arbiter
JournalsPricingAccount

Machine learning and a Hamilton–Jacobi–Bellman equation for optimal decumulation: a comparison study

Ming-Fu Chen et al.

Journal of Computational Finance2025https://doi.org/10.21314/jcf.2025.006article
AJG 1ABDC C
Weight
0.37

What the paper says

No abstract available.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.21314/jcf.2025.006

Or copy a formatted citation

@article{ming-fu2025,
  title        = {{Machine learning and a Hamilton–Jacobi–Bellman equation for optimal decumulation: a comparison study}},
  author       = {Ming-Fu Chen et al.},
  journal      = {Journal of Computational Finance},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.21314/jcf.2025.006},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Machine learning and a Hamilton–Jacobi–Bellman equation for optimal decumulation: a comparison study

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.