負面新聞事件在財務危機預測：以台灣上市上櫃公司為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：29

、訪客IP：3.129.67.248

姓名

劉雅文(Ya-Wen Liu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

負面新聞事件在財務危機預測：以台灣上市上櫃公司為例
(Negative News events in financial distress problem: Taiwan-listed company)

相關論文

★ 基於最大期望算法之分析陶瓷基板機器暗裂破片率	★ 基於時間序列預測的機器良率預測
★ 基於OpenPose特徵的行人分心偵測	★ 建構深度學習CNN模型以正確分類傳統AOI模型之偵測結果
★ 一種結合循序向後選擇法與回歸樹分析的瑕疵肇因關鍵因子擷取方法與系統－以紡織製程為例	★ 融合生成對抗網路及領域知識的分層式影像擴增
★ 針織布異常偵測方法研究	★ 基於工廠生產資料的異常機器維修預測
★ 萃取駕駛人在不同環境之駕駛行為方法	★ 基於刮痕瑕疵資料擴增的分割拼接影像生成
★ 應用卷積神經網路於航攝影像做基於坵塊的水稻判釋之研究	★ 採迴歸樹進行規則探勘以有效同時降低多種紡織瑕疵
★ 應用增量式學習於多種農作物判釋之研究	★ 應用自動化測試於異質環境機器學習管道之 MLOps 系統
★ 農業影像二元分類：坵塊分離的檢測	★ 應用遷移學習於胚布瑕疵檢測

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

財務危機預測問題(Financial distressed prediction problem)一直以來是個重要且已被廣泛討論的問題，其中又以特徵挑選及學習演算法為兩大重心。本研究著重於找尋新的特徵以幫助預測，過往的研究大多使用財務比率(Financial Ratio)，部分使用公司治理指標(Corporate Government Indicator)進行財無危機預測，卻少有研究使用公司的負面新聞對台灣地區的公司進行未來的財務危機預測，在本研究中我使用TEJ的看門狗資料庫中所蒐集並定義的負面新聞事件分類，接著使用統計方法分析後挑選出了其中八個負面新聞事件，提取欲預測年份的前一年的發生次數作為特徵值去建模在透過DET Curve及cost ratios分析，並證實了在大部分的cost ratio 下使用ensemble Bagged Tree建模這些負面事件對預測表現是有幫助的。

摘要(英)

The financial distressed prediction problem has always been an important and widely discussed issue, with feature selection and learning algorithms as the two main focuses. This study focuses on finding new features that can help improve the prediction. Most of the previous studies used the Financial Ratio, and some used the Corporate Government Indicator for financial crisis prediction. However, few studies used the company′s negative news to predict the financial crisis. In this study, we proposed eight negative news events to build the prediction model. For each event, calculate the number of occurrences of the year before predict year as the event feature value. After we analysis the result by DET Curve and different cost ratio analysis , we learned that these negative events are helpful for predicting performance over most of the cost ratios.

關鍵字(中)

★ 負面財務新聞事件
★ 財務危機預測

關鍵字(英)

論文目次

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 2
1-4 論文架構 3
二、文獻探討 4
2-1 FDP相關文獻 4
2-1-1 Altman Z-Score 4
2-2 探討新聞事件相關文獻 6
2-3 探討分類器 6
2-3-1 支持向量機(SVM) 6
2-3-2 判別分析(DA) 12
2-3-3 邏輯斯回歸Logistic Regression 13
2-3-4 最近鄰居分類 (KNN) 14
2-3-5 Ensemble Bagged Tree 16
三、資料集 18
3-1資料集來源及簡介 18
3-2 樣本配對演算法 20
3-3 負面新聞特徵的分析 23
四、實驗設計 25
4-1資料前處理 25
4-1-1 負面新聞特徵正規化 25
4-2 實驗評估方法 25
4-2-1 DET curve(Detection error tradeoff curve) 26
4-2-2 Cost Ratio analysis 27
4-3實驗流程 28
4-3-1 N fold cross validation model 28
4-3-2 Hypothesis 實驗流程 29
4-4實驗各項參數設定 30
五、實驗結果 31
5-1 Hypothesis 實驗結果分析 31
5-2 Variable Importance and Variable Interaction 38
六、結論及未來展望 41
6-1 結論 41
6-2 未來展望 41
參考文獻 42
附錄一 44
附錄二 47
附錄三 48

參考文獻

P. .Fitzpartrick, “A comparison of ratios of successful industrial enterprises with those of failed firms,” J. Account. Res., pp. 598–605, 1932.
[2] Beaver, “Financial Ratios As Predictors of Failure,” J. Account. Res., vol. 4, no. 1966, pp. 71–111, 1966.
[3] E. I.Altman, “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” J. Finance, vol. 23, no. 4, pp. 589–609, 1968.
[4] J. A.Ohlson, “Financial Ratios and the Probabilistic Prediction of Bankruptcy,” J. Account. Res., vol. 18, no. 1, p. 109, 1980.
[5] A.Gepp, “Business failure prediction using decision trees,” 2009.
[6] F.Lin, D.Liang, C. C.Yeh, andJ. C.Huang, “Novel feature selection methods to financial distress prediction,” Expert Syst. Appl., vol. 41, no. 5, pp. 2472–2483, 2014.
[7] K.Y. Tam and M.Y. Kiang, “Managerial Applications of Neural Networks: The Case of Bank Failure Predictions”, Management Science, Vol.38, pp.926-947, 1992.
[8] Murugan Anandarajan, Picheng Lee and Asokan Anandarajan, “Bankruptcy Prediction of Financially Stressed Firms: An Examination of the Predictive Accuracy of Artiﬁcial Neural Networks”, 2001.
[9] George Guan-Ru Wu, Tony Chieh-Tse Hou , Jin-Lung Lin, “Can economic news predict Taiwan stock market returns?”, 2018.
[10] Zmijewski M., “Methodological issues related to the estimation of ﬁnancial distress prediction models”, 1984.
[11] C. Cortes, V. Vapnilk, “Support-Vector Networks”, 1995.
[12] R. A. FISHER, “THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS,” 1954.
[13] D. R. Cox, “The Regression Analysisof Binary Sequences,” ournal of the Royal Statistical Society. Series B (Methodological)Vol. 20, No. 2 (1958), pp. 215-242, 1958.
[14] Cover TM, Hart PE (1967). "Nearest neighbor pattern classification". IEEE Transactions on Information Theory 13 (1): 21–27. doi:10.1109/TIT.1967.1053964.
[15] L.Breiman, “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, 1996.
[16] A.Martin, G.Doddington, T.Kamm, M.Ordowski, andM.Przybocki, “The DET Curve in Assessment of Detection Task Performance,” Proc. Eurospeech ’97, pp. 1895–1898, 1997
[17] F.WILCOXON, “Individual comparisons of grouped data by ranking methods.,” J. Econ. Entomol., vol. 39, no. 6, p. 269, 1946.
[18] C. Kelly and K. Okada, “VARIABLE INTERACTION MEASURES WITH RANDOM FOREST CLASSIFIERS”, 2012
[19] C. Kelly and K. Okada, “VARIABLE INTERACTION MEASURES WITH RANDOM FOREST CLASSIFIERS”, 2012
[20] LIN, Fengyi; LIANG, Deron; CHEN, Enchia. Financial ratio selection for business crisis prediction. Expert Systems with Applications, 2011, 38.12: 15094-15102.
[21] E. S. Pearson, W. S. Gosset, R. L. Plackett, and G. A. Barnard, Student: a statistical biography of William Sealy Gosset: Oxford University Press, USA, 1990.
[22] C. Y. Lu, “Time series accruals apply in financial distress problem with dimensionality reduction: taking US-listed company for example”, 2018.
[23] T. D.Janes, “Accruals, Financial Distress, and Debt Covenants,” Univ. Michigan Bus. Sch., no. January, 2003.
[24] P.duJardin, D.Veganzones, andE.Séverin, “Forecasting Corporate Bankruptcy Using Accrual-Based Models,” Comput. Econ., pp. 1–37, 2017.
[25] F.Lin, D.Liang, C. C.Yeh, andJ. C.Huang, “Novel feature selection methods to financial distress prediction,” Expert Syst. Appl., vol. 41, no. 5, pp. 2472–2483, 2014.
[26] LIN, Fengyi; LIANG, Deron; CHEN, Enchia. Financial ratio selection for business crisis prediction. Expert Systems with Applications, 2011, 38.12: 15094-15102.

指導教授

梁德容

審核日期

2019-7-23

推文