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Pricing assets has attracted significant attention from the financialtechnology community. We observe that the existing solutions overlook thecross-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 theassets. Our framework possesses two main properties: 1) We propose Equity2Vec,a graph-based component that effectively captures both long-term and evolvingcross-sectional interactions. 2) The framework simultaneously leverages all theavailable heterogeneous alpha sources including technical indicators, financialnews signals, and cross-sectional signals. Experimental results on datasetsfrom the real-world stock market show that our approach outperforms theexisting state-of-the-art approaches. Furthermore, market trading simulationsdemonstrate that our framework monetizes the signals effectively.

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