Journal of Economic Modeling Research

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This study investigates predictability, chaos analysis, wavelet decomposition and the performance of neural network models in forecasting the return series of the Tehran Stock Exchange Index (TEDPIX). For this purpose, the daily data from April 24, 2009 to May 3, 2012 is used. Results show that TEDPIX series is chaotic and predictable with nonlinear effect. Also, according to obtained inverse of the largest lyapunov exponent, we are able to predict the future values of the series up to 31 days. Besides, our findings suggest that multi-layer feed forward neural network model and fuzzy model based on decomposed data, are of superior performances in predicting the return series. It is worth mentioning that, among these models, MFNN reveals the best performance.

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  Storing the electrical energy in large scale is impossible. So, it is necessary to identify the factors affecting the electricity demand. Researchers have used different methods to forecast the future demand of electricity, among them intelligent methods and fuzzy based methods are more popular. Since ANFIS structure is based on researcher’s experience about phenomenon, the created structure may not have the best result. Therefore, we used PSO-ANFIS structure.

  In this paper long term electricity demand is forecasted until the year 2025 by hybrid PSO-ANFIS algorithm. The results confirm the high power of the Adaptive Neural based Fuzzy Inference System in forecasting the electricity demand. Results also indicate that the forecasted electricity demand will be 401 billion KWh in 2025. The prediction performance of the proposed technique is more accurate than the ARIMA model.

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Abstract

 Forecasting of crude oil price plays a crucial role in optimization of production, marketing and market strategies. Furthermore, it plays a significant role in government’s policies, because the government sets and implements its policies not only according to the current situation but also according to short run and long run predictions of important economic variables like oil price. The main purpose of this study is modeling and forecasting spot oil price of Iran by using GARCH model and A Gravitational Search Algorithm. Performed forecasts of this study are based in static and out-of-sample forecasting and each subseries data is divided in to two parts: data for estimation and data for forecasting. The forecast horizon is next leading period and its length is one month. In this study the selected models for forecasting spot oil of Iran are GARCH(2,1) and a Cobb Douglas function which is functional of prices of 5 days ago. Finally, the performances of these models are compared. For comparison of these models MSE, RMSE, MAE, and MAPE criteria are used and the results indicate that except in MAPE criterion, the mentioned criteria are smaller for GARCH model in comparison to GSA algorithm.

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The main objective of this study is to investigate the effects of oil shocks on GDP, prices level, money and exchange rates in Iran by using the structural vector error correction (SVEC) approach model covering the period 1980Q2-2010Q1. The findings of this study reveal that positive shock in oil real price has significant and positive effect on the real GDP in the short, medium and long. The impact of oil price shocks on domestic prices in the short, medium and long term is negative and significant, such as creating a positive shock to the real price of crude oil, reduce the domestic price. In addition, a positive shock to the real price of crude oil has the negative effect of the exchange rate in the short, medium and long term. However, the impact of oil price shock on the real exchange rate is permanent. Imports also will increase, due to the increase in wealth and demand for intermediate products. On the other hand, a positive shock to the real residual money in the short run cause to immediate increases in real out put.

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Cigarette and tobacco products in the VAT Law is considered as one of the particular goods and in order to contorlingit’s consumption by price tools, higher tax rates than the standard rate will be levied on it. In this paper, forecasting of revenues of this tax using an approach based on the estimating of tax base has been considered. Thus the first stage, tax base (consumption expenditure) is forecasted for the period 2012 to 2015 and then tax related years by applying the tax rates, will be calculated. In this regard, Because of concerns that policy makers have access to accurate predictions of tax revenues, Supervised neural networks Method to prediction and back-propagation algorithm to train is used. The results indicate that the average annual growth of revenue from value added tax on Cigarette consumption will have 20 percent during the forecasting years.

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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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The main purpose of this study, is to evaluate the effect of diversion earnings forecast and earnings realized on returns stocks in Tehran Stock Exchange. In fact, this research aims to examine the diversion of earnings resulting from the diversion of corporates managers forecasts earnings, what impact these diversion of earnings have on the returns of stock price. To achieve this, 194 companies listed in the Tehran Stock Exchange selected in the period of 2005-2013.
In this study, two groups of companies experienced the highest returns and lowest returns over the period studied, have been selected. Multi-factor model of Fama and French (1993) was used as the theoretical basis. The results indicate that forecasts of companies have experienced highest returns in comparison with lowest returns are more cautious and accurate than prediction of their future earnings. Changes in earnings realized and Tehran Stock Exchange index returns have positive and considerable relationship with stock returns as well, but these relationships for companies with highest returns are stronger than companies with lowest returns.

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Forecasting tax revenues is vitally important issue for optimal allocation of taxable resources, planning and budgeting in national and regional levels and knowing the potential national participation in public expenditures.  The classical optimization based on mathematical methods may not be reliable in real world and mostly inefficient and inapplicable in complicated world due to their restricted assumptions. The smart optimization may help us to find the solution. This essay based on modified  PSO  methodology .The initial trial based on the data during 1971- 2007 in case of various direct and indirect taxes , and  using updated data  during 2008- 2012 for final forecasting , to estimate tax revenues for upcoming next three years (2013 up to 2016) by MATLAB software.

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In the Iranian economy, which has experienced various sanctions, it was necessary to anticipate macroeconomic variables when imposing new sanctions. On the other hand, in the context of sanctions, it is possible to make a more accurate assessment of economic policies in order to be able to respond in a timely manner to these shocks and the need for appropriate planning and security against them. Therefore, in the present study, a macroeconomic model with Mixed-frequency data sampling  has been used,While having a high accuracy in prediction, it is possible that when new information about multivariate variables is obtained, based on it, the previous prediction for the dependent variable of the pattern is revised. The model consists of 27 behavioral equations, 8 communication equations and 33 definitional and union relations and the parameters of the model are estimated using time series data in the period 1338 to 1396. Predictive results show that the use of new observations in high frequency variables in the model has led to improved accuracy in predicting the endogenous variables of the model.