Researchers have developed a model that uses data from Twitter to help predict the traded volume and value of a stock the following day.
While past research has looked at the sentiment, positive or negative, of tweets to predict stock price, little research has focused on the volume of tweets and the ways that tweets are linked to other tweets, topics or users. Past work has also mostly studied the overall stock market indexes, and not individual stocks.
[For more on the trading strategy, read Academic's Twitter Model Predicts Stock Returns.]
Vagelis Hristidis, an associate professor at the Bourns College of Engineering, one of his graduate students and three researchers at Yahoo! in Spain, set out to study how activity in Twitter is correlated to stock prices and traded volume.
They obtained the daily closing price and the number of trades from Yahoo! Finance for 150 randomly selected companies in the S&P 500 Index for the first half of 2010. Then, they developed filters to select only relevant tweets for those companies during that time period. For example, if they were looking at Apple, they needed to exclude tweets that focused on the fruit.
“They expected to find the number of trades was correlated with the number of tweets. Surprisingly, the number of trades is slightly more correlated with the number of what they call “connected components.” That is the number of posts about distinct topics related to one company. For example, using Apple again, there might be separate networks of posts regarding Apple’s new CEO, a new product it released and its latest earnings report,” according to a statement.
The trading strategy outperformed other baseline strategies by between 1.4 percent and nearly 11 percent and also did better than the Dow Jones Industrial Average during a four-month simulation.
Still, Hristidis notes several potential weaknesses in the study. First, the trading strategy worked in a period when the Dow Jones dropped, but it may not produce the same results when the Dow Jones is rising. There is also sensitivity related to the duration of the trading. For example, it took 30 days in the simulation to start outperforming the Dow Jones.