Abstract

The paper looks at the impact of information on stock prices within the context of the German Market. Using data set from the Thomson Reuters, a new platform using a self-written Java Program, between the time period of 27 August and 29 September 2013, we analysed the impact of information on stock prices in the German Market. We developed an Information Based Return Model (IBRM) to analyse how information drive stock prices. We counted certain words within newspaper articles to understand their meaning. We analyse the impact of those word-clusters on different trading intervals. Our Information Based return Model shows that stock prices anticipate news from the non-trading time within the first minute of trading. We also analysed the time drifts between news release and personal reception. Our results show that the German Market anticipates new information as effectively as the US Market. 

This work is licensed under a Creative Commons Attribution 4.0 License.