Deep Learning for Event-Driven Market Prediction: A Transformer-Based Model for Misinformation-Induced Volatility

Abstract

Misinformation events introduce abrupt, nonlinear distortions in financial markets, posing significant challenges to conventional time-series models that often fail to capture such structural breaks due to assumptions of stationarity and limited contextual awareness. To address this limitation, we develop an Enhanced Event-Adaptive Transformer (EET) architecture that jointly models global contextual dependencies and localized temporal dynamics under event-driven scenarios. The proposed framework integrates event embedding layers and multi-scale con volutional feature extractors into a Transformer encoder, enabling explicit encoding of exogenous disruption signals alongside multi-resolution pattern recognition. We evaluate the model using stock-level data from China's power sector (2017-2022), with the misinformation event of October 16th, 2019, serving as a causal boundary for temporal segmentation. Trained exclusively on pre-event data and tested on post-event sequences, the model achieves superior predictive performance in three-fold cross-validation (mean $\mathrm{R}^{\mathrm{2}}=\text{0. 9 8 8 4} \pm \text{0. 0 0 0 5}$; MAE ${=}\text{0. 1 1 7 2} \pm 0.0264)$. Ablation studies confirm that both event embedding and convolutional components significantly enhance accuracy (R2 declines by 21.0% and 17.1% upon their removal, respectively). Residual diagnostics validate statistical robustness, with errors exhibiting near-normal distributions and no temporal bias. This study establishes a novel theoretical and practical framework for quantifying the impact of exogenous shocks on financial systems. The architecture provides an interpretable and high-fidelity solution for event-driven forecasting, with implications for financial risk management and algorithmic decision-making.