Meta-Learning Approaches for Recovery Rate Prediction
Paolo Gambetti (),
Francesco Roccazzella and
Frédéric Vrins ()
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Paolo Gambetti: CRIF S.p.A., via M. Fantin, 1-3, 40131 Bologna, Italy
Frédéric Vrins: LFIN/LIDAM, UCLouvain, Voie du Roman Pays 34, B-1348 Louvain-la-Neuve, Belgium
Risks, 2022, vol. 10, issue 6, 1-29
While previous academic research highlights the potential of machine learning and big data for predicting corporate bond recovery rates, the operations management challenge is to identify the relevant predictive variables and the appropriate model. In this paper, we use meta-learning to combine the predictions from 20 candidates of linear, nonlinear and rule-based algorithms, and we exploit a data set of predictors including security-specific factors, macro-financial indicators and measures of economic uncertainty. We find that the most promising approach consists of model combinations trained on security-specific characteristics and a limited number of well-identified, theoretically sound recovery rate determinants, including uncertainty measures. Our research provides useful indications for practitioners and regulators targeting more reliable risk measures in designing micro- and macro-prudential policies.
Keywords: finance; forecasting; credit risk; machine learning; recovery rate (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
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Working Paper: Meta-learning approaches for recovery rate prediction (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:10:y:2022:i:6:p:124-:d:837184
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