A Generative Adaptive Decomposition Hierarchical Transformer for Stock Price Forecasting

Abstract

Forecasting stock prices is challenging due to the non-stationarity and volatility of financial time series. We propose the Generative Adaptive Decomposition Hierarchical Transformer (GADHT), a hybrid framework that combines adaptive decomposition, masked self-supervised pretraining, and hierarchical attention. GADHT applies Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose financial signals into intrinsic mode functions (IMFs) that capture multi-scale temporal dynamics. A masked self-supervised pretraining task based on IMF reconstruction is used to learn spectral–temporal representations without labeled data, while a hierarchical transformer with energy-weighted attention emphasizes informative IMFs during forecasting. Experiments on large-cap equities across multiple forecasting horizons show that GADHT achieves competitive and stable forecasting performance. The model maintains stable predictive behavior during stress periods such as the 2008 financial crisis and the 2020 COVID-19 crash, and shows positive economic performance under the adopted backtesting assumptions. Zero-shot and cross-market experiments further suggest that the learned representations can transfer to unseen large-cap equities and selected international markets. Interpretability analysis shows that spectral attention patterns are associated with financial characteristics such as momentum, volatility, and liquidity. Overall, the results suggest that GADHT provides a coherent and interpretable framework for multi-horizon stock forecasting.