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研究生:蔡維家
研究生(外文):Wei-Chia Tsai
論文名稱:資產評價模型於台灣集中交易市場之實證研究
論文名稱(外文):Asset Pricing Model: Evidence from Taiwan Stocks Exchange Market
指導教授:杜建衡杜建衡引用關係馬泰成馬泰成引用關係
指導教授(外文):Chien-Heng TuTay-Cheng Ma
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:金融資訊研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:44
中文關鍵詞:兩階段估計非同步交易因子負荷量風險溢酬
外文關鍵詞:two-stage estimationnon-synchronous tradingfactor loadingsrisk premium
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本研究以台灣上市的權益證券為對象,採用兩階段檢定檢驗資本資產訂價模型(CAPM)與Fama-French三因子模型,也藉由變更投資組合形成之方式檢視檢定結果是否穩定;實證結果發現,當beta作為單一解釋變數時,其無法解釋股票報酬之橫斷面,意涵CAPM於台股市場並不成立;另一方面,當規模與淨值市價比因子納入模型時,每單位HML-beta溢酬之均值為顯著正數,然而每單位beta與SMB-beta溢酬之均值皆不顯著,意味著在三因子模型中唯有HML-beta具解釋能力,此外,截距估計值之平均為顯著負值,因而推論存在有待發現之未知風險因子;而不論投資組合的形成方式為何,皆符合上述之檢定結果,最後在考量非同步交易後,對結果亦沒有太大的影響。
This paper adopts the two-stage test to examine the capital asset pricing model(CAPM)and Fama-French three-factor model for equity listed in the Taiwan market. Also, we judge whether the results for t-tests are stable through changing the way to grouping. The results find that beta can’t explain the cross-section of stock returns alone, implying that the CAPM is not hold. On the other hand, when the two factors related to size and book-to-market equity are brought into the model, the average premium per unit of HML-beta is significantly positive at the 5% level. Neither of the average premiums per unit of beta and SMB-beta, however, is significant different from zero at the same level. It means that only HML-beta has explanatory power in the three-factor model. Besides, the average of the estimated intercepts is significantly negative value. Hence, we make an inference that there could be unknown risk factor. No matter how the portfolios are formed, the above is never violated. Adjustments for non-synchronous trading don’t affect the results as well.
1. Introduction 1
2. Literature Review 3
3. Methodology 8
3.1 The Theoretical Model 8
3.1.1 The capital asset pricing model(CAPM) 8
3.1.2 Fama and French three-factor model 10
3.2 The Approach to Test 12
3.2.1 Fama and MacBeth two-stage procedure 12
3.2.2 Adjustment for non-synchronous trading 13
4. Empirical Results 15
4.1 Data 15
4.2 Descriptive Statistics 16
4.2.1 The characteristics 17
4.2.2 The dependent variables 19
4.2.3 The explanatory variables 20
4.3 Time-series Regression Analysis 21
4.3.1 Test on the 9 size-BE/ME portfolios formed independently 21
4.3.2 Test on the 25 size-BE/ME portfolios formed independently 24
4.3.3 Test on the 9 size-BE/ME portfolios formed dependently 29
4.4 Cross-sectional Regression Analysis 31
4.5 Non-synchronous Trading Adjustment Analysis 33
5. Conclusion 36
Appendix Ⅰ 38
Appendix Ⅱ 41
Reference 43
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