Stocks that are fundamentally connected with each other tend to move together. Considering such common trends is believed to benefit stock movement forecasting tasks. However, such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data. Motivated by this observation, we propose a framework that incorporates the inter-connection of firms to forecast stock prices. To effectively utilize a large set of fundamental features, we further design a novel pipeline. First, we use variational autoencoder (VAE) to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure (fundamentally clustering). Second, a hybrid model of graph convolutional network and long-short term memory network (GCN-LSTM) with an adjacency graph matrix (learnt from VAE) is proposed for graph-structured stock market forecasting. Experiments on minute-level U.S. stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods. The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

中文翻译：

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ST-Trader：用于模拟股市走势的时空深度神经网络


基本面相互关联的股票往往会一起波动。考虑到这种共同趋势被认为有利于股票走势预测任务。然而，此类信号的建模并非易事，因为股票之间的联系并未实际呈现，需要根据不稳定的数据进行估计。受这一观察的启发，我们提出了一个框架，该框架结合了公司之间的相互联系来预测股票价格。为了有效地利用大量的基本特征，我们进一步设计了一种新颖的管道。首先，我们使用变分自动编码器（VAE）来降低股票基本面信息的维度，然后将股票聚类成图结构（基本面聚类）。其次，提出了一种图卷积网络和长短期记忆网络（GCN-LSTM）与邻接图矩阵（从 VAE 学习）的混合模型，用于图结构股票市场预测。对分钟级美国股市数据的实验表明，我们的模型有效地捕获了空间和时间信号，并比基线方法取得了卓越的改进。所提出的模型对于其他应用来说是有希望的，在这些应用中存在可能但隐藏的空间依赖性来改进时间序列预测。