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

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...

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Main Authors: Husiani, Noor Aida, Gan, Yee Jing, Ghazali, Rozaida, Mohmad Hassim, Yana Maswin, Yap, Jie Shen, Joseph, Jerome Subash
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
HG Finance
Online Access:http://eprints.uthm.edu.my/11972/1/Predictive%20modeling%20of%20Gold%20Prices.pdf
http://eprints.uthm.edu.my/11972/
https://doi.org/10.1007/978-3-031-66965-1_38
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spelling my.uthm.eprints.119722025-01-10T08:03:21Z http://eprints.uthm.edu.my/11972/ Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy Husiani, Noor Aida Gan, Yee Jing Ghazali, Rozaida Mohmad Hassim, Yana Maswin Yap, Jie Shen Joseph, Jerome Subash HG Finance 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. 2024-07-30 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/11972/1/Predictive%20modeling%20of%20Gold%20Prices.pdf 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. https://doi.org/10.1007/978-3-031-66965-1_38
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic HG Finance
spellingShingle HG Finance
Husiani, Noor Aida
Gan, Yee Jing
Ghazali, Rozaida
Mohmad Hassim, Yana Maswin
Yap, Jie Shen
Joseph, Jerome Subash
Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy
description 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.
format Conference or Workshop Item
author Husiani, Noor Aida
Gan, Yee Jing
Ghazali, Rozaida
Mohmad Hassim, Yana Maswin
Yap, Jie Shen
Joseph, Jerome Subash
author_facet Husiani, Noor Aida
Gan, Yee Jing
Ghazali, Rozaida
Mohmad Hassim, Yana Maswin
Yap, Jie Shen
Joseph, Jerome Subash
author_sort Husiani, Noor Aida
title Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy
title_short Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy
title_full Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy
title_fullStr Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy
title_full_unstemmed Predictive modeling of gold prices: integrating technical indicators for enhanced accuracy
title_sort predictive modeling of gold prices: integrating technical indicators for enhanced accuracy
publishDate 2024
url http://eprints.uthm.edu.my/11972/1/Predictive%20modeling%20of%20Gold%20Prices.pdf
http://eprints.uthm.edu.my/11972/
https://doi.org/10.1007/978-3-031-66965-1_38
_version_ 1821004333861306368
score 13.232389

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