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Application of machine learning with asymptotic expansion to unconstrained optimal portfolio

Makoto Naito & Kohta Takehara

International Journal of Financial Engineering2025https://doi.org/10.1142/s2424786325500100article
ABDC C
Weight
0.41

What the paper says

This paper proposes a numerical method for solving unconstrained optimal portfolio problems. This method combines an asymptotic expansion method applied to the optimal portfolio problem in complete markets, a technique of reformulating the optimal portfolio problem into a corresponding backward stochastic differential equation (BSDE), and a method for BSDEs using machine learning. Numerical examples show that this method may give a better estimate for the optimal portfolio compared to existing methods.

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https://doi.org/https://doi.org/10.1142/s2424786325500100

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@article{makoto2025,
  title        = {{Application of machine learning with asymptotic expansion to unconstrained optimal portfolio}},
  author       = {Makoto Naito & Kohta Takehara},
  journal      = {International Journal of Financial Engineering},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1142/s2424786325500100},
}

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Application of machine learning with asymptotic expansion to unconstrained optimal portfolio

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Evidence weight

0.41

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

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 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.