Bitcoin: Learning and Predictability via Technical Analysis
51 Pages Posted: 13 Feb 2018 Last revised: 19 Mar 2019
Date Written: January 9, 2019
We document that 1- to 20-week moving averages (MAs) of daily prices predict Bitcoin returns in- and out-of-sample. Trading strategies based on MAs generate substantial alpha, utility and Sharpe ratios gains, and significantly reduce the severity of drawdowns relative to a buy-and-hold position in Bitcoin. We explain these facts with a novel equilibrium model that demonstrates, with uncertainty about growth in fundamentals, rational learning by investors with different priors yields predictability of returns by MAs. We further validate our model by showing the MA strategies are profitable for tech stocks during the dotcom era when fundamentals were hard to interpret.
Keywords: Bitcoin, Cryptocurrency, Technical Analysis
JEL Classification: G11, G12, G14
Suggested Citation: Suggested Citation