政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141333
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113648/144635 (79%)
Visitors : 51578198      Online Users : 857
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141333


    Title: 影響汽車貸款核准的決定因素
    Determinants of Auto Loan Approval from Finance House- Evidence in Taiwan
    Authors: 陳苔珍
    Chen, Tai-Chen
    Contributors: 黃智聰
    Huang, Jr-Tsung
    陳苔珍
    Chen, Tai-Chen
    Keywords: 汽車貸款核准
    信用風險
    邏輯迴歸模型
    Auto loan approval
    Credit risk
    Logistic regression
    Date: 2022
    Issue Date: 2022-08-01 18:46:16 (UTC+8)
    Abstract: 隨著銀行將獲利較高的消費性貸款視為主要的推動業務以來,近年消費者貸款的餘額不斷的墊高,其中汽車貸款餘額也隨之上升。而承做汽車貸款的各家融資租賃公司更是屢屢推出專案放寬可貸成數,試圖在可承擔的風險中掌握最大的業績量,可見目前台灣汽車貸款市場呈現高度競爭的狀況。然而獲利較高意味風險較大,因此如何有效提升放款業務量又能適當把關授信品質是各家研究的重點。
    本研究調查了影響金融機構批准個人和企業申請人汽車貸款的決定性因素,並檢驗了對汽車貸款核准具有解釋力的變數。本研究中提供關於汽車貸款申請人特徵、貸款合約內容和抵押品特徵的數據,並加入了新冠疫情以來對批准汽車貸款所產生的影響,透過所使用的 25個自變數反映了在授信決策期間可供判斷核准貸款的有效資訊,整理出影響汽車貸款核准的變數。本研究採用Logit模型對各解釋變數對汽車貸款獲得核准的機率的影響進行實證分析。
    The auto loan market in Taiwan is currently highly competitive. Since banks were driven by the benefit, they shifted to consumer loans as the main market. The balance of consumer loans has continued to boost, and the balance of auto loans has also increased. Financial leasing companies that undertake auto loans have launched different projects to loose the loan-to-value ratio and attempt to capture the maximum amount of performance. However, higher profits mean higher risks. Therefore, how to effectively increase the balance and properly check the credit quality is the focus of each financial leasing company.
    This study investigates how finance institutions approve the auto loan for both individual and enterprise applicants and examines variables that explain auto loan approval. This study adopts the Logit model to conduct the empirical analysis on the effect of each explanatory variable on the probability of approving the auto loan. We provide evidence of auto loan applicants’ characteristics, loan contract contexts, collateral characteristics, and the impact on auto loan approvals since the COVID-19 pandemic. The 25 independent variables used in this study reveal available information during the credit granting decision.
    Reference: Aditya, P. D. (2015). "Will the Bubble Burst Be Revisited." https://ssrn.com/abstract=2575590

    Altman, E. I. (1968), "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy." The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933

    An, X., L. Cordell, and S. Tang (2020), "Extended Loan Terms and Auto Loan Default Risk" [Working Papers]. https://doi.org/10.21799/frbp.wp.2020.18 (Federal Reserve Bank of Philadelphia)

    Argyle, B. S., T. D. Nadauld, and C. J. Palmer (2020), "Monthly Payment Targeting and the Demand for Maturity." Review of Financial Studies, 33(11), 5416-5462. https://doi.org/10.1093/rfs/hhaa004

    Baklouti, I. (2013), "Determinants of Microcredit Repayment: The Case of Tunisian Microfinance Bank." African Development Review-Revue Africaine De Developpement, 25(3), 370-382. https://doi.org/10.1111/j.1467-8268.2013.12035.x

    Calder, L. (1999), Financing the American Dream: A Cultural History of Consumer Credit. Princeton University Press.

    Firafis, H. (2015), "Determinants of Loan Repayment Performance: Case Study of Harari Microfinance Institutions." Journal of Agricultural Extension and Rural Development. https://doi.org/10.5897/JAERD2014.0622

    Bierman, Harold Jr. and Warren H. Hausman (1970), "The Credit Granting Decision." Management Science, 16(8), B519-B532.

    Heitfield, E. and T. Sabarwal (2004), "What Drives Default and Prepayment on Subprime Auto Loans?" Journal of Real Estate Finance and Economics, 29(4), 457-477. https://doi.org/10.1023/B:REAL.0000044023.02636.e6.

    Hembruff, J. and S. Soederberg (2015), "Debtfarism and the Violence of Financial Inclusion: The Case of the Payday Lending Industry." Forum for Social Economics, 48(1), 49-68. https://doi.org/10.1080/07360932.2015.1056205

    Ionescu, F. and N. Simpson (2016), "Default Risk and Private Student Loans: Implications for Higher Education Policies." Journal of Economic Dynamics & Control, 64, 119-147. https://doi.org/10.1016/j.jedc.2015.12.003

    Kurysheva, A. and A. Vernikov (2021), "Veblen Was Right: Conspicuous Consumption and Car Loans in Russia." Available at SSRN: https://ssrn.com/abstract=3857764 or http://dx.doi.org/10.2139/ssrn.3857764. https://doi.org/10.13140/RG.2.2.35331.02081

    Levin, A., C. F. Lin, and C. S. J. Chu (2002), “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties.” Journal of Econometrics, 108, 1-24.

    Lin, T. T., C. C. Lee, and C. H. Chen (2011), "Impacts of the Borrower`s Attributes, Loan Contract Contents, and Collateral Characteristics on Mortgage Loan Default." Service Industries Journal, 31(9), 1385-1404, Article Pii 928028323. https://doi.org/10.1080/02642060903437535

    Malik, M. and L. C. Thomas (2010), "Modelling credit risk of portfolio of consumer loans." Journal of the Operational Research Society, 61(3), 411-420. https://doi.org/10.1057/jors.2009.123

    Rose, P. S. and S. C. Hudgins (2013), Bank Management & Financial Services 9th ed. New York, NY: McGraw-Hill.

    Schmidt, A. (2019), "Pump the Brakes: What Financial Regulators Should Consider in Trying to Prevent a Subprime Auto Loan Bubble." California Law Review, 107(4), 1345. https://doi.org/10.15779/Z389P2W65P

    Sheikh, M. A., A. K. Goel, and T. Kumar (2020), "An Approach for Prediction of Loan Approval using Machine Learning Algorithm." 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC).

    Shu, Yang and Qiuyi Yang (2015), "Research on Auto Loan Default Prediction Based on Large Sample Data Model." Management Review, 29(9), 59-71.

    Zhu, J., J. Janowiak, L. Ji, K. Karamon, and D. McManus (2015), "The Effect of Mortgage Payment Reduction on Default: Evidence from the Home Affordable Refinance Program." Real Estate Economics, 43(4), 1035-1054. https://doi.org/10.1111/1540-6229.12104
    Description: 碩士
    國立政治大學
    亞太研究英語碩士學位學程(IMAS)
    105926005
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105926005
    Data Type: thesis
    DOI: 10.6814/NCCU202200951
    Appears in Collections:[International Program in Asia-Pacific Studies\n(IMAS/IDAS)] 學位論文

    Files in This Item:

    File Description SizeFormat
    600501.pdf772KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback