论文标题
一种基于多模式的基于嵌入的金融市场行业分类方法
A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets
论文作者
论文摘要
行业分类方案为根据业务活动进行细分公司提供了分类学。它们在工业和学术界依赖于许多类型的财务和经济分析的组成部分。但是，即使是现代的分类方案也未能接受大数据时代，并且仍然是一个主观的，易于不一致和错误分类。为了解决这个问题，我们为培训公司嵌入提供了一种多模式神经模型，该模型利用历史定价数据和财务新闻的动态来学习捕获细微差异关系的客观公司表示。我们详细解释了我们的方法，并通过几个案例研究和应用于行业分类的下游任务来强调嵌入的实用性。
Industry classification schemes provide a taxonomy for segmenting companies based on their business activities. They are relied upon in industry and academia as an integral component of many types of financial and economic analysis. However, even modern classification schemes have failed to embrace the era of big data and remain a largely subjective undertaking prone to inconsistency and misclassification. To address this, we propose a multimodal neural model for training company embeddings, which harnesses the dynamics of both historical pricing data and financial news to learn objective company representations that capture nuanced relationships. We explain our approach in detail and highlight the utility of the embeddings through several case studies and application to the downstream task of industry classification.
