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

TL;DR

Equity2Vec is a deep learning framework that captures cross-sectional effects and leverages heterogeneous data sources to improve asset pricing accuracy and trading performance.

Contribution

The paper introduces Equity2Vec, a novel graph-based deep learning model that effectively models cross-sectional interactions and integrates diverse alpha signals for asset pricing.

Findings

Outperforms existing state-of-the-art methods in real-world datasets.

Effectively captures evolving cross-sectional interactions.

Demonstrates profitable trading simulations.

Abstract

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.