Anticipating cryptocurrency prices using machine learning
This addresses the problem of improving trading profits for cryptocurrency investors, but it is incremental as it applies existing methods to a new dataset.
The study tackled predicting cryptocurrency prices by testing if market inefficiencies could yield abnormal profits, finding that simple trading strategies with state-of-the-art machine learning algorithms outperformed standard benchmarks using data from 1,681 cryptocurrencies from 2015 to 2018.
Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.