论文标题
库存嵌入：学习金融资产的分布式表示形式
Stock Embeddings: Learning Distributed Representations for Financial Assets
论文作者
论文摘要
在各种财务应用中，确定金融资产价格变动之间有意义的关系是一个具有挑战性但重要的问题。然而，随着最近的研究，特别是那些使用机器学习和深度学习技术的研究，主要集中在价格预测上，研究资产相关性建模的文献却有所落后。为了解决这一问题，受到自然语言处理的最新成功的启发，我们提出了一种用于培训库存嵌入的神经模型，该模型利用了历史回报数据的动态，以了解金融资产之间存在的细微关系。我们详细描述了我们的方法，并讨论了可以在金融领域中使用的多种方法。此外，我们提出了评估结果，以证明与几个重要的基准相比，在两项现实世界中的财务分析任务中，这种方法的实用性。
Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications. However with recent research, particularly those using machine learning and deep learning techniques, focused mostly on price forecasting, the literature investigating the modelling of asset correlations has lagged somewhat. To address this, inspired by recent successes in natural language processing, we propose a neural model for training stock embeddings, which harnesses the dynamics of historical returns data in order to learn the nuanced relationships that exist between financial assets. We describe our approach in detail and discuss a number of ways that it can be used in the financial domain. Furthermore, we present the evaluation results to demonstrate the utility of this approach, compared to several important benchmarks, in two real-world financial analytics tasks.
