Stock Embeddings

Stock embeddings represent financial assets as numerical vectors, capturing complex relationships between them for improved financial analysis. Current research focuses on enhancing these embeddings by incorporating textual data (e.g., company descriptions) and network information (e.g., company relationships) using techniques like transformer models and graph neural networks. This approach improves applications such as portfolio optimization, industry classification, and company similarity quantification, offering more nuanced insights than traditional methods relying solely on numerical data. The resulting advancements have significant implications for investment strategies, risk management, and market understanding within the financial industry.