Unfair trade visualization device, method and program

Abstract

To provide an unfair trade visualization device capable of visualizing in an easy-to-comprehending manner stock transactions suspected of being unfair.SOLUTION: A candidate list output unit 81 outputs a list of unfair trade candidates indicating one or multiple trades suspected unfair of the specified types. A candidate input unit 82 receives a selection of an unfair trade candidate from the list of the output unfair trade candidates. An unfair trade output unit 83 displays a piece of target transaction data that includes transaction data indicating a trade that is carried out from a trade that caused the selected unfair trade candidate suspected of being unfair to a trade that ended in a manner capable of distinguishing from other transaction data for the same bland.SELECTED DRAWING: Figure 12

Description

[Technical field]

[0001] The present invention relates to an unfair trade visualization device, an unfair trade visualization method, and an unfair trade visualization program for visualizing stock transactions suspected of being unfair. [Background technology]

[0002] In recent years, online trading via the Internet has developed, making it easy for many investors to trade stocks. However, as stock trading has become easier, the risk of unfair practices such as intentional manipulation of stock prices has also increased, and so it is desirable to be able to properly monitor such unfair trading.

[0003] Among unfair stock trading, sham orders and virtual trading are known as types of unfair trading that can be detected by AI (Artificial Intelligence). Sham orders refer to the repeated placing, canceling, and amending of large volumes of buy and sell orders without the intention of executing the trade, with the aim of misleading other investors into believing that a certain stock is being traded actively, and inducing them to trade. Virtual trading refers to transactions that are not intended to transfer rights, such as when the same person places both buy and sell orders at the same price at the same time.

[0004] For example, Patent Document 1 describes a method that enables automatic or manual detection of suspicious activity on client accounts using information provided in visualization. The method described in Patent Document 1 visualizes the progress of client account balances and detects abnormal deposits and withdrawals, as well as large swings in trading activity for a particular stock immediately before a major announcement that may affect the stock price, as unfair trading. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Special Publication No. 2016-520918 Summary of the Invention [Problem to be solved by the invention]

[0006] On the other hand, the final decision as to whether a detected transaction is truly unfair or whether a bona fide user has engaged in similar transactions based on economic judgment is made by the stock exchange or securities company staff. Therefore, there is a need for a method to clearly display transactions that AI has determined to be potentially unfair.

[0007] The method described in Patent Document 1 detects unfair trading based on the relationship with an event. However, the above-mentioned sham orders and virtual trading are not necessarily transactions that are judged based only on the relationship with a specific event. Therefore, the method described in Patent Document 1 has a problem in that it cannot visualize unfair trading that is not linked to an event.

[0008] In addition, the method described in Patent Document 1 also detects large deposits and withdrawals. However, there are many cases where such transactions themselves cannot be said to be unfair, and there is a problem that the amount of content to be checked increases if all transactions are detected and visualized. Therefore, it is desirable to focus on stock transactions suspected of being unfair and visualize them in a manner that is easy for personnel to understand.

[0009] Therefore, an object of the present invention is to provide an unfair trade visualization device, an unfair trade visualization method, and an unfair trade visualization program that can visualize stock transactions suspected of being unfair in an easy-to-understand manner. [Means for solving the problem]

[0010] The information processing device according to the present invention includes a means for receiving an input of a stock, and a display device for displaying first transaction data for the stock and second transaction data for the stock in a distinguishable manner. to make Means and a means for outputting information indicating a reason why the data is identified as the first transaction data; wherein the first transaction data is identified using a model that identifies a characteristic transaction based on the transaction data. The output means outputs information indicating the factor by which the model identifies the first transaction data.It is characterized by:

[0011] The information processing method according to the present invention comprises: The computer Accepts brand input, The computer Display the first transaction data for that stock separately from the second transaction data for that stock Let The first transaction data is identified using a model that identifies a characteristic transaction based on the transaction data. The computer outputs information indicating the factors that caused the model to identify the first transaction data. It is characterized by:

[0012] The program according to the present invention includes a process for receiving input of a stock on a computer. ,So The first transaction data for a stock is displayed separately from the second transaction data for that stock. to make process and outputting information indicating the reason why the transaction data is identified as the first transaction data. and executing a process for identifying a first transaction data using a model for identifying a characteristic transaction based on the transaction data. In the output process, information indicating the factors that caused the model to identify the first transaction data is output. It is characterized by: Effect of the Invention

[0013] According to the present invention, stock transactions suspected of being unfair can be visualized in an easy-to-understand manner. [Brief description of the drawings]

[0014] [Figure 1] 1 is a block diagram showing an example of the configuration of an embodiment of an unfair transaction visualization device according to the present invention. [Diagram 2] FIG. 13 is an explanatory diagram showing an example of a screen for accepting input of a type of unfairness. [Diagram 3] FIG. 13 is an explanatory diagram showing an example of a screen that outputs a list of unfair trading candidates. [Figure 4] FIG. 13 is an explanatory diagram showing an example of a first screen for visualizing candidates for unfair trading. [Diagram 5] FIG. 13 is an explanatory diagram showing an example of a second screen for visualizing candidates for unfair trading. [Figure 6] FIG. 13 is an explanatory diagram showing an example of a third screen for visualizing candidates for unfair trading. [Figure 7]FIG. 13 is an explanatory diagram showing an example of a fourth screen for visualizing candidates for unfair trading. [Figure 8] FIG. 13 is an explanatory diagram showing another example of the fourth screen for visualizing unfair trading candidates. [Figure 9] FIG. 13 is an explanatory diagram showing an example of displaying a plurality of screens that visualize candidates for unfair trading. [Figure 10] 10 is a flowchart showing an example of the operation of the unfair trade visualization device. [Figure 11] 13 is a flowchart showing another operation example of the unfair trade visualization device. [Figure 12] 1 is a block diagram showing an outline of an unfair transaction visualization device according to the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0016] 1 is a block diagram showing an example of the configuration of an embodiment of an unfair transaction visualization device according to the present invention. The unfair transaction visualization device 100 of this embodiment includes a storage unit 10, a transaction data input unit 20, an unfair transaction estimation unit 30, an unfair transaction type input unit 40, a candidate list output unit 50, a candidate input unit 60, and an unfair transaction output unit 70.

[0017] The storage unit 10 stores information necessary for the unfair transaction visualization device 100 to perform various processes. The storage unit 10 may store, for example, transaction data input by a transaction data input unit 20 described below. The storage unit 10 is realized, for example, by a magnetic disk or the like.

[0018] The transaction data input unit 20 inputs transaction data to be visualized. Specifically, the transaction data input unit 20 inputs transaction data for a predetermined period (e.g., one day's worth) as transaction history. The transaction data may be for one type of stock, or may be for multiple types of stocks. The transaction data input unit 20 may also store the input transaction data in the storage unit 10.

[0019] In this embodiment, the transaction data includes various information related to stock trading. Specifically, the transaction data includes a list of orders, such as sell orders, buy orders, sell order contracts, buy order contracts, cancellation orders, or order corrections. The transaction data may also include not only the order list, but also the best bid or ask price. Furthermore, the transaction data may include the stock price of the stock at the time of the transaction and the order book information of the stock.

[0020] The unfair trading estimation unit 30 estimates one or more transactions suspected of being unfair and the type of the suspected unfairness (hereinafter sometimes referred to as the method) based on the input transaction data. A transaction suspected of being unfair is associated with the type of unfair trading (i.e., the method), the customer who conducted the transaction, the stock in which the transaction was conducted, and the time when the transaction was conducted. In the following description, a transaction suspected of being unfair is referred to as a candidate unfair trading.

[0021] The unfair transaction estimation unit 30 may estimate a specific transaction suspected of being unfair as a candidate for unfair transaction, or may estimate a series of transactions as a candidate for unfair transaction. The series of transactions may include, for example, a set of a triggering transaction and a transaction identified as having been completed. It may also include not only transactions identified as having been completed, but also transactions estimated to have been completed. Estimated transactions refer to, for example, a predetermined number of transactions or a period of time from the transaction identified as having been completed.

[0022] The unfair transaction estimating unit 30 may estimate the unfair transaction candidates by any method. For example, the unfair transaction estimating unit 30 may estimate the unfair transaction candidates using a model that inputs transaction data and predicts whether a transaction is unfair for each type of unfairness. For example, if the model that predicts whether a transaction is unfair calculates a score indicating the likelihood of unfairness, the unfair transaction estimating unit 30 may estimate as an unfair transaction candidate a transaction indicated by transaction data in which the calculated score is greater than a predetermined threshold. Furthermore, the unfair transaction estimating unit 30 may rank the unfair transaction candidates according to the score.

[0023] Also, assume that the model predicting whether a transaction is unfair is expressed by a linear regression equation. In this case, it can be said that the explanatory variable with a larger coefficient included in the model has a greater influence on the estimation of unfair transactions. Therefore, the unfair transaction estimation unit 30 may normalize the coefficient of the model and estimate the factor (explanatory variable) that determined the transaction as a candidate for unfair trading according to the magnitude of the coefficient of the explanatory variable included in the model. The unfair transaction estimation unit 30 may store the estimated unfair transaction candidate and the determined factor in the memory unit 10.

[0024] However, the method by which the unfair trading estimating unit 30 estimates whether a transaction is unfair is not limited to using the above-mentioned model. The unfair trading estimating unit 30 may, for example, detect a transaction history similar to a transaction pattern according to the type of unfairness to estimate an unfair trading candidate. For example, if the placing, canceling, and correcting of buy and sell orders of a predetermined amount or more are repeated a predetermined number of times or more, the unfair trading estimating unit 30 may estimate that series of transactions is an unfair trading candidate indicating a false order. Also, for example, if the same person places both buy and sell orders at the same price at the same time, the unfair trading estimating unit 30 may estimate that series of transactions is an unfair trading candidate indicating a fictitious buy and sell.

[0025] The unfairness type input unit 40 accepts from the user a specification of the type of transaction (i.e., the method) suspected to be unfair (e.g., sham orders, virtual buying and selling, etc.). For example, the unfairness type input unit 40 may display a list of types of unfairness that the unfair trading estimation unit 30 has estimated on the screen of a display device (not shown) and accept the user's selection from the list. The specification accepted by the unfairness type input unit 40 corresponds to the conditions for the candidate list output unit 50, described later, to extract unfair trading candidates.

[0026] If the types of presumed unfair transaction candidates are not limited, the unfair transaction visualization device 100 does not need to include the unfair transaction type input unit 40. However, it is preferable that the unfair transaction visualization device 100 includes the unfair transaction type input unit 40 so that the person in charge can easily recognize the types of transactions suspected of being unfair. Furthermore, the unfair transaction type input unit 40 may input not only the type of transaction suspected of being unfair, but also information such as the period during which the transaction was conducted, the stock, the market, and the customer.

[0027] FIG. 2 is an explanatory diagram showing an example of a screen on which the unfairness type input unit 40 accepts input of the type of unfairness. In the example shown in FIG. 2, the type of unfairness is selected in the "detection item" on the screen to limit the types of unfair trading candidates. The screen shown in FIG. 2 shows an example of accepting, in addition to the input of the type of unfairness, the period, stock code, customer ID, dealer, score threshold, and judgment (A to C, etc.) as conditions for narrowing down the unfair trading candidates. The score shown in FIG. 2 indicates a score indicating the unfairness calculated by the model described above. As shown in FIG. 2, the sort order of the unfair trading candidates may be specified as a condition for display.

[0028] The candidate list output unit 50 outputs a list of unfair trading candidates based on the specification received by the unfair trading type input unit 40. Specifically, the candidate list output unit 50 outputs a list of transactions suspected of the specified type of unfairness (i.e., unfair trading candidates). Furthermore, when the unfair trading type input unit 40 receives a specification of the conditions for the unfair trading candidates, the candidate list output unit 50 may output only the unfair trading candidates that meet the conditions. As described above, the unfair trading candidates include one or more transactions (for example, a set of the triggering transaction and the end).

[0029] Fig. 3 is an explanatory diagram showing an example of a screen that outputs a list of unfair trading candidates. The example shown in Fig. 3 is an example of a search result when a period, a detection item which is a type of unfairness, a stock code, a market, and a score threshold are specified.

[0030] The candidate input unit 60 accepts the selection of an unfair trading candidate desired by the user from the output list of unfair trading candidates. The candidate input unit 60 may detect an unfair trading candidate selected by the user from the list of unfair trading candidates via a pointing device, for example. The candidate input unit 60 may also detect an unfair trading candidate tapped by the user from the list of unfair trading candidates displayed on a touch panel.

[0031] In addition, when the factors (explanatory variables) that have led to the determination that a transaction is a candidate for unfair trading have been estimated, the candidate input unit 60 may input information indicating the factors (e.g., the coefficient weights of each explanatory variable, top factors, etc.) to the unfair trading output unit 70.

[0032] The unfair transaction output unit 70 visualizes the selected unfair transaction candidate. Specifically, the unfair transaction output unit 70 displays transaction data (hereinafter, referred to as target transaction data) showing transactions that occurred from the transaction that caused the selected unfair transaction candidate to be suspected to be unfair to the transaction that ended the transaction, in a manner that can be distinguished from other transaction data. For example, the unfair transaction output unit 70 may display the target transaction data in a manner that can be distinguished from other transaction data of the same stock.

[0033] The unfair trading output unit 70 may also display the target trading data in a manner that is distinguishable from other trading data for the same stock and for the same day, in accordance with the daily stock price fluctuations. The unfair trading output unit 70 may also display trading data indicating transactions that were conducted from the time of the transaction that triggered the suspicion of unfairness of the selected unfair trading candidate to the time of the transaction that ended, in a manner that is distinguishable from other trading data.

[0034] Fig. 4 is an explanatory diagram showing an example of a first screen for visualizing an unfair trading candidate. The first screen 11 shown in Fig. 4 is a screen that displays a trading history for a predetermined period. Fig. 4 shows an example in which the unfair trading output unit 70 displays a trading history, which is a list of orders according to the stock of the unfair trading candidate, and highlights the target trading data T1 from the trading history so as to distinguish it from trading data for other periods.

[0035] FIG. 5 is an explanatory diagram showing an example of a second screen for visualizing unfair trading candidates. The second screen 12 shown in FIG. 5 is a screen displaying a stock price chart included in the trading data. FIG. 5 shows an example in which the unfair trading output unit 70 displays a stock price chart for a predetermined period, and highlights stock price fluctuations in a period T2 that includes target trading data showing a false order in the stock price chart so as to be distinguished from stock price fluctuations in other periods. If the trading data includes the best bid and best ask prices as stock prices, the unfair trading output unit 70 may display the best bid and best ask prices on the stock price chart as shown in FIG. 5.

[0036] FIG. 6 is an explanatory diagram showing an example of a third screen for visualizing unfair trading candidates. The third screen 13 shown in FIG. 6 is a screen that displays an enlarged portion of a stock price chart. A stock price chart is generally created for a predetermined period such as one day or one week. On the other hand, the period that includes the target transaction data is often shorter than the predetermined period. Therefore, in order to make the stock price fluctuations during that period easier to see, as shown in FIG. 6, the unfair trading output unit 70 may enlarge and display the stock price chart for the period that includes the target transaction data. FIG. 6 shows an example of an enlarged stock price chart for the period shown in FIG. 5.

[0037] Fig. 7 is an explanatory diagram showing an example of a fourth screen for visualizing unfair trading candidates. The fourth screen 14 shown in Fig. 7 is a screen for displaying board information. Fig. 7 shows an example in which, when a specific transaction is specified from the transaction history shown in Fig. 4, the unfair trading output unit 70 displays board information at the time the transaction was made.

[0038] Furthermore, the unfair trading output unit 70 may display the order content indicated by the specified trade in association with the order book information. Fig. 8 is an explanatory diagram showing another example of the fourth screen for visualizing an unfair trading candidate. For example, when the specified trade is a buy order for 1000 shares at a stock price of 1550 yen, the fourth screen 15 shown in Fig. 8 shows an example in which the unfair trading output unit 70 displays the number of ordered shares of the trade in association with the stock price corresponding to the order book information.

[0039] Furthermore, when a factor (explanatory variable) determined to be an unfair trading candidate is input, the unfair trading output unit 70 may display the display item corresponding to the factor determined to be an unfair trading candidate in a manner that makes it distinguishable from other display items, depending on the information indicating the factor determined to be an unfair trading candidate. For example, when an order quantity is input as one of the factors determined to be an unfair trading candidate, the unfair trading output unit 70 may highlight the order quantity of the corresponding transaction. Furthermore, when a score of a factor determined to be an unfair trading candidate is input, the unfair trading output unit 70 may change the brightness or thickness of the corresponding display item depending on the score.

[0040] In this embodiment, the screens for visualizing the unfair trading candidates are illustrated in Figs. 4 to 7, but the unfair trading output unit 70 may display each screen together on one screen. Fig. 9 is an explanatory diagram showing an example of displaying a plurality of screens for visualizing the unfair trading candidates. The screen 16 illustrated in Fig. 9 shows an example of a screen that combines the first screen 11, the second screen 12, the third screen 13, and the fourth screen 14 illustrated in Figs. 4 to 7 into one. As illustrated in Fig. 9, by displaying a plurality of screens together, it becomes easier to grasp the mutual relationship. For example, when the topmost transaction is selected from the target transaction data illustrated in the first screen 11, the unfair trading output unit 70 may display the corresponding board information in which the selected transaction was performed on the fourth screen 14.

[0041] 4 to 9 show examples of screens in which the unfair trading output unit 70 displays the target trading data in a manner that allows the target trading data to be distinguished from other trading data for the same stock and on the same day. However, the unfair trading output unit 70 may display the target trading data for multiple days. In this case, the unfair trading output unit 70 may display the trading history shown in FIG. 4 for multiple days, or may display the stock price chart shown in FIG. 5 for multiple days (for example, the number of days that include the target trading data, or one week).

[0042] The transaction data input unit 20, the unfair transaction estimation unit 30, the unfair type input unit 40, the candidate list output unit 50, the candidate input unit 60, and the unfair transaction output unit 70 are realized by a computer processor (e.g., a CPU (Central Processing Unit), GPU (Graphics Processing Unit)) that operates according to a program (unfair transaction visualization program).

[0043] For example, the program may be stored in the storage unit 10, and the processor may read the program and operate, in accordance with the program, as the transaction data input unit 20, the unfair transaction estimation unit 30, the unfair type input unit 40, the candidate list output unit 50, the candidate input unit 60 and the unfair transaction output unit 70. Furthermore, the functions of the unfair transaction visualization device may be provided in the form of SaaS (Software as a Service).

[0044] The transaction data input unit 20, the unfair transaction estimation unit 30, the unfair type input unit 40, the candidate list output unit 50, the candidate input unit 60, and the unfair transaction output unit 70 may each be realized by dedicated hardware. Also, some or all of the components of each device may be realized by general-purpose or dedicated circuits, processors, etc., or combinations of these. These may be configured by a single chip, or may be configured by multiple chips connected via a bus. Some or all of the components of each device may be realized by a combination of the above-mentioned circuits, etc., and programs.

[0045] Furthermore, when some or all of the components of the unfair trade visualization device are realized by multiple information processing devices, circuits, etc., the multiple information processing devices, circuits, etc. may be centrally or decentralized. For example, the information processing devices, circuits, etc. may be realized as a client-server system, cloud computing system, etc., in which each is connected via a communication network.

[0046] Next, an example of the operation of the unfair trade visualization device of this embodiment will be described. Fig. 10 is a flowchart showing an example of the operation of the unfair trade visualization device 100 of this embodiment. The candidate list output unit 50 outputs a list of unfair trade candidates of a specified type (step S11). The candidate input unit 60 accepts the selection of an unfair trade candidate from the output list of unfair trade candidates (step S12). The unfair trade output unit 70 displays the transaction that triggered the selected unfair trade candidate to the transaction that ended in a manner that can be distinguished from other transaction data of the same issue (step S13).

[0047] Fig. 11 is a flow chart showing another example of the operation of the unfair transaction visualization device 100 of this embodiment. The transaction data input unit 20 inputs transaction data to be visualized (step S21). The unfair transaction estimation unit 30 estimates unfair transaction candidates based on the input transaction data (step S22). When the unfair transaction type input unit 40 receives a type of transaction suspected to be unfair (step S23), the candidate list output unit 50 outputs a list of unfair transaction candidates based on the received specification (step S24). Thereafter, the candidate input unit 60 receives the selection of the unfair transaction candidate, and the unfair transaction output unit 70 displays the target transaction data in a manner that can be distinguished from other transaction data, similar to the process from step S12 to step S13 illustrated in Fig. 10.

[0048] As described above, in this embodiment, the candidate list output unit 50 outputs a list of unfair trading candidates, the candidate input unit 60 accepts the selection of an unfair trading candidate from the list, and the unfair trading output unit 70 displays the target trading data in a manner that makes it distinguishable from other trading data of the same stock. Therefore, stock trading suspected of being unfair can be visualized in an easy-to-understand manner.

[0049] Next, an outline of the present invention will be described. Fig. 12 is a block diagram showing an outline of an unfair trade visualization device according to the present invention. An unfair trade visualization device 80 (e.g., an unfair trade visualization device 100) according to the present invention includes a candidate list output unit 81 (e.g., a candidate list output unit 50) that outputs a list of unfair trade candidates indicating one or more transactions suspected of a specified type of unfairness (e.g., sham orders, virtual buying and selling), a candidate input unit 82 (e.g., a candidate input unit 60) that accepts (e.g., from a user) a selection of an unfair trade candidate from the output list of unfair trade candidates, and an unfair trade output unit 83 (e.g., an unfair trade output unit 70) that displays target transaction data including transaction data indicating transactions conducted from a transaction that triggered the suspicion of unfairness of the selected unfair trade candidate to a transaction that ended the transaction in a manner that can be distinguished from other transaction data of the same stock.

[0050] With such a configuration, stock transactions suspected of unfairness can be visualized in an easy-to-understand manner.

[0051] The trade data may include information indicative of a sell order, a buy order, a filled sell order, a filled buy order, a cancel order, or an order modification.

[0052] The trading data may also include a best bid or ask price.

[0053] In addition, the unfair trading output unit 83 may display trading data showing transactions that took place from the time of the transaction that triggered the suspicion of unfairness of the selected unfair trading candidate to the time of the transaction that is estimated to have ended in a manner that is distinguishable from other trading data for the same stock.

[0054] Furthermore, the unfair trading output unit 83 may display the target trading data in a manner that makes it possible to distinguish it from other trading data of the same issue and on the same day.

[0055] Specifically, the unfair trading output unit 83 may display trading history according to the stock of the unfair trading candidate, and display the target trading data from the trading history in a manner that makes it distinguishable from trading data for other periods (for example, a screen display as exemplified in FIG. 4). Such a screen allows the trading history of the unfair trading candidate to be visualized in an easy-to-understand manner.

[0056] At that time, the unfair trading output unit 83 may display the order book information at the time when the specified transaction in the trading history was made, and display the number of ordered shares for that transaction in correspondence with the stock price corresponding to the order book information (for example, a screen display as shown in FIG. 8).

[0057] Alternatively, the unfair trading output unit 83 may display a stock price chart for a predetermined period, and display the stock price fluctuations during the period that includes the target transaction data in a manner that makes them distinguishable from stock price fluctuations during other periods. With such a screen, stock price fluctuations when a transaction involving a potential unfair trading is conducted can be visualized in an easy-to-understand manner.

[0058] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes.

[0059] (Supplementary Note 1) An unfair transaction visualization device comprising: a candidate list output unit that outputs a list of unfair transaction candidates indicating one or more transactions suspected of a specified type of unfairness; a candidate input unit that accepts a selection of an unfair transaction candidate from the output list of unfair transaction candidates; and an unfair transaction output unit that displays target transaction data including transaction data indicating transactions that took place from the transaction that caused the selected unfair transaction candidate to be suspected of being unfair to the transaction that ended the transaction, in a manner distinguishable from other transaction data for the same stock.

[0060] (Appendix 2) The unfair trading visualization device according to Appendix 1, wherein the trading data includes information indicating a sell order, a buy order, a fulfilled sell order, a fulfilled buy order, a cancellation order, or an order correction.

[0061] (Appendix 3) The unfair trading visualization device according to appendix 1 or appendix 2, wherein the trading data includes a best bid price or a best ask price.

[0062] (Appendix 4) An unfair trading visualization device as set forth in any one of Appendices 1 to 3, wherein the unfair trading output unit displays trading data showing transactions that took place from the time of the transaction that triggered the suspicion of unfairness of the selected candidate unfair trading to the time of the transaction that is estimated to have ended in a manner that is distinguishable from other trading data for the same stock.

[0063] (Appendix 5) An unfair trading visualization device according to any one of Appendices 1 to 4, wherein the unfair trading output unit displays the target trading data in a manner that is distinguishable from other trading data for the same stock and on the same day.

[0064] (Appendix 6) An unfair trading visualization device as described in any one of Appendices 1 to 5, wherein the unfair trading output unit displays a trading history according to stocks that are candidates for unfair trading, and displays target trading data from the trading history in a manner that makes it distinguishable from trading data for other periods.

[0065] (Appendix 7) The unfair trading visualization device according to appendix 6, wherein the unfair trading output unit displays order book information at the time when a specified transaction in the trading history was made, and displays the number of ordered shares for the transaction in correspondence with the stock price corresponding to the order book information.

[0066] (Appendix 8) An unfair trading visualization device according to any one of Appendices 1 to 7, wherein the unfair trading output unit displays a stock price chart for a predetermined period and displays stock price fluctuations for a period that includes target trading data in the stock price chart in a manner that makes them distinguishable from stock price fluctuations for other periods.

[0067] (Appendix 9) The unfair trade visualization device according to appendix 8, wherein the unfair trade output unit enlarges and displays a stock price chart for the period that includes the target trade data.

[0068] (Appendix 10) An unfair trading visualization device according to any one of Appendices 1 to 9, in which the type of unfair trading, the customer who conducted the trading, the stock in which the trading was conducted, and the time when the trading was conducted are associated with the candidate unfair trading.

[0069] (Appendix 11) An unfair transaction visualization device described in any one of Appendices 1 to 10, wherein the unfair transaction output unit displays a display item corresponding to a factor that has been determined to be a candidate for unfair transaction in a manner that is distinguishable from other display items, depending on information indicating the factor.

[0070] (Appendix 12) A method for visualizing unfair transactions, comprising: outputting a list of unfair transaction candidates indicating one or more transactions suspected of a specified type of unfairness; accepting a selection of an unfair transaction candidate from the output list of unfair transaction candidates; and displaying target transaction data including transaction data indicating transactions that took place from the transaction that caused the selected unfair transaction candidate to become suspected of being unfair to the transaction that ended the transaction, in a manner distinguishable from other transaction data for the same stock.

[0071] (Supplementary Note 13) An unfair transaction visualization program for causing a computer to execute a candidate list output process that outputs a list of unfair transaction candidates indicating one or more transactions suspected of a specified type of unfairness, a candidate input process that accepts the selection of an unfair transaction candidate from the output list of unfair transaction candidates, and an unfair transaction output process that displays target transaction data including transaction data indicating transactions that took place from the transaction that caused the selected unfair transaction candidate to become suspected of being unfair to the transaction that ended the transaction, in a manner that is distinguishable from other transaction data for the same stock.

[0072] Although the present invention has been described above with reference to the embodiments and examples, the present invention is not limited to the above-mentioned embodiments and examples. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. [Explanation of symbols]

[0073] 10 Storage section 20 Transaction data entry section 30 Unfair Trade Practices Presumption Division 40 Unfair Type Input Unit 50 Candidate list output section 60 Candidate input section 70 Unfair Trade Output Unit 100 Unfair Trade Visualization Device

Claims

1. A means for accepting input of a stock; a means for displaying first transaction data for the issue and second transaction data for the issue in a distinguishable manner; a means for outputting information indicating a reason why the first transaction data is identified; the first transaction data is identified using a model that identifies characteristic transactions based on the transaction data; The output means outputs information indicating a factor by which the model identifies the first transaction data.

23. An information processing apparatus comprising:

2. The characteristic transactions include unfair transactions.

2. The information processing device according to claim 1.

3. The first transaction data includes transaction data indicative of transactions conducted during a period from when a characteristic transaction is indicated to when the characteristic transaction is terminated.

2. The information processing device according to claim 1.

4. A computer receives input of a stock, the computer displays the first transaction data for the issue and the second transaction data for the issue in a distinguishable manner; the first transaction data is identified using a model that identifies characteristic transactions based on the transaction data; The computer outputs information indicating factors that caused the model to identify the first transaction data.

23. An information processing method comprising:

5. The characteristic transactions include unfair transactions.

5. The information processing method according to claim 4.

6. The first transaction data includes transaction data indicative of transactions conducted during a period from when a characteristic transaction is indicated to when the characteristic transaction is terminated.

5. The information processing method according to claim 4.

7. On the computer, A process for accepting stock inputs; A process of displaying first transaction data for the issue and second transaction data for the issue in a distinguished manner; and Executing a process of outputting information indicating a cause for identifying the first transaction data; the first transaction data is identified using a model that identifies characteristic transactions based on the transaction data; In the outputting process, information indicating a factor by which the model identifies the first transaction data is output. Program for.

8. The characteristic transactions include unfair transactions. The program according to claim 7.

9. The first transaction data includes transaction data indicative of transactions conducted during a period from when a characteristic transaction is indicated to when the characteristic transaction is terminated. The program according to claim 7.

10. The information indicating the factor is estimated according to the magnitude of the coefficient of the explanatory variable included in the model. The program according to claim 7.

11. A computer comprising: According to the information indicating the cause, a display item corresponding to the cause is displayed in a manner that can be distinguished from other display items. The program according to claim 7.

12. A computer comprising: Ranking the first transaction data according to the score calculated by the model. The program according to claim 7.

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