Intertemporal Defaulted Bond Recoveries Prediction Via Machine Learning

Posted: 5 Dec 2018

See all articles by Abdolreza Nazemi

Abdolreza Nazemi

Karlsruhe Institute of Technology

Konstantin Heidenreich

Karlsruhe Institute of Technology

Frank J. Fabozzi

EDHEC Business School

Date Written: November 6, 2018

Abstract

The recovery rate on defaulted corporate bonds has a time-varying distribution. We propose machine learning approaches for intertemporal analysis of U.S. corporate bonds' recovery rates with a large number of predictors. The most informative macroeconomic variables are selected from a broad range of macroeconomic variables using econometrics and machine learning methods. In addition to considering a large set of macroeconomic variables, bond characteristics, and industry characteristics, we study the relation between the recovery rates and news-based measures. We find that machine learning techniques significantly outperform traditional approaches not only out-of-sample as documented in the literature but also out-of-time. Examining the permutation importance of each group of explanatory variables, we find that bond characteristics, seniority dummy variables, stock market indicators, international competitiveness, and news are the most informative groups of variables for recovery rate prediction.

Keywords: credit risk, recovery rates, machine learning, news-based analysis, high-dimensional

JEL Classification: G17, G21, G28

Suggested Citation

Nazemi, Abdolreza and Heidenreich, Konstantin and Fabozzi, Frank J., Intertemporal Defaulted Bond Recoveries Prediction Via Machine Learning (November 6, 2018). Available at SSRN: https://ssrn.com/abstract=3279537

Abdolreza Nazemi (Contact Author)

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

Konstantin Heidenreich

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

Frank J. Fabozzi

EDHEC Business School ( email )

France
215 598-8924 (Phone)

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