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Algorithmic Strategies for Precious Metals Price Forecasting

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  • Gil Cohen

    (Department of Management, Western Galilee Academic College, P.O. Box 2125, Acre 2412101, Israel)

Abstract

This research is the first attempt to create machine learning (ML) algorithmic systems that would be able to automatically trade precious metals. The algorithm uses three forecast methodologies: linear regression (LR), Darvas boxes (DB), and Bollinger bands (BB). Our data consists of 20 years of daily price data concerning five precious metals futures: gold, silver, copper, platinum, and palladium. We found that all of the examined precious metals’ current daily returns are negatively autocorrelated to their former day’s returns and identified lagged interdependencies among the examined metals. Silver futures prices were found to be best forecasted by our systems, and platinum the worst. Moreover, our system better forecasts price-up trends than downtrends for all examined techniques and commodities. Linear regression was found to be the best technique to forecast silver and gold prices trends, while the Bollinger band technique best fits palladium forecasting.

Suggested Citation

  • Gil Cohen, 2022. "Algorithmic Strategies for Precious Metals Price Forecasting," Mathematics, MDPI, vol. 10(7), pages 1-12, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1134-:d:785241
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    References listed on IDEAS

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    1. Shahzad, Syed Jawad Hussain & Rehman, Mobeen Ur & Jammazi, Rania, 2019. "Spillovers from oil to precious metals: Quantile approaches," Resources Policy, Elsevier, vol. 61(C), pages 508-521.
    2. Sensoy, Ahmet, 2013. "Dynamic relationship between precious metals," Resources Policy, Elsevier, vol. 38(4), pages 504-511.
    3. Zheng, Yao, 2015. "The linkage between aggregate investor sentiment and metal futures returns: A nonlinear approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 58(C), pages 128-142.
    4. Bosch, David & Pradkhan, Elina, 2015. "The impact of speculation on precious metals futures markets," Resources Policy, Elsevier, vol. 44(C), pages 118-134.
    5. Qadan, Mahmoud, 2019. "Risk appetite and the prices of precious metals," Resources Policy, Elsevier, vol. 62(C), pages 136-153.
    6. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
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    Cited by:

    1. Choi, Insu & Kim, Woo Chang, 2024. "Practical forecasting of risk boundaries for industrial metals and critical minerals via statistical machine learning techniques," International Review of Financial Analysis, Elsevier, vol. 94(C).
    2. Gil Cohen, 2022. "Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies," Mathematics, MDPI, vol. 10(18), pages 1-13, September.

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