<b>ECAWE: Stock Price Predictions based on Extreme Cross Attention</b> <b>and Weighted Extreme Loss Function</b>

Stock prices exhibit complex dynamics influenced by political events, economic cycles, and investor sentiment, posing substantial challenges to accurate forecasting. A particularly challenging aspect is the non-stationarity and heavy-tailed distribution of price series, where empirical analysis shows extreme values (exceeding the 80th percentile) account for over 40% of the total mean squared error (MSE) in conventional machine learning models. To address this, we propose a novel framework incorporating an extreme cross-attention (ECA) mechanism that explicitly captures the interplay between normal patterns and extreme events. Our approach: (1) processes multi-sector stock data to extract multidimensional features (e.g., technical indicators); (2) identifies extreme values using quantile thresholds of stock price sequences; (3) computes bidirectional attention weights between normal-state and extreme-value embeddings via ECA; (4) optimizes predictions through a hybrid loss function that combines Huber loss (for normal fluctuations) and a Fréchet-based extreme loss (for extreme points). Extensive experiments across seven industry stock datasets demonstrate that our model reduces MSE by 17-36% against both classical (e.g., LSTM) and state-of-the-art deep learning benchmarks (e.g., iTransformer). Ablation studies confirm the contribution of each component, and a portfolio case study demonstrates its practical utility.