IntraFormer : Intra‐Asset Channel Transformer for Stock Price Forecasting Across Global Industries

ABSTRACT Stock price time‐series presents inherent difficulties due to their nearly random‐walk behavior characterized by low signal‐to‐noise ratios and frequent regime shifts, whereas distinct industries add further complexity through unique volatility profiles and driving factors, which render cross‐sector generalization especially demanding for predictive models. This study introduces IntraFormer, a novel Transformer architecture designed specifically for multivariate stock price prediction, integrating specialized intra‐series attention mechanisms that enforce strict temporal causality, prioritize short‐range dependencies, and incorporate gated residuals to combat noise overfitting while enhancing robustness against non‐stationarity. Comprehensive experiments on 30 stocks spanning technology, healthcare, and financial sectors demonstrate IntraFormer's clear dominance: it consistently delivers the lowest MAE and RMSE, reducing them by 70%–90% compared with state‐of‐the‐art Transformer variants, whose MAPE often exceeds 60%–99% in volatile cases; relative to recurrent baselines, IntraFormer achieves 20%–60% lower errors overall, with 30%–50% MAPE improvements. IntraFormer's portoflio construction returns nearly 11% on investment. This work establishes IntraFormer as a generalizable framework, offering potential for improving accuracy in trading strategies and portoflio risk managment across diverse market conditions.