Contrastive Learning of Asset Embeddings from Financial Time Series

Rian Dolphin, Barry Smyth, Ruihai Dong
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Abstract

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.
从金融时间序列对比学习资产嵌入
表征学习已成为从复杂的高维数据中提取有价值的潜在特征的强大范例。在金融领域,学习资产的信息表征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的挑战。我们提出了一种新的对比学习框架,用于从金融时间序列数据中生成资产嵌入。我们的方法利用许多子窗口中资产回报的相似性来生成信息丰富的正样本和负样本,并使用基于假设检验的统计抽样策略来解决金融数据的噪声特性。我们探索了各种对比损失函数,它们以不同的方式捕捉资产之间的关系,从而学习出一个具有区分性的表示空间。在真实世界数据集上的实验证明了所学资产嵌入在基准行业分类和投资组合优化任务中的有效性。在每种情况下,我们的新方法都明显优于现有的基线,突出了对比学习在捕捉金融数据中有意义和可操作的关系方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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