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.
By Emad Izadifar, Zahed Rahmati
arXiv:2605.28853v2 Announce Type: replace-cross
Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
By Rahul Fernandes, Travis Desell
The paper introduces Future‑Aligned Soft Contrastive Learning (FASCL), a representation learning framework that uses pairwise future return correlations as continuous supervision to improve asset retrieval. FASCL’s soft contrastive loss aligns retrieved assets with correlated future returns, and the authors propose a new evaluation protocol to directly assess future trajectory similarity. Experiments on 5,631 US‑listed securities outperform 14 baselines in future return correlation, rank information coefficient, trend consistency, and gross Sharpe ratio across various retrieval depths and basket sizes.
By Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon, Yoon Kim, Alejandro Lopez-Lira, Wonbin Ahn, Justin Xu, Srijan Sood, Qingsong Wen, Chun-Li Yang, Yongjae Lee
arXiv:2606. 28933v1 Announce Type: cross Abstract: Venture capital (VC) investment decisions face distinct challenges, such as multi-source heterogeneous data, non-stationary time series, and the demand for explainable predictions in high-stakes, low-data settings.
By Junyan Tan, Yifan Li, Minghao Wang, Zihan Chen, Haoyu Zhang
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.
By Yoonsik Hong, Diego Klabjan
arXiv:2606. 29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets.
By Dishan Sarkar