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 4,229 US equities demonstrate that FASCL consistently outperforms 13 baselines across all future-behavior metrics. The source code will be available soon.
Abstract (translated)
资产检索——在金融宇宙中找到相似的资产——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，但这些基于过去的指标无法保证对未来的准确预测。我们认为，有效的资产检索应该与未来保持一致：即所检索到的资产应该是那些最有可能在未来表现出相关收益走势的资产。为此，我们提出了未来导向的软对比学习（Future-Aligned Soft Contrastive Learning, FASCL），这是一种表示学习框架，其软对比损失函数使用成对未来的收益关联性作为连续监督目标。此外，我们还引入了一种评估协议，旨在直接检验检索到的资产是否在未来轨迹上表现出相似性。实验结果基于4229只美国股票显示，在所有未来行为指标上，FASCL始终优于13个基准方法。源代码将很快发布。
URL
https://arxiv.org/abs/2602.10711
