A Deep Learning Framework for Pricing Financial Instruments
A Deep Learning Framework for Pricing Financial Instruments
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发表时间：
2019-09
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通讯作者：
Qiong Wu;Zheng Zhang;A. Pizzoferrato;Mihai Cucuringu;Zhenming Liu
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作者：
Qiong Wu;Zheng Zhang;A. Pizzoferrato;Mihai Cucuringu;Zhenming Liu
我们提出了一个用于股票走势预测的集成深度学习架构。我们的架构同时利用了所有可用的alpha源。这些来源包括技术信号、金融新闻信号和横截面信号。我们的架构拥有三个主要属性。首先，我们的架构避免了过度拟合问题。虽然我们消耗了大量的技术信号，但比线性模型具有更好的泛化性能。其次，我们的模型有效地捕捉了来自不同类别的信号之间的相互作用。第三，我们的架构具有较低的计算成本。我们设计了一个基于图形的组件，它可以提取横截面的相互作用，从而避免使用标准模型中所需的SVD。在真实股票市场上的实验结果表明，我们的方法优于现有的基线。同时，不同交易模拟器的结果表明，我们可以有效地将信号货币化。
We propose an integrated deep learning architecture for the stock movement prediction. Our architecture simultaneously leverages all available alpha sources. The sources include technical signals, financial news signals, and cross-sectional signals. Our architecture possesses three main properties. First, our architecture eludes overfitting issues. Although we consume a large number of technical signals but has better generalization properties than linear models. Second, our model effectively captures the interactions between signals from different categories. Third, our architecture has low computation cost. We design a graph-based component that extracts cross-sectional interactions which circumvents usage of SVD that's needed in standard models. Experimental results on the real-world stock market show that our approach outperforms the existing baselines. Meanwhile, the results from different trading simulators demonstrate that we can effectively monetize the signals.