Deep Learning for Disentangling Liquidity-Constrained and Strategic Default

58 Pages Posted: 28 Dec 2020

See all articles by Arka Bandyopadhyay

Arka Bandyopadhyay

City University of NY, Baruch College, Zicklin School of Business

Yildiray Yildirim

Zicklin School of Business, Baruch College - The City University of New York

Date Written: December 26, 2020

Abstract

We disentangle liquidity-constrained default and the incentives for strategic default using Deep Neural Network (DNN) methodology on a proprietary Trepp data set of commercial mortgages. Our results are robust (insensitive) to severe Financial Crisis (2008) and plausible economic catastrophe ensuing from COVID-19 pandemic (2020-2021). We demonstrate an identification strategy to retrieve the motive of default from observationally equivalent delinquency classes by bivariate analysis of default rate on Net operating income (NOI) and Loan-to-Value (LTV). NOI, appraisal reduction amount, prepayment penalty clause, balloon payment amongst others co-determine the delinquency class in highly nonlinear ways compared to more statistically significant variables such as LTV. Prediction accuracy for defaulted loans is higher when DNN is compared with other models, by increasing flexibility and relaxing the specification structure. These findings have significant implications for investors, rating agencies and policymakers.

Keywords: Strategic default, CMBS, Machine learning, Stress Test 2008, COVID-19

Suggested Citation

Bandyopadhyay, Arka Prava and Yildirim, Yildiray, Deep Learning for Disentangling Liquidity-Constrained and Strategic Default (December 26, 2020). Available at SSRN: https://ssrn.com/abstract=3755672 or http://dx.doi.org/10.2139/ssrn.3755672

Arka Prava Bandyopadhyay (Contact Author)

City University of NY, Baruch College, Zicklin School of Business ( email )

United States

Yildiray Yildirim

Zicklin School of Business, Baruch College - The City University of New York ( email )

55 Lexington Ave., Box B13-260
New York, NY 10010
United States

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