# Stock Embeddings: Learning Distributed Representations for Financial Assets

arXiv: https://arxiv.org/abs/2202.08968 · HTML: https://mlsanity.com/paper/2202.08968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Item2Vec: Neural Item Embedding for Collaborative Filtering](https://mlsanity.com/paper/1603.04259/) (2016)
- [Multi-layer Representation Learning for Medical Concepts](https://mlsanity.com/paper/1602.05568/) (2016)
- [Towards Understanding Linear Word Analogies](https://mlsanity.com/paper/1810.04882/) (2018)
- [Algorithmic Factors Influencing Bias in Machine Learning](https://mlsanity.com/paper/2104.14014/) (2021)
- [Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network](https://mlsanity.com/paper/2010.01197/) (2020)
- [Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities](https://mlsanity.com/paper/2107.03926/) (2021)

## Built on by (most cited first, 4 of 4)

- [Evaluating Financial Relational Graphs: Interpretation Before Prediction](https://mlsanity.com/paper/2410.07216/) (2024)
- [Industry Classification Using a Novel Financial Time-Series Case Representation](https://mlsanity.com/paper/2305.00245/) (2023)
- [Contrastive Learning of Asset Embeddings from Financial Time Series](https://mlsanity.com/paper/2407.18645/) (2024)
- [Latent-Regime Bias Auditing for Volatility Forecasting](https://mlsanity.com/paper/2608.01599/) (2026)

## Abstract (arXiv)

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.