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研究生:洪崇熙
研究生(外文):Chung-Hsi Hung
論文名稱:資訊科技投資多宣告窗口認定與股價異常報酬關聯性之研究
論文名稱(外文):The Relationship Between Identification of Multi-Announcement Windows and Abnormal Return for Information Technology Investment Events
指導教授:陳昭宏陳昭宏引用關係
指導教授(外文):Jao-Hong Cheng
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
畢業學年度:92
語文別:中文
論文頁數:94
中文關鍵詞:事件研究法多宣告效率市場循環平方累加演算法資訊科技投資
外文關鍵詞:Event StudyMarket EfficiencyInformation Technology InvestmentsIterated Cumulative Sums of Squares (ICSS) AlgorMulti-announcement
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隨著資訊科技的進步與發展,企業不斷增加資訊科技投資以維持長期競爭優勢與提升組織績效。由於資訊科技投資的潛在利益難以估計,導致資訊科技投資對公司價值的影響常被錯估。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。因此,我們可以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。檢視相關文獻後,我們發現國內學者仍甚少有資訊科技投資宣告與股價報酬關聯性之研究,而國外學者對於此事件宣告之窗口大小尚無客觀標準,且通常以主觀試誤方式設定為固定時間窗口。然而此做法若窗口設定過大,可能會產生其它干擾事件的影響,失去所要研究事件的特徵;反之,若設定過小,則可能會產生條件異質性問題。其次,資訊科技投資大多存在多宣告的情況,在隱含市場效率完全的假設下,以往學者通常主觀認定首次見報日作為宣告日,並且忽略其它重複的宣告,假設單一宣告日處理之。然而若市場效率不完全,此做法將極易忽略事件多宣告所隱含的資訊殘留價值。
鑑於上述種種問題,本研究採用循環平方累加(Iterated Cumulative Sums of Squares;ICSS)演算法,來客觀認定事件窗口的範圍,以避免多宣告窗口重疊的問題。並以Fama-French三因子模式,結合動能與交易量之五因子模式,做為股票報酬的預測模型。實證結果指出,資訊科技基礎建設投資與資訊科技應用投資多宣告皆會產生正向的累積平均異常報酬,並且累積平均異常報酬會隨著宣告的次數逐次下降,符合資訊衰減的特性。其次,次宣告日所產生的累積異常報酬顯著異於零。此亦證明在市場效率不完全下,事件多宣告的確會隱含資訊的殘留價值。此外,為進一步凸顯本研究與以往研究在窗口認定之差異,本研究取以往學者常用的固定時間窗口,進行不同資訊遺漏風險值下之累積平均異常報酬敏感度分析。實證結果顯示,事件窗口大小的認定的確會改變累積平均異常報酬的顯著程度和正負方向,若未考慮資訊遺漏風險值下所設定的事件窗口將極易產生衡量上的偏誤。
Along with the advancement and development of Information Technology (IT), enterprises continuously increase IT investments to maintain the long-term competitive advantage and raise organization performance. Because the potential value of IT investments is difficult to measure, the influence of IT investments on firm value is often assessed wrong. According to Efficiency Market Hypothesis (EMH), security price will quickly reflect the announcement of all related information. Therefore, we could discuss the influence of IT investment announcements on firm value by the fluctuation of stock price. Reviewing the related literatures, there are few researches about the relationship between IT investment announcements and stock return in domestic. Furthermore, because foreign researchers still haven’t an objective approach for identifying the size of event window, they often set a fixed time window by trial-and-error subjectively. However, if the window size is set too long, it might be affected by confounding events and then loses the information. On the contrary, if the window size is set too short, it might cause the problem of conditional heteroscedasticity. Moreover, IT investments are mostly in the situation of multi-announcement. Under an efficient market, former researchers often subjectively identified first announcement of the newspapers as announcement day, neglected the duplicate announcements and assumed single announcement day. However, under an inefficient market, the approach mentioned above will be very easy to neglect the multi-announcement residual information value.
In order to avoid the problem of multi-announcement window overlapping, Iterated Cumulative Sums of Squares (ICSS) algorithm will be adopted in this study to objectively identify the event window size. And this paper uses five-factor model, combining Fama-French three-factor model, momentum and trading volume, as the prediction model of stock return. According to the empirical analysis, we first found significant evidences, positive cumulative average abnormal returns (CAAR), associated with both multi-announcement of IT infrastructure investments and IT application investments. Moreover, These two CAARs decrease along the increasing number of announcement times and confirm to the characteristics of information weakness. Second, CAARs of the following announcements doesn’t significantly equal to zero. This also proves that event multi-announcement will produce the residual value of information in an inefficient market. Finally, in order to strongly show the difference between this research and former researches in the way of the identification of the event window size, we adopted fixed time windows, often used by former researches, to implement sensitive analysis of CAAR. The empirical result shows that the identification of the event window size will affect the significant degree and the positive/negtive direction of CAAR and lead to measurement biases.
摘要 i
Abstract ii
致謝 iv
目錄 v
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 4
1.4 研究方法 4
1.5 研究範圍與限制 4
1.6 研究架構與流程 5
第二章 文獻回顧 7
2.1 效率市場假說 7
2.2 事件多宣告現況探討 9
2.3 事件多宣告窗口設定問題 11
2.4 多結構轉變研究方法回顧與比較 13
2.5 資訊科技投資宣告與股價關聯性之分析 24
2.6 股票報酬率實證模式 27
第三章 研究設計與實證模型 31
3.1 研究樣本選取及資料來源 31
3.2 變數定義與衡量 32
3.3 事件窗口求取模式 34
3.4 異常報酬之衡量 35
第四章 實證結果與分析 37
4.1 實證樣本蒐集與整理 37
4.2 最適事件窗口分析 40
4.3 事件多宣告之異常報酬分析 57
4.4 事件窗口大小之敏感度分析 66
第五章 結論與建議 71
5.1 結論 71
5.2 建議 73
參考文獻 74
附錄一 循環平方累加演算法原始碼 79
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網頁部份
【1】行政院主計處電子處理資料中心「九十一年度電腦應用概況調查」http://www.dgbas.gov.tw/eyimc/eyimc-2.h
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