This study introduces a Spatio-Temporal Graph Attention Network (STGAT) for non-stationary financial systems to address challenges in stock price prediction, such as non-stationarity and complex dependencies, by integrating a gated causal temporal convolution module and a novel graph attention module. STGAT employs causal time-modeling to prevent future information leakage, a dynamic industry-correlation graph attention mechanism for adaptive weight learning, and a multi-scale MIMO framework for industry-grouped feature extraction. Its symmetric integration of temporal and spatial modeling balances time-series dynamics with inter-stock correlations. Experiments on the New York Stock Exchange’s commercial banking and metals sectors demonstrate that STGAT substantially outperforms XGBoost, LSTM, and SARIMA in predictive accuracy, particularly in high-volatility scenarios. This research underscores the efficacy of graph neural networks in modeling interconnected financial systems and offers insights for advancing portfolio optimization and risk management.
Wei et al. (Tue,) studied this question.
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