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In this paper, the authors use a new methodology, called paper affiliation index, to create finance journal ranking using expert judgment and research impact, both of which are based on secondary, objective measures, thus making it possible to produce lists every year without human manipulation at virtually no cost.

Bibliometrics. Python implementation.

A new ranking with 65 finance journals.

This procedure helps to reduce bias and to deal with known problems associated with current methodologies. The data used in the methodology comes from public sources; the procedure is therefore easily replicable. This methodology is not subject-dependent and thus can be transferred to other realms of knowledge. Once the bibliometric institutional data has been gathered, the procedure is not computationally costly: a Python implementation of the algorithm executes the whole computation in a few seconds. Results seem to correct the pernicious Matthew effect which is so evident in citation-based methods.

The institutional classification created includes all institutions that have contributed papers to the field of finance. The procedure helps to reduce bias and to deal with known problems associated with current methodologies. The data used in the methodology comes from public sources, the procedure is therefore easily replicable. The methodology is not subject-dependent and thus can be transferred to other realms of knowledge. Once the bibliometric institutional data has been gathered, the procedure is not computationally costly.