每股盈餘預測之分析

Analysis of Predicting Earnings Per Shares

劉錦花(Ching-Hua Liou) ； 黃耀銜(Yaw-Shyan Huang) ； 李仁棻(Ren-Fen Lee)

《商業現代化學刊》 5卷2期 (2009/11) Pp. 125-140

https://doi.org/10.6132/JCM.2009.5.2.10

摘要

每股盈餘常被用來評估一個企業之獲利能力、股票投資之風險及作為投資決策之依據。本研究採用廻歸分析法，以2007年我國上市櫃公司18個產業共495家公司為研究對象，探討企業未來每股盈餘之預測。
傳統上，在建廻歸模型時，有一假設是E(Y|X=x)=d(X, θ)，而d是一己知函數型式並建立在X和有限參數集合θ=(θ1, θ2, ...)上，其中Y是反應變數，而向量X是自變數。但在實際應用時，d的函數型式通常是未知也不容易辨認的。本文將採CART(classification and regression trees)方式來整合資料和模型。文中以資料為基礎，其中每股盈餘為應變數，選出有影響的重要自變數，並判斷那個自變數最重要，並建立適當的模型。

關鍵字

廻歸分析 ； CART ； 選變數

並列摘要

The main purpose of this paper is to build the model about the earnings per shares of the public traded the listed companies on TSEC in 2007. The regression analysis is used.
Traditionally, in regression models, predictors have been constructed using a parametric approach under the assumption that E(Y|X=x)=d(x, θ), where d has known functional form depending on x and a finite set of parameters θ=(θ1, θ2,…, θm). Then θ is estimated by the least squares method. In practical applications, however, the functional form of d is usually unknown. In such a situation, it is difficult to determine the regression function and we will use Classification And Regression Tress (CART) methods based on integrating the data and the model. We utilize selection procedures to select important predictor variables in the regression model based on data to predict Earnings per shares. Some criteria for selecting the important variables will also be discussed.

並列關鍵字

Regression analysis ； CART ； Variables selection