Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
This addresses the challenge of making investment decisions based on future behavior rather than historical patterns, offering a practical improvement for quantitative finance.
The paper tackled the problem of asset retrieval in finance by proposing a future-aligned approach to find assets with correlated future returns, and demonstrated that their method outperformed 13 baselines on 4,229 US equities.
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 learning framework whose soft contrastive loss uses pairwise future return correlations as continuous supervision targets. We further introduce an evaluation protocol designed to directly assess whether retrieved assets share similar future trajectories. Experiments on 4,229 US equities demonstrate that FASCL consistently outperforms 13 baselines across all future-behavior metrics. The source code will be available soon.