Twitter Mood Predicts the Stock Market

The zeitgeist on Twitter predicts stock market behavior by several days, according to research by Johan Bollen, Huina Mao, and Xiao-Jun Zeng. They examined whether or not “measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DIJA) over time”.

The short answer is, “yes” (with some limitations, as stated in their paper about this research).

CNBC interviewed Dr. Bollen about the team’s research results in this interview below.


Bloomberg also interviewed Dr. Bollen about these research results in this short video. (Bloomberg did not allow embed code, sorry!)

The full paper has been archived online. I have included the abstract with a link to the paper below.

Twitter mood predicts the stock market
Johan Bollen, Huina Mao, Xiao-Jun Zeng
(Submitted on 14 Oct 2010)

Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e., can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public’s response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%.

Subjects: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph)

Cite as: arXiv:1010.3003v1 [cs.CE]

What do you think? Have Bollen, Mao, and Zeng found the Holy Grail of stock predictions?

Please let me know what you think....