1. Abstract
2. Summary Charts


3. Keywords


4. Research Field Charts


5. Interval Charts


6. Usage Charts


7. Similariy Demo


8. NER Model

Abstract:
Using numeric and textual data extracted from over 50,000 finance articles in SSRN during 2001--2019, we examine the relationship between measured qualities and a paper’s readership, eventual outlet, and impact. Conventionality (semantic similarity with existent research) helps boost readership and publication prospects. However, novelty in the forms of emerging topics and databases are associated with better publishing outcomes. Studies that do not easily map into established finance subfields or that introduce non-finance elements face a higher hurdle. Finally, papers whose research questions span multiple fields are a hard sell, but those building on prior knowledge from multiple fields are valued.

Keywords: Finance Research; Impact; Publication; Innovation; Machine Learning; Textual Analysis
JEL Classification Number: A14, D85, G00
Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3803654

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