FinKAN: KAN-Based Predictive Structure Learning for Stock Forecasting under Market Shifts

Heterogeneous stock forecasting requires learning stable predictive structures from historical periods and generalizing to unseen future periods under market shifts. Existing distribution-aware, multi-frequency, and graph methods address parts of this task, but still leave two issues: stable temporal representations and re- liable relational aggregation. We propose FinKAN, a KAN-based framework with stability-selected predictive Markov-blanket stock aggregation. FinKAN pretrains a Data Shift Aligner only on temporally separated subdomains inside the training period, extracts nonlinear multi-frequency patterns with frequency-aware KAN modules, and builds a predictive Markov-blanket inclusion prior through sparse conditional neighborhood selection with temporal resampling. The prior gates direct, indirect, and residual cross-stock message passing without claiming inter- ventional causality. Experiments on real-world stock datasets show that FinKAN improves prediction accuracy and investment metrics over competitive baselines and its backbone variant. Ablations and visualizations further show that predic- tive Markov-blanket aggregation yields a more robust and interpretable relational structure for stock forecasting.