Do Google Trend Data Contain More Predictability than Price Returns?
- Damien Challet and Ahmed Bel Hadj Ayed
- A version of the paper can be found here.
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Abstract:
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.
Data Sources:
Google Trends Data
Alpha Highlight:
Quote from the paper:
We therefore conclude that price returns and GT contain about the same amount of predictive information, at least with the methods we have used and challenge to community to do any better.
First, a look at different keywords and their ability to predict SP 500 returns. Clearly, there is some evidence for data-mining in the “Google predicts returns” literature.
What about performance? Looks like good old fashioned past returns predicts just as well as the fancier Google Trends data.
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.
Google Trends Data Needs Some Work!
About the Author: Wesley Gray, PhD
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