An Improved Validation Framework Using Multi-Resolution Transformers and Sentiment-Aware Graph Neural Networks for Robust Financial Forecasting

In predicting trends related to future stock prices, conventional models are less effective and do not capture the most critical multi-scale trends, sentiment developments, and instantaneous market shocks.Most existing transformer-based architectures fail when tested under highly volatile conditions due to the simplistic data modalities used and sentiments that propagate dynamically among various companies.This research proposes a hybrid stock prediction model based on five new methods to increase accuracy, robustness, and portfolio performance such that the Hybrid Stock Prediction Model can comprise the following: a novel multiresolution transformer with wavelet decomposition (MR-TWD) that captures long and short market dynamics by processing stock price signals along different frequency bands; quantitative measurement of sentiment impact through the Sentiment-Aware Financial Graph Neural Network (SA-FGNN); generating a synthetic yet realistic market crisis with an aid of GANs to better train models on tail-risk events for better robustness under rare and adverse conditions.The model iteratively improves upgrades and enhances predictive performance with the maximum combination of novel methods and the Hybrid Stock Prediction Model for robustness and portfolio performance.The TMF VA module integrates stock data, sentiment signals, and volatility indicators through an adaptive attention mechanism that re-weights input features according to market conditions.Finally, introducing CRL-PO for modelling investment configurations into the multistage systematic course of portfolios will yields a contrastive signal for identifying high-yield strategies to generate risk-adjusted BUY/SELL decisions from a well-prepared dataset.Empirical evaluation shows a +10% increase in forecasting accuracy (R²), +8% enhancement in sentiment correlation, +25% robustness gain (Sharpe Ratio), and +7.8% increase in cumulative returns, hence backing the hybrid model's claim as superior technically and robustly for decision-making in modern financial markets.