IDEAS home Printed from https://ideas.repec.org/a/spr/digfin/v3y2021i3d10.1007_s42521-021-00036-4.html
   My bibliography  Save this article

Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009

Author

Listed:
  • Vikram Ojha

    (Standard & Poor’s (S&P Global Ratings))

  • JeongHoe Lee

    (Standard & Poor’s (S&P Global Ratings))

Abstract

The rapidly growing mortgage market corresponds with the growth of mortgage backed securities. Since the economic crisis in 2008–2009, financial institutions that deal with mortgages have been working to develop more accurate numerical models for Residential Mortgage Backed Securities (RMBS) to minimize credit risk. Within this context, there is an increasing use of big data and artificial intelligence techniques accordingly. This research focuses on the U.S. RMBS analysis using machine learning to predict the Probability of Default (PD). Primary analysis involves the loan origination and performance characteristics and economic characteristics like home performance index (HPI) to investigate default probability in terms of credit risk. In this research, various machine learning models such as Logistic Regression, Random Forest, Linear Discriminant Analysis, K-Nearest Neighbors (KNN), Multi-layer Neural Network (MNN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) are used. Ultimately, this research provides comprehensive understanding and comparison in applying various machine learning algorithms to the financial discipline of RMBS to develop predictive models for calculating mortgage credit risk using the Fannie Mae loan data that include around 1.5 million of mortgage loans originating from 2005 to 2009 in the United States.

Suggested Citation

  • Vikram Ojha & JeongHoe Lee, 2021. "Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009," Digital Finance, Springer, vol. 3(3), pages 249-271, December.
    • Handle: RePEc:spr:digfin:v:3:y:2021:i:3:d:10.1007_s42521-021-00036-4
    DOI: 10.1007/s42521-021-00036-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42521-021-00036-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42521-021-00036-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:agr:journl:v:4(621):y:2019:i:4(621):p:75-84 is not listed on IDEAS
    2. Justin Sirignano & Apaar Sadhwani & Kay Giesecke, 2016. "Deep Learning for Mortgage Risk," Papers 1607.02470, arXiv.org, revised Mar 2018.
    3. Bhattacharya, Arnab & Wilson, Simon P. & Soyer, Refik, 2019. "A Bayesian approach to modeling mortgage default and prepayment," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1112-1124.
    4. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    5. B Baesens & T Van Gestel & M Stepanova & D Van den Poel & J Vanthienen, 2005. "Neural network survival analysis for personal loan data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1089-1098, September.
    6. Selçuk BAYRACI & Orkun SUSUZ, 2019. "A Deep Neural Network (DNN) based classification model in application to loan default prediction," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 75-84, Winter.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Shunqin & Guo, Zhengfeng & Zhao, Xinlei, 2021. "Predicting mortgage early delinquency with machine learning methods," European Journal of Operational Research, Elsevier, vol. 290(1), pages 358-372.
    2. Luong, Thi Mai & Scheule, Harald, 2022. "Benchmarking forecast approaches for mortgage credit risk for forward periods," European Journal of Operational Research, Elsevier, vol. 299(2), pages 750-767.
    3. Sheikh Rabiul Islam & William Eberle & Sheikh K. Ghafoor & Sid C. Bundy & Douglas A. Talbert & Ambareen Siraj, 2019. "Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy," Papers 1911.09858, arXiv.org.
    4. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
    5. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.
    6. Donglin Wang & Don Hong & Qiang Wu, 2023. "Prediction of Loan Rate for Mortgage Data: Deep Learning Versus Robust Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1137-1150, March.
    7. R T Stewart, 2011. "A profit-based scoring system in consumer credit: making acquisition decisions for credit cards," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1719-1725, September.
    8. Adil EL Fakir & Richard Fairchild & Youssef Lamrani Alaoui & Dora Chan & Mohamed Tkiouat & Zaid Amer, 2024. "Kinship, gender and social links impact on micro group lending defaults," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2527-2542, April.
    9. Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.
    10. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    11. Feng Xu & Dazhong Wu & Jian Hua & Tih Koon Tan, 2022. "Preventive Maintenance for Mortgage Loans of Low-Income Borrowers," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 14(3), pages 1-66, February.
    12. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    13. Ting Sun & Miklos A. Vasarhelyi, 2018. "Predicting credit card delinquencies: An application of deep neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(4), pages 174-189, October.
    14. Shi, Yong & Qu, Yi & Chen, Zhensong & Mi, Yunlong & Wang, Yunong, 2024. "Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation," European Journal of Operational Research, Elsevier, vol. 315(2), pages 786-801.
    15. Oliver Blümke, 2020. "Estimating the probability of default for no‐default and low‐default portfolios," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(1), pages 89-107, January.
    16. Congjin Zhou & Guojing Wang & Yinghui Dong & Pin Wang, 2024. "The Valuation at Origination of Mortgages with Full Prepayment and Default Risks," Methodology and Computing in Applied Probability, Springer, vol. 26(2), pages 1-26, June.
    17. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    18. Stefania Albanesi & Domonkos F. Vamossy, 2024. "Credit Scores: Performance and Equity," NBER Working Papers 32917, National Bureau of Economic Research, Inc.
    19. Justin A. Sirignano & Gerry Tsoukalas & Kay Giesecke, 2016. "Large-Scale Loan Portfolio Selection," Operations Research, INFORMS, vol. 64(6), pages 1239-1255, December.
    20. Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

    More about this item

    Keywords

    Machine learning; Deep learning; Ensemble machine learning (Voting); Residential mortgage backed securities (RMBS); Probability of default (PD); Default coverage ratio and credit risk;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:digfin:v:3:y:2021:i:3:d:10.1007_s42521-021-00036-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.