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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研究生：	呂澄宇 CHENG YU LU
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
指導教授：	梁德容 Deron Liang
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
論文出版年：	2018
畢業學年度：	106
語文別：	中文
論文頁數：	72
中文關鍵詞：	應計項目、財務危機預測、時間序列資料、特徵降維
外文關鍵詞：	accruals, time series data, dimension reduction, FDP
相關次數：	點閱：16 下載：0
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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。

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.

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

                                

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