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WaVeFuse: Regime-Adaptive Equity Index Forecasting via Channel-Wise Wavelet Denoising and Vertical Attention Fusion

Aashish Bohra, Vivek Vijay · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.14733 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

在股票指数预测中,HYbrid CROSS 152 有效的COSD 学习是由三个问题所限制的:OHLCV框架噪音的扩散到衍生技术指标(TIs)中,无差别的通道多尺度分解(MAD 水平 2 无差别的多尺度分解),在OHLCV中压制异性频率签名的微观分解,3 静态多层合并,无法适应市场模式的变化。WaVeFuse 通过统一的双分支架构(Morlet,32 尺度)来解决这些局限性。Symlet-4 wavelet denoising(MAD 水平 2 软门槛)压抑OHLCV中的微观结构噪音,3 个静态多层合并的 TI 计算从无差别价格分

Machine translation (Meta M2M100); the English original follows.

Abstract

Hybrid Deep Learning for equity index forecasting is limited by three problems: propagation of OHLCV noise into derived technical indicators (TIs), channel-indiscriminate multi-scale decomposition that conflates heterogeneous frequency signatures, and static multi-branch fusion that cannot adapt to market regime shifts. WaVeFuse addresses these limitations through a unified dual-branch architecture. Symlet-4 wavelet denoising (level 2, MAD soft threshold) suppresses microstructure noise in OHLCV. Seven low-lag TIs computed from denoised prices are encoded by a causal channel-wise continuous wavelet transform (Morlet, 32 scales) into a per-timestep scale-space matrix. A CNN-BiLSTM branch captures temporal dynamics, while a dual-layer Transformer (heads=4, dk in {16, 32}) models inter-scale spectral dependencies, and their representations are integrated by a 2-token softmax gate Vertical Attention Fusion (VAF) that dynamically reweights branches as market regimes shift. Evaluated under walk-forward validation (WFV) on KOSPI, DAX, NYSE Composite, and Russell 2000 (2010-2023), WaVeFuse achieves R2 = 0.81-0.96 and directional accuracy 70.5-78.3%. It outperforms seven state-of-the-art models by 8.9-20.2% MAE across twelve dataset-period configurations. Diebold-Mariano statistics (4.62-10.38, p<0.001) confirm superiority over a well-tuned XGBoost benchmark across four indices. Ablation verifies component-wise contributions. Under realistic backtesting with 10 basis point transaction costs, WaVeFuse's directional strategy achieves a mean Sharpe ratio of 3.69 across four markets and limits maximum drawdown to 7.5% during the COVID-19 crash. With 152k parameters (0.68MB) and sub-1.3ms GPU inference, WaVeFuse delivers a computationally efficient, regime-robust framework suitable for research and decision-support deployment.

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Topics: Wavelet, Computer science, Econometrics, Artificial intelligence, Softmax function, Noise reduction

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