Decoding the Unique Price Behavior in the Japanese Stock Market with Convolutional Neural Networks
A 2023 paper by Okada, Hamuro and Nakasuji on machine learning return prediction in equities, using a feedforward neural network: the abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Our findings suggest that the features extracted from past stock charts possess predictive power for subsequent returns, particularly in larger, more liquid stocks.”
AssetEquities
StrategyMachine learningmomentum
TechniqueFeedforward networkconvolutional network
GeographyJapan
Also stated: Price and volume
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
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