درخت حوزه‌های تخصصی

This research investigates the impact of inventory management on company performance, specifically focusing on Return on Assets (ROA) andReturn on Equity (ROE) across different life cycle stages. Utilizing panel data from 192 firms listed on the Tehran Stock Exchange over the period from 2019 to 2023, the study employs regression analysis to explore these relationships. The findings reveal significant influences of inventory management on financial performance metrics. Notably, while effective inventory management enhances ROE, its relationship with ROA is more complex, indicating potential inefficiencies in larger firms. Specifically, a one-unit increase in inventory management correlates with a decrease in ROA but positively affects ROE. The analysis further shows that during growth and maturity phases, there is a statistically significant negative relationship between inventory management and performance metrics, whereas a positive relationship is observed during the decline phase. Additionally, control variables such as financial leverage consistently demonstrate negative correlations with both ROA and ROE. This study contributes valuable insights into the intricate dynamics between inventory management and company performance across various life cycle stages

Industrial investment in Iran, especially in the wake of sanctions’ intensification in the 2010s, decreased significantly. The government implemented fiscal policy which has been associated with uncertainty. In this paper, using a dynamic panel model and generalized method of moments, we examined the effect of fiscal policy uncertainty on investment in 24 industrial groups in Iran over the period 2002-2020. The results indicated that the growth of fiscal level shock,resulted in a low but positive effect on investment directly. However,the coefficient of cross effect between the growth of fiscal level shock and the fiscal policy uncertainty undermined the positive effect on industrial investment. The government has had two effects on industrial investment indirectly through the demand side shock. By creating demand, it has had a positive effect on industrial investment with the cross effect between the growth of the fiscal level shock and industrial sales. However, on the other hand, the growth of the fiscal level shock has been associated with the creation and growth of fiscal policy uncertainty, so that the cross effect between the growth of fiscal policy uncertainty and the growth of industrial sales on industrial investment shows a high negative coefficient. The results of these two effects suggest that due to high uncertainty,the indirect government effect on investment in 24 industrial groups is negative, which happens through the demand-side shock. Moreover, the growth of fiscal policy uncertainty, sanctions, and interest rates, respectively, have had the most adverse effect on investment in 24 industrial groups in Iran.

Inflation as an undesirable economic phenomenon in recent years has had devastating effects on Iran economy. This made economic researchers devote plenty of reports and researches to the causes or ways to cope with inflation in Iran. In the present study, considering the importance of inflation, it was tried to take advantage of the econometric model to examine the effect of in Iran economy by using annual information from 1974 to 2021 and by using the Generalized Method of Moments (GMM). The results of the GMM model showed that the variables of labor wage index, liquidity volume, time-lag inflation and unofficial exchange rate growth have a positive and significant effect on inflation in Iran. Also, the GDP growth variable has a negative and significant effect on the dependent variable. The study suggests that in order to control inflation, the government in Iran should consider issues such as stability in economic policy-making, adopting appropriate fiscal policies, especially through budgetary discipline, and consistency of monetary and fiscal policies to curb inflation, to be important.

The price of Crude Oil is Exposed to Various Factors That Cause Random, Sudden and Chaotic Price Fluctuations. Accurate Forecasting of Oil Prices Has a Central Impact on The Macro Economy. The aim of This Study is to Predict the Fluctuations of OPEC Crude Oil in The  long-term Using Chaos Theory and GMDH-GA Algorithm. First, the Daily Oil Price Time Series is Decomposed by Wavelet Transformation. Then, Chaos is Tested Using Embedding Dimension, Lyapunov Power and GA tests. Finally, Time Series Noises are Reduced by Reconstructing the Wavelet Phase Space. Three Nonlinear Models, GMDH-GA model, GMDH-GA Wavelet Model, and GMDH-GA Extended Model, Were Used to Forecast Time Series. Although the Results Showed that All three Models are Favorable in Terms of Root Mean Square Error (RMSE) and Correlation Coefficient, But the Developed GMDH-GA  Neural Network Model with low RMSE and High Correlation Coefficient is the Most Effective in Predicting the Daily Price of OPEC Crude Oil. has it.

The oil and gas industry, as one of the most complex industries with unique characteristics, plays a vital role in the world's energy supply. In order to be present in this field, gain and maintain a competitive advantage, the actors of this industry are condemned to create, develop or acquire innovative technologies. With the change in the approach of companies active in the oil and gas industry compared to the past, intellectual achievements have been considered as a valuable asset and their support has become inevitable. Companies active in the field of oil and gas are well aware of the fact that obtaining maximum profits requires managing and supporting intellectual achievements and defining an intellectual property strategy aligned with the company's business goals. Therefore, each company defines its intellectual property strategy according to its role in the energy supply chain and operates based on it. Investigating the cost and profit of intellectual assets are two important criteria in determining the appropriate strategy of an oil company.