(2019) Niemelä, Elmeri; Spickers, Theresa; Rahoituksen laitos; Kauppakorkeakoulu; School of Business
Previous research has developed a word list specifically designed to classify the negative tone of
financial texts. Research has shown evidence that classifying 10-K documents by frequency of
negative words can predict returns after the filing date. Using this negative word list, I analyze if
the return predictability patterns still emerge. In order to control for implementation differences,
I collect two samples of 10-K documents with daily stock data: from 1994 to 2008 as the control
sample and a new sample from 2008 to 2018. Negative tone analysis with the frequency based
method is not sufficient enough, to consistently predict stock returns. Results from previous
research using this method can only be replicated in their original time frame from 1994 to 2008.