Industrial sector

This paper investigates the variability of real business activities in the form of discretionary expenditures (accounts receivable, selling, general and administrative expenses, and net change in accruals), and the firm’s stock price performance concerning the frequency of meeting or beating analysts’ earnings forecast. Also, the study investigated the relationship between the firm’s stock price performance and the variability of these financial statement accounts. The objective was to examine if these selected financial statements of accounts could be used by management to manipulate earnings in order to meet and/or beat analysts’ earnings forecasts so as to enhance the firm’s stock performance. The study found that there is a significant difference between the variance of SG&A, NCA and stock returns of the firms that meet and/or beat the analysts’ forecast and those that do not. It was also found that there is a significant difference in stock returns based on meeting and/ or beating the analysts’ estimate.

The aim of this paper is to develop a prediction model of energy demand of Iran’s industrial sector. For that matter a Markov Chain Grey Model (MCGM) has been proposed to forecast such energy demand. To find the effectiveness of the proposed model, it is then compared with Grey Model (GM) and regression model. The comparison reveals that the MCGM model has higher precision than those of the GM and the regression. The MCGM is then used to forecast the annual energy demand of industrial sector in Iran up to the year 2020. The results provide scientific basis for the planned development of the energy supply of industrial sector in Iran.

The rapid growth of Emerging Economics in previous decades have made them an important player in the international arena, along with Developed Economics, which have a significant impact on international economic transactions. This study investigate the way oil price fluctuations affect energy-intensive sectors in Emerging and Developed Economics using quarterly data from 2005 Q1 to 2024 Q2. Particularly, in this study NARDL framework is employed to examine the asymmetric impact of changes in oil prices by decomposing oil price shocks into positive and negative partial sums. The results show significant and asymmetric effect of oil price shocks on energy-intensive industrial sectors. The results show that the effect of negative changes in oil prices on both economics is similar, while Developed Economics react differently to positive changes in oil prices compared to Emerging Economics. overall, energy-intensive industries in Developed Economics show better management and are less vulnerable to oil price fluctuations in comparison with Emerging Economics.