This study examines the predictability of three major cryptocurrencies—bitcoin,
ethereum, and litecoin—and the profitability of trading strategies devised upon
machine learning techniques (e.g., linear models, random forests, and support vector
machines). The models are validated in a period characterized by unprecedented
turmoil and tested in a period of bear markets, allowing the assessment of whether the
predictions are good even when the market direction changes between the validation
and test periods. The classification and regression methods use attributes from trading
and network activity for the period from August 15, 2015 to March 03, 2019, with
the test sample beginning on April 13, 2018. For the test period, five out of 18 individual
models have success rates of less than 50%. The trading strategies are built on
model assembling. The ensemble assuming that five models produce identical signals
(Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized
Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional
round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results
support the claim that machine learning provides robust techniques for exploring the
predictability of cryptocurrencies and for devising profitable trading strategies in these
markets, even under adverse market conditions.

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