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Machine Learning for Cobalt Price Prediction

In: Sustainable Development in Banking and Finance

Author

Listed:
  • 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)

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.

Suggested Citation

  • Erdem Öncü & Veclal Gündüz, 2024. "Machine Learning for Cobalt Price Prediction," Springer Proceedings in Business and Economics, in: Nesrin Ozatac & Nigar Taspinar & Bezhan Rustamov (ed.), Sustainable Development in Banking and Finance, pages 65-75, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-65533-3_5
    DOI: 10.1007/978-3-031-65533-3_5
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