A Textual Analysis Algorithm for the Equity Market : The European Case

The general topic of the paper is the use of the crowd to interpret text, and the power of that interpretation to predict future events. This topic is addressed through an experiment, in which news sentiment is evaluated by crowds and experts in different configurations. Their classifications are used as training sets for machine learning algorithms. The testing is done based on Reuters news stories and the returns of the stocks mentioned right after the stories appear. This paper explores a simple trading strategy where the trader takes a long position when the forecast of the assets return is positive, and liquidate the position after 1 minute. Using this trading strategy, support vector machine, trained with the sentiment of several human groups, shows the highest Sharpe ratio after transaction costs and outperforms the STOXX 50 index