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

Author

Listed:
  • Qiong Wu
  • Christopher G. Brinton
  • Zheng Zhang
  • Andrea Pizzoferrato
  • Zhenming Liu
  • Mihai Cucuringu

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.

Suggested Citation

  • Qiong Wu & Christopher G. Brinton & Zheng Zhang & Andrea Pizzoferrato & Zhenming Liu & Mihai Cucuringu, 2019. "Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing," Papers 1909.04497, arXiv.org, revised Oct 2021.
    • Handle: RePEc:arx:papers:1909.04497
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    References listed on IDEAS

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    Cited by:

    1. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets," Papers 2211.06378, arXiv.org.
    2. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
    3. Xing Wang & Yijun Wang & Bin Weng & Aleksandr Vinel, 2020. "Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network," Papers 2010.01197, arXiv.org.
    4. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2020. "Company classification using machine learning," Papers 2004.01496, arXiv.org, revised May 2020.

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