让股票检索瞄准未来收益相关性，提升投资决策准确性。
Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
- 用未来收益相关性做软对比学习的监督信号
- 在4229只美股上优于13个基线模型
- 适合量化投资与资产配置研究者使用
资产检索——在金融标的中寻找相似资产——是量化投资决策的核心。现有方法依赖历史价格模式或行业分类定义相似性，但这类回溯性标准无法保证未来行为一致。我们提出未来对齐的软对比学习（FASCL），其软对比损失以成对未来的收益相关性作为连续监督信号。我们还设计了直接评估检索资产是否共享相似未来路径的评估协议。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个基线模型。代码将很快开源。
原文摘要 · Abstract (English)
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.
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