Investigating the Determinant of Return on Assets and Return on Equity and Its Industry Wise Effects in TSE (Tehran Security Exchange Market)

The  purpose of this study  is to find out that from the components of Dupont identity of  Return on Equity which one is most consistent or volatile among profit margin, total assets turnover and equity multiplier in Fuel and Energy Sector, Chemicals Sector, Cement Sector, Engineering Sector, Textiles Sector and Transport and Communication Sector of TSE 100 index. The purpose of the study was served by taking data from 2004 to 2012 of 51 companies (falling under six mentioned industries) of TSE 100 as Paradigm of Panel Data. The F-Statistics of One Way ANOVA (Analysis of Variance) show that it is Assets Turnover which significantly varies from industry to industry whereas Equity Multiplier and Profit Margin are not much volatile among indifferent industries. Moreover, Adjusted R Square in Panel OLS Analysis has confirmed Industry  Effect on Newly established firms from Fuel and Energy Sector, Cement Sector and Transport and Communication Sector whereas others Sectors such as Chemicals Sector, Engineering Sectors and Textiles Sectors does not have that leverage. Keywords: Profitability, Dupont Identity, Panel Least Square JEL Classification: G12, G39, C2

A refined multi-seasonality weighted fuzzy time series model for short term load forecasting

Seasonal Auto Regressive Fractionally Integrated Moving Average (SARFIMA) is a well-known model for forecasting of seasonal time series that follow a long memory process. However, to better boost the accuracy of forecasts inside such data for nonlinear problem, in this study, a combination of Fuzzy Time Series (FTS) with SARFIMA is proposed. To build the proposed model, certain parameters requires to be estimated. Therefore, a reliable Evolutionary Algorithm namely Particle Swarm Optimization (PSO) is employed. As a case study, a seasonal long memory time series, i.e., short term load consumption historical data, is selected. In fact, Short Term Load Forecasting (STLF) plays a key role in energy management systems (EMS) and in the decision making process of every power supply organization. In order to evaluate the proposed method, some experiments, using eight datasets of half-hourly load data from England and France for the year 2005 and four data sets of hourly load data from Malaysia for the year 2007, are designed. Although the focus of this research is STLF, six other seasonal long memory time series from several interesting case studies are employed to better evaluate the performance of the proposed method.The results are compared with some novel FTS methods and new state-of-the-art forecasting methods. The analysis of the results indicates that the proposed method presents higher accuracy than its counterparts, representing an efficient hybrid method for load forecasting problems