The Colour of Finance Words

Abstract

Our paper relies on stock price reactions to color words, in order to provide new dictionaries of positive and negative words in a finance context. We extend the machine learning algorithm of Taddy (2013), adding a cross-validation layer to avoid over-fitting. 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 to color finance discourse better.

Keywords: measuring sentiment, machine learning, earnings calls, 10-Ks, WSJ

JEL Classification: D82, G14