特徵降維方法之時間序列應計項目指標在財務危機預測：以美國上市公司為例

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姓名

呂澄宇(CHENG YU LU) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

特徵降維方法之時間序列應計項目指標在財務危機預測：以美國上市公司為例
(Time series accruals apply in financial distress problem with dimensionality reduction: taking US-listed company for example)

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摘要(中)

財務危機預測問題(Financial distressed prediction problem)已經經過了長時間且廣泛的討論，而本研究主旨在美國上市公司資料集中擴展FDPP的研究方向，過往的學者大多運用財務特徵來進行FDP，而本實驗希望能找出除了財務特徵之外新的特徵能夠提升預測財務危機的表現，且因特徵資料型態的不同也會影響預測的結果，過去已經有學者運用應計項目(Accruals)來進行FDP，但使用的應計項目並不全面，或是著重的問題不再FDP而是在盈餘管理(Earning management)，且所使用資料型態都為年資料，因此本實驗著重於運用所有的應計項目(Accruals)，跟使用時間序列(Time series)季資料來進行研究，之後會輔以特徵降維來降低維度以提高特徵表現跟進行特徵權重分析。

摘要(英)

The financial distressed prediction problem(FDPP) has been discussed for a long time and extensively. The main purpose of this thesis is to focus on US listed companies data to extend FDPP research direction. Most of previous scholars and researcher used financial ratio(FR) to do the prediction. This thesis is hopes to find out new feature besides financial ratio which can improve the performance of FDP result. And we know difference data type will also affect the prediction result. In the past, some scholars had used accruals as feature to do prediction, but its accruals are not comprehensive, or the research question is not focus on FPD but Earning management, and also the data type are year data. Therefore, this thesis focuses on use comprehensive accruals and using time series quarter data to do the research. After all we will dimension reduction to reduce dimensions to improve feature performance and perform feature weight analysis.

關鍵字(中)

★ 應計項目
★ 財務危機預測
★ 時間序列資料
★ 特徵降維

關鍵字(英)

★ accruals
★ time series data
★ dimension reduction
★ FDP

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 4
1-4. 論文架構 4
二、文獻探討 5
2-1.FDP相關文獻探討 5
2-2. Accruals相關文獻探討 6
2-3.分類器介紹 9
2-3-1支持向量機(SVM) 9
2-3-2判別分析(DA) 14
2-3-3 最近鄰居分類(KNN) 15
2-3-4 分類回歸樹(CART) 16
2-3-5 單純貝式分類器 17
2-3-6 Bagging Ensemble 18
2-3-7 Boosting Ensemble 20
三、研究資料集 22
3-1. 資料來源 22
3-2. 實驗所用的Accruals 23
四、實驗設計 26
4-1資料前處理 26
4-1-1 正規化處理 26
4-1-2 特徵降維 26
主要成分分析(PCA) 26
4-2 實驗評估方法 29
4-2-1 DET curve(Detection error tradeoff curve) 29
4-2-2 Wilcoxon signed-rank test 31
4-3實驗流程 32
4-3-1 N-Folds模型 32
4-3-2 Hypothesis 實驗流程 33
4-4實驗各項參數設定 35
五、實驗結果 36
5-1 Hypothesis 1 實驗結果分析 36
5-2 Hypothesis 2 實驗結果分析 45
5-3 實驗結果總結 48
六、結論及未來展望 50
6-1 結論 50
6-2 未來展望 53
參考文獻 54
附錄一 - 變數表（FRs + Accruals） 56
附錄二 - 公司配對表 57

參考文獻

[1] 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] W.SenChen andY. K.Du, “Using neural networks and data mining techniques for the financial distress prediction model,” Expert Syst. Appl., vol. 36, no. 2 PART 2, pp. 4075–4086, 2009.
[8] F.Barboza, H.Kimura, andE.Altman, “Machine learning models and bankruptcy prediction,” Expert Syst. Appl., vol. 83, pp. 405–417, 2017.
[9] M. Y.Chen, “Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches,” Comput. Math. with Appl., vol. 62, no. 12, pp. 4514–4524, 2011.
[10] L.-S.Liang, Deron;Chang, “國立中央大學資訊工程學系碩士論文 Corporate government indicators apply in financial distress problem based on ensemble method?: taking US-listed Company for example.”
[11] T. D.Janes, “Accruals, Financial Distress, and Debt Covenants,” Univ. Michigan Bus. Sch., no. January, 2003.
[12] P.duJardin, D.Veganzones, andE.Severin, “Forecasting Corporate Bankruptcy Using Accrual-Based Models,” Comput. Econ., pp. 1–37, 2017.
[13] R. G.Sloan, “Do Stock Prices Fully Refelct Information in Accruals and Cash Flows About Future Earnings?,” Account. Rev., vol. 71, no. 3, pp. 289–315, 1996.
[14] R. A. FISHER, “THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS,” 1954.
[15] X.Wu et al., Top 10 algorithms in data mining, vol. 14, no. 1. 2008.
[16] L.Breiman, J. H.Friedman, R. A.Olshen, andC. J.Stone, “Classification and Regression Trees,” Cole Publ. Monterey, vol. 535, p. 358, 1984.
[17] L.Breiman, “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, 1996.
[18] R. E.Schapire, “The Strength of Weak Learnability (Extended Abstract),” Mach. Learn., vol. 227, no. October, pp. 28–33, 1989.
[19] Y. F.Schapire andE. Robert, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” J. Comput. Syst. Sci., vol. 12, no. 0, 1997.
[20] 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.
[21] F.WILCOXON, “Individual comparisons of grouped data by ranking methods.,” J. Econ. Entomol., vol. 39, no. 6, p. 269, 1946.

指導教授

梁德容(Deron Liang)

審核日期

2018-7-27

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