TWI716172B - Critical information risk warning apparatus and method - Google Patents
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本發明係關於一種重大訊息風險預警裝置及方法。具體而言，本發明係關於一種基於重大訊息來判斷風險的風險預警裝置及方法。The invention relates to a major information risk early warning device and method. Specifically, the present invention relates to a risk early warning device and method for judging risks based on important information.
在現今公開原則下的證券市場，主管機關對於上市櫃公司有嚴格的資訊揭露規定，上市櫃公司須依規定揭露其自身的重大訊息，投資者可透過各個上市櫃公司公布的重大訊息，例如：增資發行新股、公告股利配發基準日、獨立董事辭職、變更會計事務所等等重大訊息，來了解各個上市櫃公司的相關資訊、重要時程、甚至是營運狀況等等。In the current securities market under the open principle, the competent authority has strict information disclosure requirements for listed companies. The listed companies must disclose their own major information in accordance with the regulations. Investors can use the major information announced by each listed company, such as: Increase capital, issue new shares, announce the basis date of dividend distribution, resign independent directors, change accounting firms and other important information to understand relevant information, important schedules, and even operating conditions of each listed company.
進一步言，在這些大量的重大訊息中，可能存在某些能反應出該上市櫃公司營運狀況有潛在隱憂之徵兆（即，不利的重大訊息），可作為投資者決策時的參考。舉例而言，公司變更會計事務所，發生的原因之一可能是原知名會計事務所不願意為其背書，因而轉由較不知名的會計事務所負責；公司的獨立董事辭職、公司的財務主管辭職，發生的原因之一可能是相關人士不願意為有風險的決策背負相關法律責任，而這些可能不利的重大訊息都可能造成該上市櫃公司股價的波動。Furthermore, among these large amounts of material information, there may be some signs (that is, unfavorable material information) that can reflect the potential risks of the listed company's operating conditions, which can be used as a reference for investors in making decisions. For example, one of the reasons why the company changes its accounting firm may be that the original well-known accounting firm is unwilling to endorse it, so it is transferred to a less well-known accounting firm; the company’s independent director resigns, and the company’s financial supervisor One of the reasons for the resignation may be that the relevant persons are unwilling to bear relevant legal responsibilities for risky decisions, and these potentially unfavorable major messages may cause the stock price of the listed company to fluctuate.
然而，由於上市櫃公司數量眾多，且與其相關的重大訊息數量更為可觀，本領域缺乏一種能夠協助評估各個重大訊息的風險，且能夠蒐集、整理及分析大量的重大訊息，以即時的計算各個上市櫃公司的風險評估並提出預警的機制。However, due to the large number of listed companies and the larger amount of significant information related to them, the field lacks a method that can assist in assessing the risks of each important information, and can collect, sort and analyze a large number of important information to calculate each in real time. The risk assessment of listed companies and the early warning mechanism.
有鑑於此，如何基於公開的重大訊息，提供一風險預警機制，以達到提醒投資者可能的風險，來協助投資者預先的對於這些風險過高的上市櫃公司作出反應，以降低可能面對的虧損，乃業界亟需努力之目標。In view of this, how to provide a risk early warning mechanism based on publicly significant information to remind investors of possible risks and to assist investors in responding to these high-risk listed companies in advance to reduce possible risks. Loss is an urgent goal for the industry.
1本發明之一目的在於提供一種重大訊息風險預警裝置。該重大訊息風險預警裝置包含一儲存器、一收發器及一處理器，且該處理器電性連接至該儲存器及該收發器。該儲存器儲存複數個目標關鍵字，且各該目標關鍵字各自對應至一風險參數。該收發器，接收複數筆重大訊息，其中各該重大訊息各自對應至一上市櫃公司及一公布時間。該處理器根據該等重大訊息與該等目標關鍵字，決定各該重大訊息各自對應的該風險參數。該處理器統計各該上市櫃公司於一時間區間內所對應到之該等重大訊息的各該風險參數，產生各該上市櫃公司各自的一風險估計值。該處理器判斷當該等風險估計值中至少其中之一超過一閾值時，產生一風險預警。1 One purpose of the present invention is to provide a major information risk early warning device. The important information risk early warning device includes a memory, a transceiver and a processor, and the processor is electrically connected to the memory and the transceiver. The storage stores a plurality of target keywords, and each target keyword corresponds to a risk parameter. The transceiver receives a plurality of important messages, each of which corresponds to a listed company and an announcement time. The processor determines the risk parameter corresponding to each of the major messages according to the major messages and the target keywords. The processor counts each of the risk parameters of the major messages corresponding to each of the listed companies in a time interval, and generates a respective risk estimate of each of the listed companies. The processor determines that when at least one of the risk estimates exceeds a threshold, a risk warning is generated.
本發明之另一目的在於提供一種重大訊息風險預警方法，其適用於一電子裝置。該電子裝置儲存複數個目標關鍵字，且各該目標關鍵字各自對應至一風險參數，該重大訊息風險預警方法包含下列步驟：接收複數筆重大訊息，其中各該重大訊息各自對應至一上市櫃公司及一公布時間；根據該等重大訊息與該等目標關鍵字，決定各該重大訊息各自對應的該風險參數；計算各該上市櫃公司於一時間區間內所對應到之該等重大訊息的各該風險參數，以產生各該上市櫃公司各自的一風險估計值；以及判斷當該等風險估計值中至少其中之一超過一閾值時，產生一風險預警。Another object of the present invention is to provide an early warning method for major information risks, which is suitable for an electronic device. The electronic device stores a plurality of target keywords, and each target keyword corresponds to a risk parameter. The method for early warning of major information risk includes the following steps: receiving a plurality of important messages, each of which corresponds to a listing cabinet. Companies and an announcement time; determine the risk parameters corresponding to each of the major messages according to the major messages and the target keywords; calculate the corresponding major messages of each listed company in a time interval Each of the risk parameters is used to generate a risk estimate of each listed company; and it is judged that when at least one of the risk estimates exceeds a threshold, a risk warning is generated.
本發明所提供之重大訊息風險預警技術（至少包含裝置及方法）藉由接收複數個重大訊息，根據該等重大訊息及目標關鍵字決定各個重大訊息的風險參數，並判斷各上市櫃公司於一時間區間內所公布的重大訊息的風險估計值是否超過預定值，當風險估計值中至少其中之一超過預定值時，產生相應的風險預警。本發明更提供了產生目標關鍵字對應的風險參數的方法，透過分析過去歷史資料（包含股價資料與重大訊息），決定目標關鍵字的風險參數權重分配。透過前述運作，本發明所提供之重大訊息風險預警技術藉由分析大量的重大訊息，即時的提供各個上市櫃公司的風險評估並提出預警的機制，提醒可能的風險來協助投資者預先的對於這些風險過高的上市櫃公司作出反應，以降低可能面對的虧損，解決習知技術無法解決的問題。The major message risk early warning technology (including at least the device and method) provided by the present invention receives multiple major messages, determines the risk parameters of each major message based on the major messages and target keywords, and determines whether each listed counter company is in one Whether the risk estimate of the major information published in the time interval exceeds a predetermined value, when at least one of the risk estimates exceeds the predetermined value, a corresponding risk warning is generated. The present invention further provides a method for generating risk parameters corresponding to the target keywords. By analyzing past historical data (including stock price data and important information), the risk parameter weight distribution of the target keywords is determined. Through the foregoing operations, the important information risk early warning technology provided by the present invention analyzes a large number of important information, provides real-time risk assessment of each listed company and proposes an early warning mechanism, reminding possible risks to help investors deal with these in advance Over-risk listed companies will respond to reduce possible losses and solve problems that cannot be solved by conventional technology.
以下結合圖式闡述本發明之詳細技術及實施方式，俾使本發明所屬技術領域中具有通常知識者能理解所請求保護之發明之技術特徵。The detailed technology and implementation manners of the present invention are described below in conjunction with the drawings, so that those with ordinary knowledge in the technical field of the present invention can understand the technical features of the claimed invention.
以下將透過實施方式來解釋本發明所提供之一種重大訊息風險預警裝置及方法。然而，該等實施方式並非用以限制本發明需在如該等實施方式所述之任何環境、應用或方式方能實施。因此，關於實施方式之說明僅為闡釋本發明之目的，而非用以限制本發明之範圍。應理解，在以下實施方式及圖式中，與本發明非直接相關之元件已省略而未繪示，且各元件之尺寸以及元件間之尺寸比例僅為例示而已，而非用以限制本發明之範圍。The following will explain the major information risk early warning device and method provided by the present invention through implementations. However, these embodiments are not intended to limit the present invention to be implemented in any environment, application or method as described in these embodiments. Therefore, the description of the embodiments is only for the purpose of explaining the present invention, rather than limiting the scope of the present invention. It should be understood that, in the following embodiments and drawings, elements that are not directly related to the present invention have been omitted and are not shown, and the size of each element and the size ratio between the elements are only examples, and are not used to limit the present invention. The scope.
本發明之第一實施方式為一重大訊息風險預警裝置1，其架構示意圖係描繪於第1圖。重大訊息風險預警裝置1包含一儲存器11、一收發器13及一處理器15，且處理器15電性連接至儲存器11及收發器13。儲存器11可為一記憶體、一通用串列匯流排（Universal Serial Bus；USB）碟、一硬碟、一光碟、一隨身碟或本發明所屬技術領域中具有通常知識者所知且具有相同功能之任何其他儲存媒體或電路。The first embodiment of the present invention is a major information risk
收發器13為一可接收及傳輸訊息之介面或本發明所屬技術領域中具有通常知識者所知悉之其他可接收及傳輸訊息之介面。處理器15可為各種處理器、中央處理單元、微處理器、數位訊號處理器或本發明所屬技術領域中具有通常知識者所知之其他計算裝置。於一些實施方式中，重大訊息風險預警裝置1可單獨的被設置，或是將重大訊息風險預警裝置1整合至交易處理伺服器中，本發明未限制其內容。The
於本實施方式中，重大訊息風險預警裝置1的儲存器11預先儲存複數個目標關鍵字（關於目標關鍵字的定義及產生方式，容後段說明）。接著，接收複數筆重大訊息，再根據該等重大訊息與該等目標關鍵字，決定各該重大訊息各自對應的該風險參數。隨後，藉由統計各該上市櫃公司於一時間區間內所對應到之該等重大訊息的各該風險參數，產生各該上市櫃公司各自的風險估計值，藉此判斷當該等風險估計值中至少其中之一超過閾值時，產生風險預警。需說明者，重大訊息風險預警裝置1的運作尚包含其他相關細節，惟本發明之重點在於重大訊息及目標關鍵字相關之運算及分析，故以下段落將僅詳細說明與本發明相關之實施細節。In this embodiment, the storage 11 of the important information risk
於本實施方式中，儲存器11儲存複數個目標關鍵字（未繪示），且每個目標關鍵字各自對應至一個風險參數。須說明者，在本發明中將對於公司股價／營運具有不利影響的重大訊息中可能出現的關鍵字，定義為目標關鍵字，而目標關鍵字可透過多種的方式產生（例如：由投資者本身決定、專業人士判斷、經由機器學習分析產生結果等等）。舉例而言，專業人士判斷當重大訊息出現「獨立董事異動」、「財務主管異動」、「變更會計事務所」等等目標關鍵字時對於公司的公司股價／營運將有不利影響。於某些實施方式中，目標關鍵字亦可基於投資者自身的投資策略或投資習慣，由各個投資者透過操作介面來決定其認為重要的目標關鍵字。此外，複數個目標關鍵字可由外部直接接收或是由重大訊息風險預警裝置1直接產生，本發明並未限制目標關鍵字的來源及產生方式。In this embodiment, the storage 11 stores a plurality of target keywords (not shown), and each target keyword corresponds to a risk parameter. It should be noted that in the present invention, keywords that may appear in major messages that have an adverse impact on the company’s stock price/operations are defined as target keywords, and target keywords can be generated in a variety of ways (for example, by investors themselves) Decisions, professional judgments, results generated through machine learning analysis, etc.). For example, professionals judge that when target keywords such as "independent director change", "financial director change", "change of accounting firm" and other target keywords appear on major messages, it will have an adverse effect on the company's company stock price/operation. In some implementations, the target keywords may also be based on the investor's own investment strategy or investment habits, and each investor may determine the target keywords that he considers important through the operation interface. In addition, a plurality of target keywords can be directly received from the outside or directly generated by the important information risk
於本實施方式中，風險參數是對應於各個重大訊息的風險估計。具體而言，當一個目標關鍵字對應至的風險參數越高時，代表出現該目標關鍵字的該重大訊息風險越高，反之，當目標關鍵字對應至的風險參數越低時，代表出現該目標關鍵字的該重大訊息風險越低。為便於理解，請參考第2A圖所示之複數個目標關鍵字及其對應風險參數之一具體範例，但其非用以限制本發明。於該具體範例中，目標關鍵字「獨立董事辭職」、「監察人辭職」、「會計事務所變更」、「財務主管異動」及「發言人異動」等對應至的風險參數分別為12分、10分、10分、6分及2分。由該表可知，重大訊息中出現目標關鍵字「獨立董事辭職」的風險顯然高於出現目標關鍵字「發言人異動」。另外，關於產生目標關鍵字對應的風險參數的方式，容後詳述。In this embodiment, the risk parameter is a risk estimate corresponding to each major message. Specifically, when a target keyword corresponds to a higher risk parameter, it means that the risk of the major message of the target keyword is higher. Conversely, when the target keyword corresponds to a lower risk parameter, it means that the The lower the risk of this important message of the target keyword. For ease of understanding, please refer to a specific example of multiple target keywords and their corresponding risk parameters shown in Figure 2A, but they are not intended to limit the present invention. In this specific example, the target keywords "resignation of independent director", "resignation of supervisor", "change of accounting firm", "change of financial supervisor" and "change of spokesperson" correspond to risk parameters of 12 points, 10 points, 10 points, 6 points and 2 points. It can be seen from the table that the risk of the target keyword "resignation of independent directors" in important messages is obviously higher than that of the target keyword "spokesperson change". In addition, the method of generating the risk parameters corresponding to the target keywords will be described later.
接著，重大訊息風險預警裝置1透過收發器13，接收複數筆重大訊息，其中各該重大訊息各自對應至一上市櫃公司及一公布時間。具體而言，重大訊息為證券市場上的公開訊息，由各家上市櫃公司公布，重大訊息可由多種公開平台上取得（例如：台灣證券交易提供的公開資訊觀測站），本發明未限制重大訊息的取得方式，且本發明所屬技術領域中具有通常知識者應可理解其內容，故不贅言。Then, the major message risk
為便於理解，請參考第2B圖所示之複數個重大訊息的一具體範例，但其非用以限制本發明。於該具體範例中，其例示了來自C1、C2、C3、C4及C5等公司於2018年7月16日公布其主旨分別為「公告本公司發言人異動」、「公告本公司獨立董事辭職事宜」、「公告本公司變更會計師」、「公告董事會決議股利分派」、「公告股利配發基準日」的5筆重大訊息。需說明者，一般重大訊息尚包含該筆重大訊息的股票代號、公司名稱、發言時間、詳細內容等等其他相關資料，本發明為了方便例示而僅保留與本發明運算及分析相關之內容，故以下段落將僅詳細說明與本發明相關之實施細節。For ease of understanding, please refer to a specific example of a plurality of important messages shown in Figure 2B, but it is not intended to limit the present invention. In this specific example, it exemplifies that companies from C1, C2, C3, C4 and C5 announced on July 16, 2018 that their main thrusts were "Announcement of the company’s spokesperson change" and "Announcement of the resignation of independent directors of the company." ”, “Announcement of the Company’s Change of Accountant”, “Announcement of the Board of Directors Resolution of Dividend Distribution”, and “Announcement of the Dividend Distribution Base Date”. It should be noted that generally important messages still include the stock code, company name, speaking time, detailed content and other related data of the important message. For the convenience of illustration, the present invention only retains the content related to the calculation and analysis of the present invention, so The following paragraphs will only describe the implementation details related to the present invention in detail.
隨後，處理器15根據接收到的重大訊息與儲存的該等目標關鍵字，決定各該重大訊息各自對應的該風險參數。具體而言，處理器15將各該重大訊息的主旨與該等目標關鍵字進行比對（例如：關聯性比對），當該重大訊息的主旨與一目標關鍵字比對符合後，處理器15即可決定該重大訊息的風險參數為該目標關鍵字對應的風險參數。Subsequently, the processor 15 determines the risk parameter corresponding to each important message according to the received important message and the stored target keywords. Specifically, the processor 15 compares the subject of each important message with the target keywords (for example: relevance comparison), and when the subject of the important message matches a target keyword, the processor 15 15 can determine the risk parameter of the important message as the risk parameter corresponding to the target keyword.
舉例而言，請同時參考第2A圖及第2B圖，處理器15比對來自C1的重大訊息「公告本公司發言人異動」與目標關鍵字中的「發言人異動」符合，因此處理器15決定來自C1的該筆重大訊息的風險參數即為2。須說明者，由於不同的重大訊息對於同一件事件的用語可能不一致（例如：異動、變更、辭任、辭職等）。因此，處理器15運作的關聯性比對可透過一機器學習訓練，於訓練階段時，可透過大量已標記的同類型詞彙進行訓練，並根據已生成的比對結果反饋進行訓練及整合，以處理用語不一致的問題。如此一來，可提高比對的準確性。本發明所屬技術領域中具有通常知識者應可理解比對之演算法及如何透過機器學習訓練，故不贅言。For example, please refer to Figure 2A and Figure 2B at the same time. The processor 15 compares the important message from C1 "Announcement of the company’s spokesperson change" with the "speaker change" in the target keyword. Therefore, the processor 15 The risk parameter that determines the significant message from C1 is 2. It should be clarified that the terminology of the same event may be inconsistent due to different important messages (for example: change, change, resignation, resignation, etc.). Therefore, the correlation comparison performed by the processor 15 can be trained through a machine learning. During the training phase, it can be trained through a large number of labeled vocabularies of the same type, and trained and integrated according to the generated comparison result feedback. Deal with inconsistencies in terms. In this way, the accuracy of the comparison can be improved. Those with ordinary knowledge in the technical field to which the present invention pertains should be able to understand the algorithm of the comparison and how to train it through machine learning, so it will not be repeated.
於某些實施方式中，為了增加比對的精準度，處理器15亦可先對於各該重大訊息的主旨／內容進行一斷詞處理（例如：JIEBA斷詞器），以產生對應各該重大訊息的一斷詞結果。 接著，處理器15再將各該斷詞結果與該等目標關鍵字進行一關聯性比對。於某些實施方式中，該關聯性比對亦可透過如BM25（Best Match 25）、TF/IDF（Term frequency–inverse document frequency）及根據機器學習等演算法實現，其更可包含去雜訊、斷句、斷詞等運作。需說明者，有關斷詞處理的細節、關聯性比對的內容為何並非本發明之重點，本發明所屬技術領域中具有通常知識者應可理解其內容，故不贅言。In some embodiments, in order to increase the accuracy of the comparison, the processor 15 may also perform a word segmentation process (for example: JIEBA word breaker) on the subject/content of each important message to generate a corresponding word The result of a word segmentation of the message. Then, the processor 15 then performs a correlation comparison between each of the word segmentation results and the target keywords. In some implementations, the correlation comparison can also be achieved through algorithms such as BM25 (Best Match 25), TF/IDF (Term frequency—inverse document frequency), and machine learning, etc., which can also include denoising , Segmentation, word segmentation and other operations. It should be clarified why the details of word segmentation processing and the content of relevance comparison are not the focus of the present invention. Those with ordinary knowledge in the technical field of the present invention should understand the content, so it will not be repeated.
於本實施方式中，當處理器15決定各該重大訊息各自對應的該風險參數後，處理器15接著統計各該上市櫃公司於一時間區間內所對應到之該等重大訊息（即，公布時間落於此時間區間的重大訊息）的各該風險參數，以產生各該上市櫃公司各自的一風險估計值。最後，處理器15判斷當該等風險估計值中至少其中之一超過一閾值時，產生一風險預警。因此，後續投資者可根據這些風險預警調整投資策略，或是由投資公司的風險控制人員／專業財金人事進一步進行專業判斷。於某些實施方式中，該時間區間為從一現在時間點開始的前N個月，且N為一正整數。In this embodiment, after the processor 15 determines the risk parameter corresponding to each of the major messages, the processor 15 then counts the major messages corresponding to each of the listed companies in a time interval (ie, announces Time falls within this time interval of each of the risk parameters of the major information) to generate a risk estimate for each of the listed companies. Finally, the processor 15 determines that when at least one of the risk estimates exceeds a threshold, a risk warning is generated. Therefore, subsequent investors can adjust their investment strategies based on these risk warnings, or the risk control personnel/professional financial personnel of the investment company can make further professional judgments. In some embodiments, the time interval is the first N months from a current time point, and N is a positive integer.
具體而言，處理器15可透過加總各該上市櫃公司於該某一時間區間（例如：近6個月內）內所對應到之該等重大訊息的各該風險參數，據以產生各該上市櫃公司各自的該風險估計值。舉例而言，請參考第3圖，其例示了處理器15對於C1、C2、C3、C4及C5等公司在近6個月內所發布的重大訊息的統計結果（意即，將各公司在6個月內所發布的重大訊息的風險參數加總），透過該統計結果可知C1、C2、C3、C4及C5等公司各自的風險估計值分別為「12」、「8」、「24」、「6」及「10」。因此，當閾值設為20時，處理器15判斷C3公司的風險估計值已超過閾值，處理器15產生對於C3公司的風險預警。Specifically, the processor 15 may sum up the risk parameters of the major information corresponding to the listed companies in the certain time interval (for example: within the past 6 months) to generate each The respective risk estimates of the listed companies. For example, please refer to Figure 3, which illustrates the statistical results of the processor 15 on the major information released by companies such as C1, C2, C3, C4, and C5 in the past 6 months (meaning that each company is The total risk parameters of major information released within 6 months), and the statistical results show that the respective risk estimates of C1, C2, C3, C4, and C5 are "12", "8", and "24". , "6" and "10". Therefore, when the threshold is set to 20, the processor 15 determines that the risk estimate of the C3 company has exceeded the threshold, and the processor 15 generates a risk warning for the C3 company.
以下段落將詳述關於處理器15產生目標關鍵字對應的風險參數的方式。具體而言，處理器15可透過分析／統計過去股價發生重大崩跌的歷史數據，來推測哪些出現次數較頻繁的重大訊息可能相對重要，而給予此類的重大訊息更高的風險比重。於某些實施方式中，各該目標關鍵字各自對應至的該風險參數是透過以下運作計算：根據一預設規則及一歷史股價，判斷符合該預設規則的複數個目標上市櫃公司（例如：每次處理一個目標上市櫃公司）；擷取該等目標上市櫃公司於一第一時間區（例如：N個月）間所對應的複數個歷史重大訊息，產生一歷史重大訊息集合；統計各該目標關鍵字於該歷史重大訊息集合的一出現次數；以及基於該出現次數，決定各該目標關鍵字各自對應至的該風險參數。於某些實施方式中，其中該預設規則為符合股價連續M天跌停板的該等目標上市櫃公司，且M為一正整數。The following paragraphs will detail the manner in which the processor 15 generates the risk parameter corresponding to the target keyword. Specifically, the processor 15 may analyze/statistically analyze historical data of major stock price crashes in the past to infer which major messages appearing more frequently may be relatively important, and give such major messages a higher risk weight. In some implementations, the risk parameter corresponding to each target keyword is calculated through the following operation: According to a preset rule and a historical stock price, a plurality of target listed companies (for example, : Process one target listed OTC company at a time); extract a plurality of historical important information corresponding to these target listed OTC companies in a first time zone (for example: N months) to generate a historical important information set; An occurrence number of each target keyword in the historical important information set; and based on the occurrence number, the risk parameter corresponding to each target keyword is determined. In some embodiments, the predetermined rule is the target listed companies that meet the stock price limit for M consecutive days, and M is a positive integer.
舉例而言，若投資者希望找出歷史數據中股價連續數日跌停板的上市櫃公司，並分析這類公司過去公布的重大訊息。投資者可將「連續跌停板5日」作為預設規則，由處理器15根據該預設規則搜尋歷史股價中「連續跌停板5日」的上市櫃公司。接著，處理器15擷取這些上市櫃公司從第一天跌停板往前推算6個月內所發布的所有重大訊息。隨後，處理器15根據前段所述的該等目標關鍵字（例如：「獨立董事辭職」、「財務主管異動」及「發言人異動」），統計出現該等目標關鍵字的重大訊息的次數。最後，處理器15根據該等目標關鍵字出現的次數，即可將出現次數最高的目標關鍵字（例如：「獨立董事辭職」）對應的風險參數調整至最高、將出現次數次高的目標關鍵字（例如：「財務主管異動」）對應的風險參數調整至次高、將出現次數最低的的目標關鍵字（例如：「發言人異動」）對應的風險參數調整至最低，依此類推。For example, if an investor wants to find a listed company whose share price has fallen limit for several consecutive days in historical data, and analyze the major information released by such companies in the past. Investors can use the "5 consecutive down limit for 5 days" as the default rule, and the processor 15 will search for listed companies with the "5 consecutive down limit for 5 days" in historical stock prices based on the default rule. Then, the processor 15 retrieves all the major information released by these listed companies in the 6 months from the first day's downward limit. Subsequently, the processor 15 counts the number of occurrences of major messages of the target keywords based on the target keywords described in the preceding paragraph (for example, "independent director resignation", "financial director change" and "spokesperson change"). Finally, the processor 15 can adjust the risk parameter corresponding to the target keyword with the highest number of occurrences (for example: "Resignation of Independent Director") to the highest and the second highest number of occurrences according to the number of occurrences of the target keywords. The risk parameter corresponding to the word (for example: "Finance Director Change") is adjusted to the second highest, the risk parameter corresponding to the target keyword with the lowest occurrence (for example: "Speaker Change") is adjusted to the lowest, and so on.
由上述說明可知，本發明所提供之重大訊息風險預警裝置1藉由接收複數個重大訊息，根據該等重大訊息及目標關鍵字決定各個重大訊息的風險參數，並判斷各上市櫃公司於一時間區間內所公布的重大訊息的風險估計值是否超過預定值，當風險估計值中至少其中之一超過預定值時，產生相應的風險預警。此外，重大訊息風險預警裝置1更提供了產生目標關鍵字對應的風險參數的方法，透過分析過去歷史資料（包含股價資料與重大訊息），決定目標關鍵字的風險參數權重分配。透過前述運作，重大訊息風險預警裝置1藉由分析大量的重大訊息，即時的提供各個上市櫃公司的風險評估並提出預警的機制，提醒可能的風險來協助投資者預先的對於這些風險過高的上市櫃公司作出反應，以降低可能面對的虧損，解決習知技術無法解決的問題。From the above description, it can be seen that the major information risk
本發明之第二實施方式為一重大訊息風險預警方法，其流程圖係描繪於第4圖。重大訊息風險預警方法適用於一電子裝置，例如：第一實施方式所述之重大訊息風險預警裝置1。該電子裝置儲存複數個目標關鍵字，且各該目標關鍵字各自對應至一風險參數，例如：第一實施方式之複數個目標關鍵字。重大訊息風險預警方法透過步驟S401至步驟S407產生一風險預警。The second embodiment of the present invention is a major information risk early warning method, and the flowchart is depicted in FIG. 4. The important information risk early warning method is suitable for an electronic device, such as the important information risk
於步驟S401，由該電子裝置接收複數筆重大訊息，其中各該重大訊息各自對應至一上市櫃公司及一公布時間。In step S401, the electronic device receives a plurality of important messages, and each of the important messages corresponds to a listed company and an announcement time.
於步驟S403，由該電子裝置根據該等重大訊息與該等目標關鍵字，決定各該重大訊息各自對應的該風險參數。於某些實施方式中，決定各該重大訊息各自對應的該風險參數的步驟包含：對於各該重大訊息分別進行一斷詞處理，以產生對應各該重大訊息的一斷詞結果； 以及將各該斷詞結果與該等目標關鍵字進行一關聯性比對，以決定各該重大訊息各自對應的該風險參數。In step S403, the electronic device determines the risk parameter corresponding to each of the important messages according to the important messages and the target keywords. In some embodiments, the step of determining the risk parameter corresponding to each of the major messages includes: performing a word segmentation processing on each of the major messages to generate a word segmentation result corresponding to each of the major messages; and The word segmentation result is compared with the target keywords to determine the risk parameter corresponding to each of the important messages.
接著，於步驟S405，由該電子裝置計算各該上市櫃公司於一時間區間內所對應到之該等重大訊息的各該風險參數，以產生各該上市櫃公司各自的一風險估計值。於某些實施方式中，產生該風險估計值的步驟包含：加總各該上市櫃公司於該時間區間內所對應到之該等重大訊息的各該風險參數，以產生各該上市櫃公司各自的該風險估計值。於某些實施方式中，該時間區間為從一現在時間點開始的前N個月，且N為一正整數。Then, in step S405, the electronic device calculates each of the risk parameters of the major messages corresponding to each of the listed companies in a time interval to generate a risk estimate of each of the listed companies. In some embodiments, the step of generating the risk estimate includes: adding up each of the risk parameters of the material information corresponding to each of the listed companies in the time interval to generate each of the listed companies The estimated value of this risk. In some embodiments, the time interval is the first N months from a current time point, and N is a positive integer.
隨後，於步驟S407，由該電子裝置判斷當該等風險估計值至少其中之一超過一閾值時，產生一風險預警。Subsequently, in step S407, the electronic device determines that when at least one of the risk estimates exceeds a threshold, a risk warning is generated.
於某些實施方式中，各該目標關鍵字各自對應至之該風險參數是透過步驟S501至步驟S507計算。於步驟S501，由該電子裝置根據一預設規則及一歷史股價，判斷符合該預設規則的複數個目標上市櫃公司。接著，於步驟S503，由該電子裝置擷取該等目標上市櫃公司於一第一時間區間所對應的複數個歷史重大訊息，產生一歷史重大訊息集合。隨後，於步驟S505，由該電子裝置統計各該目標關鍵字於該歷史重大訊息集合的一出現次數。最後，由該電子裝置於步驟S507，基於該出現次數，決定各該目標關鍵字各自對應至的該風險參數。於某些實施方式中，該預設規則為符合股價連續M天跌停板的該等目標上市櫃公司，且M為一正整數。In some embodiments, the risk parameter corresponding to each target keyword is calculated through step S501 to step S507. In step S501, the electronic device determines a plurality of target listed companies that meet the preset rule according to a preset rule and a historical stock price. Then, in step S503, the electronic device captures a plurality of historical important messages corresponding to the target listed companies in a first time interval to generate a historical important message set. Subsequently, in step S505, the electronic device counts the number of occurrences of each target keyword in the historical important message set. Finally, in step S507, the electronic device determines the risk parameter corresponding to each target keyword based on the number of occurrences. In some embodiments, the preset rule is the target listed companies whose stock prices meet the M consecutive down limit, and M is a positive integer.
除了上述步驟，第二實施方式亦能執行第一實施方式所描述之重大訊息風險預警裝置1之所有運作及步驟，具有同樣之功能，且達到同樣之技術效果。本發明所屬技術領域中具有通常知識者可直接瞭解第二實施方式如何基於上述第一實施方式以執行此等運作及步驟，具有同樣之功能，並達到同樣之技術效果，故不贅述。In addition to the above steps, the second embodiment can also execute all the operations and steps of the important information risk
綜上所述，本發明所提供之重大訊息風險預警技術（至少包含裝置及方法）藉由接收複數個重大訊息，根據該等重大訊息及目標關鍵字決定各個重大訊息的風險參數，並判斷各上市櫃公司於一時間區間內所公布的重大訊息的風險估計值是否超過預定值，當風險估計值中至少其中之一超過預定值時，產生相應的風險預警。另外，本發明更提供了產生目標關鍵字對應的風險參數的方法，透過分析過去歷史資料（包含股價資料與重大訊息），決定目標關鍵字的風險參數權重分配。透過前述運作，本發明所提供之重大訊息風險預警技術藉由分析大量的重大訊息，即時的提供各個上市櫃公司的風險評估並提出預警的機制，提醒可能的風險來協助投資者預先的對於這些風險過高的上市櫃公司作出反應，以降低可能面對的虧損，解決習知技術無法解決的問題。To sum up, the major information risk early warning technology (including at least the device and method) provided by the present invention receives multiple major messages, determines the risk parameters of each major message based on the major messages and target keywords, and judges each Whether the risk estimate of the major information announced by the listed company in a time interval exceeds the predetermined value, when at least one of the risk estimates exceeds the predetermined value, a corresponding risk warning is generated. In addition, the present invention further provides a method for generating risk parameters corresponding to the target keywords. By analyzing past historical data (including stock price data and important information), the risk parameter weight distribution of the target keywords is determined. Through the foregoing operations, the important information risk early warning technology provided by the present invention analyzes a large number of important information, provides real-time risk assessment of each listed company and proposes an early warning mechanism, reminding possible risks to help investors deal with these in advance Over-risk listed companies will respond to reduce possible losses and solve problems that cannot be solved by conventional technology.
上述實施方式僅用來例舉本發明之部分實施態樣，以及闡釋本發明之技術特徵，而非用來限制本發明之保護範疇及範圍。任何本發明所屬技術領域中具有通常知識者可輕易完成之改變或均等性之安排均屬於本發明所主張之範圍，而本發明之權利保護範圍以申請專利範圍為準。The above-mentioned embodiments are only used to exemplify part of the implementation aspects of the present invention and explain the technical features of the present invention, rather than to limit the protection scope and scope of the present invention. Any change or equal arrangement that can be easily completed by a person with ordinary knowledge in the technical field of the present invention belongs to the scope of the present invention, and the scope of protection of the rights of the present invention is subject to the scope of the patent application.
1:重大訊息風險預警裝置 11:儲存器 13:收發器 15:處理器 C1、C2、C3、C4、C5:公司代號 S401~S407:步驟 S501~S507:步驟 1: Important information risk warning device 11: Storage 13: Transceiver 15: processor C1, C2, C3, C4, C5: company code S401~S407: steps S501~S507: steps
第1圖係描繪第一實施方式之重大訊息風險預警裝置1之架構示意圖；Figure 1 is a schematic diagram depicting the structure of the important information risk
第2A圖係描繪複數個目標及其對應風險參數之一具體範例；Figure 2A depicts a specific example of multiple targets and their corresponding risk parameters;
第2B圖係描繪複數個重大訊息的一具體範例；Figure 2B depicts a specific example of multiple important messages;
第3圖係描繪複數個上市櫃公司與其風險估計值之一具體範例；Figure 3 depicts a specific example of multiple listed companies and their risk estimates;
第4圖係描繪第二實施方式之重大訊息風險預警方法之部分流程圖；以及Figure 4 depicts a partial flowchart of the method for early warning of major information risks in the second embodiment; and
第5圖係描繪某些實施方式所會執行之方法之部分流程圖。Figure 5 depicts a partial flowchart of the method performed by some embodiments.
S401~S407:步驟 S401~S407: steps
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090018891A1 (en) * | 2003-12-30 | 2009-01-15 | Jeff Scott Eder | Market value matrix |
| US20100161508A1 (en) * | 2002-04-30 | 2010-06-24 | Newriver, Inc. | Processing securities-related information |
| US7856388B1 (en) * | 2003-08-08 | 2010-12-21 | University Of Kansas | Financial reporting and auditing agent with net knowledge for extensible business reporting language |
| US8180713B1 (en) * | 2007-04-13 | 2012-05-15 | Standard & Poor's Financial Services Llc | System and method for searching and identifying potential financial risks disclosed within a document |
| CN102708421A (en) * | 2012-05-18 | 2012-10-03 | 苏州万图明电子软件有限公司 | Securities investment risk assessment and pre-warning system |
| CN106056449A (en) * | 2016-05-26 | 2016-10-26 | 黑龙江省容维投资顾问有限责任公司 | Stock information push system and push method |
-
2019
- 2019-10-30 TW TW108139291A patent/TWI716172B/en active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100161508A1 (en) * | 2002-04-30 | 2010-06-24 | Newriver, Inc. | Processing securities-related information |
| US7856388B1 (en) * | 2003-08-08 | 2010-12-21 | University Of Kansas | Financial reporting and auditing agent with net knowledge for extensible business reporting language |
| US20090018891A1 (en) * | 2003-12-30 | 2009-01-15 | Jeff Scott Eder | Market value matrix |
| US8180713B1 (en) * | 2007-04-13 | 2012-05-15 | Standard & Poor's Financial Services Llc | System and method for searching and identifying potential financial risks disclosed within a document |
| CN102708421A (en) * | 2012-05-18 | 2012-10-03 | 苏州万图明电子软件有限公司 | Securities investment risk assessment and pre-warning system |
| CN106056449A (en) * | 2016-05-26 | 2016-10-26 | 黑龙江省容维投资顾问有限责任公司 | Stock information push system and push method |
Non-Patent Citations (1)
| Title |
|---|
| 劉郁靖, 上市公司重大訊息揭露對經營績效與風險影響之探討, 碩士論文, 2012 * |
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