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

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      Review of Finance
      Oxford University Press (OUP)

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          Abstract

          Using numeric and textual data extracted from over 50,000 finance articles in Social Science Research Network (SSRN) during 2001–19, 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 nonfinance 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.

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          Most cited references52

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          The Pricing of Options and Corporate Liabilities

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            Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories

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              Distributed Representations of Words and Phrases and their Compositionality

              The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.
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                Author and article information

                Journal
                Review of Finance
                Oxford University Press (OUP)
                1572-3097
                1573-692X
                February 01 2023
                January 06 2023
                March 18 2022
                February 01 2023
                January 06 2023
                March 18 2022
                : 27
                : 1
                : 79-141
                Article
                10.1093/rof/rfac018
                49d6b156-1d75-4e70-bae2-78ac0b53d127
                © 2022

                https://academic.oup.com/pages/standard-publication-reuse-rights

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