Representation learning has emerged as a powerful paradigm for extracting valuable latent features from complex, high-dimensional data. In financial domains, learning informative representations for assets can be used for tasks like sector classification, and risk management. However, the complex and stochastic nature of financial markets poses unique challenges. We propose a novel contrastive learning framework to generate asset embeddings from financial time series data. Our approach leverages the similarity of asset returns over many subwindows to generate informative positive and negative samples, using a statistical sampling strategy based on hypothesis testing to address the noisy nature of financial data. We explore various contrastive loss functions that capture the relationships between assets in different ways to learn a discriminative representation space. Experiments on real-world datasets demonstrate the effectiveness of the learned asset embeddings on benchmark industry classification and portfolio optimization tasks. In each case our novel approaches significantly outperform existing baselines highlighting the potential for contrastive learning to capture meaningful and actionable relationships in financial data.

翻译：表征学习已成为从复杂高维数据中提取有价值潜在特征的强大范式。在金融领域，学习资产的表征信息可用于行业分类和风险管理等任务。然而，金融市场的复杂性和随机性带来了独特挑战。本文提出一种新颖的对比学习框架，从金融时间序列数据中生成资产嵌入。该方法利用资产收益率在多个子窗口内的相似性生成信息量丰富的正负样本，并采用基于假设检验的统计采样策略以应对金融数据的噪声特性。我们探索了多种对比损失函数，这些函数以不同方式捕捉资产间关系，从而学习具有判别性的表征空间。在真实数据集上的实验表明，所学习的资产嵌入在行业分类基准测试和投资组合优化任务中均表现出有效性。在各项实验中，我们提出的新方法均显著优于现有基线，这凸显了对比学习在捕捉金融数据中有意义且可操作关系方面的潜力。