arXiv:2607. 19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and cross-sectional dependencies.
By Haoran Guo, Yutong Lu, Li Zhang
arXiv:2607. 14416v1 Announce Type: new Abstract: The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults.
By Rabimba Karanjai, Hemanth Madhavarao, Lei Xu, Weidong Shi
arXiv:2602. 03981v2 Announce Type: replace-cross Abstract: Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies.
By Aijie Shu, Wenbin Wu, Gbenga Ibikunle, Fengxiang He
arXiv:2609.37446v1 Announce Type: new
Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pai...
By Pingchuan Ma, Rui Ding, Bojun Huang, Shuai Wang
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
By Yifan Hu, Yuante Li, Peiyuan Liu, Yuxia Zhu, Naiqi Li, Tao Dai, Shu-tao Xia, Dawei Cheng, Changjun Jiang
TabCausal is a causal discovery foundation model that learns to map datasets directly to causal graphs by pretraining across diverse causal environments. It uses a dynamic task construction strategy to expose the model to varied graph priors, mechanisms, noise models, dimensions, sample sizes, and intervention regimes, improving transferability from observational and mixed‑interventional data. On large synthetic benchmarks and a new protocol‑guided semantic benchmark, TabCausal outperforms many classical baselines and shows robust structure recovery, especially when interventional evidence is available.
By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye