陳國泰臺灣大學:會計學研究所魏逢佑Wei, Feng-YouFeng-YouWei2010-05-052018-06-292010-05-052018-06-292009U0001-0806200921492500http://ntur.lib.ntu.edu.tw//handle/246246/179956對財務報表使用者而言，企業盈餘為相當重要之資訊。然而，由於許多的因素及動機，管理階層企圖透過某些方法或程序來操縱盈餘。爲強調盈餘管理之重要性，許多文獻試圖找出影響盈餘管理之關鍵性因素。期望藉由考慮這些因素後，能有效預防或避免盈餘管理之發生。 本研究應用支援向量機並根據過去文獻指出之20個影響變數建立預測盈餘管理模型。此外，我們採用特徵選取方式篩選出關鍵性變數。爲驗證支援向量機之預測能力，以邏輯斯迴歸模型為評估預測準確率之標竿。 本研究採用格子點演算法與五摺交互驗證來調整支援向量機的參數，並經由過濾法與混合法執行特徵選取分析2003年至2007年台灣上市（櫃）公司資料。實驗結果顯示，僅投入六個關鍵性變數至支援向量機可獲得最高預測準確率78.05%。此六個變數分別為：績效門檻、股價淨值比、極端盈餘、營運活動現金流量、負債比率與公司規模。逐步迴歸邏輯斯篩選出11個變數僅獲得66.84%預測準確率。Corporate earning is a very important information for financial statements users. However, due to various reasons and motivations, corporate management might attempt to manipulate earning through certain methods or processes. To address the concern of earnings management, many studies have attempted to investigate its determinant factors. It is hoped that by discerning these factors, earnings management can be prevented or detected. Based on the 20 determinant factors found by pervious studies, this research applies support vector machines to build classifiers for earning management prediction. In addition, we adopt a process of features selection to filter out the most important factors. To validate the prediction power of the SVM classifier, we compare its prediction accuracy against that of the logistic regression model. We use the grid search technique with 5-fold cross-validation and perform features selection by filter model and hybrid model to analyze the data of Taiwan’s listed firms during the period of years 2003 to 2007. The experiment results show that a SVM classifier with only 6 determinant factors possess the highest prediction accuracy rate of 78.05%. These six factors are: performancehreshold, market value/book value, extreme earning, cash flow from operations, debt ratio, and corporate size. By comparison, the step-wise logistic model with 11 factors has a prediction accuracy rate of just 66.84%.口試委員審定書 i謝 ii要 iiibstract iv一章 緒論 1一節 研究背景與動機 1二節 研究目的 3三節 研究架構 4二章 文獻探討 5一節 盈餘管理之相關文獻 5二節 支援向量機(Support Vector Machine；SVM) 19三節 特徵選取(Feature Selection) 24三章 研究設計 27一節 期間定義、樣本選取與資料來源 27二節 研究變數定義與衡量 28三節 建立預測模型流程圖 35四章 實驗結果與分析 41一節 敘述性統計 41二節 SVM實驗結果 43三節 邏輯斯迴歸分析(Logistic) 52四節 SVM與Logistic 結果比較 53五章 研究結論與建議 54一節 研究結論 54二節 研究限制 56三節 未來研究建議 56考文獻 58application/pdf754118 bytesapplication/pdfen-US盈餘管理支援向量機邏輯斯迴歸格子點演算法特徵選取Earnings managementSupport vector machineLogistic RegressionGrid searchFeature selectionhttp://ntur.lib.ntu.edu.tw/bitstream/246246/179956/1/ntu-98-R96722018-1.pdf