University of Warwick researcher Tobias Preis and colleagues have found a method of predicting whole stock market shifts based on publicly available data on Google Trends search terms. The team tracked 98 search terms such as "debt" and "derivatives" from 2004 to 2011, and compared the searches to Dow Jones Industrial Average closing prices. To test whether the terms searched in the week prior to any given closing day could predict market direction, the researchers developed an investing game. If searches for financial terms went down, they opted to buy stocks and hold them with the expectation of value rising, and if searches went up, the researchers would "short" the market by selling stocks they did not own, with the intention of buying later at a lower price. The researchers theorize that if investors feel concerned about the stock market, they will search for financial information before selling stock. The researchers were able to increase their mock portfolio by 326 percent by holding onto stock when searches on the word "debt" decreased and shorting the market when searches increased. Beyond predicting the stock market, online activity on Google and other sources, such as Wikipedia, could aid in forecasting disease spread, civil unrest, and election outcomes.