WO2011055413A1 - Open market system - Google Patents

Abstract

Provided is an open market system capable of objectively evaluating securities price forecast data. The open market system comprising a server, seller terminals connected to the server, and buyer terminals connected to the server is prepared. The server is provided with a performance analysis section for matching and analyzing the results of the securities price forecast data so as to create performance data. In addition, the server is provided with a form section for outputting a form for inputting securities price forecast data exhibited from the seller terminals. The seller terminals input stylized securities price forecast data using the form. The server outputs the analyzed performance data and the exhibited securities price forecast data from a securities price exhibiting section. In addition, the server is provided with a settlement section for making settlement associated with the exhibited securities price forecast data.

Description

[Name of invention determined by ISA based on Rule 37.2] Public market system

The present invention relates to an open market system, an open market system server, and an open market system control method, and more particularly to an open market system for buying and selling securities price forecast data, an open market system server, and an open market system control method.

In recent years, day trading, which is a daily trading of securities such as stocks and bonds by individual investors, is becoming common in Japan due to the relaxation of financial regulations.
In day trading, investment behavior is performed to obtain profit margins by repeatedly buying and selling in a short period of time such as a day time unit. In other words, immediate trading is important in day trading. For example, in stock day trading, individual investors buy and sell stock certificates in real time using programs and websites provided by securities companies. Yes.

As a system for such a conventional day trader, there is a system in which stock price information is sequentially displayed, and the day trader itself buys and sells stocks from the displayed stock price information. For example, referring to Patent Document 1, there is described a system for buying and selling stocks in units of seconds for day traders, which is easy to operate and provides appropriate stock price data (hereinafter referred to as Prior Art 1). .)
Prior art 1 uses RSS (RDF Site Summary, Real Simple Synchronization, Rich Site Summary) to automatically update and view necessary stock price information via the Internet, enabling users to view a large amount of stock price data. Describes a stock investment support program for day traders that can automatically replace all stock price information automatically with the latest information without direct manual input.

JP 2007-157096 A

Here, conventionally, forecasts of prices of securities such as stock prices have been disclosed in the form of target prices as forecasts of securities companies and analysts.
That is, the target price of securities was provided as a “rough target” at that time.
However, the stock investment support program of Prior Art 1 has a problem that the stock price information is only updated in real time. That is, depending on the prior art 1, there is a problem that the day trader cannot use the target price of the conventional securities as it is for trading.
In addition, the target price of conventional securities may be updated from time to time due to changes in circumstances or the acquisition of new information. Therefore, it was difficult to verify whether the target price was realized or not.

This invention is made in view of such a situation, and makes it a subject to eliminate the above-mentioned subject.

The public market system of the present invention is a public market system comprising a server, a seller terminal connected to the server, and a buyer terminal. The server collates and analyzes the prediction results of past securities price prediction data. A performance analysis unit for creating actual data, a form unit for outputting a form for inputting the expected price of securities to be exhibited from the seller terminal, the analyzed actual data, and the marketed securities A securities price listing unit that outputs price forecast data to the buyer terminal, and a settlement unit that performs settlement related to the listed securities price forecast data, and the seller terminal is standardized using the form The marketed securities price forecast data is inputted from the seller terminal and exhibited.
The open market system of the present invention is characterized in that, as the analysis, the performance analysis unit calculates statistics and ranking and creates performance data.
The open market system according to the present invention is characterized in that the securities price listing section searches the actual data in accordance with an instruction from the buyer terminal.
The server of the open market system of the present invention further includes a result acquisition unit that acquires the result of the listed securities price forecast data, and the performance analysis unit includes the listed securities price forecast data, the result The data is collated and the result data is updated.
The server of the open market system according to the present invention inputs a result analysis unit that collates and analyzes results of past securities price prediction data and creates results data, and securities price prediction data exhibited from a seller terminal. A form part for outputting the form for the analysis, a security price listing part for outputting the analyzed data of the actual results and the expected price of the offered securities, and a settlement relating to the expected price of the offered securities And a settlement unit for performing the above.
The public market system control method of the present invention is a public market system provided with a server, a seller terminal connected to the server, and a buyer terminal, and compares the security price forecast data with the result of the security price forecast data. Analyze to create actual data, output a form to enter the expected price of securities to be listed, enter the expected price of securities priced using the form, and submit for analysis The data of the actual performance that has been performed and the securities price prediction data that has been exhibited are output.

According to the present invention, it is possible to objectively compare past forecast results by collecting and publishing securities price forecast data exhibited by sellers, and efficiently using forecasts with higher probability. Day trading can be done.

It is a conceptual diagram of the system configuration | structure of the open market system X which concerns on embodiment of this invention. It is a block diagram which shows the control structure of the server 10 which concerns on embodiment of this invention. It is a flowchart of the open market process which concerns on embodiment of this invention. It is a conceptual diagram of securities price forecast data according to an embodiment of the present invention. It is an example of a screen of the results / securities price prediction search displayed on the browser of the buyer terminal 21-1 according to the embodiment of the present invention. It is an example of the screen of the results / securities price prediction search result displayed on the browser of the buyer terminal 21-1 according to the embodiment of the present invention. It is a screen example of a web form displayed on the browser of the seller terminal 23-1 according to the embodiment of the present invention.

<Embodiment>
[Configuration of Open Market System X]
First, with reference to FIG. 1, the structure of the open market system X which concerns on embodiment of this invention is demonstrated. In the open market system X of the present embodiment, an example of a configuration in which forecast data related to the price of securities (securities price forecast data) is bought and sold as an open market will be described. In other words, in the present embodiment, the seller of forecast data does not directly buy and sell securities such as stocks, but provides information on the price increase / decrease of the brand as a forecast, and buys and sells this forecast data.

In the open market system X according to the embodiment of the present invention, a server 10, buyer terminals 21-1 to 21-n, seller terminals 23-1 to 23-n, and an administrator terminal 25 are connected to a wireless telephone network. Or a network 5 such as the Internet or an intranet.
Specifically, the network 5 is a wireless communication line such as a mobile phone network, a PHS network, WiMAX, a wireless LAN, a wired telephone line, a LAN, a power line LAN, a cLink, and a dedicated line. Further, as the form of the network 5, an IP network or other star-shaped or ring-shaped network can be used.
The server 10 is a server that uses a PC / AT compatible machine, a dedicated machine, a blade server, or the like and is connected to the network 5 to provide various services related to the securities price market.
The buyer terminals 21-1 to 21-n, the seller terminals 23-1 to 23-n, and the manager terminal 25 are web browsers capable of browsing HTML (Hyper Text Markup Language) and the like. PC / AT compatible machine, PDA (Personal Data Assistant), mobile terminal, smart phone, mobile phone, and the like.
The buyer terminals 21-1 to 21-n are terminals used by buyers (buyer users) of securities price prediction data.
The seller terminals 23-1 to 23-n are terminals used by sellers (seller users) of securities price prediction data.
The administrator terminal 25 is a terminal used by the administrator of the server 10.

[Control Configuration of Server 10]
Next, the control configuration of the server 10 that provides the securities price market will be described in detail with reference to FIG. The server 10 mainly includes a network input / output unit 110, an open market server 120, a database server 130, and a DNS / mail server 140.

The network input / output unit 110 is a router, a load balancer (load balancer), or the like. The network input / output unit 110 receives IP packets from the network 5 and transmits them to each unit, and collectively transmits the IP packets from each unit to the network 5.
The network input / output unit 110 also supports packet filters, firewalls, syslogs, and the like.

The public market server 120 is a server that provides a public market service accessed by buyers and sellers. A control unit such as a CPU includes a form unit 121, a performance analysis unit 122, a securities price listing unit 124, and a result acquisition unit 125. Various programs and classes such as the web server unit 126 and the settlement unit 127 are executed using hardware resources. These various programs, classes, etc. are services (daemons), CGIs (Common Gateway Interface), servlets, ASPs (Active Server Pages), PHP, various scripts, and other web applications, as a group of programs that perform specific operations. Can be provided.
The form part 121 is a part such as CGI, servlet, XML data or the like related to a data form for inputting securities price forecast data. The form unit 121 creates a data form for the seller to input standardized securities price forecast data described later. Further, when the form unit 121 receives data input using the data form from the seller terminals 23-1 to 23-n, the form unit 121 stores the data in the user database 131.
The performance analysis unit 122 refers to a performance database 132, a securities price database 133, and the like, which will be described later, and calculates and creates performance data such as a prediction result that becomes a “result” such as a price increase during the target period from the seller's prediction. It is a part to do. Further, the performance analysis unit 122 calculates a correlation coefficient based on data such as confidence level D106 (FIG. 4) in the securities price forecast data (to be described later) and the performance, and various statistical methods and artificial intelligence methods. Used to calculate the trend of seller data. Further, it is possible to create forecast data “likely to be correct” by performing maximum likelihood estimation by combining seller rankings and securities price data.
The securities price listing unit 124 is a part having a function of outputting data that can be displayed on a browser or the like in accordance with an instruction from a terminal, such as CGI, servlet, ASP, PHP. The securities price listing unit 124 can transmit the securities price forecast data transmitted by the seller and the data related to the results aggregated / analyzed / calculated / evaluated by the performance analysis unit 122 through a web server unit 126 such as HTML or XML. To correct data. In addition, the securities price listing unit 124 can set various conditions and perform a search from the user database 131 when the buyer searches the securities price forecast data for purchase. The securities price listing section 124 has functions such as SNS (Social Network Service), BBS (Bulletin Board System), and chat in addition to the function of displaying comments and the like. It is also possible to make an inquiry. Thereby, the transaction which does not go through an open market can be suppressed. The securities price listing unit 124 also has a natural language processing function such as creating a text of an email to be transmitted.
The result acquisition unit 125 uses HTML or XML, text data, binary data, or the like by using a timer or the like from a server (not shown) on which the price of securities such as a server selling stock price data is disclosed. This is a part for acquiring securities price data provided by data. For this securities price data, various data relating to the price of securities such as VWAP (Volume Weighted Average Price), moving average data, trend, initial price, closing price, etc. can be used. In addition, the result acquisition unit 125 can also convert the data into a format suitable for the securities price database 133 using a parser.
The web server unit 126 is a service (daemon) of a WWW (World Wide Web) server, and can use various modules such as IIS (Internet Information Server), Apache, and the like. In addition, the web server unit 126 can access the execution output of each program from each terminal by a WWW web browser by using an access control by an account using an interface such as a common gateway interface (CGI), a servlet, and PHP. To. The web server unit 126 is for building a WWW website including HTML, CHTML (compact / HTML) data, image data such as JPG, GIF, FLASH, and AVI, audio data, and other data. Data can also be read from each database or storage medium and transmitted.
The settlement unit 127 is a part that performs credit settlement, settlement at an Internet bank, and settlement in the Internet currency using SSL (Secure Socket Layer). At this time, processing such as e-mail transmission and web page creation can be performed according to conditions such as a time limit set by the buyer or seller.

The database server 130 is a part configured using a control unit such as a CPU, an SQL server, various storages, and hardware resources such as a program, and includes a user database 131, a performance database 132, a securities price database 133, and ID creation. A part like the part 135 is provided.
The user database 131 stores seller / buyer user IDs (identifications), passwords, payment credit card information, securities price forecast data, purchase / sales history, search history, comments, description / viewing history of SNS, BBS, etc. A database for storing other data such as addresses and telephone numbers.
The performance database 132 is a database for storing securities price forecast data, performance, etc. exhibited by the seller.
The securities price database 133 is a database that stores securities price data in a format such as time series data. The securities price database 133 includes various economic indicators in addition to values such as VWAP (Volume Weighted Average Price), moving average data, trends, initial prices, and closing prices, such as the above-described securities price data. It also memorizes the attributes used for securities price forecast data such as index of securities price such as stock price index of each country and economic events.
The ID creation unit 135 is a part that issues a password, key data, and the like related to an ID in order to allow each user of a seller and a buyer to access each database. The ID creation unit 135 also inputs data on the user database 131 of each seller and buyer in the user database 131 according to signals from the buyer terminals 21-1 to 21-n and seller terminals 23-1 to 23-n. You can also

The DNS / mail server 140 is a general DNS (domain name server) or an SMTP (Simple Mail Transfer Protocol) server for delivering electronic mail to users. As the DNS, Windows (registered trademark) DNS can be used, and as a service (daemon, program) for SMTP, a known SMTP program, for example, Mail Server manufactured by ArGoSoft or the like can be used.
In addition to being used as a DNS, this DNS / mail server 140 can also send the user ID and password of the account to the user who has entered the registration form on the official site.

[Public Market Processing of Open Market System X]
Next, an outline of the flow of open market processing in the open market system X will be described.
As described above, the public market according to the embodiment of the present invention is a public market for securities price forecast data that can clearly analyze, compare, and disclose the past performance of sellers as data. In this open market, buyers can select securities price forecast data with reference to performance data and purchase at the same price. In other words, this open market is a market service where sellers and buyers are free to participate.
More specifically, the seller (seller user) accesses the server 10 using the seller terminals 23-1 to 23-n and submits securities price forecast data for sale. The seller for submitting the forecast data is not necessarily a human, but may be a computer that executes a program such as a so-called “stock robot”.
The server 10 provides past forecast results, forecast data prices, and the like as a public market for the securities price forecast data.
Then, the buyer (buyer user) purchases forecast data from the securities price market using the buyer terminals 21-1 to 21-n.
When a predetermined forecast target period has passed and the price result of the securities is known, the server 10 exchanges money related to the buying and selling of forecast data and updates the results.
After that, the purchase price of the forecast data is collected from the buyer, the commission is subtracted, and the sales money is given to the seller.

[Data structure of securities price forecast data]
Here, with reference to FIG. 4, an example of each attribute related to the securities price forecast data provided by the seller will be described. The securities price forecast data is transmitted to the server 10 by the seller terminals 23-1 to 23-n, and the form section 121 of the server 10 stores it in the result database 132.

The securities type D101 is an attribute for specifying the type of securities targeted for the forecast data. For example, stock certificates are composed of data such as predetermined numbers and characters such as 0 for FX and 1 for FX. In addition, it is possible to store a forecast target period of forecast data, which will be described later.

The investment type D102 is an attribute indicating the type of price data of the securities to be predicted. Specifically, as a forecast target brand predicted by a seller, a brand stock of a predetermined stock, an arbitrary brand, or a plurality of brands can be set as a forecast target.
In addition, a portfolio like a collection of stocks can be targeted. Moreover, in addition to index indicators used for trust investment, it is also possible to make a target such as price data of a special technique such as a contract price of credit selling.
In addition, only combinations of brands whose prices “rise” and “decrease” can be used as prediction targets in correspondence with formats such as by industry or brand.

The securities price D103 is an attribute indicating an expected securities price corresponding to the investment type D102. As this attribute, a “securities price” that is a value indicating how much the predetermined brand of the securities increases / decreases is stored.
This securities price stores a value such as an expected price for price increase / decrease and a rate of increase / decrease in the price of the security. In addition, when the seller selects a combination of brands whose price increases / decreases, an expected brand and its securities price are stored.

The amount D104 is an attribute of the amount when selling securities price forecast data. This amount is not simple money, and for example, a virtual currency unit such as “point” or “credit” or an amount of what percentage of the winning amount can be used.
In addition, an index such as “number of hits” indicating the result of a virtual simulation can be used as a monetary unit.
In addition, when the buyer pays, information such as prepaid or postpaid can be stored. In the case of post-payment, it is possible to determine a value such as purchasing securities from a server (not shown) of a securities company via the server 10 and paying what percentage of the margin at the time of purchase. In the case of postpay, it is possible to prepare a system in which a deposit or a commission is paid in advance to the administrator of the server 10 in order to avoid a situation where a seller with the same ID prepares a lot of random securities price prediction data. Further, in the case of advance payment, it is also possible to store information such as depositing a fee first and remitting money to the seller only when an expectation is made.
In addition, in the amount of money D104, a flag or the like indicating that “automatic continuation” is possible can be set in a case where forecast data is automatically created by a forecast program or the like for listing. As a result, when the seller with the same ID transmits the forecast data of the securities type D101, the investment type D102, and the securities price D103, it is determined as “continue” and the data is continuously sent to the user who purchases the forecast data. Can be sold.

The forecast method D105 is an attribute that stores a factor that is regarded as most important when the seller makes a securities price forecast. As this prediction method, a statistical method, an artificial intelligence method, a heuristic method using data, and other methods can be categorized and stored in about 10 types. For each method, for example, artificial intelligence can store subcategories such as “kernel machine”, “neural network”, and “decision tree”.

The confidence level D106 is an attribute for storing the confidence level with respect to the prediction as a self-evaluation. Specifically, the evaluation of about 3 to 5 levels of A, B, C, etc. self-reported by the seller is stored.

Comment D107 is a column for freely entering and storing a message to the buyer regarding the forecast. In this comment, when a contact or spam not via the server 10 is described, the form part 121 of the server 10 notifies the administrator terminal 25 via the DNS / mail server 140, and the ID Warn users.

Hereinafter, processing for buying and selling such securities price forecast data in the open market will be described in detail based on the processing of the server 10 with reference to the flowchart / timing chart of FIG.
In the following, an example of a series of processing flows with the server 10 will be described with the seller terminal 23-1 and the buyer terminal 21-1 as representative examples.

(Step S101)
First, the performance analysis unit 122 of the server 10 performs past performance aggregation processing.
Specifically, first, the performance analysis unit 122 refers to the performance database 132 and the securities price database 133. Then, the result analysis unit 122 calculates the expected score, the operation rate, the collected amount, the sales amount from the result data acquired by the result acquisition unit 125 and the securities price prediction data previously sold by the seller of the prediction data with the same ID. Calculate “actual results”. In this process, the previous “actual result” is calculated.
Specifically, the performance analysis unit 122 calculates an expected score that is the difference between the price fluctuation forecast result within the forecast target period of the securities and the actual price movement. As the forecast target period, it is possible to specify a predetermined period such as daily (daily unit), weekly (one week unit), or monthly (monthly unit). In the present embodiment, for the day trader, calculation in a weekly period that allows easy comparison of expected periods will be mainly described, but the present invention is not limited to this. A specific method for calculating the predicted score will be described later. Further, the performance analysis unit 122 calculates various values used in later statistical processing.
Hereinafter, the evaluation of the predicted score among the “actual results” will be described in detail.

[Evaluation of expected score]
Securities can have values such as whether the price will drop or rise, except for special conditions such as delisting.
As a result of intensive studies by the inventors of the present invention, it is preferable to evaluate the forecast data of securities prices for day traders based on an “expected score” using the achievement rate within the forecast period. I found something.
In the present embodiment, if the expected price of the securities price data is achieved within the forecast target period, the value of the forecast price is evaluated as the “predicted score”. . Conversely, in the case of a price drop expectation, it can be evaluated as an “expected score” with positive and negative reversed. As a result, for a day trader who buys and sells in a relatively short period of time, the price forecast data of the securities can be appropriately evaluated and can be bought and sold in the open market.
More specifically:

(If you expect price increases)
Expected score = (Achieved the expected price during the forecast period? (Yes) Expected price: (No) Price at the end of the forecast period)

(If you expect a drop in price)
Expected score = 100% + {100%-(Achieved expected price during forecast period? (Yes) Expected price: (No) Price at the end of forecast period)}

Specific examples of obtaining this expected score are:

(If the stock is expected to rise 120% as the expected price)
・ If the stock price rises to 130% within the forecast period and reaches 110% on the last day of the forecast period, the forecast price has been achieved, so the forecast score is 120% (expected price).
・ If the stock price rises to 115% within the forecast period and reaches 110% on the last day of the forecast period ...... Because the price could not be raised to the forecast price, the forecast score is 110% (the last day of the forecast period) Price).
・ If the stock price rises to 115% within the forecast period and reaches 90% on the last day of the forecast period ...... Because the price could not be raised to the forecast price, the forecast score is 90% (the last day of the forecast period) Price).

(If the stock is expected to fall by 80% as the expected price)
・ If the stock price drops to 70% within the forecast period and reaches 90% on the last day of the forecast period ... Because the forecast price was achieved, 100% + (100% -80% (expected price)) Calculate and assume an expected score of 120%.
・ If the stock price drops to 85% within the forecast period and reaches 90% on the last day of the forecast period ... Because the price could not be reduced to the expected price, 100% + (100% -90% (forecast The price on the last day of the target period)) is calculated and the expected score is 110%.
・ If the stock price drops to 85% within the forecast period and reaches 110% on the last day of the forecast period ... Because the price could not be lowered to the forecast price, 100% + (100% -110% (forecast The price on the last day of the target period)) is calculated and the expected score is 90%.

In order to evaluate this predicted score, it is possible to evaluate the relative value from the absolute value by, for example, obtaining an absolute value with respect to the predicted price or a ratio of price increase / decrease.
In addition, when forecasts for price increases / decreases are performed, whether the price has increased / decreased to a predetermined ratio, for example, 120% / 80%, etc. within the prediction target period is similarly calculated as an expected score. be able to.
Furthermore, when the seller makes predictions using a plurality of brands, an expected score for the average value of the predictions can be obtained.

In addition, the performance analysis unit 122 purchases securities according to the seller's forecast data according to a predetermined purchase amount, and subtracts predetermined fees, taxes, etc. when the securities are sold at the time of the stock price fluctuation forecast period. Calculate the recovery amount.
Thereby, it is possible to calculate the “operation rate” as a value of how much money the seller with the same ID can collect. At this time, various indices of the price of securities, for example, the deviation of the average price of the market price such as the Nikkei average from the seller's expectation to the up and down of the overall price, a test of statistical significance, etc. This can be added to the operation rate.
Further, the “operation rate” can be calculated as a cumulative operation rate based on the amount collected in the past predetermined period. As this predetermined period, a value such as a multiple of the expected period can be used. For example, when the prediction target period is weekly, values such as the past 2 to 3 weeks, the past 1 month (4 weeks), and the past 3 months (12 weeks) can be used as the predetermined period.
In addition, the performance analysis unit 122 can calculate the operation rate and the standard deviation of the value related thereto, and calculate the certainty of the seller's operation in a format such as “deviation value at the operation rate”. .

The performance analysis unit 122 can also calculate the number of sales and the sales amount when the seller sells the securities price prediction data.
These values related to “actual results” can be calculated and analyzed by taking an average over a plurality of stock price fluctuation forecast periods or a predetermined period such as 1 to 3 months.
The performance analysis unit 122 stores the calculated and analyzed “result” in the performance database 132 as performance data.

(Step S102)
Next, the performance analysis unit 122 performs statistics / ranking creation processing.
The performance analysis unit 122 calculates and evaluates the ranking from the totals of “actual results” of sellers of each ID. At this time, the result analysis unit 122 can analyze the type and attribute of the securities and the “result” using each condition relating to each value such as the stock price fluctuation forecast period and the type of price data. Further, based on the “actual record” of the seller of each ID, the statistical significance of the securities price forecast can be calculated and evaluated.
Further, the performance analysis unit 122:
(1) Not only publishing past rankings but also rankings that can be recommended in terms of data.
Here, by analyzing the performance of each seller, a correlation coefficient between the “confidence level”, “expected score”, and “operation rate” for each seller is calculated. The correlation coefficient is used to predict the “expected score” and “operation rate” in the next period. By combining the “expected score” and “operation rate” with the securities attributes narrowed down, it is possible to create a recommendation ranking and create ranking data.
(2) Not only looking at individual seller data, but also counting by “investment type”, “amount”, and “forecast method” to clarify the problem of which forecast method is most efficient and ranking Create data.

In addition to this, the performance analysis unit 122 can use, for example, a statistical method such as Bayesian estimation based on each securities price forecast data, and can obtain securities price forecast data with high likelihood and certainty. . This data can be provided to buyers for a fee and distributed to each seller at a predetermined rate.
Further, by calculating the statistical significance between the securities price forecast and the specific forecast data, it is possible to point out the fact that operations by speculative sources are being performed.
The performance analysis unit 122 also stores rankings and various statistical values in the performance database 132 as performance data.
The calculation method of “operation rate” can be disclosed on the website, and the evaluation of each rank, “operation rate”, deviation value in “operation rate”, etc. should be clearly analyzed, compared and released. Can do.

(Step S103)
Next, the securities price listing unit 124 uses the web server unit 126 to perform a result disclosure process.
Specifically, the securities price listing unit 124 browses HTML, XML, or the like displayed on the web server unit 126 on the results data such as the results, rankings, and the results obtained by the performance analysis unit 122. Is created and stored in the results database 132.
When the buyer user browses the browsing data with a web browser, the web server unit 126 transmits each calculation result, ranking HTML, XML, and the like.

Further, the securities price listing unit 124 can search the actual data by an instruction from the web browser of the buyer terminal 21-1.
Referring to FIG. 5, the buyer has the securities type in the input field 610, the investment type in the input field 620, the money amount in the input field 630, and the input field 640 in the web browser screen of the buyer terminal 21-1. In the input field 650, a search period that is a target of the predicted data to be searched is input, and the search can be performed using any of these attributes.
Specifically, as the securities type, it is possible to select the securities to be the target of the forecast data and the forecast correspondence period. That is, the user of the buyer terminal 21-1, which is a day trader, can cope with forecast data when buying and selling various patterns such as daily, weekly, and monthly.
As the investment type, it is possible to select the type of price data of the securities to be expected by the buyer.
With respect to the amount of money, it is possible to obtain securities price forecasts according to the buyer's budget and purchase method. That is, it is possible to select the price of the securities price prediction data and information such as whether to pay at a fixed amount / percentage of the acquisition amount, whether to pay in advance or postpay. Furthermore, if the buyer wishes to continue the purchase automatically, it is also possible to automatically purchase forecast data of the same securities type and investment type of the same seller.
The forecasting method can be selected if there is a method that the buyer particularly trusts among the forecasting methods D105 of the securities price forecasting data.
In the search period, the seller's expected score of the securities price forecast is searched from the past results after the forecast period has ended. At this time, it is possible to select a search period for the actual data, such as only the previous week, the past month, three months, and one year.
By filtering with each of these attributes, it is possible to obtain a search result suitable for the corresponding securities type and investment type.

Referring to FIG. 6, the securities price listing unit 124 converts actual data into browsing data based on the seller ID that matches the search condition, and transmits the data to the web server unit 126. The converted result data can be displayed on the web browser of the buyer terminal 21-1.
Rankings can also be displayed, and the actual data is ranked according to the ranking of the average result of the expected score, the operation rate result, the deviation value at the operation rate, the sales amount result, etc., and converted into data for viewing. It can also be displayed after conversion.
Furthermore, in the browser display screen example of FIG. 6, by selecting a seller ID link 700 or the like, it is possible to browse in detail the securities price forecasts exhibited by the seller.
Further, when the seller of the ID has exhibited the securities price forecast data described later, the purchase data purchase screen is displayed by clicking a link 710 or the like. Thereby, the buyer can purchase the securities price prediction data via the server 10.

(Step S104)
Next, the form unit 121 performs form distribution processing.
Specifically, after the analysis result disclosure process, the seller is further provided with a new security price forecast. At this time, the seller is authenticated by the ID and password. Here, in the case of a user who desires a new seller, an ID creation unit 135 is used to create a seller ID, and the user database 131 is accessed to input each information.
When the authentication is completed, the form unit 121 transmits a web form of a predetermined format for inputting the securities price forecast data according to the access from the web browser of the seller terminal 23-1 (timing T101).

When the browser of the seller terminal 23-1 receives and displays the web form, the user who desires the seller inputs the securities price forecast data using the web form.
Here, with reference to FIG. 7, the example which inputs this securities price forecast data specifically using the web form which is an input form is demonstrated.
In the screen example of FIG. 7, first, a user name and a password are input to the web form displayed on the browser using the input field 500.
The seller then enters the input column 510 into the securities type D101, the input column 520 into the investment type D102, the input column 530 into the securities price D103, the input column 540 into the amount D104, and the input column 550 into the forecast method D105. The input field 560 inputs data corresponding to the confidence level D106, and the input field 570 inputs data corresponding to the comment D107.
Thereafter, when it is detected that the seller clicks the transmission button 580 with a pointing device such as a mouse, the seller terminal 23-1 transmits the input data to the server 10 as securities price prediction data (timing T102).
Upon receiving the securities price forecast data via the network input / output unit 110, the form unit 121 stores it in the results database 132 in association with the seller's ID.
This form distribution and securities price forecast data transmission can be repeated because there are usually multiple forecasts created by the seller.
Further, when “automatic continuation” is set for the securities price D103, the data is displayed separately as data having different start dates and end dates (final dates) of the forecast period, for example, next week data and next week data. be able to. Furthermore, it is also possible to list separately the data of, for example, weekly and monthly data with different ranges of the target period.
Note that the input items of each attribute can be input using selection fields such as a slide bar, radio box, check box, etc., numerical values, or symbols so as not to leave ambiguity. Furthermore, the attributes can be made consistent with each other so as to be transmitted from the program by WPUT or the like.

(Step S105)
Next, the securities price listing section 124 performs securities price forecast market listing processing.
Specifically, the securities price listing unit 124 indicates the portion of the securities price forecast data according to each seller ID that may be disclosed, for example, information other than the seller ID, the securities price D103, etc. The ranking information is combined into HTML, XML, etc., and stored in the performance database 132 as exhibition data.
When the buyer terminal 21-1 accesses the exhibition data, the web server unit 126 transmits the exhibition data.
Further, the exhibition data can be searched in the same manner as the above-mentioned record data.
The buyer searches the exhibition data using the web browser of the buyer terminal 21-1, and compares and examines the results of analysis and ranking of the results data to determine purchase.
Upon receiving this, the settlement unit 127 presents the contract conditions and makes a “purchase reservation” (timing T103).

In addition, as a purchase unit of the exhibition data, it is preferable to purchase forecast data of “sellers” that have been exhibited in a lump. That is, it is possible to configure so that forecast data of only a predetermined brand cannot be purchased. Thereby, the effect of facilitating performance evaluation is obtained.
However, since there is a demand for “buyers” holding specific stocks to obtain prediction information about the stock, it is possible to make a batch purchase across the stocks. In this case, it is possible to configure such that the contract conditions in the seller's exhibition data are set separately or sold at a predetermined ratio of the amount presented by the seller.
Further, in the present embodiment, a one-to-one trading relationship is described, but it is also possible to use it for financial products and investment trusts by concluding a secondary usage contract. In this case, the securities price listing unit 124 provides the forecast data listed on a server of a securities company (not shown).

(Step S106)
Next, the settlement unit 127 performs a sales process.
In the case of postpay, the settlement unit 127 that has received the purchase reservation signal makes a purchase reservation with another server in cooperation with an off-site ticketing site or the like. This purchase reservation is preferably configured to be canceled only via the server 10. For example, the ID creation unit 135 can create and store an ID, a password, and the like, make a purchase reservation via the ID, a password, and the like and store them in the user database 131. The ID and password at this time are stored in a storage unit that the user of the user database 131 cannot access, and the buyer can know only that the purchase has been made.
Further, in the case of prepaid, the settlement unit 127 can also settle the price by referring to the user database 131 using SSL or the like.
Thereafter, the settlement unit 127 uses the web server unit 126 or the DNS / mail server 140 to convert the purchased “securities price forecast” forecast data (securities price forecast data) into a viewable state for the buyer. Etc. (Timing T104)

(Step S107)
Next, the result acquisition unit 125 performs a result acquisition / collation process.
In this process, first, the result acquisition unit 125 acquires price data of each security and the like related to the displayed security price prediction data from another server or the like connected to the network 5 by fetching or the like. This acquisition can be performed by setting the timing with a timer. Further, the administrator of the server 10 can directly input price data using the administrator terminal 25.
Then, the result acquisition unit 125 compares the acquired result data with the displayed securities price prediction data, and calculates a preliminary price difference.
The result acquisition unit 125 stores the collated data in the result database 132.

(Step S108)
Next, the performance analysis unit 122 performs performance totaling / analysis processing.
This performance aggregation / analysis process is a process of calculating, analyzing, and evaluating the seller's performance again after the results are known.
Specifically, the performance analysis unit 122 performs the same processing as the past performance tabulation processing in step S101 and the statistics / ranking creation processing in step S102, with the above-described collated data added.
As a result, the performance of the seller of each ID is updated and stored in the performance database 132.

(Step S109)
Next, the securities price listing unit 124 uses the web server unit 126 to perform the latest results disclosure process.
This latest result release process performs the same process as the result release process in step S103. That is, when the buyer user browses the securities price listing unit 124, the web server unit 126 is used to transmit each calculation result, ranking HTML, XML, or the like.
In addition to this, if multiple sellers sell securities price forecast data for the same investment type or brand of securities, create a ranking or daily ranking to create a “breaking news”. Or can be transmitted by the DNS / mail server 140.
In addition, as the latest results, it is possible to make public disclosure suitable for comparing securities, such as the superiority or inferiority of the portfolio of sellers, ranking on the price increase / decrease surface of overall securities.

(Step S110)
Next, the settlement unit 127 performs price collection / sales payment processing.
Specifically, in the case of postpay, the settlement unit 127 first collects the purchase price from the buyer (timing T105).
The settlement unit 127 performs settlement with reference to the user database 131 using SSL or the like in the case of prepaid collection.
Thereafter, the settlement unit 127 deducts the fee and remits (pays) the sales to the seller (timing T106).
Further, in the case of advance payment, the purchase price that was first settled and collected is subtracted from the fee of the administrator of the server 10 and sent as sales after the result is disclosed. As a result, it is possible to prevent the seller's action to receive a fee by simply listing random securities price forecast data first, and to give the seller a sense of responsibility.
Further, even when the settlement unit 127 is set to send in advance payment after the result is disclosed, the settlement unit 127 can remit the money by deducting the fee. Thereby, since the buyer can pay the fee after confirming the sales, the purchase can be made with confidence.

With the configuration described above, the following effects can be obtained.
First, Prior Art 1 was able to quickly browse stock price increases and decreases for day traders. However, there has been a problem that it is difficult to compare the actual results of whether or not the predictions were actually made by applying the prediction of the price of the securities provided in the past.
In contrast, the open market system according to the embodiment of the present invention allows a seller to provide securities price forecast data using a form. Thereby, the reliability of the forecast data can be clarified by collating the securities price forecast data with the price of the securities such as the last day after the forecast period and creating a forecast score. In addition, it is possible to present the operation result of the expected data after a predetermined period of time as an “operation rate” in an easy-to-understand manner. These can be aggregated and published as a result using a ranking or the like.
Thereby, the transparency and convenience of prediction data can be improved. Therefore, it becomes easy for the buyer to analyze, compare and examine the performance of the seller. Moreover, the open market system which concerns on embodiment of this invention can also improve transparency and convenience by specifying a transaction form and a price.
Thus, day trading can be performed efficiently using more accurate prediction data.

In other words, the price forecast of securities such as stock prices has been publicly disclosed as “target price” by the forecast of securities companies and analysts. However, the target price was just a “rough target” at that time, and it was unclear what time it would be realized.
In addition, the target price may be updated due to changes in economic conditions or new information. Therefore, it is difficult to verify whether the target price has been realized or not, and it has not been actually verified.
Because of this situation, there is a problem that the target price is not very useful for investors who repeat buying and selling in a short period of time, such as day traders. That is, conventionally, expected price data required by an investor who repeats buying and selling in a short period of time has not been provided.
As described above, the open market system according to the embodiment of the present invention can quantitatively verify the expected price data of securities and the expected results.
By clearly analyzing, comparing, and disclosing the verified results as data, it is possible to establish price forecast data such as fluctuations in stock prices that are useful and necessary for day traders as “products” and can be sold and sold as open markets. Can be the target of.
As a result, it is possible to provide expected price data for investors who repeat buying and selling in a short period of time.

In addition, the forecast data of the past has been unclear and has high barriers to entering as a seller because the types of securities, types of investments, transaction forms, and prices vary depending on the seller.
Furthermore, in order to enter into the provision of securities forecast data as a seller, it is necessary to gain some credit. However, since an objective evaluation method has not been established, it has been difficult for individuals to calculate operation results and the like, like a securities company. For this reason, it was difficult for individuals to obtain trust in the predicted data.
On the other hand, the open market system according to the embodiment of the present invention can be used simply by the seller inputting the securities price using a form and listing it. As for the expected data that has been exhibited, an expected score is calculated by the server 10 as an actual result after the prediction target period has elapsed, an objective operation rate and the like are also calculated, and is specifically and objectively evaluated by ranking and actual results. For example, in the case of a stock price, a portfolio or the like that is a combination of several brands can also be evaluated as securities price forecast data.
As a result, the seller who has predicted the “correct” securities price is objectively evaluated and can surely earn income. Thus, the seller can be motivated to make a better prediction. In addition, there is an effect that anyone can easily enter the public market of expected price data of securities. Therefore, it is expected that the public market of expected securities price data is activated, and buyers can obtain forecast data for securities prices with higher accuracy.

In addition, since the open market system according to the embodiment of the present invention can objectively present the evaluation of the forecast data, it is possible to remove a psychological barrier to buyer purchase. Furthermore, the transparency can be further enhanced by unifying the transaction form such as prepaid and postpaid.
In addition, for the seller who provided excellent forecast data, the provider of the server 10 can be appointed as an investment trust manager with an actual securities company by mediating the ranking and the like. It can also be used for financial products and investment trusts by signing secondary usage contracts.

Conventionally, in order to sell forecast data, many initial costs, labor, and time have been required, such as establishment of an Internet site and a store, permission and approval, advertisement, and establishment of a method for collecting sales. For this reason, there existed a problem that initial cost started.
On the other hand, since the server 10 provides a public market for expected securities price data, the seller needs little initial cost, labor, and time.

In the past, the reliability of forecast data in operations where the forecast data itself, which predicts some numerical result related to monetary and social benefits for complex events, has commercial value. And value evaluation was a difficult problem.
Such events include public game win / loss predictions. Furthermore, as a collective knowledge of external factors, market conditions, speculation of each person, etc., the price of bids, etc. whose prices are determined by the “market”, the prediction of winning / losing elections, the sales volume of content such as videos / games / programs, public There are predictions of winning and losing “winning eyes” of the competition.
The open market system according to the embodiment of the present invention can convert events that can be specifically evaluated as virtual “securities” into data. On top of this, it is also possible to apply to these by allowing sellers to list as price forecast data. For example, in the case of an election, it is possible to buy and sell the winning or losing prediction in the constituency as securities price prediction data.

In addition, when applying the open market system which concerns on embodiment of this invention to securities, you may provide the investment kind preparation part (investment kind preparation means).
This investment type creation unit is created by combining multiple sellers with high expected scores and management rates, and calculating portfolios, etc. as investment types in a permutation and combination based on the expected values of each recovery rate, etc. can do.

It should be noted that the configuration and operation of the above-described embodiment are examples, and it is needless to say that the configuration and operation can be appropriately changed and executed without departing from the gist of the present invention.

The present invention is industrially applicable to provide an expected market price market for securities by providing a public market system that can objectively evaluate securities price forecast data.

5 Network 10 Servers 21-1 to 21-n Buyer terminal 23-1 to 23-n Seller terminal 25 Administrator terminal 110 Network input / output unit 120 Open market server 121 Form unit 122 Performance analysis unit 124 Securities price listing unit 125 Result Acquisition unit 126 Web server unit 127 Settlement unit 130 Database server 131 User database 132 Performance database 133 Securities database 135 ID creation unit 140 DNS / mail servers 500, 510, 520, 530, 540, 550, 560, 570, 610, 620 , 630, 640, 650 Input field 580, 670 Send button 700, 710 Link 680 Clear button X Open market system

Claims (6)

In an open market system comprising a server, a seller terminal connected to the server, and a buyer terminal,
The server
A performance analysis unit that collates and analyzes past security price forecast data to create actual data;
A form part for outputting a form for inputting securities price forecast data to be exhibited from the seller terminal;
A securities price listing section that outputs the analyzed performance data and the listed securities price forecast data to the buyer terminal;
A settlement unit that performs settlement relating to the securities price forecast data that has been exhibited,
The seller terminal is
The open market system, wherein the security price forecast data standardized using the form is inputted from the seller terminal and exhibited. The open market system according to claim 1, wherein as the analysis, the performance analysis unit creates statistics data by calculating statistics and rankings. The open market system according to claim 1 or 2, wherein the securities price listing section searches the actual data according to an instruction from the buyer terminal. The server
And further comprising a result obtaining unit for obtaining a result of the securities price forecast data exhibited,
The said performance analysis part collates the said securities price forecast data exhibited with the data of a result, and updates the said data of a performance. The one of the Claims 1 thru | or 3 characterized by the above-mentioned. Open market system. A performance analysis unit that collates and analyzes past security price forecast data results to create actual data;
A form part that outputs a form for inputting securities price forecast data to be exhibited from the seller terminal,
A securities price listing section that outputs data of the analyzed results and the price forecast data of the listed securities;
A server of a public market system, comprising: a settlement unit configured to perform settlement related to the securities price prediction data exhibited. In an open market system comprising a server, a seller terminal connected to the server, and a buyer terminal,
Compare and analyze the securities price forecast data and the results of the securities price forecast data to create actual data,
Output a form for entering the expected price of securities to be listed
Enter the securities price forecast data standardized using the form and submit it,
A method for controlling an open market system, characterized in that the analyzed performance data and the listed securities price forecast data are output.

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