Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards
This work presents a PPO-based dynamic portfolio optimization method featuring regret rewards, synthetic data training, and adaptive transaction cost scheduling.
What it examines
This paper uses deep reinforcement learning with Proximal Policy Optimization to enhance dynamic portfolio rebalancing. It improves a traditional 60/40 portfolio by integrating a regret-based reward, synthetic data via circular block bootstrap, and transaction cost scheduling to balance returns and risks in constantly changing markets.
What it concludes
The study shows that using a regret-based DRL approach with cost-aware rebalancing can outperform traditional portfolios. Its methods may apply to finance, supply chains, project management, and more. Future work should refine hyperparameters and improve market crisis responses, enhancing real-time asset allocation.
Evidence objects
A study uses deep reinforcement learning (PPO) to upgrade a classic 60/40 portfolio strategy by introducing a unique negative Sharpe regret reward that compares agent choices against an Oracle-optimal allocation.
key_findings bullet 1 · key_findings · validation V0
The study integrates synthetic data using circular block bootstrap and applies a transaction cost scheduler that raises fees incrementally, enabling the agent to adjust allocations and control drawdown risks dynamically.
key_findings bullet 2 · key_findings · validation V0
Tests across diverse market phases reveal the approach outperforms benchmarks in annual returns and risk-adjusted performance, despite underperforming CNN and imitation learning trials, underscoring a forward-looking reward and improvement scope.
key_findings bullet 3 · key_findings · validation V0
This paper introduces deep reinforcement learning for dynamic portfolio rebalancing by combining a regret-based \$Sharpe\$ reward function, circular block bootstrap synthetic data training, and an integrated transaction cost scheduler. Its innovative fusion of established techniques delivers practical robustness and theoretical depth, offering original insights that challenge conventional financial management paradigms.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
Abstract: This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO). Rather than focusing solely on traditional portfolio construction, our approach aims to improve an already high-performing strategy through dynamic rebalancing driven by PPO and Oracle agents. Our target is to enhance the traditional 60/40 benchmark (60% stocks,… ▽ More This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO). Rather than focusing solely on traditional portfolio construction, our approach aims to improve an already high-performing strategy through dynamic rebalancing driven by PPO and Oracle agents. Our target is to enhance the traditional 60/40 benchmark (60% stocks, 40% bonds) by employing the Regret-based Sharpe reward function. To address the impact of transaction fee frictions and prevent signal loss, we develop a transaction cost scheduler. We introduce a future-looking reward function and employ synthetic data training through a circular block bootstrap method to facilitate the learning of generalizable allocation strategies. We focus on two key evaluation measures: return and maximum drawdown. Given the high stochasticity of financial markets, we train 20 independent agents each period and evaluate their average performance against the benchmark. Our method not only enhances the performance of the existing portfolio strategy through strategic rebalancing but also demonstrates strong results compared to other baselines. △ Less
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