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研究生:高玫芳
研究生(外文):Mei Fang Kao
論文名稱:在相同行為方程式中利用追蹤資料分群捕捉異常報酬-以台灣電子產業為例
論文名稱(外文):Applying Panel Data Model to Capture the Anonmaly Effects Underly the Same Behavior Functoin Condition-- an Application on Taiwan''s Electronic Industry
指導教授:陳文典陳文典引用關係
指導教授(外文):Wen Den Chen
學位類別:碩士
校院名稱:東海大學
系所名稱:經濟系
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2004
畢業學年度:93
語文別:中文
論文頁數:52
中文關鍵詞:追蹤資料異常報酬行為方程式
外文關鍵詞:Panel dataAnomaly returnBehavior function
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一般用於衡量異常報酬的變數,如本益比及公司規模,雖是最常被使用也是易於取得的公開資料,但並不易藉由模型發現異常報酬的現象。不論是單純利用橫斷面資料,或廠商行為方程式差異大的追蹤資料,傳統方法認為較多的資料可以提供較多的訊息,但在實證分析上卻不易捕捉到異常報酬,即資料的廠商行為方程式差異過大,大量的資料就無助於異常報酬的捕捉,反而會影響資訊的揭露,因此本研究根據結合性檢定來檢定其行為方程式,藉以將具有相同行為方程式的公司合併在一起做分析,以利於捕捉異常報酬的現象。實證方面以民國 89 年元月至民國 92 年 5 月底的台灣上市電子公司為樣本資料,利用追蹤資料分群捕捉相近行為方程式公司的異常報酬,公司規模及本益比等異常報酬的變數就會很容易顯著,接著再比較 CAPM 及 APT 兩模型的市場效率性,並證實出台灣上市的高科技產業在實證應用上完全符合 APT 的解釋能力比 CAPM 好的理論,善用到了追蹤資料比單純使用橫斷面或縱斷面資料能提供更多訊息情報的優點。
It is an important issue to discover whether the stock market has anomaly returns, which implies an imperfect market. In practice, we usually apply P/E ratio, size, or M/B ratio etc., variables to explain this anomaly effects. However, in estimation experience even a huge number data is implemented, it is not easy to discover the phenomena. Why? Does it really have no anomaly return, or other reasons? This article provides an alternative thought to solve this problem.
This article supposes that if the firms have different behavior functions then should not put together for estimation, which could interfere with each other and disadvantage for the discovery of anomaly effects. This article applies the poolability test to filter these companies. In addition, through the same behavior function companies we can easily gather the anomaly effects.
The data encompass the whole electronic industry between January 2000 and March 2003 from the Taiwan New Post is available for this research. Two familiar models, CAPM and APT, are used to demonstrate the performance. From the result analysis, we can see APT demonstrate an excellent performance which can easily discover the anomaly effects.
1 緒論 1
1.1 前言 1
1.2 論文架構 3
2 文獻回顧與理論模型 4
2.1 文獻回顧 4
2.2 理論模型 6
2.2.1 資本資產定價模型 6
2.2.2 套利定價理論 8
2.2.3 因子的選擇 9
2.2.4 因子的個數 10
3 研究方法 12
3.1 追蹤資料結合性檢定 12
3.2 固定效果模式:最小平方虛擬變數法 17
3.3 隨機效果模式 18
3.4 固定效果與隨機效果的比較 21
4 實證研究 22
4.1 樣本來源與選擇 22
4.2 實證過程 23
4.3 追蹤資料的分組結果 25
4.4 CAPM 與 APT 實證結果比較 26
5 結論與後續研究建議 30
5.1 結論 30
5.2 後續研究建議 30
參考文獻 32
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