Using Twitter Sentiment for predicting stock price movement

I just finished giving a presentation titled “Will Twitter Make you a better investor?”…and like I always do with these presentations, I recorded one of my rehearsal’s to share.

In this presentation, I provide an overview of my research into using twitter sentiment and message volume as inputs into modeling stock price movements. A quick and dirty linear regression model using Twitter Sentiment, the Number of Tweets per day, the VIX Closing price and the VIX Price change delivers a simple model for the S&P 500 SPY ETF that has an accuracy of 57% over 6 months (tested on out-of sample data). This model was built using data from July 11 2011 to August 11 2011. Note: Accuracy is a measure of predicting the direction of movement.   Being accurate and making money from that accuracy is two different things.

Update:  Please note that the Linear Regression model described in this presentation is far from ideal. When modeling Time Series data, the linear regression model must be used with care due to autocorrelation issues.  

If you don’t want to listen to me yammer, you can jump down to the bottom of this post and take a look at the slides.

The presentation (if you don’t see anything…jump over to Vimeo to watch it there (~30 minutes)):

Twitter Sentiment & Investing – modeling stock price movements with twitter sentiment. from Eric D Brown on Vimeo.

The slides (if you don’t want to listen to me yammer):