The Colour of Finance Words

40 Pages Posted: 21 Aug 2020 Last revised: 10 May 2021

See all articles by Diego Garcia

Diego Garcia

University of Colorado at Boulder - Leeds School of Business

Xiaowen Hu

University of Colorado at Boulder - Leeds School of Business

Maximilian Rohrer

NHH - Norwegian School of Economics

Date Written: June 14, 2020

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.

Keywords: measuring sentiment, machine learning

JEL Classification: D82, G14

Suggested Citation

Garcia, Diego and Hu, Xiaowen and Rohrer, Maximilian, The Colour of Finance Words (June 14, 2020). Available at SSRN: https://ssrn.com/abstract=3630898 or http://dx.doi.org/10.2139/ssrn.3630898

Diego Garcia (Contact Author)

University of Colorado at Boulder - Leeds School of Business ( email )

Boulder, CO 80309-0419
United States

Xiaowen Hu

University of Colorado at Boulder - Leeds School of Business ( email )

Boulder, CO 80309-0419
United States

Maximilian Rohrer

NHH - Norwegian School of Economics ( email )

Helleveien 30
N-5045 Bergen
Norway

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