Factors Effective on Timeliness of Financial Reporting: Using Synthetic Neural Networks and Decision Trees Techniques

In decision making, timeliness is one of the useful information features that have increasingly attracted the attention of regulatory agencies. This paper examines factors which are related to the timeliness of annual reporting financial statements in Tehran stock exchange listed firms. The effect on financial reporting timeliness of factors such as the ratio of the investment institutes, major shareholders and institutional shareholders ownership, the ratio of the shares held by the CEO and board, the quantity of the majority shareholders, and the ratio of the non-executive members, duality, big auditor, auditor change, size, age, ROE, good news, consolidate financial reporting and the quality of the costing system, opinion of the independent auditor and industry during the years 2008 to 2011 have been studied. Neural networks and decision trees techniques is utilized for data analysis. The Results show that the institutional shareholders ownership, quantity of the majority shareholders, industry, big auditor, opinion of the independent auditor, ratio of the non-executive members, consolidate financial reporting and ROE have the greatest effect on quality of timeliness, respectively. Also average accuracy of neural networks technique is higher than decision trees technique.