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What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance

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Abstract

Experts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is made with publicly available bibliometric data, namely the Thomson Reuters ISI Web of Science citations database (hereafter ISI) for the Business - Finance (hereafter Finance) category. The paper analyses the leading international journals in Finance using expert scores and quantifiable Research Assessment Measures (RAMs), and highlights the similarities and differences in the expert scores and alternative RAMs, where the RAMs are based on alternative transformations of citations taken from the ISI database. Alternative RAMs may be calculated annually or updated daily to answer the perennial questions as to When, Where and How (frequently) published papers are cited (see Chang et al. (2011a, b, c)). The RAMs include the most widely used RAM, namely the classic 2-year impact factor including journal self citations (2YIF), 2-year impact factor excluding journal self citations (2YIF*), 5-year impact factor including journal self citations (5YIF), Immediacy (or zero-year impact factor (0YIF)), Eigenfactor, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, PI-BETA (Papers Ignored - By Even The Authors), 2-year Self-citation Threshold Approval Ratings (2Y-STAR), Historical Self-citation Threshold Approval Ratings (H-STAR), Impact Factor Inflation (IFI), and Cited Article Influence (CAI). As data are not available for 5YIF, Article Influence and CAI for 13 of the leading 34 journals considered, 10 RAMs are analysed for 21 highly-cited journals in Finance. The harmonic mean of the ranks of the 10 RAMs for the 34 highly-cited journals are also presented. It is shown that emphasizing the 2-year impact factor of a journal, which partly answers the question as to When published papers are cited, to the exclusion of other informative RAMs, which answer Where and How (frequently) published papers are cited, can lead to a distorted evaluation of journal impact and influence relative to the Harmonic Mean rankings. A linear regression model is used to forecast expert scores on the basis of RAMs that capture journal impact, journal policy, the number of high quality papers, and quantitative information about a journal. The robustness of the rankings is also analysed.

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Bibliographic Info

Paper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico in its series Documentos de Trabajo del ICAE with number 2013-09.

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Length: 43 pages
Date of creation: 2013
Date of revision: Feb 2013
Handle: RePEc:ucm:doicae:1309

Note: The authors are grateful to Shing-yang Hu (National Taiwan University) for providing the data on Expert Scores. For financial support, the first author wishes to thank the National Science Council, Taiwan, and the second author wishes to acknowledge the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science.
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Related research

Keywords: Expert scores; Journal quality; RAMs; Impact factor; IFI; C3PO; PI-BETA; STAR; Eigenfactor; Article Influence; h-index; harmonic mean; robustness.;

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References

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  1. Chia-Lin Chang & Michael McAleer & Les Oxley, 2011. "How are Journal Impact, Prestige and Article Influence Related? An Application to Neuroscience," KIER Working Papers 756, Kyoto University, Institute of Economic Research.
  2. Chia-Lin Chang & Michael McAleer, 2013. "Ranking journal quality by harmonic mean of ranks: an application to ISI statistics & probability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(1), pages 27-53, 02.
  3. Chia-Lin Chang & Esfandiar Maasoumi & Michael McAleer, 2013. "Robust Ranking of Journal Quality: An Application to Economics," Tinbergen Institute Discussion Papers 13-081/III, Tinbergen Institute.
  4. Chang, C-L. & McAleer, M.J. & Oxley, L., 2010. "What Makes a Great Journal Great in the Sciences? Which Came First, the Chicken or the Egg?," Econometric Institute Research Papers EI 2010-75, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  5. Chia-Lin Chang & Michael McAleer & Les Oxley, 2010. "Great Expectatrics: Great Papers, Great Journals, Great Econometrics," Working Papers in Economics 10/36, University of Canterbury, Department of Economics and Finance.
  6. Chia‐Lin Chang & Michael McAleer & Les Oxley, 2011. "What Makes A Great Journal Great In Economics? The Singer Not The Song," Journal of Economic Surveys, Wiley Blackwell, vol. 25(2), pages 326-361, 04.
  7. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2009. "Expert opinion versus expertise in forecasting," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 334-346.
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Cited by:
  1. Chia-Lin Chang & Michael McAleer, 2013. "Ranking Leading Econometrics Journals Using Citations Data from ISI and RePEc," Documentos de Trabajo del ICAE 2013-34, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  2. Chia-Lin Chang & Michael McAleer, 2014. "Just how Good are the top Three Journals in Finance? An Assessment based on Quantity and Quality Citations," Tinbergen Institute Discussion Papers 14-062/III, Tinbergen Institute.

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