SETN：利用文本和网络信息增强的股票嵌入
SETN: Stock Embedding Enhanced with Textual and Network Information
AI总结：
针对股票向量表示需求，提出融合领域自适应 transformer 文本嵌入与图神经网络网络信息的 SETN，并在关联公司抽取和主题基金构建任务上验证其优于基线。
摘要：
股票嵌入是一种对股票进行向量表示的方法。财富管理行业对股票的向量表示（即股票嵌入）需求日益增长，该方法已被应用于股价预测、投资组合优化和相似基金识别等多种任务。股票嵌入的优势在于能够量化股票之间的相对关系，并可从文本和网络数据等非结构化数据中提取有用信息。在本研究中，我们提出了利用文本和网络信息增强的股票嵌入（SETN），采用领域自适应预训练的基于 transformer 的模型来嵌入文本信息，并使用图神经网络模型来捕捉网络信息。我们在关联公司信息抽取任务上评估了所提模型的性能。我们还证明，与基线方法获得的股票嵌入相比，由所提模型获得的股票嵌入在构建主题基金方面表现更好，从而为财富管理行业的各类应用提供了一条颇具前景的途径。
英文摘要：
Stock embedding is a method for vector representation of stocks. There is a growing demand for vector representations of stock, i.e., stock embedding, in wealth management sectors, and the method has been applied to various tasks such as stock price prediction, portfolio optimization, and similar fund identifications. Stock embeddings have the advantage of enabling the quantification of relative relationships between stocks, and they can extract useful information from unstructured data such as text and network data. In this study, we propose stock embedding enhanced with textual and network information (SETN) using a domain-adaptive pre-trained transformer-based model to embed textual information and a graph neural network model to grasp network information. We evaluate the performance of our proposed model on related company information extraction tasks. We also demonstrate that stock embeddings obtained from the proposed model perform better in creating thematic funds than those obtained from baseline methods, providing a promising pathway for various applications in the wealth management industry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
