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Asset Allocation: From Markowitz to Deep Reinforcement Learning

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  • Ricard Durall

Abstract

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.

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  • Ricard Durall, 2022. "Asset Allocation: From Markowitz to Deep Reinforcement Learning," Papers 2208.07158, arXiv.org.
    • Handle: RePEc:arx:papers:2208.07158
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    References listed on IDEAS

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    5. Yunan Ye & Hengzhi Pei & Boxin Wang & Pin-Yu Chen & Yada Zhu & Jun Xiao & Bo Li, 2020. "Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States," Papers 2002.05780, arXiv.org.
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    Cited by:

    1. Marc Velay & Bich-Li^en Doan & Arpad Rimmel & Fabrice Popineau & Fabrice Daniel, 2023. "Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management," Papers 2306.10950, arXiv.org.
    2. Jiwon Kim & Moon-Ju Kang & KangHun Lee & HyungJun Moon & Bo-Kwan Jeon, 2023. "Deep Reinforcement Learning for Asset Allocation: Reward Clipping," Papers 2301.05300, arXiv.org.

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