Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
cs.CE, cs.AI
Submitted: 2026-02-11
Updated: 2026-09-17
Comments: 14 pages, 3 figures, 10 tables
Code: https://github.com/HyeongminLEE/fascl
License: http://creativecommons.org/licenses/by/4.0/
The gist: Asset retrieval (finding similar assets in a financial universe) is central to quantitative investment decision-making.
Terminology
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 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 5,631 US-listed securities against 14 baselines show that FASCL attains the best future return correlation at every retrieval depth and the best rank information coefficient at every depth and horizon, leads on trend consistency in 13 of 16 cells, and gives the highest gross Sharpe ratio in a spread trading backtest at every basket size. Code is available at https://github.com/HyeongminLEE/fascl.
Sources
- Representation Learning with Contrastive Predictive Coding
- Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework
- Retrieval-augmented Large Language Models for Financial Time Series Forecasting
- HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information
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