mlsanityData to 7 Oct 2026

Asset Allocation: From Markowitz to Deep Reinforcement Learning

arXiv 2208.07158 ↗First posted 14 Jul 2022q-fin.PM cs.AI cs.LGcited by 3

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Abstract (arXiv)

Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every individual. As a result, investors may use different asset allocations' strategy to try to fulfil their financial objectives. In this work, we conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques. In particular, we focus on traditional approaches based on Modern Portfolio Theory, and on machine-learning approaches based on deep reinforcement learning. We assess the model's performance under different market tendency, i.e., both bullish and bearish markets. For reproducibility, we provide the code implementation code in this repository.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 51 references that are in the map. First posted on arXiv 14 Jul 2022.

Builds on

15 of 51 references.

References · 15

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3 more
  1. 2020Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction Statescited by 55
  2. 2016Cryptocurrency Portfolio Management with Deep Reinforcement Learningcited by 31
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2023Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Managementcited by 1
  2. 2023Deep Reinforcement Learning for Asset Allocation: Reward Clippingcited by 0
  3. 2026The Enhanced Physics-Informed Kolmogorov-Arnold Networks: Applications of Newton's Laws in Financial Deep Reinforcement Learning (RL) Algorithmscited by 0