A team of researchers from Warwick Business School in the UK and Boston University in the US has developed a method to automatically identify topics that people search for on Google before subsequent stock market falls.
Applied to data between 2004 and 2012, the method shows that increases in searches for business and politics preceded falls in the stock market. The researchers suggest that this method could be applied to help identify warning signs in search data before a range of real world events.
"Search engines, such as Google, record almost everything we search for," said Chester Curme, Research Fellow at Warwick Business School and lead author of the study."Records of these search queries allow us to learn about how people gather information online before making decisions in the real world. So there's potential to use these search data to anticipate what large groups of people may do," Curme said.
However, these findings relied on the researchers choosing an appropriate set of keywords, in particular those related to finance.In order to enable algorithms to automatically identify patterns in search activity that might be related to subsequent real world behaviour, the team quantified the meaning of every single word on Wikipedia.
This allowed the researchers to categorise words into topics, so that a "business" topic may contain words such as "business", "management", and "bank". The algorithm identified a broad selection of topics, ranging from food to architecture to cricket. The team then used Google Trends to see how often each week thousands of these words were searched for by Internet users in the US between 2004 and 2012.
Researchers found that changes in how often users searched for terms relating to business and politics could be connected to subsequent stock market moves.
"By mining these datasets, we were able to identify a historic link between rises in searches for terms for both business and politics, and a subsequent fall in stock market prices," said Moat.