@inproceedings{equity2vec,
author = {Wu, Qiong and Brinton, Christopher G. and Zhang, Zheng and Pizzoferrato, Andrea and Liu, Zhenming and Cucuringu, Mihai},
title = {Equity2Vec: End-to-End Deep Learning Framework for Cross-Sectional Asset Pricing},
year = {2022},
isbn = {9781450391481},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3490354.3494409},
doi = {10.1145/3490354.3494409},
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 Eqity2Vec, 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.},
booktitle = {Proceedings of the Second ACM International Conference on AI in Finance (ICAIF)  },
articleno = {44},
numpages = {9},
location = {Virtual Event},
series = {ICAIF '21}
}