Return Predictability in News : Naive-Bayes based investment model using Bloomberg News

Abstract

We investigate the return predictability in news using a large sample of electronic news article diseminated from the Bloomberg. Using Naive-Bayes classifier, we create models that predict return of the Nikkei 225 futures. Our test reveals two models that beat the market by substantial margin. Based on the two selected models, we conduct out of sample money management simulation for the period between 2001 to 2011 and achieve 11.3% return per annum with the Sharpe ratio of 0.693. The result is better than the top 5% of the 1000 random models.