The Colour of Finance Words

Abstract

We study a standard machine learning algorithm (Taddy, 2013) to measure sentiment in financial documents. Our empirical approach relies on stock price reactions to colour words, providing as output dictionaries of positive and negative words. In head-to-head comparisons, our dictionaries outperform the standard bag-of-words approach (Loughran and McDonald, 2011) when predicting stock price movements out-of-sample. By comparing their composition, word-by-word, our method refines and expands the sentiment dictionaries in the literature. The breadth of our dictionaries and their ability to disambiguate words using bigrams both help colour finance discourse better.