#embeddings

1 post

• 2025-04

  Stock Embeddings - Learning Distributed Representations for Financial Assets

  Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in finance. While recent machine learning research often focuses on price forecasting, modeling asset correlations has lagged. Inspired by successes in natural language processing (NLP), we propose a neural model for training *stock embeddings*. This model uses historical returns data to learn nuanced relationships between financial assets. We describe our approach, discuss its potential applications, and present evaluation results demonstrating its utility in real-world financial analytics tasks compared to benchmarks.

  [research]