Title: Audit report lag determinants: a panel data regression model with all companies listed on the Dow Jones Stock Index

Authors: David Daniel Hammes Junior; Luísa Karam De Mattos; Leonardo Flach

Addresses: Accounting Department, Federal University of Santa Catarina, UFSC, Brazil ' Management Department, Federal University of Santa Catarina, UFSC, Brazil ' Accounting Department, Federal University of Santa Catarina, UFSC, Brazil

Abstract: This study aims to analyse the determinants of audit report lag (ARL) in companies listed on the Dow Jones Stock Index and identify whether the companies audited by an auditing firm with expertise in the audited sector have a lower ARL than the others. We applied a panel data regression model with all companies listed on the Dow Jones Stock Index. We collected data from several variables, during the years from 2013 to 2017. We collected the data at the website of each company, in the reference forms and in the standardised financial statements. Other data we collected at the website Edgar Online. We started with the hypothesis that companies audited by expert auditors have a lower ARL than the other companies. The importance of this research lies in the fact that reducing the ARL enables the disclosure of financial statements in time and facilitates the investors' decision-making process.

Keywords: auditing; business intelligence; audit firm; audit report lag; ARL; expertise.

DOI: 10.1504/IJBEX.2020.106954

International Journal of Business Excellence, 2020 Vol.21 No.1, pp.139 - 152

Received: 25 Jul 2018
Accepted: 20 Nov 2018

Published online: 22 Apr 2020 *

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