  Identifying meaningful relationships between the price movements of financial
assets is a challenging but important problem in a variety of financial
applications. However with recent research, particularly those using machine
learning and deep learning techniques, focused mostly on price forecasting, the
literature investigating the modelling of asset correlations has lagged
somewhat. To address this, inspired by recent successes in natural language
processing, we propose a neural model for training stock embeddings, which
harnesses the dynamics of historical returns data in order to learn the nuanced
relationships that exist between financial assets. We describe our approach in
detail and discuss a number of ways that it can be used in the financial
domain. Furthermore, we present the evaluation results to demonstrate the
utility of this approach, compared to several important benchmarks, in two
real-world financial analytics tasks.
