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%. sentiment from blogs. In addition, Google search queries have been shown to provide early indicators of disease infection rates and consumer spending [14]. [9] investigates the relations between breaking financial news and stock price changes.

Twitter Mood

Category: Psychology, Think Tank, Trading

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3 Responses to “Twitter mood predicts the stock market.”

  1. [...] Disclosures var __halnet_pub = "hgn.tbp"; var __halnet_sz = "728×90"; (window.location == "http://www.ritholtz.com/blog/") ? __halnet_pub_kvs = ";section=homepage;" : __halnet_pub_kvs = ";;"; // var __halnet_zone = "ROS"; « Twitter mood predicts the stock market. [...]

  2. machinehead says:

    I’m a big advocate of incorporating sentiment into trading models. But the study by Bollen, Mao and Zeng has huge holes in it. The largest one is that the model was tested for fifteen (15) trading days between December 1 and 19, 2008. The writers conclude:

    ‘The binomial distribution indicates that the probability of achieving exactly 87.6% correct guesses over 15 trials (2o days minus weekends) with a 50% chance of success on each trial equals 0.32%.

    First, let’s note that there is no integer number of successes within 15 trials which represents 87.6% of 15. For instance, 13 of 15 correct guesses would be 86.7%. A typo? Probably.

    But even a first-year statistics student knows that you need at least 30 trials to even start inferring statistical significance. The statement quoted above is so idiotic, so fatuous, that if presented to me, I’d grab the Louisville slugger behind my desk and bodily assault the offender, screaming curses and insults.

    Lastly, let me note that this ridiculous piece of academic self-gratification was funded by the National Science Foundation — your tax dollars at work!.

    WANKERS!

  3. machinehead,

    how do you think GHG-induced AGW ‘Climate Science’ gets ‘peer-reviewed’?