درخت حوزه‌های تخصصی

This study aims to estimate the demand function for recycled paper in the Iranian economy during the seasonal period of 2000 to 2021. The Johansen-Juselius cointegration method was used to identify the factors influencing demand in both the long and short term. Quarterly data were generated from annual figures using the Denton method, and the model was specified in a linear logarithmic form. The model’s variables include per capita consumption of recycled paper, its price, the price of virgin paper, the percentage of internet users, and Gross Domestic Product (GDP). A dummy variable was also included to account for the removal of the import tariff in 2018. The results indicate that recycled paper is a necessary and inelastic good. In the long term, it becomes an inferior good, meaning that as income and welfare increase, consumers and producers shift towards higher-quality virgin paper. In the short term, demand is influenced by changes in GDP, the price of recycled paper, and the internet. The price elasticity of demand is very low (0.012%), suggesting that price changes have a negligible impact on the quantity demanded. The error correction model (ECM) coefficient of -0.51 indicates that half of the short-term disequilibrium is corrected in each period. These findings emphasize the importance of government policies in encouraging recycling and managing the market. Given Iran's limited forest resources, focusing on this sector can contribute to economic and environmental sustainability. Future research could explore the impact of tax and subsidy policies on the paper recycling industry.

This study aims to develop a working capital management model for companies listed on the Tehran Stock Exchange. The proposed model determines the expected level of working capital for a company, enabling it to create the highest possible value. Additionally, this model can be used to assess the efficiency of working capital management. The discrepancy between the actual level of working capital and the expected level serves as an indicator of inefficiency in working capital management. Initially, based on theoretical foundations and expert opinions, 28 variables affecting working capital were selected. Then, using the operational working capital index, the research models were estimated using multiple regression and genetic algorithm techniques for data from 156 companies over the period from 2011 to 2022. Influential variables were identified and filtered. Finally, suitable working capital management models were identified based on two criteria: (1) the strong correlation between the errors of the fitted models and the working capital efficiency of the company, and (2) the model’s accuracy in identifying companies prone to excess or shortage of working capital. In total, after estimating 119 different models using regression and genetic algorithm methods, four suitable working capital management models were determined. The regression method resulted in models with an average accuracy of 77.27% and 79.54% for the dependent variable of working capital and the cash conversion cycle, respectively. The genetic algorithm method resulted in models with an average accuracy of 89.03% and 82.08%. The final model, with the cash conversion cycle as the dependent variable, was identified as the best model. It includes the variables of the previous year's cash conversion cycle, company-specific risk, gross profit margin, trade credit, growth opportunities, operating cycle, economic policy uncertainty, and exchange rate changes.