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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/130520
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/130520


    Title: 羅吉斯迴歸在分期付款違約之應用
    Application of Default of Installments in Logistic Regression
    Authors: 吳柔瑾
    Wu, Jou-Chin
    Contributors: 鄭宇庭
    吳柔瑾
    Wu, Jou-Chin
    Keywords: 分期付款
    違約機率模型
    羅吉斯迴歸
    Default of installments
    Probability of default model
    Logistic regression
    Date: 2020
    Issue Date: 2020-07-01 13:36:40 (UTC+8)
    Abstract: 分期付款已成為現代消費者常有的一種消費方式,無卡分期,亦即不使用信用卡即可享有消費分期,也為當今趨勢。然而,為了有效掌控承辦此項業務的風險,提供分期付款的公司必須嚴格審核新進案件,以降低違約帶來的損失。
    本研究之對象為某借貸公司,由於借貸公司非銀行,無法依法取得聯徵中心的信用評分,因此本研究以過去顧客分期資料建立羅吉斯迴歸模型。 由於違約數與非違約數並不相同,故以 1:1精細抽樣使資料平衡後,分別以向前選取法向後選取法及逐步選取法建立模型,得向前選取法及逐步選取法較適合此資料。
    模型中顯著的自變數有年齡、教育程度、居住狀況、婚姻狀況、產品別、適用群組、正常繳款次數、延滯繳款天數介於 30至 60天、分期期數、辦理分期金額、年資、罰單總筆數等等。
    期待研究結果能為借貸公司帶來更有效率的審核方式,並提供消費者更多元且自由的消費方式。
    Installment payment has become a common way of consumption for modern consumers. Installment without cards, that is, you can enjoy installment of consumption without using credit cards, is also the trend nowadays. However, in order to effectively control the risks of undertaking this business, companies that provide installments must strictly review new cases to reduce the losses caused by default.
    The research object is a lending company. Because the lending company is not a bank, it cannot legally obtain the credit score from the Joint Credit Information Center. Therefore, this study uses the past customer installment data to establish a Logistic regression model. Since the number of defaults and non defaults are not the same, I oversample the data to be balanced, the model is established by forward, backward and stepwise selection methods respectively. The forward selection method and the stepwise selection method are more suitable.
    The significant independent variables in the model are age, education level, living status, marriage status, product type, applicable group, normal payment times, delayed payment days between 30 and 60 days, period of installments, the amount of installments, working experience, and number of fines, etc.
    It is expected that the research results will bring more efficient audit method to lending companies and provide consumers with multiple and free consumption model.
    Reference: 一、中文文獻
    1. 吳毓芳,2016,「應用改良式邏輯斯迴歸於信用評分之研究」,國立臺北科技大學資訊與財金管理系碩士班論文。
    2. 李岑志,2017,「 多重插補法在線上使用者評分之應用 」,國立政治大學統計學研究所論文。
    3. 李沃牆,2010 臺灣銀行季刊第六十一卷第一期 ,頁 177。
    4. 李沃牆、朱竣平,2008,「 信用評等、公司違約與財務危機預測之探討 」 ,真理財經學報,第 18期 ,頁 33-70。
    5. 李昂軒,2009,「 中小企業信用評等暨違約風險模型之評估 」, 國立政治大學統計學研究所論文。
    6. 李婷婷,2019,「小額消費信貸信用評分模型的建置 以台灣某銀行為例」,國立清華大學財務金融碩士在職專班論文。
    7. 周致棋,2017,「消費金融的分期付款商業模式之研究」,國立臺灣大學商學研究所論文。
    8. 周嵐葶,2008,「租賃業信用評等模型之研究 以重車分期付款為例」,銘傳大學資訊管理學系碩士在職專班論文。
    9. 張大成,2002,「 違約機率與信用評分模型 」 ,台灣金融財務期刊 第四輯第一期,頁 19-37。
    10. 梁德馨,2006,「 消費者信用貸款違約風險評估模型之研究 以 CART分類與迴歸樹建模 」,中山管理評論 。
    11. 程燕翼,2009,「信用卡違約模型建構之個案研究 依循新巴爾賽資本協定內部評等法之規範」,國立臺灣科技大學財務金融研究所論文。
    12. 溫宜凡,2004,「 信用評分模型的驗證 」,東吳大學國際貿易學系論文。
    13. 詹訓承,2012,「 違約模型的再探討及在台灣上市櫃公司之應用 」,國立中興大學財務金融系所論文。
    14. 劉長寬,2003,「應用Logit模型於消費者擔保貸款違約行為之實證研究」,模型於消費者擔保貸款違約行為之實證研究」,朝陽科技大學財務金融系碩士班論文。
    15. 戴堅,2004,「個人消費性信用貸款授信評量模式之研究」,國立中正大學國際經濟研究所論文。
    16. 謝有隆,2006,,「信用評分模型的建置與驗證信用評分模型的建置與驗證」,國立政治大學經濟研究所論文。

    二、英文文獻
    1. Hull, J., 2003, Option, Futures, and Other Derivatives, Fifth edition, Prentice-Hall Press.
    2. Kubat, M., 2015, An Introduction to Machine Learning,pp 43-48.
    3. Kutner, M. H., C. J. Nachtsheim & J. Neter, 2004, Applied Linear Regression Models, pp 555-577.
    4. Mason, S. J. & N. E. Graham, 2002, Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Q.J.R. Meteorol. Soc.
    5. Zou, K. H., A. J. O`Malley & L. Mauri, 2007, Receiver--operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation, 6.
    Description: 碩士
    國立政治大學
    統計學系
    107354005
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107354005
    Data Type: thesis
    DOI: 10.6814/NCCU202000573
    Appears in Collections:[統計學系] 學位論文

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