A Novel Algorithm for Generating a GVKEY-CIK Link Table-Reference-Cited by-同舟云学术

A Novel Algorithm for Generating a GVKEY-CIK Link Table

Published:2020-01-28 Issue:1 Volume:35 Page:27-46
ISSN:1558-7959
Container-title:Journal of Information Systems
language:en
Short-container-title:

Author:

Chu K. C. Kenneth¹^ORCID,Chen Sipeng¹,Leung Tommy²

Affiliation:

1. The Hong Kong Polytechnic University

2. The Hang Seng University of Hong Kong

Abstract

ABSTRACT This paper presents a do-it-yourself algorithm to generate the historical GVKEY-CIK link table. The proposed algorithm features a technique of pre-classifying sample data into different treatment subgroups and utilizing historical firm information available from the source data to increase (reduce) matching efficiency (errors). Simulation results show that our algorithm is superior to applying only conventional name matching operations over the whole sample: 57.5 percent of the overall matching results are error free ex ante, and for the remaining 42.5 percent of data, records without Type I errors (with Type II errors) increase (decrease) by 34.0 percent (59.4 percent) when the optimal threshold is used. JEL Classifications: C89; M40; G10; G18.

Publisher

American Accounting Association

Subject

Management of Technology and Innovation,Information Systems and Management,Human-Computer Interaction,Accounting,Information Systems,Software,Management Information Systems

Link

http://meridian.allenpress.com/jis/article-pdf/35/1/27/2815913/i1558-7959-35-1-27.pdf

Reference22 articles.

1. Amiram, D., Owens E., and RozenbaumO. 2016. Do information releases increase or decrease information asymmetry? New evidence from analyst forecast announcements. Journal of Accounting and Economics62 ( 1): 121– 138. https://doi.org/10.1016/j.jacceco.2016.06.001

2. Christen, P. 2006. A comparison of personal name matching: Techniques and practical issues. Available at: https://ieeexplore.ieee.org/document/4063641

3. Chychyla, R., and KoganA. 2015. Using XBRL to conduct a large-scale study of discrepancies between the accounting numbers in Compustat and SEC 10-K filings. Journal of Information Systems29 ( 1): 37– 72. https://doi.org/10.2308/isys-50922

4. Daniel, K., and TitmanS. 1997. Evidence on the characteristics of cross sectional variation in stock returns. The Journal of Finance52 ( 1): 1– 33. https://doi.org/10.1111/j.1540-6261.1997.tb03806.x

5. Davis, J. L., Fama E. F., and FrenchK. R. 2000. Characteristics, covariances, and average returns: 1929 to 1997. The Journal of Finance55 ( 1): 389– 406. https://doi.org/10.1111/0022-1082.00209

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