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Do Google Trend data contain more predictability than price returns?

Damien Challet and Ahmed Bel Hadj Ayed

Post-Print from HAL

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.

Keywords: market efficiency; backtest; prediction; Google Trends; big data; financial markets (search for similar items in EconPapers)
Date: 2015-03-19
Note: View the original document on HAL open archive server: https://hal.science/hal-00960875
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Citations: View citations in EconPapers (1)

Published in Journal of Investment Strategies, 2015, ⟨10.21314/JOIS.2015.064⟩

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Working Paper: Do Google Trend data contain more predictability than price returns? (2014) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00960875

DOI: 10.21314/JOIS.2015.064

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