Negative words in 10-K’s and filing period stock returns
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School of Business |
Bachelor's thesis
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Authors
Date
2019
Department
Major/Subject
Mcode
Degree programme
Rahoitus
Language
en
Pages
15
Series
Abstract
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.Description
Thesis advisor
Spickers, TheresaKeywords
textual analysis, 10-K, bag-of-words, sentiment analysis, filing period returns, word lists