Title: Dynamic Advisor-Based Ensemble (dynABE): Case Study in Stock Trend Prediction of Critical Metal Companies,
Abstract: 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.