  We study the problem of predicting whether the price of the 21 most popular
cryptocurrencies (according to coinmarketcap.com) will go up or down on day d,
using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider
the fact that the price of a cryptocurrency c might depend not only on
historical prices, sentiments, global stock indices, but also on the prices and
predicted prices of other cryptocurrencies. C2P2 therefore does not predict
cryptocurrency prices one coin at a time --- rather it uses similarity metrics
in conjunction with collective classification to compare multiple
cryptocurrency features to jointly predict the cryptocurrency prices for all 21
coins considered. We show that our C2P2 algorithm beats out a recent competing
2017 paper by margins varying from 5.1-83% and another Bitcoin-specific
prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all
cryptocurrencies considered. Moreover, we experimentally show that the use of
similarity metrics within our C2P2 algorithm leads to a direct improvement for
20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the
similarity component, C2P2 still beats competitors on 20 out of 21
cryptocurrencies considered. We show that all these results are statistically
significant via a Student's t-test with p<1e-5. Check our demo at
https://www.cs.dartmouth.edu/dsail/demos/c2p2
