Stock trend classification remains a fundamental yet challenging task, owing to the intricate time-evolving dynamics between and within stocks. To tackle these two challenges, we propose a graph-based representation learning approach aimed at predicting the future movements of multiple stocks. Initially, we model the complex time-varying relationships between stocks by generating dynamic multi-relational stock graphs. This is achieved through a novel edge generation algorithm that leverages information entropy and signal energy to quantify the intensity and directionality of inter-stock relations on each trading day. Then, we further refine these initial graphs through a stochastic multi-relational diffusion process, adaptively learning task-optimal edges. Subsequently, we implement a decoupled representation learning scheme with parallel retention to obtain the final graph representation. This strategy better captures the unique temporal features within individual stocks while also capturing the overall structure of the stock graph. Comprehensive experiments conducted on real-world datasets from two US markets (NASDAQ and NYSE) and one Chinese market (Shanghai Stock Exchange: SSE) validate the effectiveness of our method. Our approach consistently outperforms state-of-the-art baselines in forecasting next trading day stock trends across three test periods spanning seven years. Datasets and code have been released (https://github.com/pixelhero98/MGDPR).

翻译：摘要：股票趋势分类仍是一项基础而富有挑战性的任务，这归因于股票间及股票内部的复杂时变动态。为应对这两个挑战，我们提出一种基于图的表示学习方法，用于预测多只股票的未来走势。首先，我们通过生成动态多关系股票图来建模股票间复杂的时间变化关系。这通过一种新颖的边生成算法实现，该算法利用信息熵和信号能量量化每个交易日股票间关系的强度和方向性。随后，我们通过随机多关系扩散过程进一步精化这些初始图，自适应地学习任务最优边。接着，我们采用带并行记忆的解耦表示学习方案来获得最终图表示。该策略既能更好地捕捉个股内部的独特时序特征，又能保持股票图的整体结构。在来自两个美国市场（纳斯达克和纽约证券交易所）和一个中国市场（上海证券交易所）的真实数据集上进行的全面实验验证了我们的方法的有效性。在跨越七年的三个测试周期中，我们的方法在预测下一个交易日股票趋势方面始终优于最先进的基线模型。数据集和代码已公开发布于 (https://github.com/pixelhero98/MGDPR)。