Quantifying the semantics of search behavior before stock market moves
Abstract
Internet search data may offer new possibilities to improve forecasts of collective behavior, if we can identify which parts of these gigantic search datasets are relevant. We introduce an automated method that uses data from Google and Wikipedia to identify relevant topics in search data before large events. Using stock market moves as a case study, our method successfully identifies historical links between searches related to business and politics and subsequent stock market moves. We find that the predictive value of these search terms has recently diminished, potentially reflecting increasing incorporation of Internet data into automated trading strategies. We suggest that extensions of these analyses could help draw links between search data and a range of other collective actions.
- Publication:
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Proceedings of the National Academy of Science
- Pub Date:
- August 2014
- DOI:
- 10.1073/pnas.1324054111
- Bibcode:
- 2014PNAS..11111600C