TY  - JOUR
AU  - Yi, Yingnan
PY  - 2026
DA  - 2026/04/10
TI  - Wavelet Denoising and Model Parsimony in High-Frequency Momentum Strategies: Evidence from China’s ChiNext Market
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 2
IS  - 3
SP  - 172
EP  - 189
DO  - 10.62762/TMI.2025.742127
UR  - https://www.icck.org/article/abs/TMI.2025.742127
KW  - momentum investing
KW  - wavelet denoising
KW  - portfolio management
KW  - chiNext market
KW  - machine learning
KW  - financial signal processing
AB  - This study introduces a sophisticated dual-denoising framework for high-frequency momentum strategies in China's ChiNext market, integrating wavelet-based temporal filtering with isolation forest cross-sectional anomaly detection. Utilizing a comprehensive dataset of over 2 million daily observations from January 2016 to November 2025, the results demonstrate that wavelet denoising achieves exceptional efficacy for turnover series with a mean Signal-to-Noise Ratio improvement of 10.7 dB, while isolation forest robustly identifies anomalous stocks characterized by excessive trading activity and distorted risk-return profiles. Empirical results reveal that parsimonious models with wavelet denoising consistently outperform complex alternatives, with linear-kernel Support Vector Machines achieving Sharpe ratios of 0.1132 in long portfolios and 0.0649 in long-short implementations. Contrary to conventional wisdom, simpler architectures demonstrate superior performance, with single-layer neural networks and regularized linear models surpassing deeper architectures across all denoising configurations. The methodology demonstrates remarkable regime invariance, maintaining consistent performance during both low-volatility and high-volatility market states. These findings establish wavelet denoising as a critical preprocessing technique for high-frequency financial data and challenge assumptions about the necessity of complex models in quantitative finance, providing computationally efficient approaches suitable for both institutional and retail investors.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Yi2026Wavelet,
  author = {Yingnan Yi},
  title = {Wavelet Denoising and Model Parsimony in High-Frequency Momentum Strategies: Evidence from China’s ChiNext Market},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2026},
  volume = {2},
  number = {3},
  pages = {172-189},
  doi = {10.62762/TMI.2025.742127},
  url = {https://www.icck.org/article/abs/TMI.2025.742127},
  abstract = {This study introduces a sophisticated dual-denoising framework for high-frequency momentum strategies in China's ChiNext market, integrating wavelet-based temporal filtering with isolation forest cross-sectional anomaly detection. Utilizing a comprehensive dataset of over 2 million daily observations from January 2016 to November 2025, the results demonstrate that wavelet denoising achieves exceptional efficacy for turnover series with a mean Signal-to-Noise Ratio improvement of 10.7 dB, while isolation forest robustly identifies anomalous stocks characterized by excessive trading activity and distorted risk-return profiles. Empirical results reveal that parsimonious models with wavelet denoising consistently outperform complex alternatives, with linear-kernel Support Vector Machines achieving Sharpe ratios of 0.1132 in long portfolios and 0.0649 in long-short implementations. Contrary to conventional wisdom, simpler architectures demonstrate superior performance, with single-layer neural networks and regularized linear models surpassing deeper architectures across all denoising configurations. The methodology demonstrates remarkable regime invariance, maintaining consistent performance during both low-volatility and high-volatility market states. These findings establish wavelet denoising as a critical preprocessing technique for high-frequency financial data and challenge assumptions about the necessity of complex models in quantitative finance, providing computationally efficient approaches suitable for both institutional and retail investors.},
  keywords = {momentum investing, wavelet denoising, portfolio management, chiNext market, machine learning, financial signal processing},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}