Comparative Analysis of Machine Learning Algorithms For XAU/USD Prediction: Integrating Economic Indicators And Sentiment Analysis

Crasta, Manoj (2024) Comparative Analysis of Machine Learning Algorithms For XAU/USD Prediction: Integrating Economic Indicators And Sentiment Analysis. Masters thesis, Dublin, National College of Ireland.

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Abstract

XAU/USD which is an highly traded forex pair in the financial market, is known for its volatility. Its dependence on diverse factors like economic indicators and sentiment-driven market dynamics, makes it a difficult pair to predict. While existing studies focus on either quantitative data or sentiment data. This study sought to fill this gap by integrating both historical indicators and sentiment scores from news articles to predict XAU/USD hourly rate. The prominent machine learning models were implemented and evaluated using metrics like MAE, MSE and R². Random Forest was found to be the most efficient in terms of accuracy and interoperability with Mean Absolute Error of 7.2035. Deep learning models even though they are designed for sequential data were outperformed by ensemble models. While sentiment score contributed to the predictive capability of models, their influence was limited. This research can help the traders and financial analyst to effectively predict the XAU/USD trends by providing a reliable framework. Nevertheless, some additional investigation of sentiment-driven features and real-time analysis tools is necessary to enhance model generalizability and precision.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Shahid, Abdul UNSPECIFIED
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > Economics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 09:56
Last Modified:	02 Sep 2025 09:56
URI:	https://norma.ncirl.ie/id/eprint/8689

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