MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
SBCA is a reinforcement learning framework using BERT cross-modal fusion and Actor-Critic to integrate price data with sentiment text for multi-asset portfolio optimization with practical trading constraints.
KICL completes execution decisions in KOL financial discourse using offline RL, achieving top returns and Sharpe ratios with no unsupported trades or direction changes on YouTube and X data from 2022-2025.
A semi-supervised teacher-student framework enables neural networks to proxy CVaR portfolio optimization using synthetic data augmentation for scarce labels and regime shifts.
A systematic review of physics-informed neural networks and mathematical modeling approaches for portfolio optimization and management in finance.
citing papers explorer
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A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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SBCA: Cross-Modal BERT-driven Actor-Critic for Multi-Asset Portfolio Optimization
SBCA is a reinforcement learning framework using BERT cross-modal fusion and Actor-Critic to integrate price data with sentiment text for multi-asset portfolio optimization with practical trading constraints.
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When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse
KICL completes execution decisions in KOL financial discourse using offline RL, achieving top returns and Sharpe ratios with no unsupported trades or direction changes on YouTube and X data from 2022-2025.
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Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training
A semi-supervised teacher-student framework enables neural networks to proxy CVaR portfolio optimization using synthetic data augmentation for scarce labels and regime shifts.
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A Systematic Review of Recent Advancements in PINN Augmented Deep Learning and Mathematical Modeling for Efficient Portfolio Management
A systematic review of physics-informed neural networks and mathematical modeling approaches for portfolio optimization and management in finance.