MTS: A deep reinforcement learning portfolio management framework with time-awareness and short-selling
University of Liverpool · Liverpool College · +2 more institutions
Abstract
Portfolio management remains a crucial challenge in finance, with traditional methods often falling short in complex and volatile market environments. While deep reinforcement approaches have shown promise, they still face limitations in dynamic risk management, exploitation of temporal markets, and incorporation of complex trading strategies such as short-selling. These limitations can lead to suboptimal portfolio performance, increased vulnerability to market volatility, and missed opportunities in capturing potential returns from diverse market conditions. This paper introduces a Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling (MTS), offering a robust and…
Citation impact
- FWCI
- 61.53
- Percentile
- 99%
- References
- 42
Authors
6- FGFengchen Gu
University of Liverpool, Liverpool College, Xi’an Jiaotong-Liverpool University
- ZJZhengyong Jiang
Xi’an Jiaotong-Liverpool University
- ÁFÁngel F. García-Fernández
University of Liverpool, Liverpool College, Universidad Politécnica de Madrid
- ASAngelos Stefanidis
Xi’an Jiaotong-Liverpool University
- JSJionglong SuCorresponding
Xi’an Jiaotong-Liverpool University
Topics & keywords
- Reinforcement learning
- Sharpe ratio
- Portfolio
- Project portfolio management
- Investment strategy
- Adaptability
- Risk management
- Investment management