Title: FATE: Feature-Wise Graph Attention with Multi-Period Temporal Encoding for Stock Return Forecasting
Primary area: learning on time series and dynamical systems
Keywords: stock return forecasting; time-series prediction; graph learning

Abstract: Forecasting stock returns remains challenging due to the dual complexity of \textit{temporal heterogeneity} and \textit{structural instability}. On the one hand, stock return series contain signals at multiple horizons: short-term fluctuations, medium-term trends, and long-term cycles. On the other hand, relations across stocks are non-stationary and noisy: correlations strengthen or vanish under sector rotations, liquidity shocks, or macro events. However, existing temporal models often process each stock independently, neglecting cross-stock dependencies and blur signals across horizons. Moreover, structure-aware models typically rely on static or single-view relation-graphs that are brittle under market shifts. To overcome these limitations, we propose \textit{FATE}, which couples \textit{ multi-view dynamic graphs} with a \textit{multi-period temporal encoder} to jointly capture cross-stock relations and horizon-specific signals, and further employs \textit{feature-wise graph attention} with a learned graph to integrate these signals coherently while suppressing noise and enhancing interpretability. Experimental results demonstrate that FATE consistently outperforms strong temporal and graph baselines in correlation metrics and investment simulations, which provides more informative and robust signals for stock return forecasting.