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Gold Exchange Traded Funds (ETFs), Take Profit / Stop Loss Prediction using Machine Learning

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  • Tamer Shawky

    (Assistant Professor, Dept. of Management Information Systems, Modern Academy for Computer Science and Management Technology Cairo, Egypt)

Abstract

Investment vehicles called Gold-Traded Funds (ETFs) try to follow the price of gold and are traded on stock markets. Without having to actually buy or store the metal, they provide investors with access to gold. Like stocks, gold ETF shares may be purchased and sold. Stop loss and take profit are common risk-management techniques for trading and investing that are automatically generated when the price of an asset rises above or below a certain threshold. Stop loss orders are used to minimize possible losses by closing out a trade before the price falls any lower. All earlier mentions combined; Our research showed that in order for an investor to get the maximum profit or the smallest loss, the movement of the points used to halt a loss or take gains must be adjusted. In our study, we used a series of algorithms to anticipate the market's direction and the progress in that direction. The points were adjusted automatically based on the outcomes. This study demonstrates how to establish an easy-to-use machine-learning model that aims to forecast the gold market, Stop Losses and take profit. We shall predict market trends to determine stop loss or take profit pips based on supervised machine learning models. Yahoo Finance released the dataset that was utilized to forecast market trends and (Stop Loss / Take Profit) Pips. Tree, Support Vector Classifier (SVM), random forest, Neural Network and K-Nearest Neighbour (KNN) Classifier are used with an accuracy of 83%,56%,91%,99% and 92% in sequential.

Suggested Citation

  • Tamer Shawky, 2023. "Gold Exchange Traded Funds (ETFs), Take Profit / Stop Loss Prediction using Machine Learning," Journal of Business and Management Studies, Al-Kindi Center for Research and Development, vol. 5(4), pages 06-17, June.
  • Handle: RePEc:alk:jbms00:v:5:y:2023:i:4:id:5538
    DOI: 10.32996/jbms.2023.5.4.2
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