33 citations · 38 across the 12 of their papers we have counts for
5 papers · 1 filter
Factor-MCLS: Multi-agent learning system with reward factor matrix and multi-critic framework for dynamic portfolio optimization
Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang +1
Typical deep reinforcement learning (DRL) agents for dynamic portfolio optimization learn the factors influencing portfolio return and risk by analyzing the output values of the re…
MTS: A Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling
Fengchen Gu, Zhengyong Jiang, Ángel F. García-Fernández +3
Portfolio management remains a crucial challenge in finance, with traditional methods often falling short in complex and volatile market environments. While deep reinforcement appr…
A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
Ruoyu Sun, Yue Xi, Angelos Stefanidis +2
Deep Reinforcement Learning (DRL) has been extensively used to address portfolio optimization problems. The DRL agents acquire knowledge and make decisions through unsupervised int…
Kolmogorov-Arnold Networks: A Critical Assessment of Claims, Performance, and Practical Viability
Yuntian Hou, Tianrui Ji, Di Zhang +1
Kolmogorov-Arnold Networks (KANs) have gained significant attention as an alternative to traditional multilayer perceptrons, with proponents claiming superior interpretability and…
Contrastive Learning Via Equivariant Representation
Sifan Song, Jinfeng Wang, Qiaochu Zhao +6
Invariant Contrastive Learning (ICL) methods have achieved impressive performance across various domains. However, the absence of latent space representation for distortion (augmen…