Google can predict the stock market: Researchers find search engine can spot crashes BEFORE they happen

Researchers analysed business and politics searches between 2004 and 2012

Found an increase in internet searches preceded falls

Google searches for business and politics topics can predict a future stock market crash, researchers have claimed.

An analysis of search terms between 2004 and 2012 found an increase in internet searches preceded falls.



Researchers from Warwick Business School said search behaviour could provide an early warning system of concerns about the state of the economy.

An analysis of search terms between 2004 and 2012 found an increase in internet searches preceded falls.

HOW THEY DID IT 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 categorize words into topics, so that a 'business' topic may contain words such as 'business,' 'management,' and 'bank.'

Researchers then used Google Trends to see how often each week thousands of these words were searched for by Internet users in the United States between 2004 and 2012.

By using these search activity datasets in a simple trading strategy for the S&P 500, they found that changes in how often users searched for terms relating to business and politics could be connected to subsequent stock market moves.

They developed complex computer programmes that mined through the vast amount of internet traffic to identify trends in a broad range of topics or sectors from food to architecture to cricket.



Research Fellow Chester Curme said: 'Search engines, such as Google, record almost everything we search for.



'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.



'However, the number of possible things people could search for is huge. So an important challenge is to identify what types of words may be relevant to behaviours of interest.'



Previous studies showed usage data from Google and Wikipedia may contain early warning signs of stock market moves but 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 categorize words into topics, so that a 'business' topic may contain words such as 'business,' 'management,' and 'bank.'



Researchers then used Google Trends to see how often each week thousands of these words were searched for by Internet users in the United States between 2004 and 2012.



By using these search activity datasets in a simple trading strategy for the S&P 500, they found that changes in how often users searched for terms relating to business and politics could be connected to subsequent stock market moves.



Researchers then used Google Trends to see how often each week thousands of these words were searched for by Internet users in the United States between 2004 and 2012.

Assistant Professor of Behavioural Science Suzy Moat said: '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.



'No other topic was linked to returns that were significantly higher than those generated by randomly buying and selling.



'The finding that political terms were of use in our trading strategies, as well as more obvious financial terms, provides evidence that valuable information may be contained in search engine data for keywords with less obvious semantic connections to events of interest.



'Our method provides a new approach for identifying such keywords.



'Our results are in line with the hypothesis that increases in searches relating to both politics and business could be a sign of concern about the state of the economy, which may lead to decreased confidence in the value of stocks, resulting in transactions at lower prices.



Associate Professor of Behavioural Science and Finance Tobias Preis added 'the strength of this relationship, using this very simple weekly trading strategy, has diminished in recent years.



'This potentially reflects the increasing incorporation of Internet data into automated trading strategies, and highlights that more advanced strategies are now needed to fully exploit online data in financial trading.”

