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Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets.
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Human-level control through deep reinforcement learning
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The Profitability of Pairs Trading Strategies: Distance, Cointegration, and Copula Methods
Hossein Rad, Rand Kwong Yew Low, and Robert W. Faff. 2015 · 2015
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Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas. 2015 · 2015
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Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. In NIPS
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The Option-Critic Architecture
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Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Shane Gu, Honglak Lee, and Sergey Levine. 2019 · 2019
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Deep Reinforcement Learning Pairs Trading with a Double Deep Q-Network
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Hierarchical Reinforcement Learning for Open-Domain Dialog. In AAAI
Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, and Rosalind W. Picard. 2020 · 2020
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Dynamic Portfolio Management Based on Pair Trading and Deep Reinforcement Learning
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The use of deep reinforcement learning in tactical asset allocation
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Stability effects of arbitrage in exchange traded funds: an agent-based model
Megan Shearer, David Byrd, Tucker Hybinette Balch, and Michael P. Wellman. 2021 · 2021
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Improving Pairs Trading Strategies via Reinforcement Learning. In 2021 International Conference on Applied Artificial Intelligence (ICAPAI) . IEEE, 1–7
Cheng Wang, Patrik Sandås, and Peter Beling. 2021 · 2021
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Hierarchical Reinforcement Learning for Integrated Recommendation. In AAAI
Ruobing Xie, Shaoliang Zhang, Rui Wang, Feng Xia, and Leyu Lin. 2021 · 2021
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Hybrid Deep Reinforcement Learning for Pairs Trading
Sang-Ho Kim, Deog-Yeong Park, and Ki-Hoon Lee. 2022 · 2022
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Structural break-aware pairs trading strategy using deep reinforcement learning
Jing-You Lu, Hsu-Chao Lai, Wen-Yueh Shih, Yi-Feng Chen, Shengkai Huang, Hao-Han Chang, Jun-Zhe Wang, Jiun-Long Huang, and Tian-Shyr Dai. 2022 · 2022
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