The colour of finance words

• 成果类型: Article
• 署名作者: Garcia, Diego; Hu, Xiaowen; Rohrer, Maximilian
• 署名单位: University of Colorado System; University of Colorado Boulder; Southern Methodist University; Norwegian School of Economics (NHH)
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2022.11.006
• 发表日期: 2023
• 页码: 525-549
• 关键词: Measuring sentiment; Machine Learning; Earnings calls; WSJ; 10-Ks

摘要

Our paper relies on stock price reactions to colour words, in order to provide new dic-tionaries 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 ap-proach (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 abil-ity to disambiguate words using bigrams both help to colour finance discourse better.(c) 2022 Elsevier B.V. All rights reserved.