@article {Fong75, author = {Wai Mun Fong}, title = {Big Data, Small Pickings: Predicting the Stock Market with Google Trends }, volume = {7}, number = {4}, pages = {75--82}, year = {2017}, doi = {10.3905/jii.2017.7.4.075}, publisher = {Institutional Investor Journals Umbrella}, abstract = {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}, issn = {2154-7238}, URL = {https://jii.pm-research.com/content/7/4/75}, eprint = {https://jii.pm-research.com/content/7/4/75.full.pdf}, journal = {The Journal of Beta Investment Strategies} }