A Novel Algorithm for Generating GVKEY-CIK Link Table

Abstract

This paper presents a do-it-yourself algorithm to generate the historical GVKEY-CIK link table. The proposed algorithm features to pre-classify sample data into different treatment subgroups and utilizes 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.