The Application of Dual-Denoised Momentum Factors in Portfolio Management: A Study of ChiNext Stocks for Retail Investors
Yi (2025) studies momentum and trend following in equities using a feedforward neural network over 2015–2025. The abstract mentions a Sharpe ratio and excess returns but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Our methodology employs comprehensive analysis of 1,200-1,300 ChiNext stocks spanning the 2015-2025 period, utilizing multiple machine learning architectures to assess portfolio performance across both long-only and long-short implementations.”
AssetEquities
StrategyMomentum
TechniqueFeedforward network
FrequencyMonthly
Also stated: 2015–2025, Sharpe ratio, Excess return, Long-short, Long-only
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodMomentum and trend following by year
FamilyMomentum and trend following289Factor investing240Machine learning return prediction153News sentiment and text signals14818 more · 854
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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