The colour of finance words

Diego García, Xiaowen Hu and Maximilian Rohrer

Journal of Financial Economics, 2023, vol. 147, issue 3, 525-549

Abstract: 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.

Keywords: Measuring sentiment; Machine learning; Earnings calls; 10-Ks; WSJ (search for similar items in EconPapers)
JEL-codes: D82 G14 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304405X22002422
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:147:y:2023:i:3:p:525-549

DOI: 10.1016/j.jfineco.2022.11.006

Access Statistics for this article

Journal of Financial Economics is currently edited by G. William Schwert

More articles in Journal of Financial Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().