JOURNAL ARTICLE

Earnings quality of multinational corporations: Evidence from Latin America before and after IFRS implementation.

  • Published In: Journal of Corporate Accounting & Finance (Wiley), 2024, v. 35, n. 4. P. 238 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Melgarejo, Mauricio 3 of 3

Abstract

This study evaluates whether Latin American multinational corporations (MNCs) report higher quality of accounting reports than companies with only operations in their home countries. In addition, it explores whether the impact of internationalization on the quality of accounting information has changed since the implementation of the International Financial Reporting Standards (IFRS) in the region. An emerging area of research studies the effect of firms' internationalization on accounting and finance. Nevertheless, evidence of the impact of internationalization on the quality of earnings quality is scarce. Based on a sample of public organizations listed on the main stock exchanges of Brazil, Mexico, Peru, and Chile from 2000 to 2020, this study finds that companies with international operations present higher‐quality accounting reports than firms with only local operations. The impact of IFRS implementation on the quality of financial reports is significant only for companies with operations in their home countries. Latin American MNCs show a decline in the quality of accounting reports after adopting IFRS. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Corporate Accounting & Finance (Wiley). 2024/10, Vol. 35, Issue 4, p238
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:1044-8136
  • DOI:10.1002/jcaf.22729
  • Accession Number:180110584
  • Copyright Statement:Copyright of Journal of Corporate Accounting & Finance (Wiley) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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