IDEAS home Printed from https://ideas.repec.org/r/eee/ejores/v249y2016i2p427-439.html
   My bibliography  Save this item

An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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.
  2. Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
  3. 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.
  4. Chi Ming Chen & Geoffrey Kwok Fai Tso & Kaijian He, 2024. "Quantum Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financing," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 919-950, February.
  5. 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.
  6. Yosi Borochov & Boris A. Portnov, 2021. "Estimating Environmentally Adjusted Risks of Mortgage Arrears for Different Socioeconomic Groups of Borrowers," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 595-620.
  7. 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.
  8. 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.
  9. Xing, Jin & Chi, Guotai & Pan, Ancheng, 2024. "Instance-dependent misclassification cost-sensitive learning for default prediction," Research in International Business and Finance, Elsevier, vol. 69(C).
  10. Haskamp, Ulrich, 2017. "Improving the forecasts of European regional banks' profitability with machine learning algorithms," Ruhr Economic Papers 705, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  11. Choi, So Eun & Jang, Hyun Jin & Lee, Kyungsub & Zheng, Harry, 2021. "Optimal market-Making strategies under synchronised order arrivals with deep neural networks," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
  12. Masci, Chiara & Johnes, Geraint & Agasisti, Tommaso, 2018. "Student and school performance across countries: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1072-1085.
  13. 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.
  14. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
  15. Aneta Dzik-Walczak & Mateusz Heba, 2019. "A comparison of credit scoring techniques in Peer-to-Peer lending," Working Papers 2019-16, Faculty of Economic Sciences, University of Warsaw.
  16. Gupta, Mukul & Kumar, Pradeep, 2020. "Recommendation generation using personalized weight of meta-paths in heterogeneous information networks," European Journal of Operational Research, Elsevier, vol. 284(2), pages 660-674.
  17. 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.
  18. Justin Sirignano & Apaar Sadhwani & Kay Giesecke, 2016. "Deep Learning for Mortgage Risk," Papers 1607.02470, arXiv.org, revised Mar 2018.
  19. 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.
  20. 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.
  21. 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".
  22. 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.
  23. Jun†Tae Han & Jae†Seok Choi & Myeon†Jung Kim & Jina Jeong, 2018. "Developing a Risk Group Predictive Model for Korean Students Falling into Bad Debt," Asian Economic Journal, East Asian Economic Association, vol. 32(1), pages 3-14, March.
  24. 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.
  25. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
  26. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  27. Aneta Dzik-Walczak & Mateusz Heba, 2021. "An implementation of ensemble methods, logistic regression, and neural network for default prediction in Peer-to-Peer lending," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 39(1), pages 163-197.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.