Nowadays, the great benefits of cloud computing have dramatically increased the number of e-banking users. Hence, the competition in the banking industry has boosted and managers need to evaluate their branches on a regular basis. To this end, this study aims to evaluate cloud-based banking systems based on the Quality of Service (QoS) attributes using the Dynamic Network Data Envelopment Analysis (DNDEA) model. The main advantage of this research is that the efficiency of cloud-based bank branches can be estimated more realistically according to their internal structure over a specific time span. To conduct the experiment, 40 bank branches in Iran are analyzed by considering between-period and divisional interactions during 2018-2019. A cloud-based bank branch is conceptualized as a set of three inter-connected divisions including capabilities, intermediate process, and profitabilities. Some outputs of sub-DMUs 2 and 3 are treated as desirable and undesirable carry-overs between consecutive periods. In addition, the cost items and QoS attributes are considered as the inputs and outputs of divisions, respectively. The results indicate that 28 bank branches were efficient and all of the inefficiencies fall in divisions 1 and 3. Moreover, the number of efficient branches has been reduced from 2018 to 2019.
< p>The present study models the risk of investment in the petrochemical industry considering the impacts of exchange rate (US dollar to Iran''''s Rial) movements using the time series data from November 2008 to March 2019 and ARFIMA-FIGARCH framework. The empirical results prove the existence of the Fractal Market Hypothesis, FMH, and the Long Memory property in both the risk and return of the petrochemical stock index. These findings can be culminated in reaching a reliable and significant model to evaluate the investment risk in the petrochemical industry. In line with this, to analyze the idea whether considering the exchange rate movements matter for assessing the risk management in the petrochemical industry, the effects of exchange rate movements as a crucial source of systematic risk in Iran has been taken into consideration in the process of modelling the risk of investment in that industry. Our results demonstrate that the exchange rate movements have had a direct and significant effect on the investment risk of that industry so that if, on average, one percent change occurs in the exchange rate, the investment risk in this industry changes by 57% in the same direction.
The stock market has an important role in growth and development of countries. Network analysis is one of the latest method in analyzing the stock market. In quantitative science literature, It is a new concept for a macro view to whole market. Therefore, this research analyzes the interpersonal relationships’ network in the Tehran Stock Exchange (TSE). From the type of data collected and analyzed point of view, this study is a quantitative research in network analysis domain. The research period is from 2013 to 2017. Softwares such as PreMap and UCINET used for analyzing data. The research results indicated that some individuals, in comparison with others, have a better position in communicative networks. Having better position has caused these individuals to encounter fewer mediators in gaining access to others, and in turn easier access to available resources. In addition, their ability in gaining access to information enhanced via the cluster of network members. Therefore, it might be concluded that these individuals are key actors in governing structure of the TSE. Furthermore, this network follows a kind of bus morphology i.e. individuals act as a bridge for other units and connect them to the core of the communication network.
We consider the hedging problem in a jump-diffusion market with correlated assets. For this purpose, we employ the locally risk-minimizing approach and obtain the hedging portfolio as a solution of a multidimensional system of linear equations. This system shows that in a continuous market, independence and correlation assumptions of assets lead to the same locally risk-minimizing portfolio. In addition, we investigate the sensitivity of the risk with respect to the variation of correlation parameters, this enables us to select the more profitable portfolio. The results show that the risk increases, with increasing the correlation parameters. This means that to reduce risk it is necessary to invest in low correlated assets.
Efficient market hypothesis predicts that capital markets are beset with cer-tain biases which result from wrong estimation, and negatively influence shareholders’ expectations for higher returns, which in turn affects invest-ment efficiency, financial constraints and corporate performance efficacy in competitive markets, and eventually mitigates firm value. The present study aims at examining the impact of CEOs’ perceptual biases on investment efficiency and financing constraints of the firms listed on the Tehran Stock Exchange over the period 2013-2017. Earnings forecast error and CEOs’ overconfidence biases serve as the measure of CEO’s perceptual biases, the model developed by Biddle et al (2009) is employed to proxy for investment efficiency, and KZ model is also adopted to calculate financing constraints. The results reveal that both earnings forecast error and overconfidence biases negatively affect investment efficiency, while they positively influence cor-porate financing constraints.
In this study, the impact of corporate social responsibility (CSR ) disclosure on idiosyncratic risk has been investigated concerning three stakeholder theory, information asymmetry, and risk management. It also goes further and explores the impact of some corporate governance mechanisms such as ownership structure, board characteristics, and incentive contracts on this relationship. To achieve the research objectives, 142 companies from Tehran Stock Exchange were selected through the systematic elimination method from 2010 through 2018 and the research hypotheses were tested using a combined data regression model with an integrated approach. The results show that CSR disclosure; by increasing transparency, reducing uncertainty, stakeholder satisfaction, and positive market signaling; reduces idiosyncratic risk. It was also found that the ownership concentration and managers’ remuneration by reducing CSR reporting lead to increased idiosyncratic risk, but government ownership, the duality of the CEO’s duties, the independence of the board of directors and the managers' equity decrease the corporate idiosyncratic risk by increasing CSR reporting. However, the effect of managers' remunerations and state ownership on the relationship between CSR reporting and corporate idiosyncratic risk was not confirmed at the 95% confidence level. Overall, from a theoretical viewpoint, a good corporate governance system can improve the quality of CSR, thereby improving corporate social reputation and reducing corporate idiosyncratic risk.
Based on IFRS laws, British companies have started providing their reporting systems according to International Standards Requirements regarding disclosing their financial derivatives since January 2005. In 2013, Iran revised its Accounting Standard No. 15 to include the derivative instruments. The present study aims at investigating the effect of this revision on financial derivatives and instruments, and the effect of earning management on the relationship between the level of financial derivatives and instruments and risk-adjusted discount rates. From generalized least squares regression panel data, it was found that based on the first hypothesis, the companies which disclose their financial instruments based on No. 15 internal standard have a lower risk- adjusted discount rate, implying an increase in profit and a price rise in the markets. The findings also confirmed the second hypothesis, attesting to the effect of earning management on the relation-ship between financial derivatives and instruments disclosure and excess return. Findings of the research third hypothesis represent that there is a direct meaningful relationship between disclosure level of financial instruments and company value. So, it can be concluded that instruments` disclosures and financial derivatives can decrease risk-adjusted discount rate and increase companies` values in terms of standard number 15.
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.
The present study aims to investigate the relationship between comparability of financial reports and negative coefficient of skewness of firm-specific monthly returns. In this study, to measure the financial statements comparability, De Franco et al. (2012) model is employed. Sample includes the 425 firm-year observations from companies listed on the Tehran Stock Exchange during the years 2013 to 2017 and research hypothesis was tested using multivariate regression model based on panel data. The results indicate that financial statements comparability mitigates negative skewness of stock return. Our findings are robust to alternative measure of stock price crash risk, individual analysis of the research hypothesis for each year and endogeneity concern. The current study is almost the first study which has been conducted in emerging capital markets, so the findings of the study not only extend the extant theoretical literature concerning the stock price crash risk in developing countries including emerging capital market of Iran, but also help investors, capital market regulators and accounting standard setters to make informed decisions
Implementation credit rating for Corporates is influenced by Different circum-stances, systems, processes, and cultures in each country. In this study, we pro-posed a Factor analysis modified approach for determine important factors on real data set of 123 accepted corporate in Tehran Securities Exchange for the years 2009-2017 of diverse range of 52 variable. We estimated the priority score for 49 factors. The three factors, Debt to Equity Ratio, Current debt-to-equity ratio and proprietary ratio exclude due to high correlation with others. The results indicated that three macroeconomic factors: Price Index of Consumer Goods and Services, exchange rate and Interest rate determinants were more effective on the credit ratings. In addition, Financial Ratios and non-Financial Ratios such as Return on equity (ROE), Long-term debt-to-equity ratio, Benefit of the loan, ratio of com-modity to working capital, Current capital turnover, Return on Working Capital, Quick Ratio, Current Ratio, Net Profit margin, Gross profit margin, had effect on credit rating accepted corporate in Tehran Securities Exchange. The Nonparametric statistical test to validate the consistency between AHP ranking and Factor analysis revealed, the new approach has a moderated consistency with AHP. In conclusion, the Factor analysis modified approach could be applied significantly to evaluate efficiency and ranking factors with minimum loss of information.
In portfolio theory, it is well-known that the distributions of stock returns often have non-Gaussian characteristics. Therefore, we need non-symmetric distributions for modeling and accurate analysis of actuarial data. For this purpose and optimal portfolio selection, we use the Tail Mean-Variance (TMV) model, which focuses on the rare risks but high losses and usually happens in the tail of return distribution. The proposed TMV model is based on two risk measures the Tail Condition Expectation (TCE) and Tail Variance (TV) under Generalized Skew-Elliptical (GSE) distribution. We first apply a convex optimization approach and obtain an explicit and easy solution for the TMV optimization problem, and then derive the TMV efficient frontier. Finally, we provide a practical example of implementing a TMV optimal portfolio selection in the Tehran Stock Exchange and show TCE-TV efficient frontier.