Cash flow models are one of the spotlights for evaluating a project. The actual data should be modeled then it could be used for the prediction process. In this paper, 996 airplane maintenance basis data are used as a database, and 119 similar data are chosen after clustering. The project is divided into 20 equal periods and first three periods are used for simulating the next point. The predicted data for each point is achieved by using of previous points from the beginning. The model is based on artificial neural network, and it is trained by three algorithms which are Genet-ic Algorithm (GA), Estimation of Distribution Algorithm (EDA), and hybrid GA-EDA method. Two dynamic ratios are used which are dividing the population into two halves, and the other is a ratio without dividing. The ratio would give a proportion to GA and EDA models in the hybrid algorithm, and then the hybrid algorithm could model the system more accurately. For each algorithm, three main errors are calculated which are mean absolute percentage error (MAPE), mean square error (MSE), and root means square error (RMSE). The best result is achieved for hybrid GA-EDA model without dividing the population and the MAPE, RMSE, and MSE values are %0.022, 28944.59 Dollars, and 837789503.79 Dollars, respectively.

Decision support system (DSS) normally consists of a set of related computer programs and the data required to help with analysis and decision-making within an organization. Many software vendors claim their decision support, analytics or business intelligence applications may provide a competitive advantage by providing different facilities such as business intelligence tools, business performance management software, data mining tools, etc. This paper presents an empirical investigation to determine different DSS factors influencing on development of export activities on selected firms which are active in Tehran Stock Exchange. The study designs a questionnaire consists of 35 questions and distributes it among 211 randomly selected managers who were involved in export activities. Cronbach alpha has been calculated as 0.817, which is well above the minimum desirable level. Using principle component analysis, the study has detected five factors including internal resources, management approach, quality of data, efficiency of data and organizational approach, which influence the most on export activities.

Investment decisions are normally accomplished based on fundamental or technical methods. However, there are many cases where investors make their investment decisions based on their emotions. This study investigates the effects of various factors including biases representation, mental accounting and risk aversion when an investment decision is executed. In other words, the study examines the effects of emotional intelligence components on retail investors’ investment strategies on Tehran Stock Exchange (TSE). The proposed study selects a sample of 270 investors who had some experiences on TSE randomly and using a questionnaire based survey detected that there was a positive and meaningful relationship between emotional intelligence and investment decisions.

Free trade zones are important regions for development of business activities and detecting important factors for development of these regions plays essential role for development of economy. This paper presents an empirical investigation to determine the factors influencing the performance of the incentive polities of free trade zones in Iran. The study designs a questionnaire consists of 34 questions. Cronbach alpha is equal to 0.809, which is well above the minimum acceptable level. In addition, Bartlett & apos; s Test of Sphericity yields a Chi-Square value of 2392.31 with Sig. = 0.000. Using principal component analysis with rotation, the study has detected six important factors influencing on development of free trade zones including government support, financial capability, development strategy, product competition, infrastructure and consultation services.

This paper presents an empirical investigation to construct, to validate and to determine normalization factors associated with adolescents depression scale. The study is performed among 750 randomly selected guided and high school students, 364 male and 386 female, who live in city of Zanjan, Iran. Validity of Beck Depression Inventory (BDI), Validity of Simpson-Angus Scale (SAS) and divergence validity of the Coopersmith self- esteem coefficients are 0.72, 0.37 and -0.71, respectively. Result suggests that adolescents’ depression test is a reliable and valid tool for assessing depression, with utility in both research and clinical settings, counseling centers. In addition, the results of correlation test indicate there are some meaningful differences between depression levels of female and male students. In fact, our survey indicates that female students have more depression than male students do (F-value = 33.06, Sig. = 0.000). In addition, there are some meaningful differences between depression levels in various educational levels (F-value = 8.59, Sig. = 0.000). However, the study does not find sufficient evidence to believe there is any meaningful correlation between educational backgrounds and gender.

This paper presents an empirical investigation to detect important factors influencing earning adjustment on firms selected on Tehran Stock Exchange over the period 2006-2011. There are four independent variables associated with the proposed study of this paper including Proportion of shares owned by institutional investors, Return on assets, Profit change and Market value to book value. In addition, Investment restructuring is considered as control variable. The results of the implementation of regression analysis indicate that there was a reverse relationship between earning forecasted adjustment and two independent variables including size of firm as well as the ratio of market value to book value. However, Net profit has a direct and positive relationship with earning forecast adjustment.

In today' s extremely competitive environment, customers are the most important asset of any business and success of any organization depends on loyal customer retention. Therefore, it is important to learn more about different groups of customers and propose appropriate plans to take care of them. Customer segmentation is one of the most common methods to analyze customer’s behavior. The market can be divided into several smaller homogeneous groups, which helps organizations deliver targeted marketing techniques and provides optimized techniques for resource allocation. This paper uses the information gathered from Point of Sales (POS) in one of Iranian private banks and using two methods of K-Means and Kohonen, customers are clustered into four segments to detect the most profitable customers.

This paper presents an empirical investigation to study the effect of five factors on purchasing food product including brand and reputation, being green product producer, quality of packaging, taste of food products and materials used in products. The study designs a questionnaire consists of 20 questions, distributes 440 questionnaires among some consumers who were regular customers of food chains in west part of city of Tehran, Iran and managed to collect 225 properly filled ones. The study uses binomial test to verify five hypothesis of the survey and it has confirmed the effects of four variables including brand and reputation, quality of packaging, taste of food products and materials used in products. In our survey, there are some positive and meaningful correlations among different pairs of five variables of the survey where the highest correlation is between materials used in products and quality of packaging (r=0.606, Sig. = 0.000) and between Quality of packaging and being green product producer (r=0.545, Sig. =0.000).

In this paper, the problem of lot sizing for the case of a single item is considered along with supplier selection in a two-stage supply chain. The suppliers are able to offer quantity discounts, which can be either all-unit or incremental discount policies. A mathematical modeling formulation for the proposed problem is presented and a dynamic programming methodology is provided to solve it. Computational experiments are performed in order to examine the accuracy and the performance of the proposed method in terms of running time. The preliminary results indicate that the proposed algorithm is capable of providing optimal solutions within low computational times, high accuracy solutions.

Decision making about dividend payout is one of the most important decision that companies should encounter. Identifying factors that influence dividends can help managers in making an appropriate dividend policy. In the other side, companies’ dividend payouts over time and with a stable manner may influence on stock price, future earnings growth and finally investor & apos; s evaluation about owners & apos; equity. Hence, investigating the factors influencing dividend payout ratio is of high importance. In this research, we investigate the effects of various factors on dividend payout ratio of Tehran Stock Exchange (TSE) listed companies. We use time series regression (panel data) in order to test the hypothesis of this study. This study provides empirical evidences by choosing a sample of 102 companies over the time span of 2005-2010. The result shows that independent variables of free cash flow and profitability current ratio have negative and significant impact on dividend payout ratio; whereas, the independent variable of leverage ratio has a positive and significant impact on dividend payout ratio. The other independent ratio such as size of the company, growth opportunities and systematic risk do not have any significant influence on dividend payout ratio.