Bitcoin Technical Trading With Artificial Neural Network
A 2018 paper by Nakano, Takahashi and Takahashi on machine learning return prediction in crypto, using a feedforward neural network: the abstract mentions a benchmark and leaves out an out-of-sample test, transaction costs, multiple testing and the size of the universe.
“Under feasible settings of execution costs, the numerical experiments demonstrate that our approach significantly improves the performance of a buy-and-hold strategy.”
AssetCryptospot crypto
StrategyMachine learning
TechniqueFeedforward network
FrequencyIntraday
Also stated: Long-only, Price and volume, 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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