FASCL employs future-aligned soft contrastive learning using pairwise return correlations as supervision for financial asset retrieval. It outperforms historical similarity baselines on US equities. Includes protocol to evaluate future trajectory alignment.
Key Points
- 1.Future returns as soft targets
- 2.Representation learning framework
- 3.Source code soon
Impact Analysis
Enhances quantitative investing by predicting correlated future asset behaviors.
Technical Details
Soft contrastive loss with continuous future supervision.