  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.
