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題名 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貢獻者 黃智聰
Huang, Jr-Tsung
陳苔珍
Chen, Tai-Chen關鍵詞 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 上傳時間 1-八月-2022 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.參考文獻 Aditya, P. D. (2015). "Will the Bubble Burst Be Revisited." https://ssrn.com/abstract=2575590Altman, E. I. (1968), "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy." The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933An, 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/hhaa004Baklouti, 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.xCalder, 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/JAERD2014.0622Bierman, 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/10.1080/07360932.2015.1056205Ionescu, 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.2015.12.003Kurysheva, 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.2.2.35331.02081Levin, 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/10.1080/02642060903437535Malik, 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.2009.123Rose, 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/Z389P2W65PSheikh, 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 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 資料類型 thesis dc.contributor.advisor 黃智聰 zh_TW dc.contributor.advisor Huang, Jr-Tsung en_US dc.contributor.author (作者) 陳苔珍 zh_TW dc.contributor.author (作者) Chen, Tai-Chen en_US dc.creator (作者) 陳苔珍 zh_TW dc.creator (作者) Chen, Tai-Chen en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-八月-2022 18:46:16 (UTC+8) - dc.date.available 1-八月-2022 18:46:16 (UTC+8) - dc.date.issued (上傳時間) 1-八月-2022 18:46:16 (UTC+8) - dc.identifier (其他 識別碼) G0105926005 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141333 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 亞太研究英語碩士學位學程(IMAS) zh_TW dc.description (描述) 105926005 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents CHAPTER 1. INTRODUCTION 11.1 BACKGROUND AND RESEARCH MOTIVATION 11.2 RESEARCH PURPOSE 41.3 RESEARCH STRUCTURE 41.4 RESEARCH LIMITATIONS 5CHAPTER 2. LITERATURE REVIEW 72.1 BASIC CONCEPTS AND PRINCIPLES OF CREDIT GRANTING 72.2 CREDIT RISK ASSESSMENT METHODS 92.3 THE ENVIRONMENT OF THE AUTO LOAN MARKET 102.4 RELATED LITERATURES TO AUTO LOAN APPROVAL 12CHAPTER 3. METHODOLOGY 203.1 EMPIRICAL MODEL 203.2 DATA AND VARIABLES 22CHAPTER 4. EMPIRICAL ANALYSIS 314.1. MULTICOLLINEARITY TEST 314.2. ESTIMATION RESULTS FOR INDIVIDUALS 354.3. ESTIMATION RESULTS FOR ENTERPRISES 40CHAPTER 5. CONCLUDING REMARKS AND SUGGESTIONS 435.1 CONCLUDING REMARKS 435.2 SUGGESTIONS 45REFERENCES 46 zh_TW dc.format.extent 791417 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105926005 en_US dc.subject (關鍵詞) 汽車貸款核准 zh_TW dc.subject (關鍵詞) 信用風險 zh_TW dc.subject (關鍵詞) 邏輯迴歸模型 zh_TW dc.subject (關鍵詞) Auto loan approval en_US dc.subject (關鍵詞) Credit risk en_US dc.subject (關鍵詞) Logistic regression en_US dc.title (題名) 影響汽車貸款核准的決定因素 zh_TW dc.title (題名) Determinants of Auto Loan Approval from Finance House- Evidence in Taiwan en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Aditya, P. D. (2015). "Will the Bubble Burst Be Revisited." https://ssrn.com/abstract=2575590Altman, E. I. (1968), "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy." The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933An, 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/hhaa004Baklouti, 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.xCalder, 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/JAERD2014.0622Bierman, 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/10.1080/07360932.2015.1056205Ionescu, 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.2015.12.003Kurysheva, 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.2.2.35331.02081Levin, 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/10.1080/02642060903437535Malik, 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.2009.123Rose, 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/Z389P2W65PSheikh, 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 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200951 en_US
