Forecasting the future movements of stock market indexes by utilizing historical transaction data is a prominent concern within the realm of finance. The application of graph convolutional networks to incorporate the interrelationships among various indices’ patterns is a highly advanced subject within this field. Addressing the inconsistency between historical and future dynamic graphs in current graph convolution-based index prediction, we propose a method called G-Conv that constructs a graph structure based on constituent stocks of the indices for index trend prediction. This approach extracts traditional quantitative features along with deep features from one-dimensional convolutional networks as characteristics of prediction samples. The method produces index trend predictions by constructing a graph structure using constituent stock data of indices and applying graph convolution to different index sample features. The proposed methodology’s efficacy is verified by utilizing 42 widely employed indicators in the A-share market. The experimental findings demonstrate that when utilizing mean absolute error (MAE) and mean squared error (MSE) as the loss functions for model training, G-Conv outperforms classic methods such as GC-CNN and ADGAT. Specifically, G-Conv reduces the average prediction errors by 5.10% and 4.20% respectively, as evaluated by the two error criteria. Additionally, G-Conv exhibits favorable generalization performance.

利用历史交易数据预测股市指数的未来走势是金融领域的一个突出问题。应用图卷积网络来整合各种指数模式之间的相互关系是该领域内的一个非常先进的主题。针对当前基于图卷积的指数预测中历史动态图与未来动态图不一致的问题，我们提出了一种称为G-Conv的方法，该方法基于指数成分股构建图结构来进行指数趋势预测。该方法从一维卷积网络中提取传统的定量特征以及深层特征作为预测样本的特征。该方法通过使用指数的成分股数据构建图结构并将图卷积应用于不同的指数样本特征来产生指数趋势预测。利用A股市场广泛使用的42个指标验证了该方法的有效性。实验结果表明，当使用平均绝对误差（MAE）和均方误差（MSE）作为模型训练的损失函数时，G-Conv优于GC-CNN和ADGAT等经典方法。具体而言，根据两个误差标准评估，G-Conv 分别将平均预测误差降低了 5.10% 和 4.20%。此外，G-Conv 表现出良好的泛化性能。