Equity2Vec: end-to-end deep learning framework for cross-sectional asset pricing
Equity2Vec: end-to-end deep learning framework for cross-sectional asset pricing
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DOI：
10.1145/3490354.3494409
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Qiong Wu;Christopher G. Brinton;Zhenghao Zhang;A. Pizzoferrato;Zhenming Liu;Mihai Cucuringu
中科院分区：
文献类型：
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作者：
Qiong Wu;Christopher G. Brinton;Zhenghao Zhang;A. Pizzoferrato;Zhenming Liu;Mihai Cucuringu
资产定价引起了金融科技界的极大关注。我们观察到，现有的解决方案忽略了横截面的影响，并没有充分利用异构数据集，导致次优性能。为此，我们提出了一个端到端的深度学习框架来为资产定价。我们的框架具有两个主要属性：1）我们提出了Eqity2Vec，一个基于图的组件，有效地捕捉长期和不断变化的横截面相互作用。2)该框架同时利用了所有可用的异构alpha源，包括技术指标，金融新闻信号和横截面信号。在真实股票市场数据集上的实验结果表明，该方法优于现有的最先进的方法。此外，市场交易模拟表明，我们的框架货币化的信号有效。
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.