M. Ghorbani; A.H. Tohidi; P. Alizadeh
Abstract
Introduction: Organic farming plays an important role in protecting the environment, maintaining non-renewable resources, improving the food quality, reducing the production of unnecessary products, and promoting market- oriented agricultural sector. In fact, organic farming make a significant contribution ...
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Introduction: Organic farming plays an important role in protecting the environment, maintaining non-renewable resources, improving the food quality, reducing the production of unnecessary products, and promoting market- oriented agricultural sector. In fact, organic farming make a significant contribution in improving the quality of the environment and natural resources, and also it has a positive effect on the quality of food supply and the promotion of public health. Given the many benefits of organic products, the market for these products has been increasingly considered by researchers, government officials and consumers. First step in developing the market for organic products is to meet the needs and demands of consumers. Recognizing consumer behavior and investigating the factors affecting it contributes significantly in success of any economic system. Besides, in advanced marketing studies, the process of identifying consumer choice is very crucial. Contrary to economists' views, consumers give little weight to benefits and costs in their decision making, and their choices are based on people's behavior, habits and other factors that may speed up the decision making. Consumer preferences for organic products depend on many factors and the importance of each of these factors varies among different consumers. Therefore, the main aim of this study is to rate and evaluate factors affecting the consumer preferences for organic products (fruitage, vegetables and cucurbits) in Mashhad city.Materials and Methods: Many marketing researchers use regression models to evaluate consumer decisions. In these models, decision variables are definitive part of utility function which is used to calculate how to choose a product. Linearity of utility function is the vital hypothesis. To specify a non-linear model, it is necessary to use variables that can show non-linear effects (For example, including the quadratic term of variables). However, this requires the insertion of assumptions about the nature of the utility function which ultimately leads to specification bias, and subsequently misinterpretation and unreasonable applications in marketing studies. Modeling complex processes is one of the advantages of artificial neural networks, and in this approach, it is not necessary to specify a mathematical relationship between the variables. The nonlinear and complex interactions can be considered between system variables using artificial neural network model. In this study in order to rate and evaluate factors affecting consumers preferences for organic products (fruitage, vegetables and cucurbits) an artificial neural network has been used that is consist of three dependent or target variables. Also, in order to evaluate the importance of the explanatory variables of the artificial neural network, partial derivatives approach has been used. Therefore, the use of three output variables on artificial neural networks simultaneously and partial derivative approach was distinctive features of this study compared with previous ones. Data is collected through questionnaires from a total of 175 households living in Mashhad. Age, gender, education, household size, number of household members under 10 years, number of household members over 65 years, price, having information on organic products, product appearance, having information on the supply of organic products, nutritional values, ease of access, the supply of organic products during the year and having labels were the input variables of artificial neural network. Consumer preferences for the purchase of organic fruitage, vegetables and cucurbits were the target variables of the artificial neural network.Results and Discussion: The results indicate that price has the greatest influence on willingness to consume organic products among all other factors. The price effect on willingness to consume organic products is different among individual consumers, and it's independent of the product. This finding suggested that the price of organic products had a significant impact on consumer purchasing decisions in comparison with other marketing mix elements.Conclusion: The adoption and implementation of marketing strategies based on price play a very important role in the growth of organic products markets. The results of the study indicate that, for each consumer and each product, the price had almost the similar effects on willingness to choose. Hence, it is recommended that the similar pricing strategies be used for these three organic products.
A. Dourandish; A.H. Tohidi; S. Soleimani Nejad
Abstract
Introduction: Providing an alternative route for mobilizing domestic savings, stock markets increase investment growth and, consequently, economic growth. Moreover, allocating better resources (through the concessions of labor division), the stock market plays an important role in economic growth. In ...
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Introduction: Providing an alternative route for mobilizing domestic savings, stock markets increase investment growth and, consequently, economic growth. Moreover, allocating better resources (through the concessions of labor division), the stock market plays an important role in economic growth. In addition to the positive effects of the stock market on economic growth, this market has high potential to create employment (direct and indirect). According to the existing conditions of the agriculture sector in meeting the needs of the society have increased the importance of this sector. Thus, it is necessary to use the stock exchange of the agricultural sector as one of the most important tools to solve the problems of this sector, improve market efficiency, increase productivity and provide financial requirements of agricultural companies in this market. Concerning the number of shares, stock volume and variety of products, agricultural exchange is the largest stock exchange. Cash returns can be used as a criterion to identify the economic status of agricultural firms in the stock market; identifying this trend plays an important role in financial planning and investment decisions. So, the main objective of this study was to compare the performance and accuracy of different methods to predict cash returns of agricultural companies in Tehran Stock Exchange.
Materials and Methods: The stock returns prediction models can be divided into two groups of statistical models and artificial intelligence. In this study, prediction accuracy was evaluated for linear (ARIMA and ARIMAX), non-linear (NAR and NARX) and hybrid models (ARIMA-ANN and ARIMAX-ANN) using monthly cash returns data over the period 2009(4)-2015(4) and according to the Diebold and Mariano test. ARIMA linear models have dominated many areas of time series forecasting. The linear function is based on three parametric linear components: auto regression (AR), integration (I), and moving average (MA). The ARIMA models also have the capability to include external independent or predictor variables. In this case, the model is a multivariate model and this model is represented as ARIMAX, where X represents the independent external. Artificial neural networks are capable of learning, generalizing, information parallel processing and error endurance. These attributes make the ANNs powerful in solving complex problems. Moreover, the ANN is able to adapt to the data pattern and relationship between the input and output, resulting in better prediction accuracy than the statistical method. The combined ARIMA(X)-ANN model is able to use the characteristics of both models simultaneously. Adding the explanatory variables, exchange rates, interest rates, inflation, oil prices and government spending, this study tried to answer the question whether performance prediction models improve significantly for linear, nonlinear and hybrid?
Results and Discussion: In this study, augmented Dicky Fuller tests (ADF), dickey fuller generalized least squares (DF-GLS), Phillips-Perron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests were used to test the unit root. According to results, time series of cash returns was stationary. Next we estimated ARIMA (2,0,0) and ARIMAX (2,0,0) models. Based on the criteria of accuracy, the results showed that the ARIMAX model had a higher predicting accuracy compared to the ARIMA. In this study, two models were designed using artificial neural networks to predict cash returns. the first model only included the first and second lag of cash returns (infact, the first model was Nonlinear Autoregressive model (NAR)); in the second model, in addition to two lags of cash returns, the exchange rates, interest rates, inflation, oil prices and government spending variables were used as the network inputs (this pattern is known as Nonlinear Autoregressive with Exogenous Input (NARX Model)). We find that the performance and accuracy of Auto Regression artificial neural networks increases by adding explanatory variables. In this study also used the ARIMA-ANN and ARIMAX-ANN hybrid models, in order to consider the linear and nonlinear characteristics in the data. After running the hybrid models and predicting, the results indicate that ARIMA-ANN and ARIMAX-ANN hybrid models have better performance in comparison with linear models (ARIMA and ARIMA) and non-linear models (NAR and NARX). Another result shows that ARIMA-ANN model is more accurate compared to ARIMAX-ANN. Finally, we show that hybrid models have greater prediction accuracy according to the Diebold and Mariano test.
Conclusions: The results showed that the addition of external explanatory variables could improve the prediction accuracy in linear and nonlinear models and this improvement in performance was significant statistically. The results of the study revealed that making use of hybrid models increased prediction accuracy significantly. Therefore, researchers, companies and shareholders of stock exchange are suggested to use the hybrid models in their financial planning to forecast economic variables.
