2000 character limit reached
Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning
Published 24 Dec 2023 in q-fin.MF, cs.LG, and q-fin.PM | (2312.15385v1)
Abstract: This paper studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in \cite{zhou2020mv}, the discrete-time model makes more general assumptions about the asset's return distribution. Using entropy to measure the cost of exploration, we derive the optimal investment strategy, whose density function is also Gaussian type. Additionally, we design the corresponding reinforcement learning algorithm. Both simulation experiments and empirical analysis indicate that our discrete-time model exhibits better applicability when analyzing real-world data than the continuous-time model.
- Optimal execution of portfolio transactions. Journal of Risk, 3:5–40.
- A reinforcement learning approach for solving the mean variance customer portfolio in partially observable models. International Journal on Artificial Intelligence Tools, 27(08):1850034.
- Optimal multi-period mean-variance policy under no-shorting constraint. European Journal of Operational Research, 234(2):459–468.
- Survey on multi-period mean–variance portfolio selection model. Journal of the Operations Research Society of China, 10(3):599–622.
- Learning equilibrium mean-variance strategy. Mathematical Finance, 33(4):1166–1212.
- G-learner and girl: Goal based wealth management with reinforcement learning. arXiv preprint arXiv:2002.10990.
- Algorithm trading using Q-learning and recurrent reinforcement learning. positions, 1(1):1–7.
- Mean-variance hedging in continuous time. The Annals of Applied Probability, pages 1–15.
- Recent advances in reinforcement learning in finance. Mathematical Finance, 33(3):437–503.
- Policy evaluation and temporal-difference learning in continuous time and space: A martingale approach. The Journal of Machine Learning Research, 23(154):1–55.
- Policy gradient and actor-critic learning in continuous time and space: Theory and algorithms. The Journal of Machine Learning Research, 23(275):1–50.
- q-learning in continuous time. The Journal of Machine Learning Research, 24(161):1–61.
- A deep reinforcement learning framework for the financial portfolio management problem. arXiv preprint arXiv:1706.10059.
- Portfolio management using reinforcement learning. Stanford University.
- Optimal dynamic portfolio selection: Multiperiod mean-variance formulation. Mathematical Finance, 10(3):387–406.
- Dynamic mean-variance portfolio selection with no-shorting constraints. SIAM Journal on Control and Optimization, 40(5):1540–1555.
- Adversarial deep reinforcement learning in portfolio management. arXiv preprint arXiv:1808.09940.
- Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.
- Adaptive quantitative trading: An imitative deep reinforcement learning approach. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 2128–2135.
- Algorithmic aspects of mean–variance optimization in markov decision processes. European Journal of Operational Research, 231(3):645–653.
- Regime-switching recurrent reinforcement learning for investment decision making. Computational Management Science, 9:89–107.
- Transition variable selection for regime switching recurrent reinforcement learning. In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), pages 407–413. IEEE.
- Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1):77–91.
- An intelligent financial portfolio trading strategy using deep Q-learning. Expert Systems with Applications, 158:113573.
- Sato, Y. (2019). Model-free reinforcement learning for financial portfolios: a brief survey. arXiv preprint arXiv:1904.04973.
- Theodossiou, P. (1998). Financial data and the skewed generalized t distribution. Management Science, 44(12):1650–1661.
- Reinforcement learning in continuous time and space: A stochastic control approach. The Journal of Machine Learning Research, 21(198):1–34.
- Wang, H. R. (2019). Large scale continuous-time mean-variance portfolio allocation via reinforcement learning. Available at SSRN 3428125.
- Continuous-time mean–variance portfolio selection: A reinforcement learning framework. Mathematical Finance, 30(4):1273–1308.
- Weijs, L. (2018). Reinforcement learning in portfolio management and its interpretation. Erasmus Universiteit Rotterdam.
- Reinforcement learning for continuous-time mean-variance portfolio selection in a regime-switching market. Available at SSRN 4396073.
- Practical deep reinforcement learning approach for stock trading. arXiv preprint arXiv:1811.07522, pages 1–7.
- Continuous-time mean-variance portfolio selection: A stochastic LQ framework. Applied Mathematics and Optimization, 42:19–33.
Paper Prompts
Sign up for free to create and run prompts on this paper.