Advances in Mathematical Finance and Applications
Volume:4 Issue: 1, Winter 2019

One of the most important applications of data envelopment analysis tech-nique is measuring the efficiency of bank branches. Performance measure-ment in the banking industry is important for several groups, including bank managers, customers, investors, and shareholders. The purpose of this study is to examine and design a mixed structure to measure the efficiency of branches of Iranian banks according to their policies. In order to obtain the efficiency of the structure divisions proposed in this study, a slack-based NDEA model was selected to solve its mathematical model. The study sam-ple consists of 31 branches of a large commercial bank in Iran. The ad-vantage of this research to previous studies is that the result will be more realistic considering the inputs and outputs consistent with Iran's banking conditions.

Study of volatility has been considered by the academics and decision makers dur-ing two last decades. First since the volatility has been a risk criterion it has been used by many decision makers and activists in capital market. Over the years it has been of more importance because of the effect of volatility on economy and capital markets stability for stocks, bonds, and foreign exchange markets. This research first deals with the evaluation of 8 various models to forecasting volatility of stock index using daily data of Tehran stock exchange. The used models include simple ones such as random walk as well as more complex models like Arch and Garch group. Forecasting volatility index method is developed in this paper. This method is based a random walk using a fuzzy logic approach. This method is used to fore-casting volatility of Iran stock exchange index. The proposed method is assessed by comparing other methods such as Moving Average, Random walk… Results show that our proposed method is compatible with existent methods.

The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, in this paper, possibilistic range directional measure (PRDM) model is proposed to measure the efficiencies of stocks in the presence of negative data and uncertainty with input/output parameters. Using the data from insurance industry, this model is also implemented for a real case study of Tehran stock exchange (TSE) in order to analyse the performance of the proposed method.

The purpose of this study, is create a challenge and discussion concerning the existence of information about the Bitcoin price and return, which suggests the relationship of information and the strong performance it. The information trends are available at different time periods and the summary data related to the statistical descriptions for the price and return index are also discussed. In this paper we show a significant correlation between the price trend and return in the Bitcoin that has been confirmed by various statistical methodology. Using statistical tests and reviewing trends and relationships between the variables, planning can be done to invest in it and its performance or inefficiency can be tested. The results of this research shows a significant and positive relationship between the price and return of Bitcoin.

Because in the theory of economics, the value of a company is based on the current value of future cash flows and profit is used as a substitute for cash flows, profit forecasting is of particular importance. In the research, the effect of rounding and revision in predicting earnings per share on the investors' attention in Iran has been investigated. After designing the investors' attention assessment indexes, the transaction information was collected from the Stock Exchange in the five-year period of 2011-2015. The statistical sample consists of 120 companies selected by systematic elimination method and totally obtained 600 year-firm. In this research, linear regression and correlation were used to investigate the hypotheses of the research and Eviews software was used to analyze the data and test the hypotheses. What can be said in the summing up and conclusion of the test of research hypotheses is that predicting the earning per share influences investors 'attention, as well as the revision of the earnings per share influences the investors' attention. On the other hand, research studies show that the rounding in interaction with the revision in predicting earnings per share can also affect the investors' attention.

Optimal working capital management can positively effect on the Firm performance, but this relationship can be affected by major characteristics of the firm, making an important subject for research. This research investigates the moderating role of firm characteristics on the relation between working capital management and financial performance of the firms listed in TSE during 2008 – 2017 period. Based on existing researches, three characters are considered as moderating variables in this research include firm size, debt ratio, and Governmental ownership. Financial performance and working capital management are measured using return on assets (ROA) and cash conversion cycle (CCC), respectively. We use from multivariate regression model with panel data for test of research hypotheses. The Results of this study show that, firm size affects the Relation between CCC (as a measure of working capital management) and ROA (as a measure of firm performance). However, debt ratio and Governmental ownership don’t any significant effect on the relationship between working capital management and financial performance of firms

Currently, in Iran's banking system, non-repayment of facilities has become one of the biggest issues, and due to the lack of a proper system for proper allocation of facilities, they face a number of problems, including the problem of allocation of loans, the problem of failure to repay loans Of the central bank, or the amount of facilities increased from the amount of reimbursement. The solution of this problem is the credit rating of the customers, which is based on a model based on the theory of fuzzy sets for validation of real customers of the Maskan bank of the East Azer-baijan in Iran in 2016. In this research a structured model was obtained for deter-mination and categorization of input variables for application in the system by factorial analysis then a expert fuzzy system was modelled that consist of six steps. In the first step a fuzzy system is designed that its inputs are financial capacity, support, reliability, repayment record and its outputs is customer credit. In the second step input and outputs are partitioned, in the third step thee partitioned inputs and outputs are converted into fuzzy numbers. The fuzzy inference is compiled in step four. In step five the defuzzifier is conducted. Finally the designed model is tested in step six. These results indicate research model efficiency compared to bank credit measuring experts that they predicate applicants performance according their judgment and intuition.

Sustainable supply chain has become an integral part of the corporate strategy. In this paper, a real case study of the natural gas supply chain has been investigated. Using concepts related to natural gas industry and the relations among the compo-nents of gas and oil wells, refineries, storage tanks, dispatching, transmission and distribution network, a seven-level supply chain has been introduced and present-ed schematically. The aim of this paper is to optimize a case study using a fuzzy goal-programming multi-period model considering environmental and economic costs and revenue as fuzzy goals and maximize the total degree of satisfaction of goals as objective function. A small-sized problem was solved using GAMS 23.2.1 software and sensitivity analysis was conducted on its parameters. To the best of our knowledge, this is the first study that presents a fuzzy goal program-ming model for the optimization of sustainable natural gas supply chain by focus-ing on the environmental and economic costs and total revenue of gas products and the other main contribution of this research is focused to the developing of the mentioned model.