Mohammad Hasan Saleh
Verified @ju.edu.jo
Scopus Publications
Scopus Publications
S. Al Wadi, Omar Al Singlawi, Jamil J. Jaber, Mohammad H. Saleh, and Ali A. Shehadeh
MDPI AG
This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant results, the research employs various mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive explanation of all past events within the specified time frame, alongside the introduction of a novel forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock market data, particularly in identifying significant events characterized by high volatility, which thereby enhances forecasting accuracy. These results hold valuable implications for researchers and scientists across various domains, with a particular relevance to the fields of business and health sciences. The performance evaluation of the forecasting methodology is based on several mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute scaled error (MASE), and the root mean squared error (RMSE).
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