股票相似性的时间表示学习及其在投资管理中的应用
Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management
AI总结：
本文提出SimStock时间自监督学习框架，通过结合SSL和时间域泛化学习股票表示，在四个真实数据集上优于现有方法，并应用于配对交易、指数跟踪和投资组合优化，提升投资决策与风险管理。
摘要：
在快速全球化和数字化的时代，由于金融市场的非平稳性以及传统区域和行业分类的模糊性，准确识别相似股票已变得日益具有挑战性。为了应对这些挑战，我们研究了SimStock，一种新颖的时间自监督学习框架，该框架结合了自监督学习（SSL）和时间域泛化的技术，以学习金融时间序列数据的稳健且信息丰富的表示。我们研究的主要焦点是从更广阔的视角理解股票之间的相似性，考虑全球金融格局的复杂动态。我们在包含数千只股票的四个真实世界数据集上进行了大量实验，并证明了SimStock在寻找相似股票方面的有效性，其性能优于现有方法。SimStock的实际效用通过其在各种投资策略中的应用得到展示，例如配对交易、指数跟踪和投资组合优化，在这些应用中，与传统方法相比，它带来了更优越的性能。我们的研究结果实证检验了数据驱动方法在利用时间自监督学习的力量应对不断变化的全球金融格局时，增强投资决策和风险管理实践的潜力。
英文摘要：
In the era of rapid globalization and digitalization, accurate identification of similar stocks has become increasingly challenging due to the non-stationary nature of financial markets and the ambiguity in conventional regional and sector classifications. To address these challenges, we examine SimStock, a novel temporal self-supervised learning framework that combines techniques from self-supervised learning (SSL) and temporal domain generalization to learn robust and informative representations of financial time series data. The primary focus of our study is to understand the similarities between stocks from a broader perspective, considering the complex dynamics of the global financial landscape. We conduct extensive experiments on four real-world datasets with thousands of stocks and demonstrate the effectiveness of SimStock in finding similar stocks, outperforming existing methods. The practical utility of SimStock is showcased through its application to various investment strategies, such as pairs trading, index tracking, and portfolio optimization, where it leads to superior performance compared to conventional methods. Our findings empirically examine the potential of data-driven approach to enhance investment decision-making and risk management practices by leveraging the power of temporal self-supervised learning in the face of the ever-changing global financial landscape.
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