Accounting
Hamidreza Hajeb; Mohammad Banafi
Abstract
The purpose of this study is to design a model to predict the efficiency of inventory management to help creditors and actual and potential investors and other stakeholders to avoid major losses in the capital market. For this reason, 137 companies listed on the Tehran Stock Exchange during the 10-years ...
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The purpose of this study is to design a model to predict the efficiency of inventory management to help creditors and actual and potential investors and other stakeholders to avoid major losses in the capital market. For this reason, 137 companies listed on the Tehran Stock Exchange during the 10-years period 2012-2021 were examined. In this study, the predicting variables of institutional ownership, managerial ownership, corporate ownership, ownership concentration, board size, percentage of non-executive board members, and duality of CEO (Chief Executive Officer) role have been used. The efficiency of inventory management was predicted using a three-layer perceptron artificial neural network with the Backpropagation of Error algorithm. Finally, a network with the mean squared error of 0.360, 0.428, 0.261 and 0.353, respectively for training data, validation, test and total data and a coefficient of determination of more than 72%, as the best network Selected.
Elham Naeimi; Mohammad Hossein Askariazad; Kaveh Khalili-Damghani
Volume 2, Issue 1 , January 2015, , Pages 10-25
Abstract
Energy as a production process input has an effective role on economic indicators such as gross domestic production (GDP). Limitations in fossil fuel and nuclear energy sources urge utilizing renewable energies. In this paper, the impact of renewable energy consumption on economic welfare indicators ...
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Energy as a production process input has an effective role on economic indicators such as gross domestic production (GDP). Limitations in fossil fuel and nuclear energy sources urge utilizing renewable energies. In this paper, the impact of renewable energy consumption on economic welfare indicators (i.e. GDP, GDP per capita, annual income of urban households, and annual income of rural households) is investigated. For this purpose, 41 annual data sets are collected, from 1971 to 2011, mostly from Iran’s Statistical Yearbook and Iran’s Balance Sheet. Artificial neural networks (ANNs) are used for forecasting the effect of renewable energy consumption on economic welfare indicators. Advantages in using the proposed ANN-based method are demonstrated by comparing its results with the multi-layer regression (MLR) model. The comparison between the artificial neural network and the multi-layer regression model demonstrates that the artificial neural network has more accurate results than the multi-layer regression model. Both ANN and MLR models show significant effect of using renewable energies on the economic welfare. Results demonstrate the importance of using the proposed model for policy makers in implementing new policies for renewable energies. The ANN prediction results show that GDP, GDP per capita, annual income of urban households, and annual income of rural households will grow by 35.63%, 62.59%, 167.61% and 143.19%, respectively, from 2007 to 2016.
