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Predictive multiplicity, procedural multiplicity, and heterogeneous machine learning ensembles in recovery rate forecasting

Martin Hibbeln et al.

Journal of Financial Stability2026https://doi.org/10.1016/j.jfs.2026.101510article
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https://doi.org/https://doi.org/10.1016/j.jfs.2026.101510

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@article{martin2026,
  title        = {{Predictive multiplicity, procedural multiplicity, and heterogeneous machine learning ensembles in recovery rate forecasting}},
  author       = {Martin Hibbeln et al.},
  journal      = {Journal of Financial Stability},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jfs.2026.101510},
}

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Predictive multiplicity, procedural multiplicity, and heterogeneous machine learning ensembles in recovery rate forecasting

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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