Machine Learning for Cobalt Price Prediction

Erdem Öncü () and Veclal Gündüz
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Erdem Öncü: Trakya University, Keşan Yusuf Çapraz School of Applied Sciences, Department of Banking and Insurance
Veclal Gündüz: Bahçeşehir Cyprus University, Faculty of Economics, Administrative and Social Sciences, Department of Banking and Finance

A chapter in Sustainable Development in Banking and Finance, 2024, pp 65-75 from Springer

Abstract: Abstract Cobalt ore is now a valuable commodity due to its high melting temperature and high-temperature strength. It is used to create cutting materials, super alloys, surface coatings, high-speed steels, and various other products. As a result, the value of cobalt, one of the major elements in batteries that power various technological items such as smartphones, tablets, laptops, and electric automobiles, is rising. Electric cars’ engines are powered by batteries. As a consequence, no fuel is used and no greenhouse gases are emitted. Furthermore, compared to traditional cars, electric vehicles use less engine oil and fluids, contaminating the environment. As a result, the supply of cobalt metal is critical for the manufacture of next-generation electric cars as well as the environment. The price of cobalt ore was approximated in this study using machine learning approaches such as random forest and gradient boosted trees. The gradient boosted trees approach correctly estimates the price of cobalt, which has recently become an important mineral. Sector investors needing cobalt ore will profit from the quality estimates based on machine learning in this study.

Keywords: Machine learning; Price prediction; Cobalt; Precious metals (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-65533-3_5

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