Twitter Mood Predicts the Stock Market Authors: Johan Bollen, Huina Mao, Xiao-Jun Zeng Presented By: Krishna Aswani Computing ID: ka5am

Is it possible to predict Stock Markets??  Early research: Stock markets are based on the Efficient Market Hypothesis (by new information, i.e. news, rather than present and past prices) and random walk theory  Recent research: News may be unpredictable but early indicators can be extracted from online social media (blogs, Twitter feeds, etc) to predict changes in various economic and commercial indicators

Method: Twitter Feed DJIA Text Analysis Normaliz- ation Mood Indicators (Daily) Stock Markets (Daily) Granger Causality SOFNN F-statistics p-value MAPE Direction% t-1 t-2 t-3 t=0 value Predicted Value Phase 1

 Step 1 – Collecting Public Tweets (February 28 to December 19th, ,853,498 tweets posted by approximately 2.7M users), removing stopwords, normalizing them etc.  Step2- Pass it through Opinion Finder and Google Profile of Mood States (GPOMS) to create time series.  Step3 – To have a comparison of time series from Opinion Finder and Google Profile of Mood States z-score is used to normalize each:  Step 4 – Cross Validating against large socio-cultural events. Phase1: Creating sentiment time series Google Profile of Mood States classifies tweets into 6 types: Calm, Alert, Sure, Vital, Kind & Happy. Opinion Finder is a software package that classifies tweets into Positive and Negative. For each day ratio of total no. of Positive tweets to total no. of negative tweets is calculated

Method: Twitter Feed DJIA Text Analysis Normaliz- ation Mood Indicators (Daily) Stock Markets (Daily) Granger Causality SOFNN F-statistics p-value MAPE Direction% t-1 t-2 t-3 t=0 value Predicted Value Phase 2

Phase 2 – Correlation between mood time series and DJIA  Step1- Collect DJIA data for the same time duration, normalize it and plot a time series.  Step2 - Use Granger causality analysis on model 1 & 2: Granger causality analysis rests on the assumption that if a variable X causes Y then changes in X will systematically occur before changes in Y

Correlation does not mean causation

Method: Twitter Feed DJIA Text Analysis Normaliz- ation Mood Indicators (Daily) Stock Markets (Daily) Granger Causality SOFNN F-statistics p-value MAPE Direction% t-1 t-2 t-3 t=0 value Predicted Value Phase 3

Phase 3- Non-linear models for accurate stock prediction  As the relationship between DJIA and Mood time series doesn’t look linear, to predict with better accuracy Self Organizing Fuzzy Neural Network (SOFNN) are used.  Different Permutations of input variables (Mood Time series) are used:

Results: Calm Calm and Happy

Factors not considered  Geographic Location of Tweets. This approach worked because twitter base is predominantly located in the US.  These results are strongly indicative of a predictive correlation between measurements of the public mood states from Twitter feeds, but offer no information on the causative mechanisms that may connect online public mood states with DJIA values  It is highly vulnerable to twitter bombing campaigns, which very easily become viral.

Applications:  Companies like Tower Research Capital (computational investment trading)  Dataminr (social analytics company)