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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
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    References listed on IDEAS

    as
    1. 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.
    2. repec:agr:journl:v:4(621):y:2019:i:4(621):p:75-84 is not listed on IDEAS
    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. 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.
    5. 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.
    6. Justin Sirignano & Apaar Sadhwani & Kay Giesecke, 2016. "Deep Learning for Mortgage Risk," Papers 1607.02470, arXiv.org, revised Mar 2018.
    Full references (including those not matched with items on IDEAS)

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    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

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