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    题名: 影響汽車貸款核准的決定因素
    Determinants of Auto Loan Approval from Finance House- Evidence in Taiwan
    作者: 陳苔珍
    Chen, Tai-Chen
    贡献者: 黃智聰
    Huang, Jr-Tsung
    陳苔珍
    Chen, Tai-Chen
    关键词: 汽車貸款核准
    信用風險
    邏輯迴歸模型
    Auto loan approval
    Credit risk
    Logistic regression
    日期: 2022
    上传时间: 2022-08-01 18:46:16 (UTC+8)
    摘要: 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.
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    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)

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    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).

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    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
    描述: 碩士
    國立政治大學
    亞太研究英語碩士學位學程(IMAS)
    105926005
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0105926005
    数据类型: thesis
    DOI: 10.6814/NCCU202200951
    显示于类别:[亞太研究英語博/碩士學位學程(IDAS/IMAS)] 學位論文

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