# Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network

arXiv: https://arxiv.org/abs/2010.01197 · HTML: https://mlsanity.com/paper/2010.01197/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Stock Embeddings: Learning Distributed Representations for Financial Assets](https://mlsanity.com/paper/2202.08968/) (2022)
- [Stock2Vec: An Embedding to Improve Predictive Models for Companies](https://mlsanity.com/paper/2201.11290/) (2022)

## Abstract (arXiv)

We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market. With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks, while the temporal convolutional layers are used for automatically capturing effective temporal patterns both within and across series. Evaluated on S&P 500, our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.