Enhancer: A Distribution-Aware Framework with Temporal-Relational Meta-Learning for Stock Prediction

Accurate stock prediction is critical for portfolio management, where learning to adapt to market changes is the key to sustainable profitability. Financial markets, as complex interactive systems, exhibit evolution in both temporal dynamics and relational structures. While current temporal-relational models have achieved remarkable success in stock prediction, they face fundamental challenges in learning and adapting to market changes, particularly the systematic shifts in temporal and relational distributions that challenge the i.i.d. assumption underlying model training. In this study, we pioneer the research of temporal-relational distribution shifts in stock prediction and introduce Enhancer, a model-agnostic framework that can be applied to any downstream predictor. Enhancer adopts a meta-learning architecture featuring both a Temporal Meta-Learner (TML) and a Relational Meta-Learner (RML). Specifically, we introduce Reactive Point Processes Attention (RPPsAtt) within TML to overcome the limitations of missing fine-grained temporal point information, a common issue with prior methods that rely on distribution inference for mitigating temporal distribution shift. To enhance relational generalization, we introduce the Approximation-Intervention (Ant) mechanism within RML, marking the first method to mitigate relational distribution shift for quantitative investment. We conduct experiments on four long-term stock datasets, categorizing them into two tasks: stock trend prediction and stock investment recommendation. Our experimental results show that Enhancer achieves an average increase of 29.3% in profit ratio and 18.54% in the Sharpe ratio compared to the baselines across two tasks.