S$^{3}$G: Stock State Space Graph for Enhanced Stock Trend Prediction
This work addresses stock trend prediction for investors by proposing a novel method to capture time-varying stock interdependencies, though it is incremental in improving existing graph-based approaches.
The paper tackled the problem of stock trend prediction by addressing the fluid evolution of stock relationships, introducing the Stock State Space Graph (S^3G) framework, which achieved state-of-the-art performance with superior annualized returns and Sharpe ratios on CSI 500 data.
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.