arXiv Artificial Intelligence

Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning

2 hours ago 18.09.2026 07:00 👁 1 views
Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning

Quick summary

arXiv:2602.10711v2 Announce Type: replace-cross Abstract: Asset retrieval (finding similar assets in a financial universe) is central to quantitative investment decision-making. Existing approaches define similarity through historical price patterns or sector classifications, but such backward-looking criteria provide no guarantee about future behavior. We argue that effective asset retrieval should be future-aligned: the retrieved assets should be those most likely to exhibit correlated future returns. To this end, we propose Future-Aligned Soft Contrastive Learning (FASCL), a representation

Key takeaways

  • arXiv:2602.10711v2 Announce Type: replace-cross Abstract: Asset retrieval (finding similar assets in a financial universe) is central to quantitative investment decision-making.
  • Existing approaches define similarity through historical price patterns or sector classifications, but such backward-looking criteria provide no guarantee about future behavior.
  • We argue that effective asset retrieval should be future-aligned: the retrieved assets should be those most likely to exhibit correlated future returns.

Why it matters

“Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning” signals where capital and distribution power are moving in the AI market. Product continuity, pricing, workforce skills and the competitive options available to startups may all be affected.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗