PT  - JOURNAL ARTICLE
AU  - Wai Mun Fong
TI  - Big Data, Small Pickings: <em>Predicting the Stock Market with Google Trends</em>
AID  - 10.3905/jii.2017.7.4.075
DP  - 2017 Feb 28
TA  - The Journal of Index Investing
PG  - 75--82
VI  - 7
IP  - 4
4099  - https://pm-research.com/content/7/4/75.short
4100  - https://pm-research.com/content/7/4/75.full
AB  - Big data such as Google Trends has stimulated much interest in the use of search query volumes for predicting social, business, and financial market trends. A recent paper by Preis, Moat, and Stanley [2013] claimed that a simple trading strategy using the Google Trends keyword debt powerfully predicts the Dow Jones Industrial Average stock index one week ahead and outperforms the buy-and-hold strategy by a factor of 20. Using the same sample period used by Preis, Moat, and Stanley, we show that debt completely loses its predictive power once look-ahead bias is eliminated. We find a similar result with a more recent sample period, from 2011 to 2016. Search terms that do outperform the buy-and-hold strategy generally have no economic meaning and are most likely spurious.TOPICS: Big data/machine learning, performance measurement