The Role of Financial Ratios in Explaining Information Quality Using the Factor Analysis Approach

Iranian Journal of Accounting, Auditing and Finance

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 اردیبهشت 1401  

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.22067/ijaaf.2022.74693.1090

نویسندگان

Akram Karimi¹؛ Roya Darabi ²؛ Mohammad Reza Poorfakharan¹؛ hossein moghadam¹

¹Accounting Department,Qom Branch,Islamic Azad University,Qom,Iran

²Islamic Azad University, South Tehran Branch

چکیده

Objective: This study aims to investigate and predict information quality ranking with the approach of factor analysis and artificial intelligence in firms listed on the Tehran Stock Exchange for nine years from 2011 to 2019.

Research Method: The independent variable used in this study is the financial criteria of the firm, and the dependent variable is the quality measurement criteria of accounting information in which all criteria have been converted into a single variable according to the factor analysis method. The present study is considered empirical accounting research, and the artificial intelligence method has been used to test the research hypotheses.

Findings: The results indicate that according to the variable selection method of artificial intelligence, neighbourhood analysis among performance variables including "Accounts receivable to sales ratio criteria", "Firm size", "Financial risk", "Current assets to total assets ratio" and "Cost to Sales ratio ", firms have the highest correlation with information quality rating. Other results indicate that linear and nonlinear artificial intelligence methods can predict the quality rating of accounting information firms listed on the Tehran Stock Exchange.

Innovation: Due to the importance of financial information quality in financial reporting, and innovation of the present study is the simultaneous use of all information quality criteria and artificial intelligence to examine research hypotheses.

کلیدواژه‌ها

Information Quality؛ Financial Criteria؛ Artificial Intelligence؛ Factor Analysis