  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.
