Portfolio Management using Deep Reinforcement Learning
On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance
Abstract (arXiv)
Algorithmic trading or Financial robots have been conquering the stock markets with their ability to fathom complex statistical trading strategies. But with the recent development of deep learning technologies, these strategies are becoming impotent. The DQN and A2C models have previously outperformed eminent humans in game-playing and robotics. In our work, we propose a reinforced portfolio manager offering assistance in the allocation of weights to assets. The environment proffers the manager the freedom to go long and even short on the assets. The weight allocation advisements are restricted to the choice of portfolio assets and tested empirically to knock benchmark indices. The manager performs financial transactions in a postulated liquid market without any transaction charges. This work provides the conclusion that the proposed portfolio manager with actions centered on weight allocations can surpass the risk-adjusted returns of conventional portfolio managers.
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 4 of its 19 references that are in the map. First posted on arXiv 1 May 2024.
Builds on
4 of 19 references.
References · 4
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
- 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
- 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
- 2015Deep Attention Recurrent Q-Networkcited by 27
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