博碩士論文 104522114 詳細資訊




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姓名 張立昕(Li-Shin Chang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 應用集成方法之公司治理指標在財務危機預測:以美國上市公司為例
(Corporate government indicators apply in financial distress problem based on ensemble method: taking US-listed Company for example)
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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]   至系統瀏覽論文 (2022-6-30以後開放)
摘要(中) 財務危機預測一直以來都是一個重要的主題,吸引了世界各地的投資者以及學者的關注,從早期的統計學方法到現在的機器學習演算法,如何利用財務指標與其它有用的指標參數建出有效且準確的模型,更是一個被廣為討論的問題。我們過去曾使用台灣資料集証明財務指標加上公司治理指標當特徵,可以得到比較好的預測結果;但是在美國資料集底下,卻沒有這樣的趨勢。因為在美國資料集我們的公司治理指標搜集不多,所以本研究提出一個使用財務指標加上公司治理指標的stacking ensemble演算法,並且証實在特定的cost ratios底下,使用本實驗提出的演算法是更好的。
摘要(英) Financial distress prediction (FDP) is an important topic. There are many investors and researchers focus on this question, From statistical methods earlier to machine learning algorithm today, how to use financial ratios and other potential feature to build a better model is a wildly studied question. We have proved that build model by corporate governance indicator and financial ratios can improve performance in Taiwan dataset, but not in USA data set. For the reason we cannot collect as much corporate governance indicators as Taiwan dataset, we proposed a stacking ensemble algorithm by using financial ratios and corporate governance indicators, and prove that in specific cost ratios, this algorithm is a better way in FDP.
關鍵字(中) ★ 公司治理指標
★ 財務危機預測
★ 集成方法
★ 機器學習
關鍵字(英) ★ Financial distress prediction (FDP)
★ corporate governance indicator
★ financial ratios
★ machine learning
★ ensemble learning
論文目次 中文摘要 I
Abstract II
圖目錄 VI
表目錄 VIII
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 研究貢獻 5
1.5 論文架構 6
二、 文獻探討 7
2-1 FDP相關文獻探討 7
2-1-1 Altman’s Z-score 7
2-1-2 FRs使用機器學習演算法預測 8
2-2 CGIs相關文獻探討 8
2-3 研究假說 11
2-4 分類器介紹 12
2-4-1 支持向量機 12
2-5 集成方法 16
2-5-1 stacking ensemble 16
2-6 特徵篩選方法 18
2-6-1 t-test 19
2-6-2 Stepwise Discriminant Analysis (SDA) 19
2-6-3 Stepwise Logistic Regression (SLR) 20
三、 Proposed method 21
3-1 proposed stacking ensemble model in FRs + CGIs 22
3-2 10-fold 實驗架構 24
四、實驗設計 29
4-1 實驗資料集 29
4-1-1 資料來源 29
4-1-2 CGI 手動收集流程 30
4-2 資料前處理 33
4-3 評估方式 34
4-3-1 DET curve(Detection error tradeoff curve) 34
4-3-2 misclassification cost 、 cost ratios和Wilcoxon test 36
4-4 分類器參數設定 38
五、實驗結果 39
5-1 Hypothesis 1 實驗結果分析 39
5-2 Hypothesis II 實驗結果分析 43
5-3 discussion 51
5-4 實驗結果總結 53
六、結論及未來展望 54
6-1 結論 54
6-2 未來展望 55
參考文獻 57
附錄一 - 變數表(FRs + CGIs) 61
附錄二 - 公司配對表 65
附錄三 - SDA特徵篩選總表 69
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指導教授 梁德容(Deron Liang) 審核日期 2017-10-18
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