Another example is the launch of bitcoin futures contracts in December 2017 by the Chicago Board Options Exchange (CBOE) and the Chicago Mercantile Exchange, which is indicative of the traditional financial industry’s attempt not to distance itself from this market trend. For instance, “What is bitcoin?” was the most popular Google search question in the United States and the United Kingdom in 2018 (Marsh 2018). Since its inception, coinciding with the international crisis of 2008 and the associated lack of confidence in the financial system, bitcoin has gained an important place in the international financial landscape, attracting extensive media coverage, as well as the attention of regulators, government institutions, institutional and individual investors, academia, and the public in general. 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. 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. The trading strategies are built on model assembling. For the test period, five out of 18 individual models have success rates of less than 50%. The classification and regression methods use attributes from trading and network activity for the period from Augto March 03, 2019, with the test sample beginning on April 13, 2018. 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. 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).
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