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題名 台灣地雷股預警模型
Taiwan Financial Crisis Model
作者 郭昭廷
Kuo, Chao-Ting
貢獻者 謝明華<br>周冠男
郭昭廷
Kuo, Chao-Ting
關鍵詞 企業授信
財務危機預警模型
邏輯斯回歸
決策樹
支持向量機
機器學習
Python
Scikit-Learn
特徵工程
Corporate loan
Financial crisis model
Logistic regression
Decision tree
Support vector machine
Machine learning
Python
Scikit-Learn
Feature engineering
日期 2019
上傳時間 1-四月-2019 15:12:31 (UTC+8)
摘要 金融業對於國家經濟發展向來扮演重要角色，其中銀行業的企業授信的角色相當重要，若銀行擁有的是不良債權，將可能讓銀行面臨被倒帳的結果，進而可能對作為銀行一大部分的資金來源的一般或機構投資人產生負面影響，故若能有效建構財務危機預警模型，將能避免上述情形的發生。
關於建構財務危機預警模型之文獻已汗牛充棟，本研究之差別在於以邏輯斯回歸、決策樹、支持向量機等三種機器學習方法建構模型，並以不進行抽樣，且不對財務比率進行歸納之方式，並運用Python程式之套件Scikit-Learn實作模型，最後加入另一個由銀行業界專家問卷進行特徵工程所獲得的模型和未加入問卷的模型進行比較，希望讓本研究建構之模型於預測效能上有不錯的表現。
根據本研究實證發現，未加入問卷的模型的效能表現皆非常不錯，然而由銀行業界專家問卷進行特徵工程所獲得的模型的效能表現和前者相比下降居多，而兩者皆為了正確判別出財務危機資料而錯殺了不少的財務正常資料。
Financial Industry has been an issue on country’s economic growth. Among the financial industry, corporate loan has played an important role. If the loan the bank has is a non-performing loan, the bank could be faced with no payback, which could have a bad impact on personal or institutional investors. So, if we could build an effective financial crisis model, the condition mentioned above could be prevented from happening.
There has been many research related to building financial crisis model. However, there are some differences between this one and such. First of all, we used three machine learning approaches, which include Logistic Regression, Decision Tree, Support Vector Machine, to build the models. Second, we did the research without sampling. Third, we didn’t make an induction to get specific variables for modeling. Fourth, we did modeling with Python’s Scikit-Learn package. And last, we designed a questionaire to get viewpoints from the professionals in the banking industry for us to do feature engineering to create another models, and compare the models with the ones without questionaire. We expect such difference could have good influence on the models’ performance.
According to the result of empirical analysis, all of the models without questionaire have good performance. However, the performance of most of the models with questionaire have fallen when compared to the performance of the models without questionaire. And, no matter with or without questionaire, all of the models sacrificed crisis-free coporations to generate better performance on detecting corporations with crisis.
參考文獻 [1] 行政院主計總處國民所得統計及國內經濟情勢展望. Available from: https://www.stat.gov.tw/public/data/dgbas03/bs4/ninews/10711/newtotal10711.pdf.
[2] Beaver, W.H., Financial ratios as predictors of failure. Journal of accounting research, 1966: p. 71-111.
[3] Altman, E.I., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 1968. 23(4): p. 589-609.
[4] Ohlson, J.A., Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 1980: p. 109-131.
[5] Provost, F. and T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking. 2013: " O`Reilly Media, Inc.".
[6] Raschka, S., Python machine learning. 2015: Packt Publishing Ltd.
[7] Zmijewski, M.E., Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting research, 1984: p. 59-82.
[8] 台灣經濟新報上市(櫃)公司基本資料資料庫. Available from: http://tejdb.tej.com.tw/ReportListing_new/reportlisting.aspx?UserCheck=3230313930313330313433373530544348494E4553452A&Report=wind.
[9] Géron, A., Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. 2017: " O`Reilly Media, Inc.".
[10] 周志华, 机器学习. 2016: Qing hua da xue chu ban she.
[11] Scikit-Learn choosing the right estimator. Available from: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html.
[12] Scikit-Learn Logistic Regression. Available from: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html.
[13] Scikit-Learn Decision Tree Classifier. Available from: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.
[14] Scikit-Learn SVC. Available from: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.
[15] 台灣經濟新報財務資料庫科目說明. Available from: http://tejdb.tej.com.tw/ReportListing_new/reportlisting.aspx?UserCheck=3230313930313330313430333331544348494E4553452A&Report=wm3.
[16] TEJ信用風險觀測(TCRI)模組欄位說明. Available from: https://www.tej.com.tw/webtej/doc/crwatch1.htm.
描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
105363083
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105363083
資料類型 thesis
dc.contributor.advisor 謝明華<br>周冠男zh_TW
dc.contributor.author (作者) 郭昭廷zh_TW
dc.contributor.author (作者) Kuo, Chao-Tingen_US
dc.creator (作者) 郭昭廷zh_TW
dc.creator (作者) Kuo, Chao-Tingen_US
dc.date (日期) 2019en_US
dc.date.accessioned 1-四月-2019 15:12:31 (UTC+8)-
dc.date.available 1-四月-2019 15:12:31 (UTC+8)-
dc.date.issued (上傳時間) 1-四月-2019 15:12:31 (UTC+8)-
dc.identifier (其他 識別碼) G0105363083en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/122814-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所(MBA學位學程)zh_TW
dc.description (描述) 105363083zh_TW
dc.description.abstract (摘要) 金融業對於國家經濟發展向來扮演重要角色，其中銀行業的企業授信的角色相當重要，若銀行擁有的是不良債權，將可能讓銀行面臨被倒帳的結果，進而可能對作為銀行一大部分的資金來源的一般或機構投資人產生負面影響，故若能有效建構財務危機預警模型，將能避免上述情形的發生。
關於建構財務危機預警模型之文獻已汗牛充棟，本研究之差別在於以邏輯斯回歸、決策樹、支持向量機等三種機器學習方法建構模型，並以不進行抽樣，且不對財務比率進行歸納之方式，並運用Python程式之套件Scikit-Learn實作模型，最後加入另一個由銀行業界專家問卷進行特徵工程所獲得的模型和未加入問卷的模型進行比較，希望讓本研究建構之模型於預測效能上有不錯的表現。
根據本研究實證發現，未加入問卷的模型的效能表現皆非常不錯，然而由銀行業界專家問卷進行特徵工程所獲得的模型的效能表現和前者相比下降居多，而兩者皆為了正確判別出財務危機資料而錯殺了不少的財務正常資料。		zh_TW
dc.description.abstract (摘要) Financial Industry has been an issue on country’s economic growth. Among the financial industry, corporate loan has played an important role. If the loan the bank has is a non-performing loan, the bank could be faced with no payback, which could have a bad impact on personal or institutional investors. So, if we could build an effective financial crisis model, the condition mentioned above could be prevented from happening.
There has been many research related to building financial crisis model. However, there are some differences between this one and such. First of all, we used three machine learning approaches, which include Logistic Regression, Decision Tree, Support Vector Machine, to build the models. Second, we did the research without sampling. Third, we didn’t make an induction to get specific variables for modeling. Fourth, we did modeling with Python’s Scikit-Learn package. And last, we designed a questionaire to get viewpoints from the professionals in the banking industry for us to do feature engineering to create another models, and compare the models with the ones without questionaire. We expect such difference could have good influence on the models’ performance.
According to the result of empirical analysis, all of the models without questionaire have good performance. However, the performance of most of the models with questionaire have fallen when compared to the performance of the models without questionaire. And, no matter with or without questionaire, all of the models sacrificed crisis-free coporations to generate better performance on detecting corporations with crisis.		en_US
dc.description.tableofcontents 摘要 I
ABSTRACT II
目錄 IV
圖目錄 VI
表目錄 VIII
第1章 緒論 1
1.1. 研究動機與目的 1
1.2. 研究架構 2
第2章 文獻探討 3
第3章 研究方法 6
3.1. 財務危機定義 6
3.2. 研究對象與研究期間 9
3.3. 變數選取 13
3.4. 模型建構 15
3.4.1. 分類模型 15
3.4.2. 使用工具 21
3.4.3. 資料前處理 23
3.5. 專家問卷 25
第4章 實證結果 30
4.1. 模型效能評估 30
4.1.1. 混淆矩陣 30
4.1.2. ROC曲線 32
4.1.3. KS曲線 33
4.1.4. PR曲線 33
4.1.5. 評估準則 34
4.1.6. 效能評估及比較 35
4.2. 專家問卷模型效能評估 43
第5章 結論與建議 52
5.1. 研究結論 52
5.2. 未來建議 53
參考文獻 55
附錄 57
附件 1、用以建構模型之財務比率 57
附件 2、專家問卷內容 67
附件 3、變數選取運用之程式碼 69
附件 4、模型建構運用之程式碼 71		zh_TW
dc.format.extent 3299547 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105363083en_US
dc.subject (關鍵詞) 企業授信zh_TW
dc.subject (關鍵詞) 財務危機預警模型zh_TW
dc.subject (關鍵詞) 邏輯斯回歸zh_TW
dc.subject (關鍵詞) 決策樹zh_TW
dc.subject (關鍵詞) 支持向量機zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) Pythonzh_TW
dc.subject (關鍵詞) Scikit-Learnzh_TW
dc.subject (關鍵詞) 特徵工程zh_TW
dc.subject (關鍵詞) Corporate loanen_US
dc.subject (關鍵詞) Financial crisis modelen_US
dc.subject (關鍵詞) Logistic regressionen_US
dc.subject (關鍵詞) Decision treeen_US
dc.subject (關鍵詞) Support vector machineen_US
dc.subject (關鍵詞) Machine learningen_US
dc.subject (關鍵詞) Pythonen_US
dc.subject (關鍵詞) Scikit-Learnen_US
dc.subject (關鍵詞) Feature engineeringen_US
dc.title (題名) 台灣地雷股預警模型zh_TW
dc.title (題名) Taiwan Financial Crisis Modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] 行政院主計總處國民所得統計及國內經濟情勢展望. Available from: https://www.stat.gov.tw/public/data/dgbas03/bs4/ninews/10711/newtotal10711.pdf.
[2] Beaver, W.H., Financial ratios as predictors of failure. Journal of accounting research, 1966: p. 71-111.
[3] Altman, E.I., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 1968. 23(4): p. 589-609.
[4] Ohlson, J.A., Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 1980: p. 109-131.
[5] Provost, F. and T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking. 2013: " O`Reilly Media, Inc.".
[6] Raschka, S., Python machine learning. 2015: Packt Publishing Ltd.
[7] Zmijewski, M.E., Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting research, 1984: p. 59-82.
[8] 台灣經濟新報上市(櫃)公司基本資料資料庫. Available from: http://tejdb.tej.com.tw/ReportListing_new/reportlisting.aspx?UserCheck=3230313930313330313433373530544348494E4553452A&Report=wind.
[9] Géron, A., Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. 2017: " O`Reilly Media, Inc.".
[10] 周志华, 机器学习. 2016: Qing hua da xue chu ban she.
[11] Scikit-Learn choosing the right estimator. Available from: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html.
[12] Scikit-Learn Logistic Regression. Available from: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html.
[13] Scikit-Learn Decision Tree Classifier. Available from: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.
[14] Scikit-Learn SVC. Available from: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.
[15] 台灣經濟新報財務資料庫科目說明. Available from: http://tejdb.tej.com.tw/ReportListing_new/reportlisting.aspx?UserCheck=3230313930313330313430333331544348494E4553452A&Report=wm3.
[16] TEJ信用風險觀測(TCRI)模組欄位說明. Available from: https://www.tej.com.tw/webtej/doc/crwatch1.htm.		zh_TW
dc.identifier.doi (DOI) 10.6814/THE.NCCU.MBA.018.2019.F08en_US