臺灣博碩士論文加值系統

本研究旨在探討台灣股票市場上市公司盈餘組成因素之持續性及其對未來盈餘預測之準確度，研究期間自民國86年至95年為止，共計10年。為瞭解樣本公司盈餘組成因素之持續性，本研究將公司報導之淨利予以解構，建立有關盈餘之預測模型，檢測盈餘組成因素之持續性，並採用F檢定，以檢測各預測模型中盈餘組成因素之持續性是否具有差異性，亦利用Vuong Z檢定，以檢測不同預測模型對未來盈餘的解釋能力是否有所不同。為瞭解所建構之預測模型，其對於公司未來盈餘預測之準確度，首先計算各預測模型之預測錯誤絕對值，接著分別採用Paired t檢定及Wilcoxon signed rank test檢定，針對各模型進行兩兩比較，以檢測各模型預測能力之良窳。
本研究實證結果顯示：(1)盈餘組成因素持續性具有顯著差異性，繼續營業單位稅前淨利解構後，營業淨利持續性高於營業外損益，營業淨利解構後，營業毛利高於營業費用，營業收入高於營業成本，營業外損益解構後，淨利息支出持續性最高，依續為投資淨收入、其他營業外損益及處份投資及資產淨收益，可提供個別盈餘組成項目其持續性之有用資訊。(2)不同盈餘組成因素的預測模型對未來盈餘的解釋能力，伴隨盈餘解構愈為詳細，盈餘預測模型愈具備解釋能力，亦即調整後判定係數愈高。(3)將盈餘解構為營業淨利、營業外損益、所得稅費用及非經常損益，在預測下期盈餘提供額外資訊進而增加預測之準確度，然而，隨後將盈餘進行更為詳細之解構並未改善盈餘預測的準確度。

This study examines the persistence and forecast accuracy of earnings components on the Taiwan Stock Exchange. Our 5282 firm-year samples were drawn from the TEJ database. The sample set covers 624 companies listed on the Taiwan Stock Exchange with sampling period from 1997 to 2006.
Firstly,we disaggregated earnings to develop six earnings component models and to estimate the persistence coefficients of earning components.Persistence is measured by the coefficients in regressions of next period’s earnings.Secondly, I examined coefficients of earnings component s to see if there were significant differences.I provides F-tests for differences in the estimated persistence coefficients .A significant F-test indicated there are differences in persistence coefficients.Furthermore,we not only compare the adjusted R-square of each model to determine whether the explanatory power was improved if difference in persistence, but alos utilize Vuong Z statistic examine incremental improvement in adjusted R-square.Finally,the primary emphasis of the paper, we test whether the perceived benefits form earnings disaggregation result in improved forcasts of next period’s eanings.I calculate the mean absolute forecast error(AFE), also the mean difference is tested using a paired t-test and the rank difference is tested using a non-parametric Wilcoxon signed rank test.
This study found the following empirical results:
1.The results demonstrate that the persistence of the earnings components are differential.
2.The results show that the mean adjusted R-square increases with the progressive disaggregation of earnings.The results also demonstrate that disaggregated components are better able to predict next period earnings than aggregated earnings. After considering the persistence of earings components on future earnings, the explanatory power of our models can improve.
3.The results shows that disaggregating earnings into OPI, NONOPI and EXTRA as in the model provides incremental information useful for forecasting next earnings, further disaggregation does not improve next earnings forecasts one average.

摘要I
ABSTRACTIII
誌謝V
目錄VII
圖目錄IX
表目錄X
第壹章 緒論1
第一節 研究背景與動機1
第二節 研究目的4
第三節研究範圍與期間6
第四節 研究架構7
第貳章 文獻探討9
第一節盈餘組成因素與股價之關聯性9
第二節盈餘預測之相關研究11
第參章 研究設計13
第一節 預測模型之建立13
第二節 研究方法16
第三節 研究期間與樣本20
第肆章 實證結果與分析23
第一節 敘述性統計量分析23
第二節 盈餘組成因素之持續性係數31
第三節 預測模型解釋能力之差異性檢定39
第四節 預測模型之準確度之檢定41
第五節　敏感性分析50
第伍章 結論與建議59
第一節 研究結論59
第二節 研究限制62
第三節 研究建議63
參考文獻64
圖目錄
圖 1-1研究流程8
表目錄
表3-1 盈餘組成因素解構模型14
表3-2 相關係數之強度與意義16
表3-3 樣本篩選彙總表21
表3-4 樣本分布情況 22
表4-1 敘述性統計量分析(N=5282)25
表 4-2 迴歸模型之相關係數矩陣29
表4-3 估計係數及持續檢定35
表4-4預測模型(一)之迴歸分析估計結果41
表4-5預測模型(二)之迴歸分析估計結果41
表4-6預測模型(三)之迴歸分析估計結果43
表4-7預測模型(四)之迴歸分析估計結果43
表4-8 預測模型(五)之迴歸分析估計結果44
表4-9 預測模型(六)之迴歸分析估計結果46
表4-10預測錯誤絕對值敘述統計及統計檢定49
表4-11盈餘組成因素估計係數及持續性檢定51
表4-12預測模型(一)之迴歸分析估計結果 53
表4-13預測模型(二)之迴歸分析估計結果 54
表4-14預測模型(三)之迴歸分析估計結果 54
表4-15預測模型(四)之迴歸分析估計結果 55
表4-16 預測模型(五)之迴歸分析估計結果56
表4-17預測錯誤絕對值敘述統計及統計檢定58

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