mlsanityData to 14 Oct 2026

Comparing Normalization Methods for Portfolio Optimization with Reinforcement Learning

arXiv 2508.03910 ↗First posted 5 Aug 2025q-fin.CP cs.LGcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

Recently, reinforcement learning has achieved remarkable results in various domains, including robotics, games, natural language processing, and finance. In the financial domain, this approach has been applied to tasks such as portfolio optimization, where an agent continuously adjusts the allocation of assets within a financial portfolio to maximize profit. Numerous studies have introduced new simulation environments, neural network architectures, and training algorithms for this purpose. Among these, a domain-specific policy gradient algorithm has gained significant attention in the research community for being lightweight, fast, and for outperforming other approaches. However, recent studies have shown that this algorithm can yield inconsistent results and underperform, especially when the portfolio does not consist of cryptocurrencies. One possible explanation for this issue is that the commonly used state normalization method may cause the agent to lose critical information about the true value of the assets being traded. This paper explores this hypothesis by evaluating two of the most widely used normalization methods across three different markets (IBOVESPA, NYSE, and cryptocurrencies) and comparing them with the standard practice of normalizing data before training. The results indicate that, in this specific domain, the state normalization can indeed degrade the agent's performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 28 references that are in the map. First posted on arXiv 5 Aug 2025.

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10 of 28 references.

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Same direction, newest first

  1. 2024A Deep Reinforcement Learning Framework For Financial Portfolio Management3 Sep

Other references · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  5. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  6. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  7. 2021Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocketcited by 13
  8. 2014Normalized Online Learningcited by 7
  9. 2022Reinforcement Learning Applied to Trading Systems: A Surveycited by 2

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  4. 2025Reinforcement Learning for Portfolio Optimization with a Financial Goal and Defined Time Horizons
  5. 2025Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information
  6. 2024Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity
  7. 2024Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent
  8. 2024Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization