Browsing by Author "Almazyad, Sulaiman"

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Compliance of Saudi Arabia’s Government Tenders and Procurement Law with the WTO Government Procurement Agreement’s Principles: Analysing Non-Discrimination, Transparency, and Dispute Settlement System, and Evaluating Arguments Against Accession.
(University of Leeds, 2024) Almazyad, Sulaiman; Corvaglia, Maria Anna
This dissertation investigates the compliance of Saudi Government Tenders and Procurement Law (GTPL) with the WTO’s Government Procurement Agreement (GPA), despite Saudi Arabia not being a signatory to the agreement. It aims to critically analyze both the GPA and GTPL in three core areas: non-discrimination, transparency, and dispute settlement system requirements. Adopting a doctrinal methodology, the study found that the GTPL exceeds the GPA's standards regarding transparency principles and dispute settlement system requirements, showcasing its compliance and advancement in these areas. However, the study also revealed that the principle of non-discrimination is adopted differently in the GTPL, indicating divergence and non-compliance with the GPA. Consequently, it identifies the necessary amendments required for GTPL to comply with the GPA and explores alternative routes to mitigate the compliance process, such as limiting the coverage of Saudi procurement under the agreement or leveraging preferences accorded to developing countries. By doing so, Saudi Arabia could also maintain one of its objectives under the GTPL, which is enhancing its economic development. However, upon critical evaluation of the GPA, this dissertation argues that Saudi Arabia should resist the pressure to accede to the agreement and contends that its immediate accession is not advisable, as the purported benefits would be outweighed by the associated implications.
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Forecasting OPEC Basket Oil Price and Its Volatilities Using LSTM
(University College London, 2024-09) Almazyad, Sulaiman; Hamadeh, Lama
The global economy is greatly affected by oil prices, which have an impact on everything from consumer goods prices to transportation expenses. Forecasting these prices accurately is crucial for energy security, company strategy, and economic planning. Traditional statistical models such as ARIMA and SARIMA have been used for such forecasts, but struggle with the non-linear patterns inherent in oil price movements. This research explores the use of Long Short-Term Memory (LSTM) networks, a specialized form of Recurrent Neural Network (RNN) built to manage longterm dependencies, in predicting the OPEC reference basket oil price and its associated volatility, ultimately improving the accuracy of these forecasts. The model is built upon historical datasets of the OPEC Reference Basket (ORB), and its efficacy is assessed using a variety of performance indicators, including RMSE, MAE, and MAPE. The outcomes reveal that the LSTM model is
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