expert's opinion has been expressed for each stock in the past. It becomes possible to know what has been done.

In the example of FIG. 27, it is assumed that the input data is, for example, reference expert opinion information P01 to P03. Reference expert opinion information as such input data is linked to the output. In this output, stock price increase / decrease data as an output solution is displayed.

The reference expert opinion information is related to each other through three or more levels of association with the stock price increase / decrease data as this output solution. Expert opinion information for reference is arranged on the left side via this degree of association, and the increase / decrease data of each stock price is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which stock price increase / decrease data is highly relevant to the reference expert opinion information arranged on the left side. In other words, this degree of association is an index showing what kind of stock price increase / decrease data is likely to be associated with each reference expert opinion information, and the most probable stock price from the reference expert opinion information. It shows the accuracy in selecting increase / decrease data. In the example of FIG. 27, w13 to w19 are shown as the degree of association. As shown in Table 1, these w13 to w19 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the stock price increase / decrease data as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the stock price increase / decrease data as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 27. That is, the search device 2 accumulates past data as to which of the reference expert opinion information and the stock price increase / decrease data in that case was adopted in determining the actual search solution, and stores these. By analyzing and analyzing, the degree of association shown in FIG. 27 is created.

For example, suppose a reference expert opinion information announces a comment that a stock will rise. At this time, it is assumed that the stock price of the stock was often reduced by 20% at a subsequent point in time. In such a case, the degree of association of the stock price down by 20% becomes stronger. This analysis and analysis may be performed by artificial intelligence.

When the expert opinion information for reference is a comment provided by text data, the sentence may be morphologically analyzed, syntactically analyzed, and semantically analyzed through text mining. Similarly, the expert opinion information may be taken in as text data, and then morphological analysis, syntactic analysis, and semantic analysis may be performed and linked to the above-mentioned reference expert opinion information. Further, if the expert simply determines the rise, stagnation, or fall, it may be taken in as it is.

Further, the degree of association shown in FIG. 27 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 28, reference expert opinion information is input as input data, increase / decrease data of the stock price of each issue is output as output data, and at least one or more between the input node and the output node. A hidden layer may be provided for machine learning. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when the degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, in order to actually give new advice to customers regarding stock trading issues, the above-mentioned learned data will be used to predict changes in stock prices. In such a case, we will obtain expert opinion information at the time of the actual stock trading. This expert opinion information is composed of the same kind of data as the above-mentioned reference expert opinion information.

The newly acquired expert opinion information is input by the above-mentioned information acquisition unit 9.

Based on the newly acquired expert opinion information in this way, the future stock price (that is, future stock price increase / decrease data) that is likely to occur is predicted with respect to the expert opinion information. .. In such a case, the degree of association shown in FIG. 27 (Table 1) acquired in advance is referred to. For example, when the newly acquired expert opinion information is the same as or similar to P02, the increase / decrease data A2 is associated with w15 and the increase / decrease data A3 is associated with the association degree w16 via the degree of association. In such a case, the increase / decrease data A2 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A3 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, it is possible to search the stock price status of each stock that may occur in the future from the newly acquired expert opinion information through the stock price increase / decrease data and display it to the user (consultant). By looking at the search result, the user (consultant) can obtain a guideline for the stock to be bought and sold based on the searched stock price increase / decrease data. It is possible to give useful advice to the user just by showing the search result of the stock price increase / decrease data. By the way, in constructing this advice, in addition to simply displaying the searched stock price increase / decrease data, based on this increase / decrease data, specifically which stock and how much stock should be purchased or sold. The advice may be configured by displaying.

Further, also in the fourth embodiment, as in the first to third embodiments, the system side buys and sells the stock by itself based on the advice regarding the stock purchasing behavior (for example, XX company buying, XX company holding) and the like. You may do so. In such a case, if it is determined that the stock price of a certain stock will rise as a result of searching the increase / decrease data of each stock, the process of automatically purchasing the stock of that stock is performed. On the other hand, when it is determined that the stock price of a certain stock will fall, the stock of that stock is automatically sold or short-sold.

FIG. 29 shows an example in which a combination of the above-mentioned reference expert opinion information and reference natural environment information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. ing.

The reference natural environment information and the natural environment information are the same as those in the first embodiment described above.

In the example of FIG. 29, it is assumed that the input data is, for example, reference expert opinion information P01 to P03 and reference natural environment information P18 to 21. The intermediate node shown in FIG. 29 is a combination of the reference expert opinion information as such input data and the reference natural environment information at the time of acquisition of the reference expert opinion information. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference expert opinion information and the reference natural environment information is related to each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. There is. Expert opinion information for reference and natural environment information for reference are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference expert opinion information and the reference natural environment information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference expert opinion information and reference natural environment information, and is a reference expert opinion information and reference. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from the natural environment information. In addition to expert opinion information, stock prices may change depending on the actual state of the natural environment. Therefore, by combining these reference expert opinion information and reference expert opinion information, it is possible to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 29. That is, the search device 2 accumulates past data as to which of the reference expert opinion information, the reference natural environment information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 29 is created.

In the example of the degree of association shown in FIG. 29, the node 61b is a node that is a combination of the reference expert opinion information P01 and the reference natural environment information P18, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node that is a combination of the reference natural environment information P19 and P21 with respect to the reference expert opinion information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned expert opinion information, the natural environment information that reflects the information on the natural environment at the time of new stock trading is acquired.

Based on the newly acquired expert opinion information and natural environment information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 29 (Table 1) acquired in advance is referred to. For example, when the newly acquired expert opinion information is the same as or similar to P02 and the natural environment information is P21, the node 61d is associated through the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 30 shows an example in which a combination of the above-mentioned reference expert opinion information and reference fundamental information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. There is.

The reference fundamental information and the fundamental information are financial indicators, management indicators, etc. of each stock for performing so-called fundamental analysis in stock trading, for example, capital adequacy ratio, current ratio, liquidity on hand, cash flow, etc. These are indicators such as aging of EPS, sales ratio, ROE, ROA, PER, PBR, and BPS. In addition to this, ordinary financial statements (balance sheet, income statement, cash flow statement, etc. are also included in the reference fundamental information and fundamental information. Furthermore, this reference fundamental information and fundamental information are included in the company quarterly report. It also includes information such as posted information, shareholder composition, capital stock, and capital surplus.

In the example of FIG. 30, it is assumed that the input data is, for example, reference expert opinion information P01 to P03, and reference fundamental information P18 to 21 of each brand at the time of acquisition of the reference expert opinion information. The intermediate node shown in FIG. 30 is a combination of reference expert opinion information and reference fundamental information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed.

Each combination (intermediate node) of the reference expert opinion information and the reference fundamental information is related to each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. .. Even if very unfavorable expert opinion information occurs for a stock, if the fundamental financial condition of the stock is good, it may not be regarded as a big risk and the stock price decline may not be so big. Judgment is made through the degree of association shown in FIG. 30 including such cases. The reference expert opinion information and the reference fundamental information are arranged on the left side through this degree of association, and the increase / decrease data are arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference expert opinion information and the reference fundamental information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference expert opinion information and reference fundamental information, and is a reference expert opinion information and reference. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from the fundamental information. Stock prices change depending on the state of actual fundamental information in addition to expert opinion information. Therefore, by combining these reference expert opinion information and reference fundamental information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 30. That is, the search device 2 accumulates past data as to which of the reference expert opinion information, the reference fundamental information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 30 is created.

In the example of the degree of association shown in FIG. 30, the node 61b is a node of the combination of the reference expert opinion information P01 and the reference fundamental information P18, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node that is a combination of the reference fundamental information P19 and P21 with respect to the reference expert opinion information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned expert opinion information, the fundamental information of each stock at the time of stock trading is acquired.

Based on the newly acquired expert opinion information and fundamental information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 30 (Table 1) acquired in advance is referred to. For example, when the newly acquired expert opinion information is the same as or similar to P02 and the fundamental information is P21, the node 61d is associated through the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 31 shows an example in which the combination of the above-mentioned reference expert opinion information, the reference-related stock market information, and the stock price increase / decrease data of each stock for the combination are set to three or more levels of association. Shown.

The related issue market information and the related issue market information for reference are composed of the market information of other related issues previously associated with the trading candidate issue for which the stock is traded. The other related brand may be another rival automobile manufacturer, or a parts manufacturer that supplies parts to the automobile manufacturer, as long as the transaction candidate brand is an automobile manufacturer. Other related stocks include not only rival companies in the same industry, but also companies in other industries that cooperate with each other to build an ecosystem. This related issue may be preset for a transaction candidate issue in the system, or may be freely set on the user side. Further, for this related issue, when the access tendency via the Internet is analyzed and one issue is accessed, another issue that is frequently accessed may be set as the related issue.

The details of the market information are the same as those in the first embodiment described above. Therefore, the reference-related stock market information and the related stock market information are composed of information such as a time-series chart, a line graph, a Bollinger band, MACD, and a moving average line for the related stocks.

In the example of FIG. 31, it is assumed that the input data is, for example, reference expert opinion information P01 to P03 and reference related brand market condition information P18 to 21. The intermediate node shown in FIG. 31 is a combination of reference-related expert opinion information and reference-related stock market information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each trading candidate stock as an output solution is displayed. In other words, it predicts the increase or decrease in the stock price of trading candidate stocks from the market conditions of related stocks.

Each combination (intermediate node) of the reference expert opinion information and the reference-related stock market information is linked to each other through three or more levels of linkage to the stock price increase / decrease data of each trading candidate stock as this output solution. We are in contact with each other. If a very unfavorable comment is made by an expert on a certain stock and the price movement of the related stock is on a downward trend, it can be judged that the stock price of the trading candidate stock is more likely to fall. Including such cases, the judgment is made through the degree of association shown in FIG. 31. Expert opinion information for reference and market information of related stocks for reference are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference expert opinion information and the reference-related stock market information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference expert opinion information and reference related stock market information, and is a reference expert opinion information. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from the reference-related stock market information. In addition to expert opinion information, stock prices change depending on the actual price movements of related stocks. Therefore, by combining these reference expert opinion information and reference-related stock market information, the optimum stock price increase / decrease data for each stock will be searched for.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 31. That is, the search device 2 accumulates past data as to which of the reference expert opinion information, the reference-related brand market condition information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 31 is created.

In the example of the degree of association shown in FIG. 31, the node 61b is a node that is a combination of the reference expert opinion information P01 and the reference related brand market information P18, and the increase / decrease data A3 has a degree of association w15 and the increase / decrease data. The degree of association of A5 is w16. The node 61c is a node that is a combination of the reference-related stock market information P19 and P21 with respect to the reference expert opinion information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned expert opinion information, information on the market conditions of related stocks of trading candidate stocks at the time of stock trading is acquired.

Based on the newly acquired expert opinion information and market information of related stocks in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 31 (Table 1) acquired in advance is referred to. For example, when the newly acquired expert opinion information is the same as or similar to P02 and the related issue market information is P21, the node 61d is associated via the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 32 shows an example in which a combination of the above-mentioned reference expert opinion information and reference statistical information and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set. There is.

The reference statistical information and statistical information include various statistical information related to household consumption survey, household data, average working hours per week, statistical data of savings amount, statistical data of annual income, household budget, and the like. In addition, the reference statistical information and statistical information include all statistical information on real estate such as office vacancy rate, tsubo unit price, rent market price, land price, and statistical data on vacant houses. In addition, the reference statistical information and statistical information include data such as GDP, business conditions index, consumption expenditure, new car sales volume, and consumer price index as statistical information on the economy. Reference statistics and statistics include statistical information about any of households, malfunctions, and economies.

In the example of FIG. 32, it is assumed that the input data is, for example, reference expert opinion information P01 to P03 and reference statistical information P18 to 21. The intermediate node shown in FIG. 32 is a combination of reference statistical information and reference expert opinion information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed. In other words, it predicts an increase or decrease in the stock price of a stock from the market conditions of the related stock.

Each combination (intermediate node) of the reference expert opinion information and the reference statistical information is related to each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. .. While experts make very unfavorable comments on a stock, if the above-mentioned statistical information is positive, it can be judged that the stock price of the trading candidate stock does not fall so much. Including such a case, the judgment is made through the degree of association shown in FIG. 32. Expert opinion information for reference and statistical information for reference are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference expert opinion information and the reference statistical information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference expert opinion information and reference statistical information, and is a reference expert opinion information and reference. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from the statistical information. In addition to expert opinion information, stock prices change depending on the state of actual household, real estate, and economic statistics. Therefore, by combining these reference expert opinion information and reference statistical information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 32. That is, the search device 2 accumulates past data as to which of the reference expert opinion information, the reference statistical information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 32 is created.

In the example of the degree of association shown in FIG. 32, the node 61b is a node in which the reference statistical information P18 is combined with the reference expert opinion information P01, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node in which the reference statistical information P19 and P21 are combined with respect to the reference expert opinion information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned expert opinion information, statistical information of each stock at the time of stock trading is acquired. It is desirable that the type of statistical information to be acquired corresponds to the statistical information for reference. For example, if the statistical information for reference is a statistic related to real estate, the newly acquired statistical information is also a statistic related to real estate. Is desirable.

Based on the newly acquired expert opinion information and statistical information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 32 (Table 1) acquired in advance is referred to. For example, when the newly acquired expert opinion information is the same as or similar to P02 and the statistical information is P21, the node 61d is associated through the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

Further, also in the fourth embodiment, as shown in FIG. 9, three or more types of input parameters may be combined, and in such a case, in addition to the reference expert opinion information, the reference natural environment information and the reference. It is applicable even if the degree of association of the combination is composed of any two or more of the fundamental information for reference, the market information of related stocks for reference, and the statistical information for reference. In addition to this, in addition to the reference expert opinion information, any one or more of the reference natural environment information, the reference fundamental information, the reference related brand market information, and the reference statistical information are combined with other parameters. The degree of association may be configured. That is, in addition to the reference expert opinion information, the degree of association of the combination having the reference natural environment information, the reference fundamental information, the reference-related brand market information, and the reference statistical information may be configured.

Further, the degree of association of the combination of any one or more of the input parameters in the fourth embodiment and any one or more of the input parameters in the first to third embodiments may be configured. .. Then, when information corresponding to the input parameter of the degree of association of the combination is input, the solution search may be performed in the same manner as described above via the degree of association.

The present invention is not limited to the above-described embodiment. For example, as shown in FIG. 33, instead of the stock price increase / decrease data of each stock, the degree of association is set by learning the increase / decrease data of each exchange in association with the reference expert opinion information. May be good. The increase / decrease data of each exchange shows the degree of increase / decrease for each exchange such as Japanese yen-US dollar, Japanese yen-renminbi, and British pound-US dollar.

In the example shown in FIG. 33, the method of searching for the increase / decrease data of each exchange is described by replacing the output solution with the increase / decrease data of each exchange from the increase / decrease data of the stock price of each stock in the above-mentioned explanation of FIG. 27. The following description will be omitted.

When the expert opinion information that reflects the event that occurred at the time of the new exchange transaction is acquired, the reference expert opinion information shown in FIG. 33 and the increase / decrease of each exchange at the time after the event occurs. Use three or more levels of association with the data. Then, the above-mentioned increase / decrease data of each exchange is displayed by giving priority to the one having a higher degree of association between the expert opinion information for reference and the increase / decrease data of each exchange according to the acquired expert opinion information.

FIG. 34 shows an example in which a combination of the above-mentioned reference expert opinion information and reference natural environment information and three or more levels of association with each exchange rate increase / decrease data for the combination are set. In the example shown in FIG. 34, the method of searching for the increase / decrease data of each exchange is described by replacing the output solution with the increase / decrease data of each exchange from the increase / decrease data of the stock price of each stock in the above description of FIG. 29. The following description will be omitted.

When the expert opinion information reflecting the event that occurred during the new exchange transaction and the natural environment information are acquired, the reference expert opinion information shown in FIG. 34 and the reference natural environment information are combined. Use the degree of association of the increase / decrease data of each exchange with respect to. Then, there are three or more levels of association between the reference expert opinion information according to the acquired expert opinion information and the increase / decrease data of each exchange for the combination of the reference natural environment information according to the acquired natural environment information. Display the increase / decrease data of each exchange, giving priority to the higher one.

In addition to the reference expert opinion information, as an alternative to this reference natural environment information, the degree of association with any one or more of the above-mentioned reference fundamental information, reference-related stock market information, and reference statistical information is provided. It may be created with the exchange rate increase / decrease data. As a result, when the above-mentioned fundamental information, related stock market information, and statistical information are input as a substitute for this natural environment information in addition to expert opinion information, the exchange rate can be increased or decreased by using this degree of association. Can be searched and predicted.

Similarly, when searching for and predicting an increase or decrease in exchange rate, the system side may buy and sell the exchange rate by itself. In such a case, if it is determined that one currency will rise in a certain currency (combination of currencies) as a result of searching the increase / decrease data of each currency, the process of automatically purchasing the currency of that currency. I do. On the other hand, when it is determined that one currency will depreciate in a certain currency combination, the currency may be automatically sold or short-sold.

Fifth Embodiment Hereinafter, the fifth embodiment will be described. In executing the fifth embodiment, the stock transaction information display system 1, the information acquisition unit 9, the search device 2, and the database 3 used in the first embodiment are similarly used. The description of each of these configurations will be omitted below by quoting the description of the first embodiment.

In the fifth embodiment, for example, as shown in FIG. 35, it is premised that three or more levels of association between the fundamental information and the stock price increase / decrease data of each issue are set and acquired in advance. The reference fundamental information, the fundamental information, and the stock price increase / decrease data are the same as those in the first to fourth embodiments described above. This fifth embodiment focuses on a plurality of stocks instead of one stock, and predicts what stock price will rise or fall in the future based on the fundamental information of the plurality of stocks.

Through the dataset of the reference fundamental information acquired in advance and the increase / decrease data of the stock price of each stock at the time after the acquisition of the reference fundamental information, what is the stock price of any stock at the time after the acquisition of the reference fundamental information? You can see if it has increased or decreased. In other words, the data set is the result of the increase / decrease in the stock price of each company with respect to the fundamental situation of each company. Therefore, by collecting a data set of reference fundamental information and stock price increase / decrease data, it is possible to know how the stock price has increased / decreased at a later point in the fundamental situation of each stock in the past. Become.

In the example of FIG. 35, it is assumed that the input data is, for example, reference fundamental information P01 to P03. The reference fundamental information as such input data is linked to the output. In this output, stock price increase / decrease data as an output solution is displayed.

The reference fundamental information is associated with each other through three or more levels of association with the stock price increase / decrease data as this output solution. The reference fundamental information is arranged on the left side via this degree of association, and the increase / decrease data of each stock price of each stock is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to which stock price increase / decrease data is highly related to the reference fundamental information arranged on the left side. In other words, this degree of association is an index showing what kind of stock price increase / decrease data is likely to be associated with each reference fundamental information, and the most probable stock price increase / decrease data is selected from the reference fundamental information. It shows the accuracy in doing so. In the example of FIG. 35, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the stock price increase / decrease data as an output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node with the stock price increase / decrease data as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. 35. That is, in determining the actual search solution, the search device 2 accumulates past data as to whether it was the reference fundamental information of each issue or the increase / decrease data of the stock price of each issue in that case. By analyzing and analyzing these, the degree of association shown in FIG. 35 is created.

For example, the reference fundamental information for a stock is the sales ratio at one point in time, ROE, ROA, PER, PBR, BPS, profit per share, profit after tax, dividend amount per share, ordinary profit, etc. Operating income, various forecasts, shareholders' equity ratio, relationship of profit to ordinary income, market value, etc., and sales ratio at that time, ROE, ROA, PER, PBR, BPS, profit per share, Each data showed the difference in each index such as profit after tax, dividend per share, ordinary income, operating income, various expected values, shareholders' equity ratio, relationship of profit to ordinary income, market value, etc. It shall be. At this time, it is assumed that the stock price of the stock was often reduced by 20% at a subsequent point in time. In such a case, the stock price of that stock or another stock is reduced by 20%, and the degree of association becomes stronger. This analysis and analysis may be performed by artificial intelligence.

Further, the degree of association shown in FIG. 35 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 36, reference fundamental information is input as input data, increase / decrease data of the stock price of each issue is output as output data, and at least one or more hidden data is hidden between the input node and the output node. Layers may be provided for machine learning. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when the degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, in order to actually give new advice to customers regarding stock trading issues, the above-mentioned learned data will be used to predict changes in stock prices. In such a case, the fundamental information of each stock at the time of the actual new stock trading is acquired. This fundamental information is composed of the same kind of data as the above-mentioned reference fundamental information.

The newly acquired fundamental information is input by the information acquisition unit 9 described above.

Based on the fundamental information of each stock newly acquired in this way, the future stock price (that is, future stock price increase / decrease data) that is likely to occur is predicted with respect to the fundamental information. In such a case, the degree of association shown in FIG. 35 (Table 1) acquired in advance is referred to. For example, when the newly acquired fundamental information is the same as or similar to P02, the increase / decrease data A2 is associated with w15 and the increase / decrease data A3 is associated with the association degree w16 via the degree of association. In such a case, the increase / decrease data A2 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A3 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, it is possible to search the stock price status of each stock that may occur in the future from the newly acquired fundamental information through the stock price increase / decrease data and display it to the user (consultant). By looking at the search result, the user (consultant) can obtain a guideline for the stock to be bought and sold based on the searched stock price increase / decrease data. It is possible to give useful advice to the user just by showing the search result of the stock price increase / decrease data. By the way, in constructing this advice, in addition to simply displaying the searched stock price increase / decrease data, based on this increase / decrease data, specifically which stock and how much stock should be purchased or sold. The advice may be configured by displaying.
As described above, in the fifth embodiment, the fundamental information of the plurality of stocks at each time in the past is learned in relation to the increase / decrease data of the stock price of each stock at the subsequent time. Then, after that, the current fundamental information of each brand to be actually searched is input as input data. Then, the increase / decrease data of each brand according to the input data is output.
The group of stocks to be learned as this reference fundamental information and the group of stocks to be searched may be the same or may include some stocks, but such a group is defined in advance. You may leave it.

Further, also in the fifth embodiment, as in the first to fourth embodiments, the system side buys and sells the stock by itself based on the advice regarding the stock purchasing behavior (for example, XX company buying, XX company holding) and the like. You may do so. In such a case, if it is determined that the stock price of the trading target stock will rise as a result of searching the increase / decrease data of the trading target stock, the process of automatically purchasing the stock of that stock is performed. On the other hand, when it is determined that the stock price of the stock to be traded will decline, the stock of that stock is automatically sold or short-sold.

Further, in the present invention, the timing of buying and selling may be notified through the above-mentioned degree of association. In such a case, for example, if a stock of a certain stock with a fundamental index (for example, PBR) of 30 times is purchased, and after holding it, the stock is sold when the PBR drops to 10 times. You may want to send a signal to do so. The best timing for selling is also learned through the above-mentioned degree of association, and the increase / decrease in stock price is learned at the subsequent timing for the reference fundamental information with PBR of 30 times and PBR of 10 times. .. Then, if the stock price falls after the PBR drops to 10 times, a signal may be transmitted just before the PBR falls 10 times.

FIG. 37 shows an example in which the combination of the above-mentioned reference fundamental information, the reference natural environment information, and the stock price increase / decrease data of each stock for the combination are set to three or more levels of association. ..

The reference natural environment information and the natural environment information are the same as those in the first embodiment described above.

In the example of FIG. 37, it is assumed that the input data is, for example, reference fundamental information P01 to P03 and reference natural environment information P18 to 21. The intermediate node shown in FIG. 37 is a combination of the reference fundamental information as such input data and the reference natural environment information at the acquisition time of the reference fundamental information. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed. In particular, if the stock to be traded is a company whose sales are affected by temperature, such as a beer company, the stock price may be affected by the natural environment (temperature). be able to.

Each combination (intermediate node) of the reference fundamental information and the reference natural environment information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. The reference fundamental information and the reference natural environment information are arranged on the left side via this degree of association, and the increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference fundamental information and the reference natural environment information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference fundamental information and reference natural environment information, and is a reference fundamental information and reference natural environment information. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock. In addition to fundamental information, stock prices may change depending on the actual state of the natural environment. Therefore, by combining these reference fundamental information and reference fundamental information, it is possible to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 37. That is, the search device 2 accumulates past data as to which of the reference fundamental information, the reference natural environment information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 37 is created.

In the example of the degree of association shown in FIG. 37, the node 61b is a node in which the reference natural environment information P18 is combined with the reference fundamental information P01, the degree of association of the increase / decrease data A3 is w15, and the association of the increase / decrease data A5. The degree is w16. The node 61c is a node that is a combination of the reference natural environment information P19 and P21 with respect to the reference fundamental information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned fundamental information, the natural environment information that reflects the information on the natural environment at the time of new stock trading is acquired.

Based on the newly acquired fundamental information and natural environment information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 37 (Table 1) acquired in advance is referred to. For example, when the newly acquired fundamental information is the same as or similar to P02 and the natural environment information is P21, the node 61d is associated with the node 61d through the degree of association. In the node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 38 shows an example in which the combination of the above-mentioned reference fundamental information, the reference-related stock market information, and the stock price increase / decrease data of each stock for the combination are set to three or more levels of association. There is.

The related issue market information and the related issue market information for reference are composed of the market information of other related issues previously associated with the trading candidate issue for which the stock is traded. The other related brand may be another rival automobile manufacturer, or a parts manufacturer that supplies parts to the automobile manufacturer, as long as the transaction candidate brand is an automobile manufacturer. Other related stocks include not only rival companies in the same industry, but also companies in other industries that cooperate with each other to build an ecosystem. This related issue may be preset for a transaction candidate issue in the system, or may be freely set on the user side. Further, for this related issue, when the access tendency via the Internet is analyzed and one issue is accessed, another issue that is frequently accessed may be set as the related issue.

The details of the market information are the same as those in the first embodiment described above. Therefore, the reference-related stock market information and the related stock market information are composed of information such as a time-series chart, a line graph, a Bollinger band, MACD, and a moving average line for the related stocks.

In the example of FIG. 38, it is assumed that the input data is, for example, reference fundamental information P01 to P03 and reference related brand market condition information P18 to 21. The intermediate node shown in FIG. 38 is a combination of the reference fundamental information and the reference related brand market condition information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each trading candidate stock as an output solution is displayed. In other words, it predicts the increase or decrease in the stock price of trading candidate stocks from the market conditions of related stocks.

Each combination (intermediate node) of the reference fundamental information and the reference related stock market information is linked to each other through three or more levels of linkage to the stock price increase / decrease data of each trading candidate stock as this output solution. ing. If the financial situation of a certain stock is extremely severe and the price movements of related stocks are on a downward trend, it can be judged that the stock price of the trading candidate stock is more likely to fall. Judgment is made through the degree of association shown in FIG. 38 including such cases. The reference fundamental information and the reference-related stock market information are arranged on the left side via this degree of association, and the increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference fundamental information and the reference-related stock market information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference fundamental information and reference-related stock market information, and the reference fundamental information and reference-related stocks. It shows the accuracy in selecting the most probable stock price increase / decrease data of each stock from market information. In addition to fundamental information, stock prices change depending on the actual price movements of related stocks. Therefore, by combining the reference fundamental information and the reference-related stock market information, the optimum stock price increase / decrease data of each stock will be searched for.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 38. That is, the search device 2 accumulates past data as to which of the reference fundamental information, the reference related brand market condition information, and the increase / decrease data in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 38 is created.

In the example of the degree of association shown in FIG. 38, the node 61b is a node of the combination of the reference related issue market condition information P18 with respect to the reference fundamental information P01, and the degree of association of the increase / decrease data A3 is w15 and the increase / decrease data A5. The degree of association is w16. The node 61c is a node that is a combination of the reference-related stock market information P19 and P21 with respect to the reference fundamental information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned fundamental information, information on the market conditions of related stocks of trading candidate stocks at the time of stock trading is acquired.

Based on the newly acquired fundamental information and market information of related stocks in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 38 (Table 1) acquired in advance is referred to. For example, when the newly acquired fundamental information is the same as or similar to P02 and the related stock market information is P21, the node 61d is associated via the degree of association. In this node 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 39 shows an example in which a combination of the above-mentioned reference fundamental information and the reference market condition information, and three or more levels of association with the stock price increase / decrease data of each stock for the combination are set.

In the example of FIG. 39, it is assumed that the input data is, for example, reference fundamental information P01 to P03 and reference market condition information P18 to 21. The intermediate node shown in FIG. 39 is a combination of reference market condition information and reference fundamental information as such input data. Each intermediate node is further linked to the output. In this output, the increase / decrease data of the stock price of each stock as an output solution is displayed. In other words, it predicts an increase or decrease in the stock price of a stock from the market conditions of the related stock.

Each combination (intermediate node) of the reference fundamental information and the reference market condition information is associated with each other through three or more levels of association with the stock price increase / decrease data of each stock as this output solution. Even if the financial situation of a certain stock is extremely severe, if the above-mentioned market conditions are positive, it can be judged that the stock price of the trading candidate stock will not fall so much. Judgment is made through the degree of association shown in FIG. 39 including such cases. Reference fundamental information and reference market information are arranged on the left side via this degree of association, and increase / decrease data are arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the increase / decrease data with respect to the reference fundamental information and the reference market condition information arranged on the left side. In other words, this degree of association is an index showing what kind of increase / decrease data is likely to be associated with each reference fundamental information and reference market information, and is the most from the reference fundamental information and the reference market information. It shows the accuracy in selecting the stock price increase / decrease data of each stock that is likely to be. In addition to fundamental information, stock prices change depending on the state of each economic market information. Therefore, by combining these reference fundamental information and reference market condition information, it is necessary to search for the optimum stock price increase / decrease data of each stock.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 39. That is, the search device 2 accumulates past data as to which of the reference fundamental information, the reference market condition information, and the increase / decrease data in that case was suitable for determining the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 39 is created.

In the example of the degree of association shown in FIG. 39, the node 61b is a node in which the reference market information P18 is combined with the reference fundamental information P01, the degree of association of the increase / decrease data A3 is w15, and the degree of association of the increase / decrease data A5. Is w16. The node 61c is a node that is a combination of the reference market condition information P19 and P21 with respect to the reference fundamental information P02, and the degree of association of the increase / decrease data A2 is w17 and the degree of association of the increase / decrease data A4 is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the above-mentioned fundamental information, market information of each stock at the time of stock trading is acquired. It is desirable that the type of market condition information to be acquired corresponds to the reference market condition information.

Based on the newly acquired fundamental information and market information in this way, the stock price increase / decrease data of each stock is searched for in order to construct the optimum advice. In such a case, the degree of association shown in FIG. 39 (Table 1) acquired in advance is referred to. For example, when the newly acquired fundamental information is the same as or similar to P02 and the market information is P21, the node 61d is associated via the degree of association, and this node. In 61d, the increase / decrease data A3 is associated with w19, and the increase / decrease data A4 is associated with the degree of association w20. In such a case, the increase / decrease data A3 having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the increase / decrease data A4 in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

Further, also in the fifth embodiment, as shown in FIG. 9, three or more types of input parameters may be combined, and in such a case, in addition to the reference fundamental information, the reference natural environment information and the reference related It is applicable even if the degree of association of the combination is composed of any two or more of the brand market condition information and the reference market condition information. In addition to this, in addition to the reference fundamental information, the degree of association between any one or more of the reference natural environment information, the reference related brand market information, and the reference market information and the combination with other parameters is configured. It may be a thing. That is, in addition to the reference fundamental information, the degree of association of the combination having the reference natural environment information, the reference related brand market condition information, and the reference market condition information may be configured.

Further, the degree of association of the combination of any one or more of the input parameters in the fifth embodiment and any one or more of the input parameters in the first to fourth embodiments may be configured. .. Then, when information corresponding to the input parameter of the degree of association of the combination is input, the solution search may be performed in the same manner as described above via the degree of association.

Further, as shown in FIG. 40, the present invention determines the increase / decrease data of the stock price (exchange) of each issue based on the degree of association of the combination of two or more types of information, the reference information U and the reference information V. .. It is assumed that the reference information Y is the reference fundamental information, and the reference information V is any of the reference natural environment information, the reference related brand market information, and the reference market information.

At this time, as shown in FIG. 40, the output obtained for the reference information U is used as the input data as it is, and is output via the intermediate node 61 in combination with the reference information V (stock price increase / decrease data of each issue). It may be associated. For example, for reference information U (reference fundamental information), after an output solution is output as shown in FIG. 35, this is used as an input as it is, and the degree of association with other reference information V is used. The output (data of increase / decrease in stock price of each stock) may be searched.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the most suitable stock for stock trading and the most suitable forex trading for foreign exchange trading without any special skill or experience. It can be carried out. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the input data and the output data described above do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

In a situation where there are multiple possible candidates for a search solution by discriminating stocks with higher profit margins and lower risk based on the degree of association expressed by such numerical values of three or more levels. It is also possible to search and display in descending order of the degree of association.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition to market information, when event information, external environment information, household information, real estate information, expert opinion information, and natural environment information knowledge, information, and data are acquired, the degree of association is increased or decreased accordingly. Let me. Similarly, in addition to event information, if the above-mentioned expert opinion information, natural environment information, fundamental information, related brand market information, and market information are acquired as a substitute for this external environmental information, the degree of association will increase accordingly. Let or lower.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on the information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, treatises, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

Sixth Embodiment Further , in the present invention, the chart may be applied to the chart pattern of the trading signal for the market condition information and the reference market condition information. FIGS. 41 and 42 show an example of the chart pattern of the trading signal of the chart. For example, in FIG. 41 (a), when the stock price repeatedly moves up and down with respect to the moving average line, the buying signal is when the stock price falls to the moving average line. Further, in FIG. 41 (b), a buy signal is obtained when the stock price breaks out of the upside resistance line after the fraying market continues for a long time. FIG. 41 (c) is said to be a W bottom type, and when the stock price hits the low price twice in the low price range, it becomes a buy signal. FIG. 41 (d) is called the head and shoulders shoulders, which is a chart pattern in which the stock price hits the low price three times in the low price range, and the middle is the cheapest, and when this appears, it becomes a buy signal. FIG. 42 (a) shows a case where the stock price soars and then immediately plummets, and a long lower beard candlestick or the Taiyo line appears and reverses, and when this appears, it is a sign of buying. Fig. 42 (b) is said to be the bright star at the end of Mikawa. If the shape of the Taiyo line appears, it will be a signal to buy in the near future. FIG. 42 (c) is said to be Mikawa Akegarasu, and represents a V-shaped conversion with the red three soldiers after plunging with the black three soldiers (three feathers), and is a sign of buying. In Fig. 42 (d), the bright star of Mikawa evening becomes the Taiyo line in the ascending phase, and the upper mado opens and the beard and the substance are short. It will be a sign of a downturn and a sell signal.

Such signals are born from past empirical rules in stock trading, but in the present invention, the market information and the reference market information may be applied to the chart pattern type of these trading signals. ..

For this fitting, a determination model generated by machine learning as shown in FIG. 43 may be used. In this determination model, an image of a chart pattern of a trading signal consisting of the above-mentioned example is used as teacher data. The input is a chart of each stock price, and the output is a type of trading signal. When the chart is acquired, fitting is performed based on the judgment model generated by this machine learning, and it is determined what type of trading signal is applied.

For example, as a result of inputting a chart, it is possible to determine whether or not the output corresponds to the type of trading signal such as Akegarasu Mikawa and Akegarasu Mikawa evening.

In the present invention, when machine learning is performed through the degree of association between the reference market condition information and the increase / decrease data of each stock price, when the reference market condition information is acquired, the chart of each stock price representing the market condition is acquired. do. Then, it is determined what type of trading signal the acquired chart applies to through the determination model shown in FIG. 43, and each reference market condition information is applied to the type of trading signal.

As a result, this reference market information will be represented by a typified trading signal. The above-mentioned degree of association is formed by learning the increasing / decreasing tendency of the stock price at a subsequent time with respect to such a trading signal. This degree of association may be similarly applied to all the degrees of association described in the first embodiment described above.

Next, even when the market condition information is actually acquired, it is determined what type of trading signal the acquired chart applies to through the determination model shown in FIG. 43, and each reference market condition information is used as the type of trading signal. I will apply it. As a result, this market information will be represented by typified trading signals. The type of trading signal of such market information is determined through the above-mentioned degree of association to determine what type of trading signal of the reference market information is applicable. Then, using the three or more levels of linkage between the trading signal type of the reference market information corresponding to the trading signal type of the market information and the increase / decrease data of each stock price, the one with the higher degree of linkage is prioritized and described above. Display the increase / decrease data of each stock price.

The type of trading signal corresponding to the output shown in FIG. 43 is not limited to the existing proposed person, and a new type of signal may be sequentially updated.

For example, if each chart of the reference market information and the market information does not correspond to the existing type in the output shown in FIG. 43, the tendency of the chart is registered as a new type. Then, a degree of association is formed between this newly registered type and the increase / decrease data of each stock price at a subsequent time point. After that, when a chart similar to this newly registered type is input, by creating a degree of association with the increase / decrease data of each stock price in the same way, this newly registered type and the increase / decrease data of each stock price can be obtained. A weighting w of the degree of association is formed between them.

When such a degree of association is updated and market condition information is newly input and it is determined from the determination model of FIG. 43 that it is a newly registered signal type, it is determined. It is possible to search for a search solution via reference market information consisting of newly registered types.

In addition to the first embodiment, this sixth embodiment may be similarly applied to the case where the market condition information and the reference market condition information are used in association with each other in the other first to fifth embodiments. can.

1 Stock transaction information display system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 node

Claims (7)

In the stock trading information display program that displays the increase / decrease data of the stock price of each stock that trades stocks
Information acquisition steps to acquire fundamental information of each stock at the time of new stock trading,
Obtained in the above information acquisition step by using the degree of association between the reference fundamental information of each stock acquired in advance and the stock price increase / decrease data of each stock at the time after the acquisition of the reference fundamental information. A computer with a display step that displays the stock price increase / decrease data of each of the above stocks by giving priority to the one with a higher degree of association between the reference fundamental information according to the fundamental information and the stock price increase / decrease data of each issue. A stock trading information display program characterized by being executed by a stock price. In the above information acquisition step, market information that reflects market information at the time of new stock trading is acquired.
In the above display step, there are three stages: a combination of the reference fundamental information and the reference market information reflecting the market information at the time of acquisition of the reference fundamental information, and the stock price increase / decrease data of each of the above stocks. Using the above degree of linkage, a combination of reference market information according to the market information acquired in the above information acquisition step and reference fundamental information according to the acquired fundamental information, and stock price increase / decrease data of each stock The stock trading information display program according to claim 1, wherein the stock price increase / decrease data of each of the above stocks is displayed by giving priority to those having a higher degree of association of three or more levels. In the above information acquisition step, the related stock market information consisting of the market information of other related stocks associated in advance with each stock to be newly traded is acquired.
In the above display step, a combination having the above-mentioned reference fundamental information and reference-related stock market information consisting of market information at the time of acquisition of the reference fundamental information of other related stocks associated with each stock in advance. , Referencing related stock market information according to the related stock market information acquired in the above information acquisition step and reference according to the acquired fundamental information, using the degree of association with the stock price increase / decrease data of each of the above stocks in three or more stages. A claim characterized by displaying the stock price increase / decrease data of each of the above stocks by giving priority to the combination having the fundamental information for use and the data having a higher degree of association with the stock price increase / decrease data of each issue in three or more stages. 1 The stock price trading information display program described. In the above information acquisition step, statistical information on either household or real estate is acquired, and
In the display step, the combination of the reference fundamental information, the household budget and the real estate at the time of acquisition of the reference fundamental information, and the reference statistical information corresponding to the type of the statistical information, and the stock price of each of the above stocks. Using the degree of association with the increase / decrease data of 3 or more levels, a combination having reference statistical information according to the statistical information acquired in the above information acquisition step and reference fundamental information according to the acquired fundamental information, and each The stock trading information display program according to claim 1, wherein the stock price increase / decrease data of each of the above stocks is displayed by giving priority to the data having a higher degree of association with the stock price increase / decrease data of the stock. In the above information acquisition step, a chart of stock prices at the time of new stock trading is acquired, and this is applied to a chart pattern of trading signals categorized in advance.
In the above display step, a combination having the above-mentioned reference fundamental information and the above-mentioned reference market condition information obtained by acquiring a chart of past stock prices and applying this to the chart pattern of the above-mentioned trading signal categorized in advance, and the combination thereof. Using the degree of association with the stock price increase / decrease data of each stock at a later time with respect to the acquisition of the past chart, the type according to the above market condition information consisting of the type of the chart pattern applied in the above information acquisition step Prioritize the combination of the reference market condition information consisting of the reference market information and the reference fundamental information according to the acquired fundamental information, and the one with a higher degree of association of three or more levels of the stock price increase / decrease data of each of the above stocks. The stock trading information display program according to claim 1, wherein the stock price increase / decrease data of a stock is displayed. In the above information acquisition step, the changing tendency of the fundamental situation of the above brand is acquired as the above fundamental information.
The stock transaction information display program according to any one of claims 1 to 5, wherein in the display step, the changing tendency of the fundamental situation of the above-mentioned stock in the past is acquired in advance as reference fundamental information. In a stock trading program that automatically buys and sells stocks
Information acquisition steps to acquire fundamental information of each stock at the time of new stock trading,
Obtained in the above information acquisition step by using the degree of association between the reference fundamental information of each stock acquired in advance and the stock price increase / decrease data of each stock at the time after the acquisition of the reference fundamental information. A search step for searching the stock price increase / decrease data of each of the above stocks by giving priority to the data having a higher degree of association between the reference fundamental information according to the fundamental information and the stock price increase / decrease data of each issue.
A stock trading program characterized in that a computer executes an automatic trading step of buying or selling any one or more of the above stocks based on the stock price increase / decrease data searched by the above search step.

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