The colour of finance words

Our paper relies on stock price reactions to colour 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 dicti...

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Bibliographic Details
Published in:Journal of financial economics 2023-03, Vol.147 (3), p.525-549
Main Authors: García, Diego, Hu, Xiaowen, Rohrer, Maximilian
Format: Article
Language:English
Subjects:
10-Ks
Earnings calls
Machine learning
Measuring sentiment
WSJ
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Summary:Our paper relies on stock price reactions to colour 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 colour finance discourse better.
ISSN:0304-405X
1879-2774
DOI:10.1016/j.jfineco.2022.11.006