Predicting prices of S&P500 index using classical methods and recurrent neural networks
Machine learning return prediction in equities over 2000–2020, studied in a 2020 paper with a recurrent neural network. The abstract mentions an information ratio, excess returns, drawdowns and a benchmark but not an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Combination of signals from different methods was stable and outperformed benchmark of Buy & Hold strategy doubling its returns on the same level of risk.”
AssetEquitiesindices
StrategyMachine learning
TechniqueRecurrent neural networkfeedforward network
GeographyUS
Also stated: 2000–2020, Information ratio, Excess return, Drawdown, benchmark
Missing from abstract: 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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