Yazar "Assous, Hamzeh F." seçeneğine göre listele

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    Can international market indices estimate TASI’s movements? The ARIMA model
    (Multidisciplinary Digital Publishing Institute (MDPI), 2020) Assous, Hamzeh F.; Al-Rousan, Nadia; Al-Najjar, Dania; Al-Najjar, Hazem
    This study investigates the effectiveness of six of the key international indices in estimating Saudi financial market (TADAWUL) index (TASI) movement. To investigate the relationship between TASI and other variables, six equations were built using two independent variables of time and international index, while TASI was the dependent variable. Linear, logarithmic, quadratic, cubic, power, and exponential equations were separately used to achieve the targeted results. The results reveal that power equation is the best equation for forecasting the TASI index with a low error rate and high determination coefficient. Additionally, findings of the AutoRegressive Integrated Moving Average (ARIMA) model represent the most important variables to use in order to build a prediction model that can estimate the TASI index. The ARIMA model (with Expert Modeler) coefficients are described as ARIMA (0,1,14). The results show that the SP500, NIKKEI, CAC40, and HSI indices are the most suitable variables for estimating TASI with an R2 and RMSE equal to 0.993 and 113, respectively. This relationship can be used on the previous day to estimate the opening price of TASI based on the closing prices of international indices. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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    Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index
    (WILEY-HINDAWI, ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON WIT 5HE, ENGLAND, 2022) Al-Najjar, Dania; Al-Najjar, Hazem; Al-Rousan, Nadia; Assous, Hamzeh F.
    The air quality index (AQI) can be described using different pollutant indices. Many investigators study the effect of stock prices and air quality in the field to show if there is a relationship between changing the stock market and the concentration of various pollutants. This study aims to find a relationship between Saudi Tadawul All Share Index (TASI) and multiple pollutants, including particulate matter (PM10), ozone (O-3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and AQI. Based on tree models, the relationship is created using linear regression and two prediction models, Chi-square Automatic Interaction Detection (CHAID), and CR-Tree. In order to achieve the target of this research, the TASI dataset relates to six pollutants using time; afterward, the new dataset is divided into three parts-test, validate, and train-after eliminating the outlier data. In order to test the performance of two prediction models, R-2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O-3, SO2, and AQI indices are insignificant. The test dataset showed that R-2 scores are 0.995 and 0.986 for CR-Tree and CHAID, respectively, with RMSE values of 387 and 227 for CR-Tree and CHAID, respectively. The prediction models showed that the CHAID model performed better than CR-Tree because it used only three indices, namely, PM10, AQI, and O-3, with year and month. The results indicated an effect between changing TASI and the three pollutants, PM10, AQI, and O-3.
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    Impact of COVID-19 pandemic virus on G8 countries' financial indices based on artificial neural network
    (EMERALD GROUP PUBLISHING LTD, HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND, 2021) Al-Najjar, Hazem; Al-Rousan, Nadia; Al-Najjar, Dania; Assous, Hamzeh F.; Al-Najjar, Dana
    Purpose – The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the COVID-19 pandemic worldwide are causing instability in stock indices every day. These changes resulted in the G8 suffering major losses due to the spread of the pandemic. This paper aims to study the impact of COVID-19 events using country lockdown announcement on the most important stock indices in G8 by using seven lockdown variables. To find the impact of the COVID-19 virus on G8, a correlation analysis and an artificial neural network model are adopted. Design/methodology/approach – In this study, a Pearson correlation is used to study the strength of lockdown variables on international indices, where neural network is used to build a prediction model that can estimate the movement of stock markets independently. The neural network used two performance metrics including R2 and mean square error (MSE). Findings – The results of stock indices prediction showed that R2 values of all G8 are between 0.979 and 0.990, where MSE values are between 54 and 604. The results showed that the COVID-19 events had a strong negative impact on stock movement, with the lowest point on the March of all G8 indices. Besides, the US lockdown and interest rate changes are the most affected by the G8 stock trading, followed by Germany, France and the UK. Originality/value – The study has used artificial intelligent neural network to study the impact of US lockdown, decrease the interest rate in the USA and the announce of lockdown in different G8 countries.
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    Ramadan effect and indices movement estimation: a case study from eight Arab countries
    (Emerald Group Publishing Ltd, 2023) Al-Najjar, Dania; Assous, Hamzeh F.; Al-Najjar, Hazem; Al-Rousan, Nadia
    Purpose This study aims to investigate the Ramadan effect anomaly on the stock markets' indices and estimate the movement of these indices in the light of the phenomenon. Design/methodology/approach Stock market indices are used as financial indicators to show the Ramadan effect. To validate this effect, eight Arab countries, which comprises Jordan, Saudi Arabia, Oman, Qatar, United Arab Emirates, Bahrain, Kuwait and Egypt, are adopted. A linear regression with R-2, error, F-value and p-value is considered to analyze and understand the effect of Ramadan on the aforementioned Arab countries. Findings Results found that Ramadan has a strong effect on estimating and predicting the performance of stock market indices in all studied Arab countries, except Kuwait. Results found that the majority of the Ramadan effect occurred after the second 10 days of Ramadan, where the direction of stock indices is opposite of Ramadan variables in all aforementioned cases. Originality/value This study is considered as an enrichment of the existing literature review with regard to the Ramadan effect. The study presents a new methodology that can be followed to improve the predictions of stock market indices by using a weight least square method with linear regression. This study presents the most affected periods of time that could decrease or increase the stock prices. Finally, the study proves the capability of the weight least square method in building a predictive model that takes the date into consideration in predicting stock market indices.