AHAT-Fin: an adaptive hybrid attention and market-state-aware framework for financial time-series forecasting

Accurate financial time-series forecasting is essential for quantitative investment. However, existing methods often perform inadequately when handling the complex dynamics of financial data, including non-stationarity, volatility clustering, and market regime transitions. These characteristics can adversely affect both forecasting accuracy and the quality of investment decisions. To address these challenges, this study develops AHAT-Fin, an improved Transformer-based forecasting framework that integrates adaptive hybrid attention with market state perception. AHAT-Fin integrates dual-path adaptive hybrid attention, generalized autoregressive conditional heteroskedasticity (GARCH)-based dynamic volatility adjustment, and hierarchical market-state perception based on multi-window long short-term memory (LSTM) networks. The model is evaluated using daily trading data from seven representative U.S. stocks and selected constituents of the CSI 300 Index. The results show that AHAT-Fin generally outperforms the benchmark models, achieving an average reduction of 20.2% in mean squared error, an 8.4% improvement in directional accuracy, and a 63.4% increase in the Sharpe ratio. Module-level evaluation further confirms that all three components contribute to the overall predictive performance, with the market state perception module playing a particularly important role in improving risk-adjusted investment performance. Visual analyses of attention weights and market state identification further demonstrate the model's ability to extract multidimensional financial features and capture transitions across market states. These findings demonstrate the potential of combining volatility-aware modeling, adaptive attention, and market-state perception for financial forecasting and quantitative strategy design.