Trading Strategies To Exploit Blog, News & Twitter Sentiment (Paper)

Trading Strategies To Exploit Blog and News Sentiment (Paper)

I just came across this paper and wanted to document it here for something to come back to and test for myself, hopefully you will find it as interesting as I did.

The method has four Parameters:

  • Sentiment Analysis Period – How many days of previous sentiment data to use?
  • Holding Period – How long to hold a trade for?
  • Market Capitalization – Do small cap and large cap respond the same?
  • Diversification – How many stocks to have in the portfolio?

Each of the trading model parameters is also analysed and their effects explained.The paper outlines a market neutral sentiment based trading algorithm which is back tested over a five year period (2005-2009) and produces some exceptionally impressive returns almost 40% in certain years depending on configuration. 

What i like most about the paper is that the asset to trade is selected based upon a fixed criteria (ie is it in the top n most extreme sentiments), this stops positive bias effects whereby the author could just present profitable scenarios / cherry pick the results.

The sentiment is based upon analysing news posts, blog posts and tweets. Since twitter only came into existence in 2009 the authors only had half a years worth of twitter data to analyse. The great results in this paper were achieved without twitter data using normal news and blog sources.

The paper shows that corpus size matters, using blogs might be a cheaper method to collect a corpus (scrape lots of RSS feeds), whereas with twitter there are limitations to what data you can get for free (full datafeeds start at $3500 a month!!!!).




Twitter Trading & Sentiment Analysis

A standard idea in behavioral economics is that emotions play a large part in decision making and profoundly influence an agents behavior. This line of logic can be applied to the stock market, price moves are a function of the emotions of the agents in the market.

In 2011 a Paper by Johan Bollen, Huina Mao, Xiaojun Zeng called “Twitter mood predicts the stock market”, it is shown that by applying sentiment analysis to twitter posts (tweets) it is possible to gauge the current emotional state of agents. The paper then goes on to argue that the emotion of twitter is correlated with market movements and possibly even predictive of the movements.

After this landmark paper was first publish a number of hedge funds have taken the idea and produced twitter funds, the most publicly known twitter fund is run by Derwent Capital.

I plan on investigating this idea further in this blog, but if you want to get started before me the following should be useful: