Saudi Cultural Missions Theses & Dissertations
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Item Restricted Enhancing Stock Price Prediction Using Machine Learning Models: A Comparative Study of SVM, LSTM, and GRU(University College London, 2024-08) AlMohamdy, Razan; Andrea, DucciThis study evaluates the effectiveness of three machine learning models—Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU)—in predicting the stock prices of Saudi Aramco. Using historical stock price data and technical indicators, the models were assessed based on their accuracy in both long-term and short-term predictions. The findings reveal that LSTM and GRU significantly outperform SVM, with LSTM showing superior performance in capturing long-term dependencies and GRU offering a balance between accuracy and computational efficiency. Specifically, LSTM achieved a Root Mean Squared Error (RMSE) of 0.0516 and a Mean Absolute Error (MAE) of 0.0323, while GRU recorded an RMSE of 0.0539 and an MAE of 0.0234. In contrast, SVM exhibited a much higher RMSE of 0.1712 and an MAE of 0.1079, indicating its struggles with market volatility. The 30-day prediction analysis further highlighted the strengths of LSTM and GRU in short-term forecasting, with both models maintaining an R² value above 0.993, while SVM lagged behind at 0.9332. Despite their advantages, the study identified limitations such as the exclusion of external economic factors and the models' varying effectiveness across different time horizons. These findings contribute to the growing field of financial forecasting, offering practical insights for investors and analysts on model selection. Future research is recommended to incorporate broader economic indicators, explore cross-market validation, and enhance the models' responsiveness to short-term market fluctuations.22 0Item Restricted Exploring the Impact of Sentiment Analysis on Price Prediction(Lehigh University, 2024-07) Zahhar, Abdulkarim Ali Y.; Robinson, Daniel P.The integration of sentiment analysis into predictive models for financial markets, particularly Bitcoin, combines behavioral finance with quantitative analysis. This thesis investigates the extent to which sentiment data, derived from social media platforms such as X (formerly Twitter), can enhance the accuracy of Bitcoin price predictions. A key idea in the study is that public sentiment, as shown on social media, affects Bitcoin’s market prices. The research uses linear regression models that combine Bitcoin’s opening prices with sentiment scores from social media to forecast closing prices. The analysis covers the period from January 2012 to December 2019. Sentiment scores were analyzed using VADER and TextBlob lexicons. The empirical findings show that models incorporating sentiment scores enhance predictive accuracy. For example, incorporating daily average sentiment scores (v avg and B avg) into the models reduced the Mean Squared Error (MSE) from 81184 to 81129 and improved other metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), particularly at specific lag times like 8 and 76 days. These results emphasize the potential benefits of sentiment analysis to improve financial forecasting models. However, it also acknowledges limitations related to the scope of data and the complexities of accurately measuring sentiment. Future research is encouraged to explore more sophisticated models and diverse data sources to further enhance and validate the integration of sentiment analysis in financial forecasting.91 0