SETN: Stock Embedding Enhanced with Textual and Network Information
This work addresses the need for improved stock embeddings in the wealth management industry, though it appears incremental as it builds on existing methods with domain-specific enhancements.
The paper tackled the problem of creating vector representations of stocks by proposing SETN, a model that enhances stock embeddings with textual and network information, and demonstrated that it performs better in creating thematic funds than baseline methods.
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.