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題名：新事件研究法在資訊科技投資事件股價異常報酬關聯性之研究－聯合事件、多宣告和窗口認定之分析
作者：李琛瑜
作者(外文)：Chen-yu Lee
校院名稱：雲林科技大學
系所名稱：管理研究所博士班
指導教授：陳昭宏
學位類別：博士
出版日期：2009
主題關鍵詞：資訊科技投資；事件研究法；聯合事件；多宣告；窗口認定；迭代累積平方加總演算法；累積平均異常報酬；Joint Event；Event Study；Information Technology Investment；Iterated Cumulative Sums of Squares (ICSS)；Window Identification；Multiple Announcement；Cumulative Average Abnormal Return (CAAR)
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　　資訊科技(Information Technology; IT)被認為可以提升企業的競爭優勢與組織績效，在企業持續增加資訊科技投資的趨勢下，其投資效益也因此備受關注。根據效率市場假說，證券價格會快速反應所有相關訊息的宣告，所以可藉由股價的波動來探討資訊科技投資宣告對公司價值的影響，而這方面的研究通常採用「事件研究法」(Event Study)。
　　先前的學者通常將資訊科技投資區分為資訊科技應用(Application)與資訊科技基礎建設(Infrastructure)兩種投資類型並個別探討不同類型的事件對股價的影響(Dos Santos et al., 1993; Chatterjee et al., 2002)，而且大部份僅針對單一特定類型的事件來探討，並且假設市場效率完全而以首次見報日作為惟一的宣告日而忽略後續的相同重複宣告。然而根據相關文獻與實際情況所示，資訊科技投資之宣告經常同時包含上述兩種投資類型而形成聯合事件以及多次宣告的情形，倘若市場效率不完全卻僅採用首次宣告日，可能在估計異常報酬時會造成偏誤而忽略同一事件多宣告之後續宣告的資訊內涵。因此，本研究將採用事件研究法來探討資訊科技投資之聯合事件與多宣告對公司股價的影響。
　　在應用事件研究法時，對於事件窗口的認定並無客觀標準，先前的學者通常以主觀試誤之方式將其設定為固定的時間窗口，但窗口設定過大可能會受到其他不相關事件的干擾與影響，而設定過小則可能會遺漏該研究事件所造成的影響。有鑑於此，本研究採用迭代累積平方加總(Iterated Cumulative Sums of Squares; ICSS) 演算法來偵測股價報酬變異的轉折點，以便客觀認定事件窗口的範圍，並且為顯示本研究與以往研究在窗口認定之差異，亦針對本研究與以往學者採用之事件窗口，進行不同窗口大小所產生的累積平均異常報酬(Cumulative Average Abnormal Returns; CAAR)之敏感度分析。
　　實證結果指出，聯合資訊科技投資多宣告會產生正向的累積平均異常報酬，而且後續宣告日所產生的累積平均異常報酬顯著異於零，以及累積平均異常報酬隨著宣告次序遞增而呈現逐漸降低之情形，也就是愈後面宣告的累積平均異常報酬會愈小，顯示在市場效率不完全下，聯合事件多宣告確實具有資訊內涵。
It has been considered that information technology (IT) enables enterprises to raise their competitive advantage and organization performance, and enterprises tend to increase IT investments continuously, hence the benefit of IT investments has elicited great concerns. Financial theory suggests that the market value of firms will be reflected on the stock price in response to IT investment announcements; therefore, the event study method is employed in this research.
IT investments are classified into two categories including IT application and IT infrastructure to be discussed respectively by previous researchers; nevertheless, according to relative literatures and real cases, it indicates that both categories mentioned above are often included in IT investment announcements as a joint event as well as multiple announcements. It is probable to neglect the information content of subsequent duplicate announcements if just taking the first announcement day under imperfect market efficiency. Thus, the joint events of information technology investments as well as multiple announcements are taken into account in this study.
Previous researchers usually set fixed-time event windows subjectively by try-and-error (Dos Santos et al., 1993; Chatterjee et al., 2002). However, too-long windows would probably include the noises of irrelevancies, whereas too-short windows would probably exclude the effects of target events. In order to obtain objective identification, the event window in this study is measured by using the iterated cumulative sums of squares (ICSS) algorithm for detecting change points. The sensitive analysis of cumulative average abnormal returns (CAAR) under various window settings that proposed by this study and previous researchers will be implemented to manifest the effect of window identifications in this study.
The results indicate that positive CAARs are generated for the joint events of IT investments with multiple announcements, and a significance level of CAAR estimated by subsequent duplicate announcements is reached; moreover, the later duplicate announcements the less amount of CAARs; in other words, a later duplicate announcement results in a less amount of CAAR. The joint events with multiple announcements have information contents under imperfect market efficiency.
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