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Deep-MacroFin: Informed Equilibrium Neural Network for Continuous-Time Economic Models

Yuntao Wu et al.

The Journal of Financial Data Science2026https://doi.org/10.3905/jfds.2026.002article
AJG 1
Weight
0.50

What the paper says

In this article, we present Deep-MacroFin, a comprehensive framework designed to solve partial differential equations, with a particular focus on models in continuous time economics. This framework leverages deep learning methodologies, including multilayer perceptrons and the newly developed Kolmogorov-Arnold Networks. It is optimized using economic information encapsulated by Hamilton-Jacobi-Bellman (HJB) equations and coupled algebraic equations. The application of neural networks holds the promise of accurately resolving high-dimensional problems with fewer computational demands and limitations compared to other numerical methods. The framework can be readily adapted for systems of partial differential equations in high dimensions. Importantly, it offers a more efficient (5 × less CUDA memory and 40 × fewer FLOPs in 100D problems) and user-friendly implementation than existing libraries. We also incorporate a time-stepping scheme to enhance training stability for nonlinear HJB equations, enabling the solution of 50D economic models.

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https://doi.org/https://doi.org/10.3905/jfds.2026.002

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@article{yuntao2026,
  title        = {{Deep-MacroFin: Informed Equilibrium Neural Network for Continuous-Time Economic Models}},
  author       = {Yuntao Wu et al.},
  journal      = {The Journal of Financial Data Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3905/jfds.2026.002},
}

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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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