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    Predicting Precious Metal Markets using Custom Deep-Learning NARX Network: A Portfolio Application

    Access Status
    Fulltext not available
    Embargo Lift Date
    2026-10-15
    Authors
    Tjendra, Stephanie Aliana
    Date
    2024
    Supervisor
    Dhanuskodi Rengasamy
    Jobrun Nandong
    Andrew Brennan
    Filbert Hilman Juwono
    Type
    Thesis
    Award
    MPhil
    
    Metadata
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    Faculty
    Curtin Malaysia
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/96183
    Collection
    • Curtin Theses
    Abstract

    The thesis aims to predict precious metal market prices using a deep learning model known as Nonlinear AutoRegressive with eXogenous input. Market forecasts of the 58 assets selected are evaluated through portfolio techniques such as Mean-Variance and Conditional Value-at-Risk to demonstrate the real-world application. This investigation provides a framework for future value projection, including the preprocessing stage, feature selection, and dataset construction. Additionally, a novel error measure is proposed to comprehensively assess the estimation accuracy.

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