Twitter Can Predict the Stock Market Six Days Prematurely [Twitter]

The secret of the bull and the bear are within the bird and the whale: Twitter can be utilized to predict the stock market behavior with an accuracy of 87.6%. In other words: Justin Bieber controls the area’s economy.

This is the paper summary by Indiana University’s research team Johan Bollen, Huina Mao, and Xiao-Jun Zeng’s:

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 perhaps predictive of commercial indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the price of the Dow Jones Industrial Average (DJIA) through the years. 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 relation to 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 analyze 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 could 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 inside the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%.

So people, please keep your tweets happy. [ arxiv via Technology Review ]
Cartoon by Kal Walters for the Baltimore Sun

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This post is tagged: behavioral economics, dow jones industrial, dow jones industrial average, fuzzy neural network, profile of mood states, stock market behavior