Deep Learning for Mortgage Risk*

By Justin A. Sirignano, Apaar Sadhwani, Kay Giesecke. 2016

197 citations recorded by the source corpus.

We examine the behavior of mortgage borrowers over several economic cycles using an unprecedented dataset of origination and monthly performance records for over 120 million mortgages originated across the United States between 1995 and 2014. Our deep learning model of multi-period mortgage delinquency, foreclosure, and prepayment risk uncovers the highly nonlinear influence on borrower behavior of an exceptionally broad range of loan-specific and macroeconomic variables down to the zip-code level. In particular, most variables strongly interact. Prepayments involve the greatest nonlinear effects among all events. We demonstrate the significant implications of the nonlinearities for risk management, investment management, and mortgage-backed securities.

Subfields: Accounting, Economics and Econometrics, Finance, Optimization & Training

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, methodology): “[103] used the mortgage application data of 20 years for identifying the mortgage risk using various parameters.”
Deep Learning for Predicting Asset Returns (2018, background): “…economics and ﬁnance, see Gal-lant and White (1988a,b); Hornik et al. (1989); Gallant and White (1992); Kuan and White (1994); Hutchinson et al. (1994); Lo (1994); Qi (1999); Jones (2006); Sirignano et al. (2016) and Heaton et al. (2017), deep learning is new to characteristic-based asset pricing.”
Universal features of price formation in financial markets: perspectives from deep learning (2018, background): “This is one of the reasons why, except in a few instances (Sirignano et al. 2016, Kolanovic and Krishnamachari 2017, Dixon 2018a, 2018b, Sirignano 2019), largescale machine learning methods such as Deep Learning (Goodfellow et al. 2017) have not yet been deployed for quantitative modeling in…”
Mean Field Analysis of Neural Networks: A Law of Large Numbers (2018, methodology): “, E-mail: kspiliop@math.bu.edu zK.S. was partially supported by the National Science Foundation (DMS 1550918) 1 arXiv:1805.01053v4 [math.PR] 11 Nov 2019 used to model loan default and prepayment risk [43] and to model high frequency nancial data [44]. Neural networks have also been used to solve hig”
Deep learning for limit order books (2016, unknown): “Sirignano et al. (2016) model mortgage risk with deep neural networks.”
Predictably Unequal? The Effects of Machine Learning on Credit Markets (2020, unknown): “4Academic work applying machine learning to credit risk modeling includes Khandani et al. (2010) and Sirignano et al. (2017). across groups.”
Empirical Asset Pricing via Machine Learning (2018, background): “Sirignano et al. (2016) estimate a deep 5In addition to least squares regression, the literature often sorts assets into portfolios on the basis of characteristics and studies portfolio averages—a form of nonparametric regression. neural network for mortgage prepayment, delinquency, and foreclosure.”
Mean Field Analysis of Deep Neural Networks (2019, background): “[18], and [42]), and text recognition (see [44] and [39]). Neural networks also ﬁnd increasing more applications in engineering, robotics, medicine, and ﬁnance (see [19], [20], [40], [15], [29], [1], [33], [34], [35], and [36]). In this paper we characterize multi-layer neural networks (i.e., “deep ”
Enhancing the momentum strategy through deep regression (2019, methodology): “Deep learning has also been utilized for other domains in ﬁnance such as for high frequency trading applications in Dixon et al. (2018) and Sirignano and Cont (2018), for options pricing in Berner et al. (2018) and for mortgage risk assessments in Sirignano et al. (2018).”
Explainable Deep Learning: A Field Guide for the Uninitiated (2020, background): “, 2014), in the military of many countries (Lundén & Koivunen, 2016), and at insurance agencies to evaluate coverage suitability and costs for clients (Dong et al., 2016; Sirignano et al., 2016).”
Bond Risk Premia with Machine Learning∗ (2018, unknown): “Jones (2006), Heaton et al. (2017) and Sirignano et al. (2018) constitute recent applications of deep learning hierarchical models to derivatives prices, smart indexing in finance and mortgage risk, respectively.”