  Using non-linear machine learning methods and a proper backtest procedure, we
critically examine the claim that Google Trends can predict future price
returns. We first review the many potential biases that may influence backtests
with this kind of data positively, the choice of keywords being by far the
greatest culprit. We then argue that the real question is whether such data
contain more predictability than price returns themselves: our backtest yields
a performance of about 17bps per week which only weakly depends on the kind of
data on which predictors are based, i.e. either past price returns or Google
Trends data, or both.
