JP2019079482A - Prediction management method - Google Patents

Prediction management method Download PDF

Info

Publication number
JP2019079482A
JP2019079482A JP2018013645A JP2018013645A JP2019079482A JP 2019079482 A JP2019079482 A JP 2019079482A JP 2018013645 A JP2018013645 A JP 2018013645A JP 2018013645 A JP2018013645 A JP 2018013645A JP 2019079482 A JP2019079482 A JP 2019079482A
Authority
JP
Japan
Prior art keywords
value
user
forecast
prediction
management method
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
JP2018013645A
Other languages
Japanese (ja)
Other versions
JP6729859B2 (en
Inventor
加藤 寛之
Hiroyuki Kato
寛之 加藤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Individual
Original Assignee
Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Priority to JP2018013645A priority Critical patent/JP6729859B2/en
Publication of JP2019079482A publication Critical patent/JP2019079482A/en
Application granted granted Critical
Publication of JP6729859B2 publication Critical patent/JP6729859B2/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

To provide a system and a method capable of easily sharing and managing the prediction of the business performance of an enterprise by a large number of users, and also capable of recommending equity investment taking into consideration the prediction of the business performance of the users including individuals and small-sized investors.SOLUTION: Each of client terminals sends a user prediction value related to the business performance of an enterprise to a server. The server stores the user prediction value received from the client terminal in a memory, calculates market prediction from a plurality of the stored user prediction values, calculates a deviation value with respect to the market prediction about the user prediction value which is sent from at least one client terminal, and when the deviation value is not smaller than a prescribed value, sends an alert to at least one client terminal.SELECTED DRAWING: Figure 2

Description

本技術は、企業の業績予測を管理するシステム及び方法に関する。   The present technology relates to systems and methods for managing business performance forecasts.

企業の株式(銘柄)売買の推奨方法には主に以下のものが考えられる。   The following can be considered as the recommended methods for buying and selling stocks of companies.

(1)株価を基に判断する。
例えば、絶対株価が高すぎる場合には売り推奨をし、低すぎる場合には買い推奨をする。また、一定期間での株価の上昇が大きすぎる場合には売り推奨をし、下落が大きすぎる場合には買い推奨をする。
(1) Judge based on stock prices.
For example, if the absolute stock price is too high, then it is recommended to sell, and if it is too low, it is recommended to buy. In addition, if the stock price rises in a certain period is too large, we recommend selling, and if the decline is too large, we recommend buying.

(2)時価総額を基に判断する。
例えば、類似企業の時価総額と比較して割安ならば買い推奨をする。また、時価総額の絶対額が小さい小型株であれば上昇余地があるとして買い推奨をし、絶対額が大きい大型株であれば上昇余地が小さいとして売り推奨をする。
(2) Judge based on market capitalization.
For example, it recommends buying if it is cheap compared with the market capitalization of similar companies. In addition, small size stocks with small absolute value of market capitalization are recommended to buy as there is room to rise, and large size stocks with large absolute size are recommended to sell as small room to rise.

(3)証券会社や調査会社のアナリストの予測を基に判断する。
アナリストの業績予測により割安又は割高であることを判断する。例えば、電機セクターを担当しているアナリストによると、A社が業績の割に株価若しくは時価総額が低いとして買い推奨をする。
また、特定アナリストの業績予測に基づいてサプライズを予測する。例えば、日経QUICKニュース社、Bloomberg社、証券会社、調査会社等が集計する不特定多数のアナリストのコンセンサスに対し、特定のアナリストBが、A社の業績はコンセンサスより上回ると判断して買い推奨をする。
(3) Judge based on the forecasts of analysts of securities companies and research companies.
Judging that the analyst's performance forecast is cheap or expensive. For example, according to an analyst in charge of the electronics sector, company A recommends that the stock price or market capitalization be low for its performance, and that it be recommended.
It also predicts surprises based on the performance forecasts of specific analysts. For example, a particular analyst B buys the company A's performance over the consensus, as opposed to the consensus of an unspecified number of analysts that Nikkei QUICK News, Bloomberg, a securities company, a research company etc. Make a recommendation.

(4)バスケットの推奨
特定のテーマやセクターに入っている不特定多数の銘柄を推奨する。例えば、今後AI(Artificial Intelligence)が伸びるとしてAI関連の30社の株を買い推奨する。
また、配当性向やROE(Return on Equity)など、何らかの指標を基に抽出された不特定多数の銘柄を推奨する。例えば、高配当利回り銘柄を買い推奨する。ただしこれは証券会社や調査会社等の予測や各上場会社の計画若しくは実績の数字を基に判断している。
(4) Recommendation of basket Recommend a large number of unspecified stocks in a specific theme or sector. For example, we recommend buying 30 stocks of AI-related companies as AI (Artificial Intelligence) will grow in the future.
In addition, it recommends a large number of unspecified stocks that are extracted based on some index such as dividend payout ratio and return on equity (ROE). For example, we recommend buying a high dividend yield issue. However, this is judged based on the forecasts of securities companies and research companies, and the figures of plans or results of each listed company.

特開2007−264969号公報Unexamined-Japanese-Patent No. 2007-264969 特開2011−232954号公報JP, 2011-232954, A

株式価格は証券会社や調査会社により決められるものではなく、株式市場参加者の需給により決まる。証券会社や調査機関の予測でも第三者の予測でもなく、株式市場参加者となりうるユーザ自身の業績予測を基に、各ユーザの考えに沿った推奨をするシステムまたは方法が望まれる。   The stock price is not determined by the securities company or the research company, but is determined by the supply and demand of stock market participants. It is desirable to have a system or a method that makes recommendations based on each user's idea based on the user's own performance forecast that can be a stock market participant, rather than that of a securities company or research institution or a third party.

また、多くのユーザの業績予測を収集・分析可能なシステムまたは方法が望まれる。   In addition, a system or method capable of collecting and analyzing performance forecasts of many users is desired.

さらに、システム参加者の業績予測の平均・分布を基に推奨するシステムまたは方法が望まれる。 In addition, a system or method is recommended that is recommended based on the average and distribution of performance forecasts of system participants.

さらに、システム参加者の業績予測の平均の変動により推奨を修正するシステムまたは方法が望まれる。 Further, a system or method is desired that corrects the recommendations by fluctuations in the average of the system participant's performance forecasts.

さらに、株価ではなく業績予測を基に長期保有するべきかを推奨するシステムまたは方法が望まれる。 Furthermore, a system or method is recommended that recommends whether long-term holding should be based on performance forecasts rather than stock prices.

さらに、ユーザの過去の成績を基に行動に移すべきかを推奨するシステムまたは方法が望まれる。 Furthermore, a system or method is recommended that recommends whether to put into action based on the user's past performance.

本技術は、例えば、コンピュータにおいて予測を管理する方法であって、複数のユーザ予測値から市場予測を算出することと、少なくとも1つのユーザ予測値について市場予測に対する乖離値を算出することとを含む、予測管理方法を含む。   The present technology is, for example, a method of managing predictions in a computer, including calculating a market prediction from a plurality of user prediction values, and calculating a deviation from the market prediction for at least one user prediction value. , Including forecast management methods.

本技術の実施例による業績予測管理システムを示す図である。FIG. 1 is a diagram illustrating a performance forecast management system according to an embodiment of the present technology. 本技術の実施例による業績予測管理方法を示すフローチャートである。3 is a flowchart illustrating a method of managing performance forecasts according to an embodiment of the present technology.

図1に本技術の実施例による業績予測管理システム100を示す。   FIG. 1 shows a performance forecast management system 100 according to an embodiment of the present technology.

業績予測管理システム100は、インターネット140に接続されたサーバ110、クライアント端末120及びクライアント端末130を含む。 The performance prediction management system 100 includes a server 110 connected to the Internet 140, a client terminal 120 and a client terminal 130.

サーバ110は、インターネットを介してクライアント端末120及びクライアント端末130と通信する機能を有するコンピュータである。クライアント端末120は、インターネットを介してサーバ110と通信する機能を有するコンピュータ、タブレット端末又はスマートフォンであり、クライアント端末130も同様である。クライアント端末120及びクライアント端末130に限らず、さらに多くのクライアント端末が接続可能である。 The server 110 is a computer having a function of communicating with the client terminal 120 and the client terminal 130 via the Internet. The client terminal 120 is a computer, a tablet terminal or a smartphone having a function of communicating with the server 110 via the Internet, and the client terminal 130 is similar. Not only the client terminal 120 and the client terminal 130 but also more client terminals can be connected.

図2に本技術の実施例による業績予測管理方法200を示す。 FIG. 2 illustrates a method 200 for managing performance forecasts according to an embodiment of the present technology.

ステップ210で業績予測管理方法200をサーバ110において開始する。次にステップ220で企業業績に関するユーザ予測値を受信する。企業業績に関するユーザ予測値はユーザによりクライアント端末120に入力され、クライアント端末120からサーバ110に送信される。 In step 210, the performance forecast management method 200 is started on the server 110. Next, at step 220, a user forecast value regarding corporate performance is received. The user prediction value regarding corporate performance is input by the user to the client terminal 120 and transmitted from the client terminal 120 to the server 110.

企業業績に関するユーザ予測値は、例えば、企業の継続的利益に関する予測値を含む。企業の継続的利益に関する予測値は、当該企業の売上高、営業利益、税引前利益、純利益、1株当たり利益等の指標のうち少なくとも1つに関する予測値を含む。当該企業の売上高、営業利益、税引前利益、純利益、1株当たり利益のうち複数の予測値から、加重平均等の手法によって、これらのうちの1つの予測値または他の指標の予測値をサーバ110が算出してもよい。企業の継続的利益に関する予測値は、当該企業又はその属する業種によって予め指定した指標であってもよい。 The user forecast value regarding corporate performance includes, for example, a forecast value regarding the continuous profit of the company. The forecast value for the company's continuous profit includes the forecast value for at least one of the company's sales, operating profit, profit before tax, net profit, profit per share, and other indicators. The forecast value of one of these or other indicator from multiple forecasts of sales, operating profit, profit before tax, net profit, and profit per share of the company by weighted average method etc. The server 110 may calculate the The forecasted value regarding the continuous profit of the company may be an index specified in advance by the company or the industry to which it belongs.

次にステップ230でクライアント端末120から受信したユーザ予測値をサーバ110内のメモリ(図示せず)に記憶する。メモリは、ハード・ディスク・ドライブ(HDD)等の磁気記憶装置であってもよく、ソリッド・ステート・ドライブ(SSD)等の半導体記憶装置であってもよい。   Next, in step 230, the user prediction value received from the client terminal 120 is stored in a memory (not shown) in the server 110. The memory may be a magnetic storage device such as a hard disk drive (HDD) or a semiconductor storage device such as a solid state drive (SSD).

クライアント端末130及び他のクライアント端末についても、ステップ220でサーバ110が企業業績に関する他のユーザ予測値を受信し、ステップ230でサーバ110が受信した他のユーザ予測値をメモリに記憶する。   Also for the client terminal 130 and the other client terminals, the server 110 receives the other user prediction value regarding the business performance in step 220, and stores the other user prediction value received by the server 110 in the memory in step 230.

次にステップ240で、クライアント端末120、クライアント端末130及び他のクライアント端末から受信しメモリに記憶された複数のユーザ予測値を用いて企業業績に関する市場予測を算出する。複数のユーザ予測値から算出された市場予測は、例えば、複数のユーザ予測値の平均値及び標準偏差で表される。当該市場予測を算出する際に特定のユーザの予測値を含めてもよく、除いてもよい。また、当該市場予測は、一定以上の数のユーザ予測値があった場合にのみ算出され又は有効化されるようにしてもよい。   Next, in step 240, a plurality of user forecast values received from the client terminal 120, the client terminal 130 and other client terminals and stored in the memory are used to calculate a market forecast related to corporate performance. The market forecast calculated from the plurality of user forecast values is represented, for example, by an average value and a standard deviation of the plurality of user forecast values. When calculating the market forecast, the forecast value of a specific user may be included or excluded. Further, the market forecast may be calculated or validated only when there is a predetermined number or more of user forecast values.

次にステップ250で、クライアント端末の少なくとも1つ、例えばクライアント端末120から送信されたユーザ予測値について市場予測に対する乖離値を算出する。乖離値は、例えばユーザ予測値と前記平均値との差である。   Next, in step 250, a deviation value to the market prediction is calculated for the user prediction value transmitted from at least one of the client terminals, for example, the client terminal 120. The divergence value is, for example, the difference between the user prediction value and the average value.

次にステップ260で、乖離値が所定の値以上である場合にクライアント端末120にアラートを送信する。所定の値は前記標準偏差の1倍又は2倍であってもよく、前記平均値に対する所定の割合(例えば10%又は15%)であってもよい。企業の一時的な業績の大きな変動があった場合、例えば、年間の営業利益が継続して概ね100億円である企業について、一時的な要因によって前年に営業利益が10億円となった場合、翌年の業績予測については大きくばらつくことがある。このような場合には正規化や補正をしてもよい。   Next, in step 260, an alert is transmitted to the client terminal 120 when the divergence value is greater than or equal to a predetermined value. The predetermined value may be one or two times the standard deviation, and may be a predetermined ratio (for example, 10% or 15%) to the average value. If there is a temporary change in the company's performance, for example, if the company's annual operating profit continues to be approximately 10 billion yen, the operating profit will be 1 billion yen in the previous year due to temporary factors The forecast for the next year's performance may vary widely. In such a case, normalization or correction may be performed.

ステップ260において、さらにユーザの過去の成績が一定以上である場合、例えば、過去のユーザ予測値と企業の業績の実績値との差が所定の範囲内である場合にのみアラートを送信するようにしてもよい。所定の範囲内とは、例えば、当該企業に対する各ユーザの過去の予測値を実績値に近い順に順位付け、当該ユーザの予測値が上位にランク付けされる場合も含む。複数年での予測値と実績値がある場合、より最近の順位に重みをつけて平均してもよく、業績の変動がより大きかった年の順位に重みをつけて平均してもよい。平均された順位は四分位又は五分位で表してもよい。   In step 260, if the user's past performance is more than a certain level, for example, an alert is sent only when the difference between the past user's predicted value and the performance value of the company's performance is within a predetermined range. May be Within the predetermined range, for example, the past predicted values of each user for the company are ranked in order of closeness to the actual value, and the predicted values of the user are ranked high. When there are forecast values and actual values in multiple years, more recent ranks may be weighted and averaged, and ranks of years in which performance fluctuation is larger may be weighted and averaged. The averaged rank may be expressed in quartiles or quintiles.

ステップ260において、さらにまたユーザの予測値に対する自信が高い場合にのみアラートを送信するようにしてもよい。   In step 260, the alert may also be sent only if the user's confidence in the predicted value is high.

アラートは、当該企業の業績予測に関して自己の予測と市場予測に一定以上の乖離があることを示し、これによってユーザは自己の予測が当たれば、市場予測にとってはサプライズとなり、当該企業の株価が大きく動くことを期待することができる。アラートは、例えば「注目銘柄」又は「売買推奨銘柄」のように表示してもよい。   The alert indicates that there is a gap between the company's performance forecast and the market forecast with respect to the company's performance forecast, which allows the user to be surprised for the market forecast if his forecast is true, and the stock price of the company is large. You can expect to move. The alert may be displayed as, for example, "notable brand" or "trade recommended brand".

次にステップ270で、業績予測管理方法200を終了する。   Next, at step 270, the performance forecast management method 200 ends.

上記の実施例において、市場予測については定期的にまたは不定期に更新することができる。例えば、特定の企業の特定の決算期の予測を行ったユーザ全員の直近の予測値を収集し、自己の予測値を除いた全員の予測値の平均及び標準偏差を算出し、更新された市場予測と自己の予測の差が以前に算出したものより小さくなったか大きくなったかを各ユーザに通知してもよい。   In the above example, the market forecasts can be updated regularly or irregularly. For example, the latest forecast values of all users who forecast a specific financial period of a specific company are collected, and the average and standard deviation of the forecast values of all except self forecast values are calculated, and the updated market Each user may be notified if the difference between the prediction and his prediction has become smaller or larger than previously calculated.

本技術は、多数のユーザによる企業の業績予測の共有及び管理を容易にすることを可能にし、個人や小規模投資家を含め各ユーザの業績予測を尊重した株式投資推奨を可能とする。また、証券会社や調査会社など予測の変更に長い社内プロセスを経て時間がかかるアナリストの数字を待たずに直近の市場予測を把握することを可能にする。例えば業績予測をしている個人、若しくは株式投資を職業としている機関投資家において銘柄の売買の判断を支援する用途にも適用できる。   The technology makes it easy to share and manage business performance forecasts by a large number of users, and enables equity investment recommendations that respect the performance forecasts of each user, including individuals and small scale investors. It also enables securities companies and research firms to take a long time in the process of changing forecasts, so that it is possible to grasp the latest market forecasts without waiting for analysts who take time. For example, the present invention can be applied to use for supporting decision making on buying and selling of stocks in individuals who are forecasting business results or in institutional investors who are engaged in equity investment.

100 業績予測管理システム
110 サーバ
120、130 クライアント端末
100 Performance Forecast Management System 110 Server 120, 130 Client Terminal

Claims (17)

コンピュータにおいて予測を管理する方法であって、
前記複数のユーザ予測値から市場予測を算出することと、
前記少なくとも1つのユーザ予測値について前記市場予測に対する乖離値を算出することとを含む、
予測管理方法。
A method of managing predictions on a computer, comprising
Calculating a market forecast from the plurality of user forecast values;
Calculating a deviation value for the market forecast for the at least one user forecast value,
Predictive management method.
さらに、前記乖離値が所定の値以上である場合にアラートを送信することを含む、
請求項1記載の予測管理方法。
And sending an alert if the deviation value is greater than or equal to a predetermined value.
The prediction management method according to claim 1.
前記ユーザ予測値が、企業の継続的利益に関する予測値を含む、
請求項2記載の予測管理方法。
The said user forecast value includes the forecast value regarding the continuous profit of the company,
The prediction management method according to claim 2.
前記企業の継続的利益に関する予測値が、前記企業の売上高、営業利益、税引前利益、純利益、1株当たり利益のうち少なくとも1つに関する予測値を含む、
請求項3記載の予測管理方法。
The forecast value on the continuous profit of the company includes a forecast value on at least one of sales, operating profit, profit before tax, net profit and profit per share of the company.
The prediction management method according to claim 3.
前記ユーザ予測値が、前記企業の売上高、営業利益、税引前利益、純利益、1株当たり利益のうち少なくとも2つから算出される、
請求項4記載の予測管理方法。
The predicted user value is calculated from at least two of sales, operating profit, profit before tax, net profit, and profit per share of the company.
The prediction management method according to claim 4.
前記市場予測が、前記複数のユーザ予測値の平均値及び標準偏差を含む、
請求項1記載の予測管理方法。
The market forecast includes an average value and a standard deviation of the plurality of user forecast values.
The prediction management method according to claim 1.
前記乖離値が、前記ユーザ予測値と前記複数のユーザ予測値の平均値との差である、
請求項6記載の予測管理方法。
The deviation value is a difference between the user prediction value and an average value of the plurality of user prediction values.
The prediction management method according to claim 6.
前記所定の値が、前記標準偏差の1倍又は2倍である、
請求項7記載の予測管理方法。
The predetermined value is one or two times the standard deviation.
The prediction management method according to claim 7.
前記乖離値が所定の値以上である場合にアラートを送信することが、前記ユーザの過去の成績が一定以上である場合にのみ行われる、
請求項2記載の予測管理方法。
Sending an alert when the deviation value is equal to or more than a predetermined value is performed only when the past performance of the user is equal to or more than a predetermined value.
The prediction management method according to claim 2.
前記ユーザ予測値の過去の成績が一定以上であることが、過去の前記ユーザ予測値と前記企業の業績の実績値との差が所定の範囲内である、
請求項9記載の予測管理方法。
The difference between the past user predicted value and the performance value of the company's performance is within a predetermined range that the past performance of the user predicted value is a certain level or more.
The prediction management method according to claim 9.
前記ユーザ予測値の過去の成績が一定以上であることが、過去の各ユーザ予測値と前記企業の業績の実績値との差が小さい順に順位付けをしたときに当該ユーザ予測値が一定以上の順位である、
請求項9記載の予測管理方法。
When the past results of the user predicted value are at least a certain level, the user predicted value is at least a certain value when ranking is performed in the order of small differences between each past user predicted value and the actual value of the business performance of the company Is the rank,
The prediction management method according to claim 9.
前記市場予測が、定期的にまたは不定期に更新される、
請求項6記載の予測管理方法。
Said market forecast is updated regularly or irregularly,
The prediction management method according to claim 6.
前記アラートを送信することが、ユーザの予測値に対する自信が高い場合にのみアラートを送信する、
請求項2記載の予測管理方法。
Sending the alert sends an alert only if the user is confident about the predicted value.
The prediction management method according to claim 2.
前記アラートが、「注目銘柄」又は「売買推奨銘柄」の表示を含む、
請求項2記載の予測管理方法。
The alert includes the display of "notable stocks" or "recommended trading".
The prediction management method according to claim 2.
プロセッサを有するサーバに、
複数のユーザ予測値から市場予測を算出することと、
少なくとも1つのユーザ予測値について前記市場予測に対する乖離値を算出することと
を実行させるためのプログラムを記録した、コンピュータ読み取り可能な記録媒体。
To a server with a processor
Calculating a market forecast from multiple user forecast values;
A computer readable recording medium having recorded thereon a program for performing: calculating a deviation value to the market prediction for at least one user prediction value.
複数のユーザ予測値から市場予測を算出することと、
少なくとも1つのユーザ予測値について前記市場予測に対する乖離値を算出することと
をコンピュータに実行させるためのプログラム。
Calculating a market forecast from multiple user forecast values;
A program for causing a computer to execute: calculating a deviation value with respect to the market forecast for at least one user forecast value.
複数のユーザ予測値と、
前記複数のユーザ予測値から算出された市場予測と、
少なくとも1つのユーザ予測値について算出された前記市場予測に対する乖離値と
を含む、データ構造。
Multiple user forecast values,
Market forecasts calculated from the plurality of user forecast values;
A data structure comprising: deviation values for the market forecast calculated for at least one user forecast value.
JP2018013645A 2018-01-30 2018-01-30 Prediction management method Active JP6729859B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2018013645A JP6729859B2 (en) 2018-01-30 2018-01-30 Prediction management method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP2018013645A JP6729859B2 (en) 2018-01-30 2018-01-30 Prediction management method

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
JP2017565331A Division JP6288662B1 (en) 2017-10-24 2017-10-24 Performance prediction management system and method

Publications (2)

Publication Number Publication Date
JP2019079482A true JP2019079482A (en) 2019-05-23
JP6729859B2 JP6729859B2 (en) 2020-07-29

Family

ID=66627891

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2018013645A Active JP6729859B2 (en) 2018-01-30 2018-01-30 Prediction management method

Country Status (1)

Country Link
JP (1) JP6729859B2 (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005031927A (en) * 2003-07-11 2005-02-03 Hitachi Ltd Device and method for predicting power demand
JP2006350484A (en) * 2005-06-14 2006-12-28 Ifis Japan Ltd Display method for various kinds of expectation and various kinds of consensus graphs
JP2009211594A (en) * 2008-03-06 2009-09-17 Chugoku Electric Power Co Inc:The System and method for warning employee portfolio deviation in corporate defined contribution pension
JP2009251938A (en) * 2008-04-07 2009-10-29 Value Resource Design Inc Evaluation system, evaluation method and evaluation program
JP2010277295A (en) * 2009-05-28 2010-12-09 Nomura Research Institute Ltd Prediction voting processor
JP2011232954A (en) * 2010-04-27 2011-11-17 Quick Corp Information providing system, information providing method, and information providing program

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005031927A (en) * 2003-07-11 2005-02-03 Hitachi Ltd Device and method for predicting power demand
JP2006350484A (en) * 2005-06-14 2006-12-28 Ifis Japan Ltd Display method for various kinds of expectation and various kinds of consensus graphs
JP2009211594A (en) * 2008-03-06 2009-09-17 Chugoku Electric Power Co Inc:The System and method for warning employee portfolio deviation in corporate defined contribution pension
JP2009251938A (en) * 2008-04-07 2009-10-29 Value Resource Design Inc Evaluation system, evaluation method and evaluation program
JP2010277295A (en) * 2009-05-28 2010-12-09 Nomura Research Institute Ltd Prediction voting processor
JP2011232954A (en) * 2010-04-27 2011-11-17 Quick Corp Information providing system, information providing method, and information providing program

Also Published As

Publication number Publication date
JP6729859B2 (en) 2020-07-29

Similar Documents

Publication Publication Date Title
KR101136696B1 (en) Stock information providing method and system for displaying firm's life stage and determining the overvaluation/undervaluation of a stock
US11475519B2 (en) Systems and methods for determining a significance index
JP6288662B1 (en) Performance prediction management system and method
CN102282551A (en) Automated decision support for pricing entertainment tickets
Cai et al. Forecasting Chinese stock market volatility with economic variables
US8694413B1 (en) Computer-based systems and methods for determining interest levels of consumers in research work product produced by a research department
KR102084389B1 (en) Company evaluation system and evaluation method therefor
Jung et al. An adaptively managed dynamic portfolio selection model using a time-varying investment target according to the market forecast
Vercammen et al. Portfolio speculation and commodity price volatility in a stochastic storage model
Jin Do futures prices help forecast the spot price?
US20150317576A1 (en) Framework for assessing the sensitivity of productivity measures to exogenous factors and operational decisions and for the computer generated proposal of optimal operating plans
Vroomen et al. Rates of return for crowdfunding portfolios: Theoretical derivation and implications
JP6474184B1 (en) Stock price prediction support system and method
Jung et al. Developing a dynamic portfolio selection model with a self-adjusted rebalancing method
Balcilar et al. The predictive power of the term spread on inequality in the United Kingdom: an empirical analysis
JP2019079482A (en) Prediction management method
CN115186101A (en) Investment management back-end system, method, equipment and storage medium
JP6381844B1 (en) Computer system, method, and program for accumulating assets whose value varies over time
JP7218037B1 (en) Transaction management system
JP6587201B1 (en) Company performance prediction management system and method
Hong et al. Optimal margin levels for margin buying in China: An extreme value method
US20150278952A1 (en) Leveraging spend behavior to create equity portfolios
JP5940048B2 (en) Information processing system and information processing method
Xu et al. Examining the impact of trading volume on liquidity and prices in China's soybean complex
Infanger Stochastic programming for funding mortgage pools

Legal Events

Date Code Title Description
A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20180130

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20190326

A601 Written request for extension of time

Free format text: JAPANESE INTERMEDIATE CODE: A601

Effective date: 20190522

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20190723

A02 Decision of refusal

Free format text: JAPANESE INTERMEDIATE CODE: A02

Effective date: 20191217

RD02 Notification of acceptance of power of attorney

Free format text: JAPANESE INTERMEDIATE CODE: A7422

Effective date: 20200221

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20200316

C60 Trial request (containing other claim documents, opposition documents)

Free format text: JAPANESE INTERMEDIATE CODE: C60

Effective date: 20200316

A911 Transfer to examiner for re-examination before appeal (zenchi)

Free format text: JAPANESE INTERMEDIATE CODE: A911

Effective date: 20200417

C21 Notice of transfer of a case for reconsideration by examiners before appeal proceedings

Free format text: JAPANESE INTERMEDIATE CODE: C21

Effective date: 20200421

TRDD Decision of grant or rejection written
A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

Effective date: 20200616

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20200618

R150 Certificate of patent or registration of utility model

Ref document number: 6729859

Country of ref document: JP

Free format text: JAPANESE INTERMEDIATE CODE: R150

S531 Written request for registration of change of domicile

Free format text: JAPANESE INTERMEDIATE CODE: R313531

R350 Written notification of registration of transfer

Free format text: JAPANESE INTERMEDIATE CODE: R350

RD04 Notification of resignation of power of attorney

Free format text: JAPANESE INTERMEDIATE CODE: R3D04

RD02 Notification of acceptance of power of attorney

Free format text: JAPANESE INTERMEDIATE CODE: R3D02