RUAA BINSADDIG

Dr. RUAA BINSADDIG

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 May 2024
Verified
Book Chapter
Closed access
PDF file
TATareq Hammad Almubaydeen
Tareq Hammad Almubaydeen
AAAhmad Mohammed Abu-Qteash
Ahmad Mohammed Abu-Qteash
NANidal Al-Ramahi
Nidal Al-Ramahi
RARiham Fathi Alkabbji
Riham Fathi Alkabbji
MAMajed Alshrouf
Majed Alshrouf
RBRUAA BINSADDIG
RUAA BINSADDIG

20 January 2024
Verified
Book Chapter
Closed access
PDF file
AAAmjad Alkhatib
Amjad Alkhatib
MAMajed Alshrouf
Majed Alshrouf
MSMohammad Shadid
Mohammad Shadid
IAImad AlZeer
Imad AlZeer
MAMousa Ajouz
Mousa Ajouz
RBRUAA BINSADDIG
RUAA BINSADDIG

Verified
Journal Article
Open access
PDF file
SHSyed Hasan Hasan
Syed Hasan Hasan
Shakeb AkhtarShakeb Akhtar
Shakeb Akhtar
Hani El-ChaaraniHani El-Chaarani
Hani El-Chaarani
PKParvez Alam Alam Khan
Parvez Alam Alam Khan
RBRUAA BINSADDIG
RUAA BINSADDIG
Predicting trends in the stock market is becoming complex and uncertain. In response, various artificial intelligence solutions have emerged. A significant solution for predicting the trends of a stock’s volatile and chaotic nature is drawn from deep learning. The present study’s objective is to compare and predict the closing price of the NIFTY 50 index through two significant deep learning methods—long short-term memory (LSTM) and backward elimination LSTM (BE-LSTM)—using 15 years’ worth of per day data obtained from Bloomberg. This study has considered the variables of date, high, open, low, close volume, as well as the 14-period relative strength index (RSI), to predict the closing price. The results of the comparative study show that backward elimination LSTM performs better than the LSTM model for predicting the NIFTY 50 index price for the next 30 days, with an accuracy of 95%. In conclusion, the proposed model has significantly improved the prediction of the NIFTY 50 index price.

29 May 2024
Verified
Book Chapter
Closed access
PDF file
MAMahmoud Alghizzawi
Mahmoud Alghizzawi
MSMotteh S. Al Shibly
Motteh S. Al Shibly
AEAbd Alrahman Ratib Ezmigna
Abd Alrahman Ratib Ezmigna
YSYousef Shahwan
Yousef Shahwan
RBRUAA BINSADDIG
RUAA BINSADDIG

Verified
Journal Article
Open access
PDF file
ZAZouhour Abiad
Zouhour Abiad
RARebecca Abraham
Rebecca Abraham
Hani El-ChaaraniHani El-Chaarani
Hani El-Chaarani
RBRUAA BINSADDIG
RUAA BINSADDIG
This study investigates the impact of corporate governance characteristics on bank financial performance in Gulf Cooperation Council countries. The board characteristics include board size, board independence, board gender diversity, and CEO duality (CEO is also Board Chair), with bank size as the moderating variable. Sixty-six commercial banks from six Gulf Cooperation Council countries—Saudi Arabia, United Arab Emirates, Kuwait, Bahrain, Oman, and Qatar—are examined from 2019 to 2023 using two-stage least squares and generalized method of moments econometric methods. Board size, board independence, and board gender diversity significantly increase return on assets and return on equity. The impact of CEO duality is mixed. The empirical findings show that CEO duality increases return on equity, with a non-significant impact on return on assets. Finally, results show that bank size moderates the impacts of board size, board independence, and gender diversity in boards on the financial performance of banks. Large banks significantly increase return on assets and return on equity due to the board characteristics examined, to a greater extent than small banks. Bank leaders should expand board membership, and add independent directors and women, to improve financial performance.