A three-phase DRL framework for personalized portfolio management using a ticker-free encoder pretrained with a time series foundation model, an objective-conditioned MoE actor-critic, and inference-time LoRA adaptation from brokerage data.
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
abstract
Financial portfolio management is the process of constant redistribution of a fund into different financial products. This paper presents a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. The framework consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. This framework is realized in three instants in this work with a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. All three instances of the framework monopolize the top three positions in all experiments, outdistancing other compared trading algorithms. Although with a high commission rate of 0.25% in the backtests, the framework is able to achieve at least 4-fold returns in 50 days.
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Frontier AI models lose 16-31% trading on Kalshi over 57 days but show better results on Polymarket, with platform design strongly affecting outcomes and prediction accuracy mattering more than research volume.
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.
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 hybrid DRL system for multi-pair crypto trading with deterministic risk shielding outperforms a heuristic baseline at 10% significance on Binance futures data.
ReCAP segments markets into regimes, builds a policy library via continual learning, and uses a regime-gate to adapt trading policies, claiming superior returns and fast adaptation on five real datasets.
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.
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