Empirical Study on the Obtaining of Audit Evidence based on Comparison of Information Derived from Stock Analysis
Author:
Univ. Prof. Marilena MIRONIUC, Ph. D.; Mihaela-Alina ROBU, Ph. D. Student; Ioan-Bogdan ROBU, Ph. D. Student
JEL:
C38, C58, C63, M41, M42
DOI:
Keywords:
audit opinion, audit evidence, stock analysis, stock performance, perfo rmance profile, logistic regression
Abstract:
The current economic environment characterized by imbalances in the economic and financial processes and transactions, caused a worldwide reappraisal of the role of financial auditor in the examination and assurance the true and fair view of the financial position and performance presented in the financial statements, increasing the quality of audit engagement carried out. To support the interests of all stakeholders, according to the requirements of the present economic environment, financial auditor should obtain sufficient and appropriate audit evidence to support the final opinion from the audit report. The constraints and the limitations of obtaining audit evidence based solely on the analysis of the reported financial statements requires also the use of the other sources to provide the necessary audit evidence to the auditor throughout his mission. So, the information derived from stock analysis regarding performance assessment can be used by the auditor, in an objective and independent manner, for reporting possible frauds or errors and for assessing the ability of the client to continue as going concern. The study aims identifying a stock performance profile of listed companies using a range of specific indicators as well as estimating the parameters of a deterministic model for performance assessment (based on the same indexes) to supports the financial auditor in obtaining audit evidence required for the final opinion of the audit report. The study is based on a sample of 100 industry companies listed on U.S. stock exchanges, NYSE and Nasdaq, in 2009-2010. To obtain the research results, multiple correspondence factorial analysis and logistic regression analysis were used as working methods. Data were treated with SPSS 19.0 as a statistical tool.\r\n