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

AFD-Net: a robust exchange rate forecasting framework integrating frequency-domain decomposition and attention mechanisms with linear modeling.

AFD-Net (Attention-Enhanced FAN with DLinear) is introduced, a novel deep learning framework designed to synergistically integrate frequency-domain decomposition, attention mechanisms, and linear time series modeling that consistently outperforms baseline models across multiple forecasting horizons.

A Generative Adaptive Decomposition Hierarchical Transformer for Stock Price Forecasting

Forecasting stock prices is challenging due to the non-stationarity and volatility of financial time series. We propose the Generative Adaptive Decomposition Hierarchical Transformer (GADHT), a hybrid framework that combines adaptive decomposition, masked self-supervised pretraining, and hierarchical attention. GADHT applies Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose financial signals into intrinsic mode functions (IMFs) that capture multi-scale temporal dynamics. A masked self-supervised pretraining task based on IMF reconstruction is used to learn spectral–temporal representations without labeled data, while a hierarchical transformer with energy-weighted attention emphasizes informative IMFs during forecasting. Experiments on large-cap equities across multiple forecasting horizons show that GADHT achieves competitive and stable forecasting performance. The model maintains stable predictive behavior during stress periods such as the 2008 financial crisis and the 2020 COVID-19 crash, and shows positive economic performance under the adopted backtesting assumptions. Zero-shot and cross-market experiments further suggest that the learned representations can transfer to unseen large-cap equities and selected international markets. Interpretability analysis shows that spectral attention patterns are associated with financial characteristics such as momentum, volatility, and liquidity. Overall, the results suggest that GADHT provides a coherent and interpretable framework for multi-horizon stock forecasting.

A Stock Market Price Prediction Model Integrating a CNN–Transformer Dual-Channel Dynamic Attention Architecture

Stock market price prediction remains a persistent challenge owing to the non-stationarity, high noise content, and intricate spatiotemporal dependencies that characterize financial time series. Existing approaches typically excel at either local pattern extraction or long-range dependency modeling, yet seldom reconcile both within a unified framework. This paper introduces a CNN–Transformer dual-channel architecture equipped with a dynamic attention fusion module for stock price forecasting. The convolutional channel applies hierarchical dilated convolutions to distill fine-grained local patterns from multi-indicator sequences while suppressing high-frequency noise. Simultaneously, the Transformer channel employs multi-head self-attention to capture long-distance temporal correlations and regime-shift dynamics. A learnable gating mechanism then fuses the two feature streams by adaptively weighting local detail against global trend information according to market conditions. Experiments conducted on four real-world stock datasets spanning the S&P 500, CSI 300, NASDAQ Composite, and Hang Seng Index show that the proposed model reduces mean absolute error by 9.7–15.3% and root mean square error by 9.5–13.8% relative to competitive baselines including LSTM, CNN–LSTM, Informer, and PatchTST. Ablation studies further indicate that both channels and the fusion module contribute to prediction accuracy, and the architecture remains effective across markets with differing volatility profiles.

Scale-Invariant Conditional VAE for Coarse-Grained Economic Time-Series Forecasting

DeRNN: Decomposed Recurrent Neural Network for Long-Term Time Series Forecasting

The Decomposed Recurrent Neural Network (DeRNN) is proposed, which decouples global trend modeling from local fluctuation extraction via an asymmetric dual-track architecture and exhibits superior robustness against noise and distribution shifts.

CASCADED GLOBAL–LOCAL REPRESENTATION LEARNING FOR FINANCIAL TIME-SERIES FORECASTING

Forecasting financial indices remains difficult because market observations combine persistent movements, short-lived disturbances, nonlinear interactions, and substantial noise. A single recurrent architecture may retain nearby temporal information yet fail to represent widely separated dependencies, whereas an attention-based encoder is effective at relating distant positions but does not by itself guarantee detailed sensitivity to local sequence dynamics. This paper reformulates the forecasting pipeline as a cascaded global–local learning problem. A Transformer encoder first converts normalized price windows into contextual representations through positional encoding, multi-head self-attention, residual normalization, and a feedforward sublayer. Those representations are then processed by a bidirectional long short-term memory network, so forward and reverse recurrent states refine the global context before a dense prediction head produces the output. The design was assessed on daily closing-price series for the S&P 500, Dow Jones Industrial Average, and Nasdaq Composite over 2 September 2003–13 July 2023. Preprocessing included interpolation of missing observations, interquartile-range screening of outliers, first differencing, min–max scaling, and windowed sample construction. Tests against recurrent, bidirectional recurrent, feedforward, Informer, and temporal-convolution baselines show that the hybrid system delivers the strongest overall error and goodness-of-fit profile across the three markets. The findings indicate that passing attention-derived context into a bidirectional memory module offers a practical means of combining long-horizon structure with local temporal variation, although computational cost remains relevant for latency-sensitive trading applications.