Uddin Ajim, Tao Xinyuan, Yu Dantong
Martin Tuchman School of Management, New Jersey Institute of Technology, 323 Dr Martin Luther King Jr Blvd, Newark, NJ 07102, USA.
Glob Financ J. 2023 Nov;58. doi: 10.1016/j.gfj.2023.100900. Epub 2023 Oct 2.
Recent studies suggest that networks among firms (sectors) play a vital role in asset pricing. This paper investigates these implications and develops a novel end-to-end graph neural network model for asset pricing by combining and modifying two state-of-the-art machine learning techniques. First, we apply the graph attention mechanism to learn dynamic network structures of the equity market over time and then use a recurrent convolutional neural network to diffuse and propagate firms' information into the learned networks. This novel approach allows us to model the implications of networks along with the characteristics of the dynamic comovement of asset prices. The results demonstrate the effectiveness of our proposed model in both predicting returns and improving portfolio performance. Our approach demonstrates persistent performance in different sensitivity tests and simulated data. We also show that the dynamic network learned from our proposed model captures major market events over time. Our model is highly effective in recognizing the network structure in the market and predicting equity returns and provides valuable market information to regulators and investors.
近期研究表明，公司（行业）间的网络在资产定价中起着至关重要的作用。本文研究了这些影响，并通过结合和改进两种先进的机器学习技术，开发了一种新颖的端到端图神经网络资产定价模型。首先，我们应用图注意力机制来学习股票市场随时间变化的动态网络结构，然后使用循环卷积神经网络将公司信息扩散并传播到所学习的网络中。这种新颖的方法使我们能够对网络的影响以及资产价格动态协同运动的特征进行建模。结果证明了我们提出的模型在预测回报和改善投资组合表现方面的有效性。我们的方法在不同的敏感性测试和模拟数据中都表现出持续的性能。我们还表明，从我们提出的模型中学习到的动态网络能够捕捉随时间变化的主要市场事件。我们的模型在识别市场网络结构和预测股票回报方面非常有效，并为监管机构和投资者提供了有价值的市场信息。
