Abstract
The increasingly meagre copper ore resources constitute one of the decisive factors influencing the price of this commodity. The demand for copper has been showing an accelerating trend since the Covid pandemic broke out. It is thereby imperative to estimate the future price movement of this material. The article focuses on a daily prediction of the forthcoming change in prices of copper on the commodity market. The research data were gathered from day-to-day closing historical prices of copper from commodity stock COMEX converted to a time series. The price is expressed in US Dollars per pound. The data were processed using artificial intelligence, recurrent neural networks, including the Long Short Term Memory layer. Neural networks have a great potential to predict this type of time series. The results show that the volatility in copper price during the monitored period was low or close to zero. We may thereby argue that neural networks foresee the first three months more accurately than the rest of the examined period. Neural structures anticipate copper prices from 4.5 to 4.6 USD to the end of the period in question. Low volatility that would last longer than one year would cut down speculators’ profits to a minimum (lower risk). On the other hand, this situation would bring about balance which the purchasing companies avidly seek for. However, the presented article is solely confined to a limited number of variables to work with, disregarding other decisive criteria. Although the very high performance of the experimental prediction model, there is always space for improvement – e.g. effectively combining traditional methods with advanced techniques of artificial intelligence.
Highlights
The trade of copper has increasingly assumed substantial significance on the commodity market
The lowest level of annual volatility of the time series was recorded for the year 1963 when the volatility reached its minimum of 0.00321
The higher efficiency of hybrid methods is confirmed by Hu, Ni & Wen (2020), whose results show that predictions made by GARCH may serve as informative features to significantly increase the predictive power of the model of the neural network and that the integration of LSTM and artificial neural networks (ANN) networks is an efficient approach to create useful structures of deep neural networks to improve the predictive power
Summary
The trade of copper has increasingly assumed substantial significance on the commodity market. Sulfide ores thereby go through a froth flotation process to gain concentrates containing ≈ 30% Cu, converting concentrates to main products offered by copper mining companies (Glöser, Soulier & Tercero-Espinoza, 2013). The market with negotiable copper concentrates lacks tangible references, public official or regulated markets which would provide stockholders with a practical guide to set a reference official price of the concentrates – yet, this information exists for basic commodities included in the concentrate (Díaz-Borrego et al, 2021). Copper concentrates are jointly negotiated between tradesmen and mining companies all over the world, which currently presents the principal source of refined copper, and as of 2017, it represents 67% of global production. SX-EW constitutes 16%, and secondary copper processing amounts to 17% of the global production (International Copper Study Group, 2018). The prediction of prices of copper was a subject of various surveys
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