以成長價值模型與變數誤差模型建構營建企業評價模型

機構典藏 > College of Engineering > Graduate Institute & Department of Civil Engineering > Thesis > Item 987654321/114613

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/114613

Title:	以成長價值模型與變數誤差模型建構營建企業評價模型
Other Titles:	Building valuation model for construction enterprise with growth value models and errors-in-variables models
Authors:	張葳;Chang, Wei
Contributors:	淡江大學土木工程學系碩士班葉怡成;Yeh, I-Cheng
Keywords:	企業評價;成長價值模型;變數誤差迴歸;sand column;Laminated;Soft clay;numerical modeling;bearing capacity
Date:	2017
Issue Date:	2018-08-03 14:58:17 (UTC+8)
Abstract:	成長價值模型認為股票的內在價值是淨值與股東權益報酬率的綜效的結果，公式為 P/B=k∙(1+ROE)^m，其中k、m為待定係數。由於產業、規模、風險這些公司的穩定的重要特性可能會影響k、m值，因此本文的目的在於建立k、m值與這些特性之間的關係，進一步改善模型的預測能力。本研究將2005年至2016年上市上櫃公司的資料分，將各年份以產業分成營建、金融、傳產、電子四個資料集，以規模或風險的大小分成十個資料集，分別估計k、m值。由於GVM模型的自變數ROE存在量測誤差，因此利用變數誤差迴歸(Errors-in-variables Regression)克服此問題，以精確估計模型參數k, m值。結果顯示 (1) 傳統迴歸分析估計的迴歸曲線有嚴重偏差，變數誤差迴歸可以有效矯正此種偏差。(2) 產業影響k, m值。營建的k值較大，金融次之，傳產、電子最小。傳產、電子的m值較大，金融次之，營建最小。(2) 公司的規模對k, m值的影響最大。規模愈大，價值係數愈小，成長係數愈高。顯示大型公司有較高的獲利持續性。(3) 公司的風險對k, m值的影響不明顯。 GVM valuation model considers that PBR is the function of ROE. There are two coefficients in the function. One of them is value coefficient (k), the other is growth coefficient (m). The main purpose of this study is to improve the GVM model by regulating coefficients k and m of the GVM model by using the industry, scale and the risk, which would result in more accurate prediction of PBR. In this research, to calculate the value of k and m, we categorized the data of listed companies from 2005 to 2016 into four data sets according to industrial types (Construction, Finance, Traditional, and Electronic industry). Also, based on the scale or risk, we divided them into ten data sets from low to high. To overcome measurement error in the independent variable(ROE) of GVM model, adopted the Errors-in-variables Regression(EIV) to precisely estimate coefficient k and m. The result shows that: (1) There are some significant biases in the regression curve generated by traditional regression analysis, but it can be correct by using the errors-in-variables regression analysis. (2) Industry category affects the value of k and m. Construction industry has the highest value of k, followed by finance, and the traditional and electronic industry have the lowest value. In contrast, the effects of industry category on the value of m appear the opposite order. (3) The scale of the company has the most significant effects to the value of k and m. The greater the scale is, the less the value coefficient k is whereas the higher the growth coefficient m is, which indicates that large companies get the higher persistence of earning. (4) The risk of the companies won’t significantly affect the value of k and m.
Appears in Collections:	[Graduate Institute & Department of Civil Engineering] Thesis

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