The demand of metals by modern technology has been shifting from common base 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," and there is a growing  interest in such critical metals and their producing companies. In this research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE), to predict the stock trend of major critical metal producers. Specifically, dynABE first utilizes domain knowledge to group the features into different "advisors , " each advisor dealing with a particular economic sector.Then through ensembles of weak classifiers, each advisorproduces a prediction result, and all the advisors are combined again in a biased online update fashion to dynamically make the final prediction. Based on a misclassification error of 32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an excess return of 130\% within one year . In addition , the feature set selected by dynABE also suggests potentially influential factors to metal criticality, because stock prices of major producers influence metal production. Therefore, not only does this research propose a novel framework for specialized stock trend prediction, it also provides domain insights into dynamic features that potentially influence metal criticality .
The demand for metals by modern technology has been shifting from common base 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," and there is a growing investment interest in such critical metals and their producing companies. In this research, we create a novel framework, Dynamic Advisor-Based Ensemble (dynABE), for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the feature set by dividing them into different "advisors . " creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy. We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12\% and excess return of 477\% compared to the stock itself in a year and a half . In addition to presenting an effective stock prediction model with decent profitabilities, this research further analyzes dynABE to visualize how it works in practice, which also yields discoveries of its interesting behaviors when processing time-series data .
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to predict the stock trend of major critical metal producers. Specifically, dynABE first utilizes
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diversify the feature set by dividing them
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each advisor dealing with a particular economic sector.Then through ensembles of weak classifiers, each advisorproduces a prediction result, and all the advisors are combined again in a biased online update fashion to dynamically make the final prediction. Based on a
creates high-level ensembles with complex base models for each advisor, and combines the advisors together dynamically during validation with a novel and effective online update strategy. We test dynABE on three cobalt-related companies, and it achieves the best-case
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32\% for Jinchuan Group's stock (HKG: 2362), we further test a simple stock trading strategy, which leads to a back-tested return of 296\%, or an
31.12\% and
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130\% within one year
477\% compared to the stock itself in a year and a half
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, the feature set selected by dynABE also suggests potentially influential factors to metal criticality, because stock prices of major producers influence metal production. Therefore, not only does this research propose a novel framework for specialized stock trend prediction, it also provides domain insights into dynamic features that potentially influence metal criticality
to presenting an effective stock prediction model with decent profitabilities, this research further analyzes dynABE to visualize how it works in practice, which also yields discoveries of its interesting behaviors when processing time-series data