Intertemporal defaulted bond recoveries prediction via machine learning
| Autor: | Abdolreza Nazemi, Frank J. Fabozzi, Friedrich Baumann |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Information Systems and Management
General Computer Science Computer science media_common.quotation_subject 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Machine learning computer.software_genre Recession Industrial and Manufacturing Engineering Recovery rate 0502 economics and business Association (psychology) Risk management media_common 050210 logistics & transportation 021103 operations research business.industry Bond 05 social sciences Ranking Modeling and Simulation Expectation propagation Default Artificial intelligence business computer |
| Zdroj: | European Journal of Operational Research. 297:1162-1177 |
| ISSN: | 0377-2217 |
| Popis: | The recovery rate on defaulted corporate bonds has a time-varying distribution, a topic that has received limited attention in the literature. We apply machine learning approaches for intertemporal analysis of U.S. corporate bonds’ recovery rates. We show that machine learning techniques significantly outperform traditional approaches not only out-of-sample as documented in the literature but also in various out-of-time prediction setups. The newly applied sparse power expectation propagation approach provides the most compelling out-of-time prediction results. Motivated by the association of systematic factors with the time-varying characteristic of recovery rates, we study the effect of text-based news measures to account for bond investors’ expectations about the future which translate into market-based recovery rates. Especially during recessions, government-related news are associated with higher recovery rates. Although machine learning is a data-driven approach rather than considering economic intuition for ranking a group of predictors, the most informative groups of predictors for recovery rate prediction are nevertheless economically meaningful. |
| Databáze: | OpenAIRE |
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