Stock trend prediction has attracted considerable attention for its potential to generate tangible investment returns. With the advent of deep learning in quantitative finance, researchers have increasingly recognized the importance of synergies between stocks, such as sector membership or upstream-downstream relationships, in accurately capturing market dynamics. However, previous work often relies on static industry graphs or constructs graphs at each time step via similarity measures, overlooking the fluid evolution of stock relationships. We observe that as companies interact competitively and cooperatively, their interdependencies change in a fine-grained, time-varying manner that cannot be fully captured by coarse, static connections or simple similarity-based snapshots. To address these challenges, we introduce the Stock State Space Graph (S$^{3}$G) framework for enhanced stock trend prediction. First, we apply wavelet transforms to denoise the inherently low signal-to-noise financial series and extract salient patterns. After that, we construct data-dependent graphs at each time point and employ state space models to characterize the evolutionary dynamics of these graphs. Finally, we perform a graph aggregation operation to obtain the predicted return. Extensive experiments on historical CSI 500 data demonstrate the state-of-the-art performance of S$^{3}$G, with superior annualized returns and Sharpe ratios compared to other baselines.

翻译：股票趋势预测因其能够产生可观的投资回报而备受关注。随着深度学习在量化金融领域的应用，研究人员日益认识到股票间协同效应（如行业隶属关系或上下游关系）对于准确捕捉市场动态的重要性。然而，以往研究往往依赖于静态行业图或通过相似性度量构建每个时间步的图，忽视了股票关系的动态演变。我们观察到，随着公司之间竞争与合作的相互影响，其相互依赖关系会以细粒度的时变方式发生变化，而这种变化无法被粗粒度的静态连接或基于简单相似性的快照完整捕捉。为应对这些挑战，我们提出了股票状态空间图（S$^{3}$G）框架用于增强股票趋势预测。首先，我们应用小波变换对固有信噪比低的金融序列进行去噪并提取显著模式；接着，在每个时间点构建数据相关的图，并采用状态空间模型刻画这些图的演化动态；最后，通过图聚合操作获得预测收益率。在历史中证500数据上的大量实验表明，S$^{3}$G取得了最先进的性能，其年化收益率和夏普比率均优于其他基线方法。