arXiv:2607. 12067v1 Announce Type: new Abstract: Institutional equity holdings disclosed in SEC Form 13F filings provide a rich temporal record of portfolio decisions by large investment managers.
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.
The paper introduces the Finance‑Aware Graph Spatio‑Temporal Network (FA‑GSTN) for forecasting realized volatility by treating the implied volatility surface as a dynamic graph. Nodes represent grid points on the surface, with edges capturing adaptive intra‑day spatial and explicit inter‑day temporal relationships, while finance‑aware node features (e.g., option Greeks) and a multi‑scale temporal smoothing gate address high‑frequency noise. Experiments on a large equity options dataset show FA‑GSTN achieves state‑of‑the‑art predictive accuracy (R² up to 0.473) and outperforms Vision Transformer baselines even with only one year of training data, demonstrating robustness during market stress.
arXiv:2608. 07158v1 Announce Type: new Abstract: Temporal graph learning has become essential for analyzing real-world systems whose interactions continuously evolve over time, including financial transaction networks, communication systems, and online social platforms.
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.
arXiv:2606. 25811v1 Announce Type: cross Abstract: Commodity futures can be represented hierarchically, with underlying assets at the upper level and individual futures contracts at the lower level.
arXiv:2606. 24509v1 Announce Type: cross Abstract: Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention.
arXiv:2606. 30997v1 Announce Type: new Abstract: We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior financial RL work: 1) ticker lock-in, 2) monolithic objectives , and 3) static user models.
Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant traction. Recent advances in Graph Neural Networks (GNNs) have demonstrated strong perfor- mance by assuming a static graph topology and aggregating information from neighboring series.
arXiv:2606. 24062v1 Announce Type: cross Abstract: Financial time series forecasting presents structural challenges absent from standard benchmarks.
arXiv:2510. 09416v4 Announce Type: replace Abstract: Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models.
arXiv:2602. 19591v3 Announce Type: replace-cross Abstract: Small and Medium Enterprises (SMEs) constitute 99.
arXiv:2608. 07333v1 Announce Type: new Abstract: Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant traction.