درخت حوزه‌های تخصصی

Resource-based view (RBV) theory analyzes and interprets company's resources in order to find out how organizations gain a competitive advantage. This theory fo-cuses on the implications of the complicated features of a company as the resources for excellent performance and comparative advantage. According to this view, capa-bilities and resources of the company are the main factors in explaining the func-tional results or competitive advantage. Resources can be considered as inputs which enable companies to do their activities. The purpose of this research is to introduce a resource-based theory for calculating the efficiency score of accepted companies in Tehran Securities Exchange by using Data Envelopment Analysis (DEA). In this regard, the financial statements of 190 companies accepted in the exchange for the 2009 – 2018 period have been analysed. Efficiency indicators, which include 4 categories of resources (10 inputs) and 5 outputs, have formed the axis of the mentioned technique. The results of implementing this model for compa-nies with the efficiency score of one, indicates first a minimal input consumption compared to competing companies in the same industry while producing more out-put, and second, using the resource-based view theory (integration of tangible and intangible resources) enables the company to push the boundaries of efficiency. Finally, it can be said that utilizing minimum and maximum resources simultaneous-ly leads to a focus strategy-type competitive advantage.

Fraud is a common phenomenon in business, and according to Section 24 of the Iranian Auditing Standards, it is the fraudulent act of one or more managers, employees, or third parties to derive unfair advantage and any intentional or unlawful conduct. Financial statements are a means of transmitting confidential management information about the financial position of a company to shareholders and other stakeholders. In this paper, by reviewing the literature, 6 indicators of current ratio, debt ratio, inventory turnover ratio, sales growth index, total asset turnover ratio, and capital return ratio as input and detection of financial fraud as output are considered for the fuzzy neural network. The database was compiled for 10 companies in the period from 2010 to 2018 after clearing and normalizing qualitatively between 1 to 5 discrete numbers with very low or very high meanings, respectively. The fuzzy neural network model with 161 nodes, 448 linear parameters, 36 nonlinear parameters, and 64 fuzzy laws with two methods of accuracy approximation of mean squared error and root mean squared error has been set to zero and 0.0000001 respectively. This neural network can be used for prediction.