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The development of modern technology is closely related to the use of metals, whose demand has been shifting from common industrial metals to a variety of minor metals, such as cobalt or indium. The industrial importance and limited geological availability of some minor metals have led to them being considered more “critical.” In this research, we develop a novel approach to investigate metal criticality. Instead of quantifying the supply risk of critical metals with static indicators, as used in conventional criticality measures, we propose a dynamic method by looking at the stock trend of the major critical metal producers, who would, in turn, influence the future supply. This allows us to utilize high-frequency time series data in creating machine learning models that predict the stock prices using metal-related features. Specifically, we use regularized linear regression, logistic regression, support vector machine and gradient boosted trees as base models. An ensemble of them is then used for the final prediction, with methods including majority vote, logistic regression stacking, and gradient boosted trees stacking. Based on a misclassification error of 34% in the validation set, we further develop a stock trading strategy, which leads to a back-tested return of 313%, or an excess return of 147%. More importantly, a set of significant features selected by our models leads to insightful suggestions that certain unseeming features, such as markets of other metal cycles and currency exchange rates, may play important roles in influencing the supply of critical metals.

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