Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach
Machine learning return prediction in equities, studied by Sun, Wang and An in 2022 with a feedforward neural network. The abstract mentions excess returns and profits but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Through extensive experiments on long-term real-world data spanning over five years on two of the most influential financial markets (US and China), we demonstrate that AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria.”
AssetEquities
StrategyMachine learning
TechniqueFeedforward network
GeographyUSChina
Also stated: Excess return, Profit, Fundamentals
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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