Stock movement prediction is a challenging task due to the complex temporal dynamics of financial time series and the interdependence among stocks. Most existing approaches either model each stock independently or rely on a single static graph, which limits their ability to capture heterogeneous and time-varying relationships in financial markets. To address this issue, this paper proposes a dynamic dual-graph GCN-GRU framework for next-day stock movement prediction. The proposed model integrates two complementary graph structures: a static industry graph to encode long-term sectoral relationships and a dynamic correlation graph to capture evolving co-movement patterns among stocks over time, where the dynamic graph is constructed in a causally consistent manner using only historical information available before the target prediction. For each graph, a graph convolutional network (GCN) is employed to learn relational stock representations, while a gated recurrent unit (GRU) is used to model temporal dependencies from sequential graph-based embeddings. The two graph-specific representations are integrated through an adaptive gated fusion mechanism. By jointly exploiting structural market information and temporal dynamics, the proposed framework provides a unified representation for next-day stock direction forecasting. Experiments are conducted on two public stock datasets constructed from the S&P 500 and VN30 markets using engineered features derived from daily trading data. The evaluation considers both standard classification metrics and finance-oriented indicators, including IC, Sharpe ratio, and maximum drawdown. The empirical results demonstrate the effectiveness of the proposed approach compared with conventional machine learning, sequence-based, and graph-based baselines. These findings suggest that combining dynamic relational modeling with temporal learning is a practical and reproducible forecasting framework for stock movement prediction in both developed and emerging markets.