Journal of Economic Modeling Research

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In this study, the electrical energy consumption in Tehran before reduction subsidies and after targeting subsidies was examined with using a dataset collected from household subscribers Tehran Electricity Distribution Company from August 2000 to November 2012. After review and analysis values, a model was proposed for predicting power consumption. The proposed model was a combination of trigonometric coefficients and power factors. The best values were obtained by using a genetic algorithm.
Procedure of electrical energy consumption in Tehran after Implementation of subsidies reduction plan was compared with the predicted model of electrical energy consumption in Tehran before Implementation that plan. The results indicated that implementation of subsidies reduction plan reduced electrical consumption growth rates and also a little reduced consumption rate. The other results of this study contain consumption patterns in order to manage the future consumption level of electrical consumers in Tehran. Also the results showed that, because demand for electricity is inelastic to price and income in the short time, as a result price policies cannot be effective in controlling the electricity demand, then should use non-price and intensive policies to reduce the consumption of electricity.

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In order to estimate the production function besides productivity and economic growth, the time series of capital stock is required. Time-series that available for capital stock is not so reliable because of Variations in suggested methods and also difficulty in the calculation of this variable. The continuously inventory method (CIM) has been more attention, among the existing methods. We improve CIM in this research. For estimating the capital stock we developed an algorithm and titled that “Programming or Recursive Algorithm”.The following can be noted in capabilities of the model that developed in this study. Unlike the previous studies we taking the variable depreciation rate of capital in different periods, considering the quality variable of war and its impact on the rate of depreciation, investigation of nonlinear and linear production function in order to increase estimation accuracy and considering energy as well as labor and capital input.The results show that compared to the time series reported by the Central Bank of Iran, the series calculated in this study are similar trend, but with some differences.The mean of depreciation rate has been calculated 5.1% for the period 2009 to 2010. The estimation results show that in war period we have always higher depreciation rate than average rate of depreciation in period of this study.

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In capital markets, stock price forecasting is affected by variety of factors such as political and economic condition and behavior of investors. Determining all effective factors and level of their effectiveness on stock market is very challenging even with technical and knowledge-based analysis by experts. Hence, investors have encountered challenge, doubt and fault in order to invest with minimum risk. In order to reduce cost and raise the profit of investment, determining effective factors and suitable time for sailing and purchase is one of the important problems that every shareholder or investor in stock market should consider. To reach this goal, a variety of approaches have been introduced, which are often intelligent, statistical, and hybrid. These approaches are mostly used to predict the stock price time series. Our proposed algorithm is hybrid and involves two stages: preprocessing and predictor. The preprocessing stage involves three steps: missing value, normalization and feature selection. Since there are many features in used datasets, genetic algorithm (GA) is used as the feature selection algorithm. In order to intelligent capability of Fuzzy Neural Network (FNN), this network with two structures (Mamdani and Sugeno) is used as a stock price prediction in second stage. This network is capable of extracting fuzzy rules automatically. Back propagation algorithm (gradient decent) is used for adapting all the parameters. 
Our algorithm is evaluated on ten datasets with seven features obtained from ten different companies. By comparing the simulation results of the simple and hybrid FNN network, we found that the lack of suitable feature selection algorithm will lead to high computational cost, and in many instances the hybrid algorithm outperforms the simple FNN. This results demonstrate the superiority of the hybrid FNN to the simple one. In general, since the number of Sugeno tuning parameters are more than Mamdani, its performance is better than mamdani. Moreover, our algorithm is comparable to the maximum precision rates of other approaches.

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Efficient portfolio management, has been attractive for financial researchers and was wished for investors from past to now. In this research, a multiperiod portfolio optimization problem for asset liability management of an investor who intends to control the probability of bankrupt is investigated. The proposed portfolio is consisting of number of risky assets, risk free asset and a type of debt. A mean variance model, with constraint of bankrupt controlling in different time horizons is proposed. Lagrangian Multiplier Method with dynamic programming is used for solving proposed model and regarding to its complexity degree, Genetic algorithm was the best selection for reaching numerical results. The proposed model is ran with real data consisting of 10 accepted company in Tehran stock exchange, bonds and bank loan as an investor debt.