The cross-sectional stock return predictions via quantum neural network and tensor network
Machine learning return prediction in equities, studied by Kobayashi and co-authors in 2024 with a feedforward neural network. The abstract mentions excess returns but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models.”
AssetEquities
StrategyMachine learning
TechniqueFeedforward networkquantum computing
GeographyJapan
Also stated: Excess return
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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