Towards Robust Predictions Under Extreme Markets: A Hybrid Framework Integrating Adaptive Trend Decomposition and STOA ‐Optimized Deep Learning

ABSTRACT Financial time series prediction faces dual challenges: deep learning models are highly sensitive to hyperparameters, and their vulnerability under extreme market conditions leads to severe performance decay. To address this, this paper proposes the AT‐STOA‐Transformer‐BiLSTM hybrid framework. The framework employs the STOA (Sooty tern optimization algorithm) for two‐stage collaborative optimization: first selecting predictive features, then tuning six key hyperparameters of the Transformer‐BiLSTM architecture. Concurrently, it integrates an AIC‐based adaptive trend decomposition module to separate non‐stationary sequences into deterministic trends and stationary residuals. In systematic experiments on China's CSI 300, CSI 500 and CSI 1000 indices, this framework demonstrates significant superiority. Compared to the unoptimized baseline, the entire model reduces the average absolute error (MAE) by approximately 24% on average and increases the coefficient of determination ( R 2 ) by 0.043 on average. In most cases, STOA is always superior to or on par with the mainstream meta‐heuristic optimizers (COA, DBO, EO, SO and SSA). Crucially, the framework exhibits considerable robustness. During the 2022 Russia–Ukraine conflict—a proxy for extreme market stress—the model maintains stable predictions, significantly outperforming baselines in the decay ratio metric. Furthermore, tests on the Dutch AEX and Swiss SMI indices confirm strong cross‐market generalization capabilities. In conclusion, the proposed hybrid framework offers improvements in predictive accuracy, stability and applicability, offering a promising tool for quantitative finance and risk management.