Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy

Husiani, Noor Aida and Gan, Yee Jing and Ghazali, Rozaida and Mohmad Hassim, Yana Maswin and Yap, Jie Shen and Joseph, Jerome Subash (2024) Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy. In: 6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND DATA MINING, SCDM 2024.

Text Predictive modeling of Gold Prices.pdf Download (1MB)

Abstract

This paper discusses the crucial requirement for reliable gold price prediction, which is necessary for financial market decision-making. We propose a comprehensive approach to develop a robust predictive model capable of predicting both the rise and fall of gold prices. For this, three (3) machine learning (ML) models - Decision Tree Regressor (DTR), Support Vector Regression (SVR), and Random Forest (RF); must be carefully chosen, and model parameters must be adjusted so that predicted values roughly match actual results. Given this, this paper investigates the influence and effectiveness of incorporating technical indicators in predicting fluctuations in gold prices, which might have an impact on the overall performance of the ML models. By achieving a prediction accuracy rate of at least 80%, the model becomes a favorable tool for informed decision-making and provides valuable insights to investors in the gold markets.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	H Social Sciences > HG Finance
Divisions:	Faculty of Computer Science and Information Technology > FSKTM
Depositing User:	Mrs. Sabarina Che Mat
Date Deposited:	10 Jan 2025 08:02
Last Modified:	10 Jan 2025 08:03
URI:	http://eprints.uthm.edu.my/id/eprint/11972

Actions (login required)

View Item