Large scale continuous-time mean-variance portfolio allocation via reinforcement learning
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- Ang, Andrew & Gorovyy, Sergiy & van Inwegen, Gregory B., 2011.
"Hedge fund leverage,"
Journal of Financial Economics, Elsevier, vol. 102(1), pages 102-126, October.
- Andrew Ang & Sergiy Gorovyy & Gregory B. van Inwegen, 2011. "Hedge Fund Leverage," NBER Working Papers 16801, National Bureau of Economic Research, Inc.
- Haoran Wang & Xun Yu Zhou, 2019. "Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework," Papers 1904.11392, arXiv.org, revised May 2019.
- Mannor, Shie & Tsitsiklis, John N., 2013. "Algorithmic aspects of mean–variance optimization in Markov decision processes," European Journal of Operational Research, Elsevier, vol. 231(3), pages 645-653.
- David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
- David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
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Cited by:
- Zhou Fang, 2023. "Continuous-Time Path-Dependent Exploratory Mean-Variance Portfolio Construction," Papers 2303.02298, arXiv.org.
- Haoran Wang & Shi Yu, 2021. "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning," Papers 2105.09264, arXiv.org.
- Haoran Wang & Xun Yu Zhou, 2020. "Continuous‐time mean–variance portfolio selection: A reinforcement learning framework," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1273-1308, October.
- Zhou Fang, 2023. "Electricity Virtual Bidding Strategy Via Entropy-Regularized Stochastic Control Method," Papers 2303.02303, arXiv.org.
- Gang Huang & Xiaohua Zhou & Qingyang Song, 2020. "Deep reinforcement learning for portfolio management," Papers 2012.13773, arXiv.org, revised Apr 2022.
- Xiangyu Cui & Xun Li & Yun Shi & Si Zhao, 2023. "Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning," Papers 2312.15385, arXiv.org.
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2019-08-26 (Big Data)
- NEP-CMP-2019-08-26 (Computational Economics)
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