درخت حوزه‌های تخصصی

By intensifying sanctions against Islamic Republic of Iran, identification and development of mechanisms to sell crude oil has gained attention among most experts and oil industry activists. Limitations for financial transactions and settlements are one of the fundamental challenges in international oil transactions; thus, various solutions are proposed to solve the limitation. Using capability of virtual currencies is one of the proposed suggestions for petroleum transaction under sanctions but there is a research gap in finding whether and which one of these currencies are able to be used in petroleum transactions in present condition. In this study, performed by analysis and quantitative approach, indices are introduced for choosing a valid currency for international transactions, and subsequently are confirmed by experts. The indices are ranked by the experts through TOPSIS method and are studied on the most suitable virtual currencies to find the best suit for petroleum transactions. The results reveal that Bitcoin, Ethereum and Tether are respectively the best, second and third choice for this end. It is noteworthy that some experts might believe the virtual currency market size may not meet the high volume of crude oil transactions, making it unsuitable. Although current study has shown that there would be no problem in covering the whole petroleum market for Iran’s crude oil by virtual currencies platform, if the concerns remain, virtual currency platform could be used for selling Iran’s crude oil temporarily and partially through various channels.

During the last decade, several studies have argued that sticky information model proposed by Mankiw and Reis (2002), in which firms update their information occasionally rather than instantaneously, explains some stylized facts about the inflation dynamics. Sticky information pricing model successfully captures the sluggish movement of aggregate prices in response to monetary policy shocks. Despite the importance of sticky information, no empirical studies have been done yet to estimate sticky information Philips Curve (SIPC) and its key parameter - the degree of information rigidity - in Iran. This paper is the first attempt to estimate the degree of information stickiness in Iran using the two stage empirical approach proposed by Khan and Zhu (2006). Having the correct structural parameter allows a better understanding of the dynamics of inflation. Results show that the average duration of information stickiness ranges from 3.2 to 4 quarters in Iran. In addition, the existence of threshold effects in SIPC is also tested in this paper. Based on the estimation of TAR model over 2002Q2- 2015Q1, firms update information faster when inflation is higher. This evidence suggests that firms are more aware of macroeconomic conditions when inflation is higher; that is, missing information during high inflation periods is costly. JEL Classifications: E31, E37, C53, D84

Based on the capital market’s nature, accountants and auditors’ information is provided by an effective influence of personal decisions and market results, derived systematically by information structure and market participants’ features. Auditors’ choices are influenced by perception, judgment and decision options processes, which may affect auditing errors. The study purpose is to investigate auditors’ different biases and decision-making factors on errors based on a cognitive approach in the capital market. The model’s objective is practical based on a descriptive-analytical methodology. The statistical population of the study includes all certified auditors of Iran's Securities and Exchange Organization (SEO), whom were provided with the researcher-made questionnaires with valid narration and reliability. The collected data were analyzed by AMOS software. The findings indicate that components of the cognitive bias are predictable by auditors’ errors based on the priority level and maximum influences, including mental accounting bias (63%), availability bias (45%), heuristic bias (60%), and ambiguity aversion bias (58%). Also, components of decision-making are predictable by auditors’ errors based on the priority level and maximum influences, including decision case (54%), job experience (57%), decision-making situation (58%) and individual features (45%).