Ensemble Predictions of Recovery Rates

João Bastos

Journal of Financial Services Research, 2014, vol. 46, issue 2, 177-193

Abstract: In many domains, the combined opinion of a committee of experts provides better decisions than the judgment of a single expert. This paper shows how to implement a successful ensemble strategy for predicting recovery rates on defaulted debts. Using data from Moody’s Ultimate Recovery Database, it is shown that committees of models derived from the same regression method present better forecasts of recovery rates than a single model. More accurate predictions are observed whether we forecast bond or loan recoveries, and across the entire range of actual recovery values. Copyright Springer Science+Business Media New York 2014

Keywords: Recovery rate; Loss given default; Forecasting; Ensemble learning; Credit risk; G17; G21 (search for similar items in EconPapers)
Date: 2014
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Working Paper: Ensemble predictions of recovery rates (2013)
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DOI: 10.1007/s10693-013-0165-3

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