基于时序超图卷积神经网络的股票趋势预测方法

传统的股票预测方法大多基于时间序列模型,忽视了股票之间复杂的关系,并且该关系往往超出成对连接,例如同行业板块内股票或者基金持仓多支股票.针对该问题,提出一种基于时序超图卷积神经网络(HGCN)的股价走势预测方法,根据金融投资事实构造超图模型以拟合股票之间的多元关系,该模型包括两大组件:门控循环单元(GRU)网络和超图卷...

Stock trend prediction method based on temporal hypergraph convolutional neural network

Traditional stock prediction methods are mostly based on time-series models， which ignore the complex relations among stocks， and the relations often exceed pairwise connections， such as stocks in the same industry or multiple stocks held by the same fund. To solve this problem， a stock trend prediction method based on temporal HyperGraph Convolutional neural Network （HGCN） was proposed， and a hypergraph model based on financial investment facts was constructed to fit multiple relations among stocks. The model was composed of two major components： Gated Recurrent Unit （GRU） network and HGCN. GRU network was used for performing time-series modeling on historical data to capture long-term dependencies. HGCN was used to model high-order relations among stocks to learn intrinsic relation attributes， and introduce the multiple relation information among stocks into traditional time-series modeling for end-to-end trend prediction. Experiments on real dataset of China A-share market show that compared with existing stock prediction methods， the proposed model improves prediction performance， e.g. compared with the GRU network， the proposed model achieves the relative increases in ACC and F1_score of 9.74% and 8.13%， respectively， and is more stable. In addition， the simulation back-testing results show that the trading strategy based on the proposed model is more profitable， with an annual return of 11.30%， which is 5 percentage points higher than that of Long Short-Term Memory （LSTM） network.