LOGISTIC REGRESSION IN DETECTING OF FRAUDMENTAL FINANCIAL STATEMENTS WITH USING FINANCIAL RATIOS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ETEMADI HOSSEIN; ZALAGHI HASAN

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF AUDIT SCIENCE Year:2013 | Volume:13 | Issue:51 Page(s): 0-0

Download Full-Text

Persian Verion

View:

2,515

Download:

Cites:

Information Journal Paper

Title

LOGISTIC REGRESSION IN DETECTING OF FRAUDMENTAL FINANCIAL STATEMENTS WITH USING FINANCIAL RATIOS

Author(s)

ETEMADI HOSSEIN | ZALAGHI HASAN | Issue Writer Certificate

Keywords

FRAUDMENTAL FINANCIAL STATEMENTSQ2

FINANCIAL RATIOSQ2

LOGISTIC REGRESSIONQ2

Abstract

We examines published data to develop a model for detecting factors associated with FRAUDMENTAL FINANCIAL STATEMENTS (FFS). Most FRAUDMENTAL FINANCIAL STATEMENTS in Tehran Stocks Exchange (Tse) Listed Companies, can be identified on the basis of the FINANCIAL RATIOS. We use a sample of 68 firms includes 34 with FFS and 34 non-FFS. Nine financial variables are selected for examination as potential predictors of FFS. Univariate and multivariate statistical techniques (logistic regression) are used to develop a model to identify factors associated with FFS. The model is accurate in classifying the total sample correctly with accuracy rates 83.8 per cent. The results therefore demonstrate that the models function effectively in detecting FFS and could be of assistance to Investors and auditors, both internal and external and taxation, Government bodies and to the banking system.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

ETEMADI, HOSSEIN, & ZALAGHI, HASAN. (2013). LOGISTIC REGRESSION IN DETECTING OF FRAUDMENTAL FINANCIAL STATEMENTS WITH USING FINANCIAL RATIOS. JOURNAL OF AUDIT SCIENCE, 13(51), 0-0. SID. https://sid.ir/paper/391237/en

Vancouver: Copy

ETEMADI HOSSEIN, ZALAGHI HASAN. LOGISTIC REGRESSION IN DETECTING OF FRAUDMENTAL FINANCIAL STATEMENTS WITH USING FINANCIAL RATIOS. JOURNAL OF AUDIT SCIENCE[Internet]. 2013;13(51):0-0. Available from: https://sid.ir/paper/391237/en

IEEE: Copy

HOSSEIN ETEMADI, and HASAN ZALAGHI, “LOGISTIC REGRESSION IN DETECTING OF FRAUDMENTAL FINANCIAL STATEMENTS WITH USING FINANCIAL RATIOS,” JOURNAL OF AUDIT SCIENCE, vol. 13, no. 51, pp. 0–0, 2013, [Online]. Available: https://sid.ir/paper/391237/en

Related Journal Papers