The “celebrities” in finance: a citation analysis of finance journals

Introduction

Highly ranked finance journals have enjoyed esteem, global readership and international recognition over the past decades. Their contribution together make up a body of scholarly research that academics and practitioners, researchers and business professionals, including students, find useful in furthering the fields of finance, banking, accounting and investment, in both theory and practice. One way to recognise successful contribution is to examine the body of research that has been published or to look at specific snapshots of particularly interesting periods.

This is also to say that to look at the future direction of the scholarly finance literature is to look back at its past achievements. Sometimes, it is useful to look at a particular journal and its position in impact factor and journal citations rankings. However, this can be somewhat self-serving. We can think that finance journals need recognition beyond impact factor and rankings. In this paper, the interest is in some of the other measures of "impact" among selected top journals in finance - their "heroes", "celebrities", "thinkers", "movers", "shakers" and "doers" (the "most influential", so to speak). Specifically, the interest is in finding out the most influential articles across selected top journals; the topics in those articles; the authors, countries and universities that contributed significantly to the finance literature; and the most used keywords in finance.

Large-scale data were used to provide a citation analysis of the impact of some highly ranked finance journals. The analysis begins with using the metadata of every available article published in these selected journals. These data have not been investigated previously. Any such analysis is a contribution to the evolving history of the journals and a contribution to an assessment of the field. This paper aims to analyse the entire history of citation data of ten highly ranked journals using the Australian Research Council Centres of Excellence in Research Australia’ s (2010) Journal Rankings List. It provides a network analysis of this data using data visualisation tools such as Gephi® and Wordle(TM). These tools are already in widespread use in other disciplines. The issues behind the Australian Research Council Centre Excellence in Research for Australia (ARC ERA) list is in the classification of accounting and finance journals (Moosa, 2011), and the need to re-rank them in future ERA lists are noted but these issues are not of interest, thereby providing no relevance, to this study.

Currently, there is no existing research on citation analysis of ten finance journals, or the use of data visualisation tools with large-scale data such as in this study. There is also no extant citation analysis of the entire history of some of the finance journals included in this study to date.

In this paper, the entire available publication history of the following journals is examined: European Financial Management , Financial Analysts Journal , Financial Management , International Review of Finance , Journal of Behavioral Finance, Journal of Corporate Finance, Journal of Empirical Finance, Journal of Financial Markets, Journal of Risk and Uncertainty , and finally, The Review of Financial Studies . This represents 178 years of combined publication history of the journals. My analysis reveals the "strongest voices" in finance research, as well as the most discussed topics and other key important statistics and milestones. This is a fitting contribution to the growing field of finance.

Background

Citation analysis is not a new research technique. A quick search in Google Australia using the phrase "citation analysis" generates 37 million results. Narrowing this search in finance using "citation analysis finance" still reveals more than 3 million results. Using Educational Resources Information Center (ERICProQuest) reveals 228 results, while using the Web of Science(TM) (WoS) database using "citation analysis" and "finance" resulted in 77 articles, with 14 of those specifically under the field code "business finance". There were a few bibliometric and citation analyses in finance research found, but there was no single article found similar to what is described in this paper. Let us examine closely, and provide a brief summary of the citation analysis research in finance.

The closest article is Ratnavelu et al.’ s (2012) article that explored the relative influence of seven prominent finance journals between 1990 and 2006. They found stable rank ordering with the Journal of Finance and the Journal of Financial Economics and observed inter-journal communication trends. A few articles used data from impact factors. An article that uses citation counts was by Borokhovich et al. (2000). They found that citations outside the finance discipline affect impact factors in finance journals.

Some other articles have approached the analysis similar to what was described here. However, they are either limited in scope (e.g. number of journals, years of publication coverage) or are focused on analysing specific topics of interest (e.g. risk, insurance). Notable are Colquitt (2003), Liu (2005), Avkiran (2013) and Richart-Ramon et al. (2011). Colquitt analysed the significance of research published in 16 risk, insurance and actuarial journals (1996-2000) by using citation counts. Colquitt ranked the citations and reported the most frequently cited articles (Haley’s, 2013) research on variability in ranking journals, in contrast to ranking citations). Liu created a cluster map of co-citations in 38 journals within a ten-year period (1992-2002) and found a higher degree of interdiscplinarity in some clusters of journals than in others; thus, revealing that topics discussed in one journal do not correlate with others. Avkiran examined whether collaboration resulted in higher impact articles by using more than 6,000 articles published in 2001-2007. Interestingly, Avkiran found that collaboration leads to higher impact but not when collaboration exceeds three authors. Lastly, the article by Richart-Ramon et al . analysed more than 2,000 publications on corporate governance and their associated 79,000+ citations. Sharing some similarity with the present study, they identified the most significant publications, authors and the journals they published in. The same approach was used by Merigo and Yang (2014), including Cornelius and Persson’s (2006) identification of the most influential researchers on venture capital research.

A few other articles have used bibliometric analysis on specific topics in selected journals. An example is Chan et al.’ s (2009) analysis of 362 journals on the topic of financial crisis. Another is Huang and Ho’s (2011) study on corporate governance. Using articles from 1992 to 2008, Huang and Ho found the most used keywords in corporate governance literature, as well as the peak production period. Durisin and Puzone’s (2009) article on the same topic uses more publications including 48,000 citations on corporate governance. Other authors have observed the increasing number of publications in journals (Gaunt, 2014).

The previous research shares resemblance to this study, where I use keywords to identify the most discussed topics and the most influential authors. However, this study is not only focused on a particular area in finance but in all areas in finance involving more than 20,000 keywords and 200,000 cited references in more than 6,000 articles in ten journals. It explores in detail the top publication years, top contributing countries and universities and, specifically, the most popularly cited authors and articles. The analysis also uses data visualisation tools to illustrate the findings in a more meaningful way.

Method

The procedures in collecting and analysing citation data for this study are discussed below. Briefly, ten top journals (A or A* classified in Excellence in Research Australia [2010] list) in finance were selected. A total of 6,029 journal articles were included in the study. Also, a total of 23,521 keywords and 208,905 cited references were found across the ten journals.

Data collection

Using the ERA (2010) list, 25 international journals whose field of research (FoR) is "banking, finance and investment" were identified. Using convenience sampling, ten of those journals were selected. Using WoS, a search was made on 30 June 2015 and 1 July 2015 to include all published articles from each of the ten journals (academic articles only; excluding editorial, proceedings, reviews and meetings) using the filter "all dates" to include all publication years. Note that WoS only reports articles from the time that they were indexed. Thus, the extracted data are complete in some journals but incomplete in others. The resulting collected data involved 6,209 journal articles (Table I).

Using WoS’s "Analyze Results" feature, specific analytics were generated. This included the top publishing authors, top countries/territories, top publishing organisations and top publication years. The top five most cited articles in each journal as reported in WoS have also been collected. Following this, the metadata for all the articles from each journal have been generated, which included information such as, at the least, the publishing authors, titles, keywords, cited references and publication years.

As earlier mentioned, a total of 23,521 keywords were found across the ten journals. The Financial Management journal records the least number of keywords, whereas the Review of Financial Studies records the most (Table II).

Data analysis

Part of the analysis was using the results obtained from WoS analytics. This was further refined by sorting the data using Excel® to find the most published authors, top countries, top organisations and top publication years. The data about the most cited articles in each of the ten journals from WoS were collected. These are reported in the Results section.

The significant part of the analysis was done by using Excel® to conduct an analysis of the most published authors, most cited authors and most cited articles; and to prepare the Gephi® files for later data visualisation. Item 1 was pursued because of an interest in finding the authors who published solo or as first-named author. This is because WoS presumably attributes publications to an author including when the author appeared as a second, third, etc. author in a published article.

By using Gephi®, a data visualisation software, diagrams were generated to establish the networks associated with the most published authors, most cited authors and most cited articles. A couple of steps were made to prepare the files for Gephi®, which included developing "nodes" and "edges" files. Nodes represent what we would like to connect (e.g. author A and a cited reference B), while edges explain the relationship in the connection. The connection can be directed (A cites B, B cites A) or undirected (A cites B, B may not cite A). An undirected graph was used for all diagrams.