Dissemination, Publication, and Impact of Finance Research: When Novelty Meets Conventionality

Using numeric and textual data extracted from over 50,000 finance articles posted in the Social Science Research Network (SSRN) between 2001 and 2019, this study examines the relationship between measured qualities and a paper's eventual outlet as well as its impact. Based on semantic characteristics uncovered with newly developed machine learning tools, we find that conventionality (semantic similarity with existent research) helps boost readership and publication prospects, after controlling for various article and author characteristics including reputation and research resources of the affiliated institution and author network. Novelty through investigating emerging topics and adopting fresh databases boosts the attention from a broader audience and also the probability of top tier publication, but novelty from introducing atypical ideas from non-finance focused fields or inter-field research does not achieve the same goals.