MDMF: A Market-Mainline-Driven Multi-Feature Fusion Model for Stock Trend Forecasting

Accurate stock prediction is crucial for investment decisions and risk management, yet remains challenging due to the non-stationary, nonlinear, and noisy nature of financial markets. Although deep learning has advanced forecasting by modeling temporal patterns and stock relationships, most methods fail to capture structured, market-wide forces. Specifically, they miss the emergence and influence of “market mainlines”—persistent directional trends in groups of stocks with shared attributes that collectively drive market movement. To address this, we propose the Market-mainline-Driven Multi-feature Fusion Model (MDMF), which dynamically identifies multidimensional market mainline characteristics and captures their differential impact on individual stocks. The model incorporates a dual-channel encoding mechanism, a dynamic stock aggregation algorithm, and a differential influence module to integrate temporal, fundamental, and stock-specific features. Extensive experiments on real-world stock datasets show that MDMF outperforms state-of-the-art baselines in predictive accuracy and profitability, demonstrating its robustness and practical utility. Our study highlights the value of explicitly modeling market mainlines for enhancing stock prediction and offers insights into systematic market behavior.