Twitter mood predicts the stock market
Behavioral economics tells us that emotions canprofoundly affect individual behavior and decision-making. Doesthis also apply to societies at large, i.e. can societies experiencemood states that affect their collective decision making? Byextension is the public mood correlated or even predictive ofeconomic indicators? Here we investigate whether measurementsof collective mood states derived from large-scale Twitter feedsare correlated to the value of the Dow Jones Industrial Average(DJIA) over time. We analyze the text content of daily Twitterfeeds by two mood tracking tools, namely OpinionFinder thatmeasures positive vs. negative mood and Google-Profile of MoodStates (GPOMS) that measures mood in terms of 6 dimensions(Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validatethe resulting mood time series by comparing their ability todetect the public’s response to the presidential election andThanksgiving day in 2008. A Granger causality analysis anda Self-Organizing Fuzzy Neural Network are then used toinvestigate the hypothesis that public mood states, as measured bythe OpinionFinder and GPOMS mood time series, are predictiveof changes in DJIA closing values. Our results indicate that theaccuracy of DJIA predictions can be significantly improved bythe inclusion of specific public mood dimensions but not others.We find an accuracy of 87.6% in predicting the daily up anddown changes in the closing values of the DJIA and a reductionof the Mean Average Percentage Error by more than 6%.
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Update: ETF Central propone el debate.