Dynamic Advisor-Based Ensemble : Case Study in Stock Trend Prediction of Critical Metal Companies

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.