Deep Learning Enhanced Multi-Day Turnover Quantitative Trading Algorithm for Chinese A-Share Market
Machine learning return prediction in equities over 2010–2020, studied by Du in 2025 with a feedforward neural network. The abstract mentions a Sharpe ratio, excess returns, profits and drawdowns but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“The strategy demonstrates exceptional scalability by maintaining 50-100 daily positions with a 9-day maximum holding period, incorporating dynamic profit-taking and stop-loss mechanisms that enhance capital turnover efficiency while preserving risk-adjusted returns.”
AssetEquities
StrategyMachine learningmarket timing
TechniqueFeedforward network
GeographyChina
Also stated: Daily, 2010–2020, Sharpe ratio, Excess return, Profit, Drawdown
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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