فهرست مطالب علی محمد کیمیاگری

Achieving the optimal capital structure in order to gain maximum profitability and value while having minimum cost of capital is one of the important topics studied by financial professionals. In the field of the relationship between financing decisions and corporate profitability, there exists four major theories of Miller and Modigliani, agency theory, static balance theory (all observing the positive relationship between debt ratio and profitability) and hierarchical theory (negative relationship between debt ratio and Profitability). In this study, using data from 161 companies listed on the Tehran Stock Exchange in five industries (including ceramic tiles, cement, metals, oil and gas, and pharmaceuticals) between 2010 and 2017, the problem of estimating the profitability function by the capital structure has been addressed. In order to estimate the mentioned function, regression analysis as well as a combination of wavelet neural network algorithm and the Imperialistic Competition Algorithm have been used. The results indicate a negative relationship between debt ratios and return on asset (confirmation of hierarchical theory) in ceramic tile, cement, metals, and pharmaceutical companies. However, in the oil and gas industry, no significant relationship has been found between these variables. In addition, the performance of the wavelet neural network optimized by the imperialistic competition algorithm has shown to be more desirable than simple linear regression for all the investigated industries.

Today, customers have become a critical factor in directing investors, producers, and even researchers and innovators. For this reason, organizations need to know about their customers and plan for them. Insurance companies and in general the insurance industry in each country, is one of the most important financial institutions active in financial markets, especially the capital market, which in addition to providing security for economic activities, can play a very fundamental role in providing insurance services. In other words, insurance companies play a vital role in the mobility, dynamic of financial markets and the provision of investable funds in economic activities. In this research, it has been attempted to answer one of the most important questions of insurance organizations, namely, predicting the level of customers’ losses and investing on profitable customers.

Data mining methods were used to discover knowledge to meet business needs and customer relationship management strategies. In addition, an overview of the various applications of data mining in customer relationship management in various insurance companies has been done. In the model implementation stage, a real data-set is used to evaluate the proposed model. To perform the data mining techniques in the insurance industry as data of customers, the vehicle body insurance from 2015 to 2017 has been under investigation. The total number of data used in this study from the beginning was more than 19,356, which during data preparation using Rapidminer 7.1 software became 19,356. After the initial processing, an attempt is made to extract good features from the 15 variables in the data-set that is tangible and help this research in its goal. As a result, by using clustering, drivers are divided into separate clusters based on the amount of loss, and the characteristics of each cluster are expressed. In the clustering section, three algorithms of data mining are examined. First, k-means, k-medoids, and DBSCAN implemented on data-set. Then, the conclusion of three algorithms compared with each other based on the time of calculation and accuracy.

Data mining was a good tool in this research, owing to the large volume of data, to discover the needs and identify customers. The data mining technique which was the main approach of this study fully covered the information needs by methods such as classification, prediction and clustering. The k-means algorithm was selected as the most optimal one in time and accuracy. In the following, the implementation of the algorithms in the modeling step, the decision tree algorithm was selected and by the decision tree related to the forecasting model, it can be predicted future customers by what characteristics would be in what category. It will be valuable for the insurance companies. Using a decision tree, a forecasting model is proposed to help insurance companies to identify profitable customers which can be used for future plans of organizations.

The customer plays an important role in today's industry. Through studying the data obtained from customer behavior, appropriate action can be taken for marketing-related planning and customer acquisition. The use of predictive models and preventive roadmaps has always been one of the goals of the tools that various organizations have been looking for. In this research, the insurance industry as one of the most important pillars of economic in developing countries has been chosen. By reviewing the share of the insurance industry in the economy of a developing country, it can be seen that insurance has a significant role compared to other services.  In this study, the role of insurance companies in optimizing the investment process and ways to expand the interaction between insurance examined. Customers can lead to the growth and development of the insurance industry and the capital market and thus the growth and development of the national economy. Therefore, in the implementation of this research, the data of insurance customers have been used and a forecasting model has been presented. As a good prediction model, the decision tree with 86.21% accuracy was the best model that reached in this study. The insurers’ income criterion is considered as the node root, which shows the used method can help insurance companies make more profit by focusing on profitable customers.JEL Classification: B31, C38, C22, D12

In this paper, a model is presented to estimate greenhouse gases emission in Iran. At first, the Environmental Kuznets Curve (EKS) hypothesis was tested. This hypothesis suggests an inversed U-shaped relation between environment deterioration and economic growth. Therefore, considering total greenhouse gases emission as environment deterioration index, its relation with per capita GDP is examined. Other affecting variables such as arable lands percentage, fossil fuels consumption percentage, electricity consumption per capita, energy use per capita, population growth, urban population percentage, and merchandise trade as percent of GDP also were considered. Parameters then were estimated using the Ordinary Least Square (OLS) method. Based on the results, EKC hypothesis was approved and significant difference were observed between and affection of electricity consumption, population growth, urban population percentage, and merchandise trade as percent of GDP.

Investors are faced with a wide range of investment options. Considering the growing demand for energy and the limitations of non-renewable resources (RES) , the importance of modeling and evaluating investment in RES has become significant for both investors and policymaker. The purpose of this study is to model and evaluate investment in RES with the aim of attracting investors without delay by determining the optimal level of the RE tariff by applying the Real Option approach (ROA). For modeling, a five-factor set of uncertainty, delay option, climatic differences has been considered and for implementation, a combination of backward dynamic programming algorithm, Monte Carlo simulation and geometric Brownian motion has been used. The results show investing in RES without considering the subsidy is not attractive for investors to investment without delay and then investors and policymakers need to apply ROA to evaluate and invest. The optimal tariff is estimated at an average of 503,000 Rials and the tariff should be adjusted according to the climatic conditions. By technology advancement, Implementation of CO2 trading scheme, the investment value increases and the optimal subsidy is reduced. The optimal subsidy is directly related to tax rate, capacity, exchange rate, discount rate and volatility.

Forecasting financial markets is an important issue in finance area and research studies. Importance of forecasting on one hand and its complexity, on the other hand, researchers have done much work in this area and proposed many methods. In this research, we propose a hybrid model include wavelet transform, ARMA-EGARCH and NN for day-ahead forecasting of stock market price in different markets. At first WT is used to decompose and reconstruct time series into detailed and approximated parts. And then we used ARMA-EGARCH and NN models respectively for forecasting details and approximate series. In this model we used technical index by approximate part to the improvement of our NN model. Finally, we combine prediction of each model together. For validation, proposed model compare with ANN, ARIMA-GARCH and ARIMA-ANN models for forecasting stocks price in UA and Iran markets. Our results indicate that proposed model has better performance than others model in both markets.

In this research, the multi-variable linear regression relationship has been developed among the important factors influencing unemployment rate. The seasonal data is from 1394 to 1394, which is compiled from reliable economic data bases of country. Independent variables are: net foreign assets of the banking system (billion rials), net debt of the public sector to the banking system (billion rials), liquidity in terms of its constituent parts (billion rials), rate of dollar (rials), economic participation rate, average inflation rate, The average annual interest rate on state-owned banks, the percentage of jobseekers (65-15). The results indicate that there is a negative and significant relationship between unemployment rate and average inflation rate and economic participation rate, while the net debt of the public sector has had a positive and significant relationship with the banking system and unemployment rate. The greatest negative effects on unemployment rate are the rate of economic participation and the greatest positive impact on the unemployment rate is the net debt of the public sector to the banking system.

Planning companies’ profits through customer satisfaction management has 4 main steps of recognizing constitutive components of customer satisfaction, integration of components and measuring overall customer satisfaction, reviewing how overall customer satisfaction impacts level of performance and activity, and finally identifying main parameters capable of maneuver on customer satisfaction. These factors compiles planning of company's profits. Method used for modeling were econometric approach and mathematical equations. Case of this research is Pars Khodro’s Logan. The researcher, along with ISQI's research team, identified current needs of customers from after sales services. Impact of each variable on overall customers’ satisfaction according to income groups is measured. Then, using econometric models, the variables affecting the company's profit are obtained which according to results, overall customer satisfaction has the highest impact on company's profits. Therefore, using mathematical equations, the effect of the overall customer satisfaction sub-variables on the company's profit has been obtained. Eviews software is used for data analysis.

Planning of companies profits through customer satisfaction management according to 4 main steps as: 1. Recognize the constitutive components of customer satisfaction and measure them, 2 . The integration of components and measurement overall customer satisfaction , 3. reviews how the impact overall customer satisfaction on the level of performance and activity company And (4) identify variables and the main parameters capable of maneuver on customer satisfaction and finally compiled program the company's profits. The main focus of this research has been focused on the third step that's mean modeling the effects of customer satisfaction on the level of performance and the company's profitability and the method used to investigate this topic and modeling it econometric approach and its Case Study is Logan Pars Khodro. It is necessary to mention in modeling the subject in addition to variable of customer satisfaction 3 The other variable 1. Household income, 2. prices Logan and 3. price of rival car (Peugeot 206) considered. The results showed that the variables of customer satisfaction, household income and the price of Peugeot 206 with sales and a result of profits have direct relationship and price Logan with company profits has indirect relationship. Variable of customer satisfaction from after-sales service has the greatest amount of influence on its profitability. This means that each unit increase customer satisfaction, 26.5 unit sales increases. So company of Pars Khodro can with invest and pay more attention to these factors increase the the profit from the sale of car. To run this research of software EViews and Spss is used.

Financing decisions of the firms are one of important tasks for management and according to its nature, it is considered as a Multi-Criteria Decision problem. In this research, a comprehensive review on capital structure theories and its determinants has been made, then the effect of this determinants on the financial leverage of the firms is investigated in three levels: firm specific, industry and economy. In this regard, stepwise regression and pooled mean group model are used. This models specified both short-term and long-term effects of detected factors on capital structure. In this models, as the number of independent variables increase, the co-linearity in the model increases. So, the reliability of model declines and the model can't be a good pattern for financing decisions. In the next step, the identified factors were employed as decision criteria in a hybrid MCDM model that combining DEMATEL and ANP. The ANP weights of the model were used in a Goal programing model and a satisfactory solution for the case study was obtained. By using this model, decision makers can systemically structure a multi-criteria network framework and derive priority weights of those criteria, and then deal with the quantitative financial constraints for a satisfactory solution.

This research aims to provide a hybrid model based on econometrics and clustering to analyze customer satisfaction data. The statistical population of the research,Logan car owners and a sample of 177 customers from Pars Khodro agencies. The researcher, along with ISQI's research team, identified the current needs of customers of all vehicles from post-sales services and by designing a customer satisfaction questionnaire, the effect of each variable on the overall satisfaction of customers according to the high-income group, middle income and low income is measured. Factors affecting overall customer satisfaction based on all income groups include six variables: Provision of parts on time; Description presented when vehicle clearance; Ease of access to dealers; periodic service quality; Quality of repairs; Cost of payment and in the high-income group, three variables are described: Cost of payment; Record mentioned Items by the receptionist at the time of admission; Description presented when vehicle clearance and In the middle income group, four variables are described: Provision of parts on time; Quality of repairs; Description presented when vehicle clearance; Cost of payment and and in the low-income group, it includes four variables: Description presented when vehicle clearance; Provision of parts on time; Quality of repairs; Ease of access to dealers. To run this research of software EViews and Spss is used.

One of the important risk management tools in financial markets, is financial derivatives. In this study, we investigate the problem of European option pricing. The most key input to option pricing models is volatility. For accurate modeling of volatility, we use three famous GARCH type models including GARCH, EGARCH, GJR-GARCH. With using the results of the best GARCH-type model, we develop two nonparametric models based on Neural Networks and Neuro-Fuzzy Networks to price call options for S&P 500 index. We compare the obtained results with those of Black-Scholes model and show that the Black-Scholes model in not appropriate for at-the-money options. Furthermore, by comparing the Neural Network and Neuro-Fuzzy approaches with Black-Scholes model, we observe that the accuracy of non-parametric models are better than the Black-Scholes model.

In this research, a new model for valuation of financial contracts is designed by using guaranteed and participation rates. In the traditional methods, valuation of these contracts is determined with the use of minimum guaranteed rate of returns such as risk free rate. However, the proposed model is based on the valuation of these contracts by guaranteed and participation rates as well as the customer's risk tolerance and investment risk. In this model, first an asset allocation model is considered in order to model market elements such as certificate of deposit, bonds and stocks. Then, conditional value at risk, as a proper risk measure, is used to determine guaranteed and participation rates and fair valuation of the contracts. To improve the validity, the model is run for both the capital markets of Iran and the United States of America. The variables used in this study are interest rate of banks, bonds and stock index from 1998 to 2012 in Iran and from 1980 to 2012 in America.

B‌u‌s‌i‌n‌e‌s‌s c‌o‌n‌d‌i‌t‌i‌o‌n‌s a‌r‌e f‌r‌a‌u‌g‌h‌t w‌i‌t‌h u‌n‌c‌e‌r‌t‌a‌i‌n‌t‌y a‌n‌d r‌i‌s‌k‌s. T‌h‌e‌s‌e u‌n‌c‌e‌r‌t‌a‌i‌n‌t‌i‌e‌s h‌o‌l‌d w‌i‌t‌h t‌h‌e‌m v‌a‌l‌u‌a‌b‌l‌ein‌f‌o‌r‌m‌a‌t‌i‌o‌n. W‌h‌e‌n u‌n‌c‌e‌r‌t‌a‌i‌n‌t‌y b‌e‌c‌o‌m‌e‌s r‌e‌s‌o‌l‌v‌e‌d t‌h‌r‌o‌u‌g‌h t‌h‌e p‌a‌s‌s‌a‌g‌e o‌f t‌i‌m‌e, m‌a‌n‌a‌g‌e‌r‌s c‌a‌n m‌a‌k‌e t‌h‌e a‌p‌p‌r‌o‌p‌r‌i‌a‌t‌e m‌i‌d-c‌o‌u‌r‌s‌e c‌o‌r‌r‌e‌c‌t‌i‌o‌n‌s t‌h‌r‌o‌u‌g‌h a c‌h‌a‌n‌g‌e i‌n b‌u‌s‌i‌n‌e‌s‌s d‌e‌c‌i‌s‌i‌o‌n‌s a‌n‌d s‌t‌r‌a‌t‌e‌g‌i‌e‌s. R‌e‌a‌l o‌p‌t‌i‌o‌n‌s i‌n‌c‌o‌r‌p‌o‌r‌a‌t‌e t‌h‌i‌s l‌e‌a‌r‌n‌i‌n‌g m‌o‌d‌e‌l, a‌k‌i‌n t‌o h‌a‌v‌i‌n‌g a s‌t‌r‌a‌t‌e‌g‌i‌c r‌o‌a‌d m‌a‌p, w‌h‌i‌l‌e t‌r‌a‌d‌i‌t‌i‌o‌n‌a‌l a‌n‌a‌l‌y‌s‌e‌s t‌h‌a‌t n‌e‌g‌l‌e‌c‌t t‌h‌i‌s m‌a‌n‌a‌g‌e‌r‌i‌a‌l f‌l‌e‌x‌i‌b‌i‌l‌i‌t‌y w‌i‌l‌l g‌r‌o‌s‌s‌l‌y u‌n‌d‌e‌r‌v‌a‌l‌u‌e c‌e‌r‌t‌a‌i‌n p‌r‌o‌j‌e‌c‌t‌s a‌n‌d s‌t‌r‌a‌t‌e‌g‌i‌e‌s. I‌n‌d‌u‌s‌t‌r‌y a‌n‌a‌l‌y‌s‌t‌s, e‌x‌p‌e‌r‌t‌s, a‌n‌d a‌c‌a‌d‌e‌m‌i‌c‌s a‌l‌l a‌g‌r‌e‌e t‌h‌a‌t r‌e‌a‌l o‌p‌t‌i‌o‌n‌s p‌r‌o‌v‌i‌d‌e s‌i‌g‌n‌i‌f‌i‌c‌a‌n‌t i‌n‌s‌i‌g‌h‌t‌s t‌o p‌r‌o‌j‌e‌c‌t e‌v‌a‌l‌u‌a‌t‌i‌o‌n t‌h‌a‌t t‌r‌a‌d‌i‌t‌i‌o‌n‌a‌l t‌y‌p‌e‌s o‌f a‌n‌a‌l‌y‌s‌i‌s, l‌i‌k‌e t‌h‌e d‌i‌s‌c‌o‌u‌n‌t‌e‌d c‌a‌s‌h f‌l‌o‌w a‌p‌p‌r‌o‌a‌c‌h, c‌a‌n‌n‌o‌t p‌r‌o‌v‌i‌d‌e. O‌n t‌h‌e o‌t‌h‌e‌r h‌a‌n‌d, t‌h‌e n‌a‌t‌u‌r‌e o‌f s‌o‌m‌e i‌n‌d‌u‌s‌t‌r‌i‌e‌s, s‌u‌c‌h a‌s o‌i‌l & g‌a‌s, i‌s v‌e‌r‌y c‌r‌i‌t‌i‌c‌a‌l, a‌n‌d e‌v‌e‌r‌y d‌e‌l‌a‌y i‌n m‌a‌k‌i‌n‌g d‌e‌c‌i‌s‌i‌o‌n‌s o‌r w‌r‌o‌n‌g d‌e‌c‌i‌s‌i‌o‌n‌s c‌a‌u‌s‌e‌s i‌r‌r‌e‌t‌r‌i‌e‌v‌a‌b‌l‌e l‌o‌s‌s. B‌e‌c‌a‌u‌s‌e o‌f t‌h‌i‌s c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌s‌t‌i‌c, t‌h‌e u‌s‌a‌g‌e o‌f t‌h‌i‌s a‌p‌p‌r‌o‌a‌c‌h h‌a‌s i‌n‌c‌r‌e‌a‌s‌e‌d i‌n t‌h‌i‌s f‌i‌e‌l‌d o‌v‌e‌r t‌h‌e y‌e‌a‌r‌s. F‌o‌r e‌c‌o‌n‌o‌m‌i‌c e‌v‌a‌l‌u‌a‌t‌i‌o‌n o‌f t‌h‌e o‌i‌l & g‌a‌s p‌r‌o‌j‌e‌c‌t w‌i‌t‌h t‌h‌e r‌e‌a‌l o‌p‌t‌i‌o‌n‌s a‌p‌p‌r‌o‌a‌c‌h, w‌e n‌e‌e‌d t‌o e‌s‌t‌i‌m‌a‌t‌e s‌o‌m‌e i‌n‌p‌u‌t p‌a‌r‌a‌m‌e‌t‌e‌r‌s, s‌u‌c‌h a‌s r‌i‌s‌k f‌r‌e‌e i‌n‌t‌e‌r‌e‌s‌t r‌a‌t‌e ($r_f$), d‌i‌s‌c‌o‌u‌n‌t‌e‌d r‌a‌t‌e a‌n‌d v‌o‌l‌a‌t‌i‌l‌i‌t‌y. A‌m‌o‌n‌g t‌h‌e‌m, t‌h‌e v‌o‌l‌a‌t‌i‌l‌i‌t‌y o‌f t‌h‌e c‌a‌s‌h f‌l‌o‌w i‌s t‌h‌e m‌o‌s‌t i‌m‌p‌o‌r‌t‌a‌n‌t, b‌u‌t e‌s‌t‌i‌m‌a‌t‌i‌o‌n o‌f t‌h‌a‌t i‌s v‌e‌r‌y c‌o‌m‌p‌l‌i‌c‌a‌t‌e‌d s‌i‌n‌c‌e m‌o‌s‌t p‌r‌o‌j‌e‌c‌t‌s a‌r‌e d‌o‌n‌e f‌o‌r t‌h‌e f‌i‌r‌s‌t t‌i‌m‌e a‌n‌d, c‌o‌n‌s‌e‌q‌u‌e‌n‌t‌l‌y, t‌h‌e‌r‌e i‌s n‌o h‌i‌s‌t‌o‌r‌i‌c‌a‌l d‌a‌t‌a f‌o‌r p‌r‌o‌j‌e‌c‌t v‌a‌l‌u‌e‌s. I‌n m‌a‌n‌y c‌a‌s‌e‌s, t‌h‌e p‌r‌o‌j‌e‌c‌t‌s a‌r‌e a‌l‌s‌o i‌r‌r‌e‌v‌e‌r‌s‌i‌b‌l‌e. I‌n t‌h‌i‌s p‌a‌p‌e‌r, w‌e h‌a‌v‌e t‌r‌i‌e‌d t‌o f‌a‌c‌i‌l‌i‌t‌a‌t‌e t‌h‌e u‌s‌e o‌f t‌h‌i‌s a‌p‌p‌r‌o‌a‌c‌h i‌n I‌r‌a‌n‌i‌a‌n o‌i‌l & g‌a‌s i‌n‌d‌u‌s‌t‌r‌i‌e‌s. D‌i‌f‌f‌e‌r‌e‌n‌t m‌e‌t‌h‌o‌d‌s f‌o‌r v‌o‌l‌a‌t‌i‌l‌i‌t‌y e‌s‌t‌i‌m‌a‌t‌i‌o‌n h‌a‌v‌e b‌e‌e‌n i‌n‌v‌e‌s‌t‌i‌g‌a‌t‌e‌d a‌n‌d a n‌e‌w m‌e‌t‌h‌o‌d h‌a‌s b‌e‌e‌n p‌r‌o‌p‌o‌s‌e‌d. W‌e u‌s‌e‌d t‌h‌e p‌r‌o‌f‌i‌t‌a‌b‌i‌l‌i‌t‌y i‌n‌d‌e‌x a‌s a c‌r‌e‌d‌i‌t‌a‌b‌l‌e f‌i‌n‌a‌n‌c‌i‌a‌l i‌n‌d‌e‌x f‌o‌r a‌p‌p‌l‌y‌i‌n‌g M‌o‌n‌t‌e C‌a‌r‌l‌o s‌i‌m‌u‌l‌a‌t‌i‌o‌n f‌o‌r v‌o‌l‌a‌t‌i‌l‌i‌t‌y e‌s‌t‌i‌m‌a‌t‌i‌o‌n. I‌n a‌d‌d‌i‌t‌i‌o‌n, a m‌e‌t‌h‌o‌d f‌o‌r d‌e‌t‌e‌r‌m‌i‌n‌a‌t‌i‌o‌n o‌f r‌i‌s‌k f‌r‌e‌e i‌n‌t‌e‌r‌e‌s‌t r‌a‌t‌e ($r_f$) a‌n‌d d‌i‌s‌c‌o‌u‌n‌t‌e‌d r‌a‌t‌e h‌a‌s b‌e‌e‌n i‌n‌t‌r‌o‌d‌u‌c‌e‌d. T‌h‌e‌n, w‌e i‌n‌v‌e‌s‌t‌i‌g‌a‌t‌e t‌h‌e a‌c‌c‌u‌r‌a‌c‌y o‌f t‌h‌e m‌e‌n‌t‌i‌o‌n‌e‌d t‌e‌c‌h‌n‌i‌q‌u‌e‌s b‌y a‌p‌p‌l‌y‌i‌n‌g a‌n o‌p‌t‌i‌o‌n t‌o d‌e‌f‌e‌r i‌n o‌u‌r c‌a‌s‌e s‌t‌u‌d‌y.

In the last 15 years, some methods have been proposed for forecasting based on fuzzy time series. One of the most important issues that affect the forecasting results in these models is the length of intervals. There are some studies on this issue but in most of them, length of intervals are predefined or even in some studies the interval’s length are the same. In this study we propose a model to determine effective intervals by using Simulated Annealing algorithm. And then we exercise this model on the FOREX currency market on some currency exchange rates (EUR/USD euro/US dollar, CAD/USD US Dollar/Canadian Dollar, GBP/USD British Pound/US Dollar, GBP/CHF British Pound/Swiss Franc) also we compare our model with other’s on forecasting Alabama University Enrollment.

Since the Third Economic-Social-Cultural Development Plan and & specially in Forth Plan، the government has noticed the importance and crucial role of TFP (Total Factor Productivity) growth in enhancing GDP (Gross Domestic Product) and therefor, has been economical situation of country & has begun trying to increase TFP of country by improving the Partial Performance Indices (PPI). Therefore the necessity of relating the computed TFP to the PPIs in companies –which without that, computing TFP alone would not be useful & applicable enough- is clear. None of the Ministries and governmental companies of Iran were successful to study this relation, so Tthis article studies & investigates the effects of PPI on TFP this subject in NIOOC, which is that is one of the main producing oil & gas sub-companies of NIOC (National Iranian Oil Company), as a case study with respect to Labor and Capital PPIs infor the period of 1997 -to 2006., based on econometrics & stasistical models We use the. Research will be done using Solow mMethod with Cobb-Douglas production fFunction as well as; & in addition to main method, also the Kendrick method. method will be used, so that results can be comparable. It can be seen that both methods nearly represent the same results. The results of this project show that in NIOOC the most effective studied Partial Performance Indexes on TFP in NIOOC is “the proportion of operating & managerial staff” that represents the high importance of structuring & suitable organizational chart. Other indices which were effective on TFP of NIOOC were experience and expertise, the number of technologies used in production of oil, & finally the degree of using educated staff respectively.

For evaluation of efficiency and yield per surface unit with using the 56 sugar beet producer countries data in year 2005 an econometric model based on COLS method has presented. With this model maximum sugar beet production efficiencies considering the harvested area and annual rainfall measure and level of mechanization has been determined. In total Iran with production efficiency 0. 43 is in rank 22 of the world and France with production efficiency 1 has the maximum sugar beet production efficiency in the world. In continuance of discussion relation between the countries sugar beet harvest area، annual rainfall measure and level of mechanization with their efficiency and yield in sugar beet production has tried to be studied.

This paper suggests a composed option pricing model based on black-scholes and binomial tree models. So at first this two models are presented and analyzed. Then we showed black-scholes model is an appropriate option pricing model for stocks with low volatility and binomial trees model is an appropriate option pricing model for stocks with high volatility. Suggested model is a composed model of black-scholes and binomial tree models and volatility is used as selecting model factor. To determine volatility limit quantity, we calculated average volatility of Iran Stock Exchange. For this calculating we selected 32 stocks in two period of time. At the end of paper, suggested model is validated by 2 methods and it’s validity is approved by both of them.

Capital structure puzzle is a challenging issue in Corporate Finance. Different researchers attempt to identify ways to determine optimized capital structure which minimizes firm’s cost of capital and maximizes its value, by proposing variety of theories. In line with forming new theories and introducing determinant variables, the absence of comprehensive research which simultaneously tests existing theories is obvious. Considering the issue literature perfectly, we attempt to identify introduced determinant variables and propose appropriate model using Forward Stepwise Data Reduction Algorithm and Econometrics tools. To achieve this objective, we select 78 Companies listed in Tehran Stock Exchange and consider the effects of Trade-off theory, Pecking Order theory and Market Timing theory from 1380 to 1384. Finally we propose a regression model which is the best estimation of existing data for the whole instance and each cluster of industry.

This study provides a model for testing stock market efficiency. Case study of this research is Tehran Stoch Exchange (TSE). To test the efficiency of TSE, Neural Network capable of learning the underlying dynamics of complex processes are used. After an explanation of the underlying theory, Neural Networks are applied to a trading simulation based on predicted value of 2 indices, over the period of Mehr 1383- Esfand 1385. An array of trading results was derived using the predicted index values and different trading parameters, four threshold levels on which tradind decisions were based, and four transaction cost levels. The simulation results indicate that in most cases, neural Networks were able to make statistically significant excess return over the passive "buy and hold strategy". This suggests that the stock market might be inefficient. For validation of this model, "run test" was used to prove that the results of simulation are correct. At last, this research provides some good ways for improving stock market efficiency.