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.

翻译：资产检索——在金融领域中寻找相似资产——是量化投资决策的核心。现有方法通过历史价格模式或行业分类来定义相似性，但这种后向视角的标准无法保证未来行为的一致性。我们认为有效的资产检索应当面向未来对齐：检索出的资产应最有可能表现出相关的未来收益。为此，我们提出了未来对齐软对比学习（FASCL），这是一种表示学习框架，其软对比损失函数以成对未来收益相关性作为连续监督目标。我们进一步设计了一种评估方案，旨在直接评估检索资产是否具有相似的未来走势。在4,229只美国股票上的实验表明，FASCL在所有未来行为指标上均持续优于13个基线方法。源代码即将公开。