# Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing

arXiv: https://arxiv.org/abs/1909.04497 · HTML: https://mlsanity.com/paper/1909.04497/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction](https://mlsanity.com/paper/2409.05698/) (2024)
- [A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data](https://mlsanity.com/paper/2211.02533/) (2022)
- [Stock2Vec: An Embedding to Improve Predictive Models for Companies](https://mlsanity.com/paper/2201.11290/) (2022)
- [Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models](https://mlsanity.com/paper/2212.00852/) (2022)
- [SETN: Stock Embedding Enhanced with Textual and Network Information](https://mlsanity.com/paper/2408.02899/) (2024)

## Abstract (arXiv)

Pricing assets has attracted significant attention from the financial technology community. We observe that the existing solutions overlook the cross-sectional effects and not fully leveraged the heterogeneous data sets, leading to sub-optimal performance. To this end, we propose an end-to-end deep learning framework to price the assets. Our framework possesses two main properties: 1) We propose Equity2Vec, a graph-based component that effectively captures both long-term and evolving cross-sectional interactions. 2) The framework simultaneously leverages all the available heterogeneous alpha sources including technical indicators, financial news signals, and cross-sectional signals. Experimental results on datasets from the real-world stock market show that our approach outperforms the existing state-of-the-art approaches. Furthermore, market trading simulations demonstrate that our framework monetizes the signals effectively.