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題名 橫斷面權益評價模式正確性與解釋變動能力之實證研究
其他題名 The Accuracy and Explainability of Cross-Sectional Equity Valuation Models
作者 林江亮;郭弘卿;吳仁杰
Lin, Chiang-Liang ; Kuo, Horng-Ching ; Wu, Jen-Chieh
貢獻者 會計系
關鍵詞 一般迴歸模式;追蹤資料迴歸模式;會計基礎評價
Panel data regression model;Cross-section model;Accounting-based valuation
日期 2008-11
上傳時間 19-Feb-2014 15:49:34 (UTC+8)
摘要 橫斷面(cross sectional)的評價模式中,大約可分成一般迴歸模式與追蹤
     資料(panel data)迴歸模式兩類。本研究以台灣證券交易所上市之公司為樣
     本,在價格模式下,以正確性及解釋變動能力為標準,嘗試比較一般與追蹤資
     料迴歸模式對於權益價值的解釋力及預測力。實證結果發現:不管是在正確性
     或解釋變動能力方面,追蹤資料迴歸模式均具有較佳解釋同期股價之能力;而
     在預測方面,也發現追蹤資料迴歸模式預測值之正確性及解釋變動能力均顯著
     優於一般迴歸模式。在考量不同產業與盈餘正負等因素後,所得到的結論亦相
     同。因此,在使用追蹤資料迴歸模式的情形下,不論是估計值對同時點價值的
     解釋力,或是對真實價值的預測力,皆具有較好的效果。
Cross-sectional valuation models include general regression model and panel data
     regression model. Using a sample of listed firms in Taiwan Security Exchange, we
     compare alternative empirical estimates of intrinsic value using two criteria: accuracy
     and explainability. The study compares the reliability of value estimates from general
     regression model and panel data regression model. The empirical results show that
     the panel data regression model’s estimates are more accurate and explain more of the
     variation in security prices than the general regression models. On the other hand, the
     accuracy and explainability of panel data regression mode’s forecasts are also better
     than general regression model’s. For the sensitivity test, we consider financial industry
     and negative earnings. The sensitivity test results are similar to previous outcomes. In
     summary, we provide evidence to support that panel data regression model’s estimate
     is a better model and can raise the effectiveness of valuation model.
關聯 會計學報, 1(1), 83-109
資料類型 article
dc.contributor 會計系en_US
dc.creator (作者) 林江亮;郭弘卿;吳仁杰zh_TW
dc.creator (作者) Lin, Chiang-Liang ; Kuo, Horng-Ching ; Wu, Jen-Chiehen_US
dc.date (日期) 2008-11en_US
dc.date.accessioned 19-Feb-2014 15:49:34 (UTC+8)-
dc.date.available 19-Feb-2014 15:49:34 (UTC+8)-
dc.date.issued (上傳時間) 19-Feb-2014 15:49:34 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/64022-
dc.description.abstract (摘要) 橫斷面(cross sectional)的評價模式中,大約可分成一般迴歸模式與追蹤
     資料(panel data)迴歸模式兩類。本研究以台灣證券交易所上市之公司為樣
     本,在價格模式下,以正確性及解釋變動能力為標準,嘗試比較一般與追蹤資
     料迴歸模式對於權益價值的解釋力及預測力。實證結果發現:不管是在正確性
     或解釋變動能力方面,追蹤資料迴歸模式均具有較佳解釋同期股價之能力;而
     在預測方面,也發現追蹤資料迴歸模式預測值之正確性及解釋變動能力均顯著
     優於一般迴歸模式。在考量不同產業與盈餘正負等因素後,所得到的結論亦相
     同。因此,在使用追蹤資料迴歸模式的情形下,不論是估計值對同時點價值的
     解釋力,或是對真實價值的預測力,皆具有較好的效果。
-
dc.description.abstract (摘要) Cross-sectional valuation models include general regression model and panel data
     regression model. Using a sample of listed firms in Taiwan Security Exchange, we
     compare alternative empirical estimates of intrinsic value using two criteria: accuracy
     and explainability. The study compares the reliability of value estimates from general
     regression model and panel data regression model. The empirical results show that
     the panel data regression model’s estimates are more accurate and explain more of the
     variation in security prices than the general regression models. On the other hand, the
     accuracy and explainability of panel data regression mode’s forecasts are also better
     than general regression model’s. For the sensitivity test, we consider financial industry
     and negative earnings. The sensitivity test results are similar to previous outcomes. In
     summary, we provide evidence to support that panel data regression model’s estimate
     is a better model and can raise the effectiveness of valuation model.
-
dc.format.extent 1198531 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) 會計學報, 1(1), 83-109en_US
dc.subject (關鍵詞) 一般迴歸模式;追蹤資料迴歸模式;會計基礎評價en_US
dc.subject (關鍵詞) Panel data regression model;Cross-section model;Accounting-based valuationen_US
dc.title (題名) 橫斷面權益評價模式正確性與解釋變動能力之實證研究zh_TW
dc.title.alternative (其他題名) The Accuracy and Explainability of Cross-Sectional Equity Valuation Modelsen_US
dc.type (資料類型) articleen