Abstract
This paper focuses on the relationship between the reader's information-extracting costs of finance articles and the article's number of citations. The reader's information-extracting costs are measured using three metrics: (i) the Flesch-Kincaid readability score, (ii) the article's length, and (iii) the number of complex words. Based on a sample of more than 14,000 full text articles published between 2000 and 2016 in 16 finance journals, we show that the information-extracting costs of finance journals have significantly increased over time, while the topics of these articles, determined by machine-learning topic modeling, remained relatively constant. We find a positive correlation between the reader's information-extracting costs and the number of citations achieved by a paper for articles that are published in the top-three finance journals (JF, JFE, RFS), but do not observe this pattern for articles published in other major finance journals.