Systematic review traces computational methods in multi-period financial planning, highlighting reinforcement learning as a scalable bridge across stochastic optimization constraints.
Key Points
To review the mathematical evolution of multi-period financial planning methods and evaluate how model-free reinforcement learning bridges gaps in classical stochastic optimization frameworks.
Traced the algorithmic transition from Markowitz's single-period mean–variance framework to multi-period scenario-based and state-space formulations.
Categorized recent reinforcement learning applications across individual and institutional financial planning domains into hybrid, scalable, and end-to-end architectures.
Identified that multi-stage stochastic programming and dynamic programming are fundamentally constrained by the curse of dimensionality, model misspecification, and intractable transition assumptions.
Showed that model-free reinforcement learning enables adaptive and scalable multi-period decision-making without requiring rigid transition models or handcrafted scenario trees.
Determined that implementing AI-driven automated investing requires resolving practical barriers related to data constraints, model interpretability, user trust, and regulatory oversight.