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 .
Stock trend prediction is a challenging task due to the market's noise, and machine learning techniques have recently been successful in coping with this challenge . In this research, we create a novel framework for stock prediction , Dynamic Advisor-Based Ensemble (dynABE) . dynABE explores domain-specific areas based on the companies of interest, diversifies the feature set by creating different "advisors " that each handles a different area, follows an effective model ensemble procedure for each advisor, and combines the advisors together in a second-level ensemble through an online update strategy we developed. dynABE is able to adapt to price pattern changes of the market during the active trading period robustly, without needing to retrain the entire model. We test dynABE on three cobalt-related companies, and it achieves the best-case misclassification error of 31.12\% and an annualized absolute return of 359.55\% with zero maximum drawdown. dynABE also consistently outperforms the baseline models of support vector machine, neural network, and random forest in all case studies .
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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
Stock trend prediction is a challenging task due to the market's noise, and machine learning techniques have recently been successful in coping with this challenge
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for stock prediction
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, for stock prediction and use critical metal companies as case study. dynABE uses domain knowledge to diversify the
. dynABE explores domain-specific areas based on the companies of interest, diversifies the
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dividing them into
creating
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.
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creates high-level ensembles with complex base models
that each handles a different area, follows an effective model ensemble procedure
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dynamically during validation with a novel and effective
in a second-level ensemble through an
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.
we developed. dynABE is able to adapt to price pattern changes of the market during the active trading period robustly, without needing to retrain the entire model.
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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
an annualized absolute return of 359.55\% with zero maximum drawdown. dynABE also consistently outperforms the baseline models of support vector machine, neural network, and random forest in all case studies