Mohammad H. Saleh, Rami S. Alkhawaldeh, and Jamil J. Jaber Elsevier BV
Tariq T. Alshammari, Mohd Tahir Ismail, Nawaf N. Hamadneh, S. Al Wadi, Jamil J. Jaber, Nawa Alshammari, and Mohammad H. Saleh Computers, Materials and Continua (Tech Science Press)
Jamil J. Jaber, Fatiha Beldjilali, Ali A. Shehadeh, Nawaf N. Hamadneh, Mohammad Saleh, Muhammad Tahir, and S. Al Wadi Hindawi Limited
In this study, we estimated the performance efficiency of the Jordanian mining and extracting sector based on Data Envelopment Analysis (DEA). The utilized dataset includes 6 out of 15 corporations that reflect around 90% of the total market capitalization under the mining and extracting sector in the Amman Stock Exchange (ASE). The sample consists of 126 observations from 2000 to 2020. It should be noted that estimating the efficiency of the sector based on time series for each company is not mentioned in the literature review. Therefore, we applied BCC (Banker–Charnes–Cooper) models to estimate performance efficiency and compared between input and output models under DEA. We also estimated the average performance efficiency of the sector to detect weaknesses/strengths among companies. The market capitalization and the operating revenue are used to evaluate the companies’ performance. In addition to the performance variables as output to the DEA models, the current assets, non-current assets, operating expenses, and general administrative expenses are also used as input variables under the DEA models. This study also examined the effect of Gross Domestic Product (GDP) growth and Return on Assets (ROA) on performance efficiency scores for BCC models. In the results, we found that there are differences in performance efficiency across time series in each company based on dynamic BCC models. It is observed that the performance efficiency of NAST Company is better than the other companies based on BCC (Input/output). The GDP growth and ROA reveal the effect on efficiency performance under BCC models. The proposed model can be used to improve the performance efficiency of companies in stock exchange markets.
Mohammad I. Almaharmeh, Ali Shehadeh, M. Iskandrani and M. H. Saleh
Tariq S. ALSHAMMARI, , Mohd T. ISMAIL, Sadam AL-WADI, Mohammad H. SALEH, and Jamil J. JABER Korea Distribution Science Association
This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.
Adnan M. Rawashdeh, Malek Elayan, Mohamed Dawood Shamout, and Mohammad H. Saleh Growing Science
Article history: Received: May 3, 2020 Received in revised format: June 3
Jamil J. Jaber, Noriszura Ismail, S. Al Wadi, and Mohammad H. Saleh Pushpa Publishing House