Merging Time‐Series Australian Data Across Databases: Challenges and Solutions

25 Pages Posted: 5 Dec 2016

See all articles by Dean Katselas

Dean Katselas

Independent

Baljit K. Sidhu

UNSW Australia Business School, School of Accounting

Chuan Yu

UNSW Australia Business School, School of Accounting

Date Written: December 2016

Abstract

This study discusses the differences in company identification across sources of Australian data and raises important issues which should be considered prior to merging across databases. In particular, we show that the practice among accounting databases of overwriting prior identifiers used by a given company, with its most recent, results in failure to match data which actually exists. We suggest a method for reconciling these differences and show that our method results in a match rate of 97 percent with the Aspect company identification file, and 94 percent after missing accounting data is considered. This contrasts with a match rate of only 71 percent when performing a direct merge.

Keywords: Accounting, Finance, Australian Data, Merging, Databases

Suggested Citation

Katselas, Dean and Sidhu, Baljit K. and Yu, Chuan, Merging Time‐Series Australian Data Across Databases: Challenges and Solutions (December 2016). Accounting & Finance, Vol. 56, Issue 4, pp. 1071-1095, 2016, Available at SSRN: https://ssrn.com/abstract=2879666 or http://dx.doi.org/10.1111/acfi.12123

Baljit K. Sidhu

UNSW Australia Business School, School of Accounting ( email )

Sydney, NSW 2052
Australia

Chuan Yu

UNSW Australia Business School, School of Accounting ( email )

Sydney, NSW 2052
Australia

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