Deakin University
Browse

File(s) under permanent embargo

A comparison of reported earnings under Chinese GAAP vs. IAS: evidence from the Shanghai Stock Exchange

Version 2 2024-06-04, 06:52
Version 1 2016-11-01, 10:31
journal contribution
posted on 2024-06-04, 06:52 authored by CJP Chen, Ferdinand GulFerdinand Gul, X Su
This paper reports the results of an empirical examination of the difference between earnings based on Chinese GAAP and those based on International Accounting Standards (IAS). Specifically, the study determines how current Chinese accounting standards are different from the IAS, whether these differences are systematlcally biased toward under- or overstated earnings, and which items from the financial statements contributed most to these differences. The findings suggest that reported accounting earnings based on current Chinese GAAP are significantly different from those based on IAS. On average, the reported earnings determined under the Chinese GAAP are 20-30 percent higher than earnings reported under IAS. After restatement, 15 percent of the B-share companies changed from a reported profit to a reported loss. The findings suggest that the differences between the two sets of earnings are caused by differences in accounting standards and financial rules, opportunistic applications of Chinese GAAP, and unusual market-wide events. An analysis of recently promulgated accounting standards indicates that the difference between the two sets of accounting earnings is likely to be significantly reduced from those reported for 1998 as a result.

History

Journal

Accounting horizons

Volume

13

Pagination

91-111

Location

[Lakewood Ranch, Fla.]

ISSN

0888-7993

eISSN

1558-7975

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

1999, American Accounting Association

Issue

2

Publisher

American Accounting Association

Usage metrics

    Research Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC